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Modeling distribution of Amazonian tree species and diversity using remote sensing measurements
Abstract
The availability of a wide range of satellite measurements of environmental variables at different spatial and temporal resolutions, together with an increasing number of digitized and georeferenced species occurrences, has created the opportunity to model and monitor species geographic distribution and richness at regional to continental scales. In this paper, we examine the application of recently developed global data products from satellite observations in modeling the potential distribution of tree species and diversity in the Amazon basin. We use data from satellite sensors, including MODIS, QSCAT, SRTM, and TRMM, to develop different environmental variables related to vegetation, landscape, and climate. These variables are used in a maximum entropy method (Maxent) to model the geographical distribution of five commercial trees and to classify the patterns of tree alpha-diversity in the Amazon basin. Maxent simulations are analyzed using binomial tests of omission rates and the area under the receiver operating characteristics (ROC) curves to examine the model performance, the accuracy of geographic distributions, and the significance of environmental variables for discriminating suitable habitats. To evaluate the importance of satellite data, we used the Maxent jackknife test to quantify the training gains from data layers and to compare the results with model simulations using climate-only data. For all species and tree alpha-diversity, modeled distributions are in agreement with historical data and field observations. The results compare with climate-derived patterns, but provide better spatial resolution and detailed information on the habitat characteristics. Among satellite data products, QSCAT backscatter, representing canopy moisture and roughness, and MODIS leaf area index (LAI) are the most important variables in almost all cases. Model simulations suggest that climate and remote sensing results are complementary and that the best distribution patterns can be achieved when the two data sets are combined.
... Increasing numbers of studies integrate remote sensing methods with SDMs to assess, model, predict or map species' distributions and therefore different aspects of biodiversity (e.g. Buermann et al., 2008;Jiang et al., 2013;Saatchi et al., 2008;Zimmermann et al., 2007). ...
... In parallel, remote sensing (RS) offers a powerful tool to derive and integrate environmental information into SDMs and generate predictions on species distribution over large areas (Cerrejón et al., 2020;He et al., 2015;. Although a considerable number of studies have successfully integrated RS predictors into SDMs (Jiang et al., 2013;Saatchi et al., 2008;Zimmermann et al., 2007), no study has generated ESMs using only RS predictors, nor has used this approach to generate ...
Cryptogams (bryophytes and lichens) are ubiquitous non-vascular species that contribute significantly to total biodiversity and play an essential ecological role in ecosystem functioning worldwide. Specifically, cryptogams influence water, carbon and nutrient cycles, as well as physical and chemical weathering, and increase stability of soils, preventing their erosion and regulating their temperature and humidity. Cryptogams facilitate ecosystem recovery following disturbances, and provide microhabitats for micro- and macroorganisms, and a food source for invertebrates and herbivores. These species are also reliable and highly sensitive indicators to environmental disturbances and currently face numerous human-induced threats mainly derived from land use and climate change. Despite this, cryptogams are generally neglected in conservation planning mostly due to current knowledge gaps in their diversity, ecology and distribution, which jeopardizes the maintenance of their species and ecological role. New technologies and data sources such as remote sensing (RS) can significantly help to fill these gaps and ultimately improve the representation of cryptogams in systematic conservation planning. The contribution of RS to cryptogam biodiversity assessments can be particularly valuable in vast and largely unknown regions such as boreal forests, where these species and their habitats face increasing human-induced threats. The general objective of this thesis is to elucidate the role that RS can play in the evaluation and generation of information on cryptogam biodiversity in a boreal context. The study region is located in the Canadian boreal forest, within the Eeyou-Istchee James Bay region in Northern Quebec. As specific objectives, Chapter II aims to predict and map diversity (species richness) patterns of i) total bryophytes, and ii) bryophyte guilds (mosses, liverworts and sphagna) using RS data; Chapter III focusses on producing predictive models of rare bryophyte species using RS-derived predictors in an Ensembles of Small Models (ESMs) framework; and Chapter IV is intended to describe and model the lichen alpha diversity (species richness) and beta diversity (species turnover) components parallelly using two set of RS-derived variables (Red and NIR; EVI2) from two sensors (Wordlview-3, WV3; Sentinel-2, S2) at different high spatial resolutions (1.2m; 10m), and ii) to identify which habitat types represent lichen biodiversity hotspots.
The Random Forest algorithm used in Chapter II allowed us to develop spatially explicit models and to generate predictive cartography at 30m resolution of total bryophyte, moss, liverwort and sphagna richness. These models explained a significant fraction of the variation in total bryophyte and guild level richness, both in the calibration (42 to 52%) and validation sets (38 to 48%), and consistently identified vegetation (mainly NDVI) and climatic variables (temperature, precipitation, and freeze-thaw events) as the most important predictors for all bryophyte groups modeled. Guild-level models identified differences in important factors determining the richness of each of the guilds and thus in their predicted richness patterns, which provide valuable information for management and conservation strategies for bryophytes. The RS-based ESMs developed in Chapter III built from Random Forest and Maxent techniques using predictors related to topography (TPI) and vegetation (EVI2, NDWI1, Vegetation Continuous fields, and PALSAR HVHH) yielded poor to excellent prediction accuracy (AUC > 0.5) for 38 of the 52 modeled species despite their low number of occurrences (< 30), with AUC values > 0.8 for 19 species. The actual presences of the 38 species modeled better than random (AUC ≤ 0.5) were accurately predicted, as supported by the high sensitivity values obtained that ranged from 0.8 to 1 with an average of 0.959 ± 0.063. The distribution of these 38 species and the richness patterns both for total rare bryophytes and rare species at the guild level were mapped at 30m resolution. Chapter III also revealed a spatial concordance between rare (present chapter) and overall bryophyte richness patterns (Chapter II) in different regions of the study area, which has important implications for conservation planning. In Chapter IV, a total of 116 lichen species were identified. While high lichen richness was generally found across our plots (36.5 ± 9 species), those richer in microhabitats often harbored more species (R2 = 0.22) regardless of the habitat type. Differences in species composition were identified among plots (25.6% explained by PCoA) and habitat types (PERMANOVA R2 = 0.35), both being supported by differences in microhabitat composition (Mantel r = 0.22 and PERMANOVA R2 = 0.29, respectively). Rocky outcrops and undisturbed coniferous forests represented the main lichen biodiversity hotspots, while other habitat types were also important for maintaining overall biodiversity. Red and NIR variables were effective for modeling alpha and beta diversity at both resolutions, while EVI2, either from WV3 or S2, was only informative for assessing beta diversity. Poisson models explained up to 32% of the variation in lichen richness. Generalized dissimilarity models described well the relationship between beta diversity and spectral dissimilarity (R2 from 0.25 to 0.30), except for the S2 EVI2 model (R2 = 0.07), confirming that more spectrally and thus environmentally different areas tend to harbor different lichen communities. While WV3 often outperformed the S2 sensor, the latter still provides a powerful tool for the study of lichens and their conservation.
This thesis demonstrated the ability for RS at medium and high spatial resolutions to characterize the habitat of inconspicuous cryptogam species, to capture diverse meaningful ecological features shaping their distribution, and thus to better understand and/or predict their biodiversity patterns. RS-based modeling frameworks proved to be informative even when the available baseline information on cryptogam biodiversity was limited. By identifying environmental drivers of cryptogam biodiversity that can guide specific management actions, and by providing predictive mapping of their spatial patterns at high level of detail across the landscape, this work unequivocally highlighted the high potential of RS technology for conservation purposes of cryptogams. This thesis thus represents a very important step to achieve the inclusion of these inconspicuous and generally overlooked species into systematic conservation planning.
... The topographic-related drivers that are commonly used spans elevation and slope, while the Emberger quotient is a key bioclimatic predictor in the Mediterranean region, while soil predictors use, is marginal (Piedallu et al., 2013;Vessella et al., 2015;Lopez-Tirado & Hidalgo, 2018;Wallas et al., 2019). Limitations related to occurrence and to the availability and accuracy of environmental predictors data triggered the development of models that rely on spatially collected data from satellite imagery or aerial photography that resulted into fine predicted distribution at local scale (Saatchi et al., 2008;He et al., 2015). The aims of this study are to (i) assess new environmental predictors for shrub species distribution models, especially adding remote sensed drivers; (ii) assess the model accuracy, based in species with a small number of presence points, and (iii) generate suitability maps for decision making for conservation and ecosystem restoration in Lebanon. ...
... These findings highlight the importance of geomorphology, topography, CC or LST predictors as generated from remote sensing in shaping the distribution of species at the local scale (Fenu et al., 2014;Muñoz et al., 2016;Bobrowsky et al., 2018;Rita et al., 2021), especially when these are provided at a fine resolution, and combined with climatic predictors (Saatchi et al., 2008;He et al., 2015). The importance of classical predictors such as EQ, Tmax or Tmin is common for Mediterranean elements such as C. villosa and S. ...
The identification of species' environmental predictors constitute a key challenge for decision making, especially when using ecological niche modeling based on these drivers and when presence points are limited. More specifically, shrub species are affected by ecosystem dynamics, and appear in degraded formations, in dense mid-stage vegetation formations, or under late climax-forest canopy. In this study, we tested novelty predictors to understand the drivers that affect the selected species distribution in the Mediterranean biome, targeting different vegetation successional stages, and further improve ecological models' performance, when presence points are limited. Land surface temperature (LST) in association with temperature related predictors, allowed differentiating between species thriving in the understory of the forest canopy, from those that are co-dominant with dense vegetation cover and a third group/species, thriving in degraded vegetation. In addition, the Normalized Difference Vegetation Level Index (NDVI) played a key role in the models for species growing in highly degraded ecological niches such as Spartium junceum, Calicotome villosa, but also forest-fringe vegetation like the climber Hedera helix. Our study highlights the importance of integrating remote sensed predictors, combined with appropriate climate drivers, when using ecological niche modeling.
... indices and spectral bands (Bradley et al., 2012;Saatchi et al., 2008;Zhang et al., 2003). The contribution of these datasets for large and inaccessible areas with their fine spatial, spectral and temporal resolutions makes them preferable (Bradley, 2014;He et al., 2015;Paz-Kagan et al., 2019). ...
... Studies carried out for large areas usually consider low spatial resolution predictor variables and consequently cannot accurately measure the difference at the landscape level Fig. 3. Relative variable influence of S2-VIs, S2-bands, TOPOCLIM and the combined model. (Engler et al., 2013;Feilhauer et al., 2013;Saatchi et al., 2008). In addition, other studies also described the problem of overestimation of models built on climate derived variables Deblauwe et al., 2016). ...
Earth observation data play a vital role for efficient modeling of invasive species. Particularly, optical Sentinel-2 (S2) data with its capability of providing high spatial, spectral and temporal resolutions creates ample opportunities. However, few studies so far evaluated the combined use of S2 derived variables and environmental variables for modeling the distribution of invasive species. This study aims to compare the performance of models using S2 derived variables with environmental variables and their integration for modeling invasive Prosopis juliflora in the lower Awash River basin of Ethiopia. A total of 680 field data were used to train and validate the Random Forest (RF) approach. Model performances were evaluated using True Skill Statistics (TSS), kappa index, correlation, area under the curve (ROC), sensitivity and specificity. Our results demonstrated that modeling using S2 vegetation indices and S2 spectral bands showed higher performance compared to topo-climatic based variables with TSS of 0.91, 0.89, and 0.74, respectively. The ROC also confirmed the higher accuracy of S2 vegetation indices, S2 spectral bands and combined models compared to a topo-climatic based modeling. Interestingly, models using the integration of S2 derived variables with topo-climatic variables showed even better performance than the individual models. Our study highlighted that S2 derived variables and their integration with topo-climatic variables are highly recommended for efficient monitoring of invasive species distribution.
... The values of the Shannon-Wiener index are intermediates in the range (2.1-3.6) reported by Campbell et al. (1992) and Damasceno-Junior et al. (2005). Furthermore, areas with prolonged drought periods are reported to show low diversity indexes (ter Steege et al., 2003;Saatchi et al., namics of tropical forests (Hartshorn, 1980;Moret et al., 2008;Ruschel, 2008;Lohbeck, 2014). Nevertheless, the decline of C. peltata from year 15 is not coincident with some reports that indicate that pioneers such as this species have a life cycle up to 50 years and that at year 20 still maintains a high proportion of the basal area community (de Oliveira, 2005). ...
Aim of study: We evaluate palm and tree species diversity in a floodplain forest and the changes affecting the plots subjected to different intensities of selective logging.Area of study: The western alluvial plains of Venezuela.Materials and Methods: A randomized complete blocks design was established 25 years ago with three felling treatments (trees with diameter greater than 20 cm, 40 cm and 60 cm). Each treatment had three replications, using 1 ha permanent plots. We have measured all trees and palms bigger than over 10 cm in diameter. The data set was used to calculate the Importance Value Index of each species, the Shannon-Wiener index, the Hill Numbers and the Chao-Sørensen index.Main results: Disturbance increases the importance value index of pioneer species like Cecropia peltata, Ochroma pyramidale and Triplaris americana. All treatments produce changes on the floristic diversity but most of them are not significant. Only the high impact treatment causes a decrease in the species richness, but after 5 year of recovery this parameter is close to its previous levels (N0= 43.5). In logged forests, species loss (9.2%) is lower than in the control plots (11.7%) and is also lower than the rate of occurrence of species input (14.6%).Research highlights: In these logged forests restoration of diversity is acceptable because is higher than 91% (Chao-Sørensen index). Selective logging, with low and medium intensity, is a disturbance that works in a similar way to natural disturbances. All the diversity indexes recovered the pre-harvest level values.Keywords: Caparo - Venezuela; ecological restoration; forest dynamics; forest management; forest succession; Intermediate Disturbance Hypothesis; permanent plots.
... The use of spectral data within this heterogeneous structure can improve the understanding of biodiversity and biogeography (Tuomisto et al., 1995). Also, these data can improve our understanding of suitable habitats, especially by providing information about species composition, forest structure, and ecosystem health (Saatchi et al., 2008). ...
Scots pine (Pinus sylvestris L.) holds a substantial position as a tree species designated for biomass energy within European forests, covering a significant part of Türkiye’s forests. We used the machine learning technique, namely, maximum
entropy (MaxEnt), to estimate the suitable areas for Scots pine and to investigate its potential future distribution under various climate change scenarios in Inner Anatolian Region, Türkiye. The distribution data of Scots pine was utilized, and a set of 20 variables was chosen from spectral, topographic, and bioclimatic datasets to train the MaxEnt model. A map depicting the potential distribution of Scots pine in
the area was generated, and alterations in its spatial distribution under SSP2-4.5 and SSP5-8.5 climate change scenarios were predicted. The results showed that the most effective factors for the distribution of Scots pine in the region were normalized difference vegetation index (NDVI), Red band of the imagery,
and Bio19 variables, and the contribution percentages were 45.6%, 18.5%, and 18.1%, respectively. Current conditions have indicated that 81.11% of the region
is not suitable for Scots pine. Highly suitable areas for Scots pine constituted 0.88% of the total area in the east and southeast parts of the region. Considering the SSP2-4.5 and SSP5-8.5 scenarios, it has been determined that there may be a partial increase in highly suitable areas. The above-ground biomass (AGB) data generated based on potential distribution areas were predicted between 0.04 and 168.76 t ha−1, and the areas with dense biomass over 120 t ha−1 were identified in the west, north, and northeast parts of the region. While actual AGB of Scots pine was 6.92 MT, its potential AGB was estimated 125.93 MT in total area. The difference may well be attributed to the wide potential distribution of Scots pine stands in the area apart from the current forest lands. Nevertheless, this research contributes to the holistic management of forests and provides substantial values for formulating well-suited silvicultural interventions, developing sustainable forest management strategies, and furthering research aimed at estimating biomass
reserves.
... These bioclimatic variables, which include regional variations of annual means, extreme or limiting climatic conditions, and seasonality (e.g. Buermann et al. 2008;Saatchi et al. 2008), have previously been used to estimate the distribution of plant species. Mean, minimum, maximum temperatures, solar radiation, precipitation and wind speed for each month were also used. ...
An effective method for identifying species and evaluating the effects of changes caused by humans on specific species is the application of species distribution modelling (SDM) in desert environments. The fact that many dry lands and deserts throughout the world are situated in inhospitable regions may be the reason why such applications are still infrequently used on plant species in Egypt's Mediterranean region. Henceforth, the current study aims to map species richness and weighted endemism of Mediterranean endemics in the Mareotis subsector in Egypt and determine the environmental variables influencing distribution of these taxa. We produced a map of species distribution range using Ensemble SDMs. Further, stacked machine learning ensemble models derived from Random Forest (RF) and MaxEnt models were applied on 382 Mediter-ranean endemics distribution data to estimate and map diversity and endemism using two indices: species richness (SR) and weighted endemism index (WEI). The best models for ensemble modelling were chosen based on Kappa values and the Area Under the Receiver Operator Curve (AUC). The results showed that the models had a good predictive ability (Area Under the Curve (AUC) for all SDMs was > 0.75), indicating high accuracy in forecasting the potential geographic distribution of Mediterranean endemics. The main bioclimatic variables that impacted potential distributions of most species were wind speed, elevation and minimum temperature of coldest month. According to our models, six hotspots were determined for Mediterranean endemics in the present study. The highest species richness was recorded in Sallum, Matrouh wadis and Omayed, followed by Burg El-Arab, Ras El-Hekma and Lake Mariut. Indeed, species richness and endemism hotspots are promising areas for conservation planning. This study can help shape policy and mitigation efforts to protect and preserve Mediterranean endemics in the coastal desert of Egypt. These hotspots should be focused on by policy makers and stake-holders and declared as protectorates in the region. The largest number of species per area would be protected by focusing primarily on the hotspots with high species richness.
... Gathering this detailed information requires significant manpower, time, and prior knowledge, rendering it unsuitable for undertaking large-scale tree species surveys. With technological advancements, remote sensing has gradually been applied in tree species classification [7]. This approach initially extracts features from data that are then combined with traditional supervised classification methods, such as support vector machines [8], maximum likelihood [9], and random forest [10], to achieve tree species classification. ...
To address the disorderliness issue of point cloud data when directly used for tree species classification, this study transformed point cloud data into projected images for classification. Building upon this foundation, the influence of incorporating multiple distinct projection perspectives, integrating depth information, and utilising various classification models on the classification of tree point cloud projected images was investigated. Nine tree species in Sanjiangkou Ecological Park, Fuzhou City, were selected as samples. In the single-direction projection classification, the X-direction projection exhibited the highest average accuracy of 80.56%. In the dual-direction projection classification, the XY-direction projection exhibited the highest accuracy of 84.76%, which increased to 87.14% after adding depth information. Four classification models (convolutional neural network, CNN; visual geometry group, VGG; ResNet; and densely connected convolutional networks, DenseNet) were used to classify the datasets, with average accuracies of 73.53%, 85.83%, 87%, and 86.79%, respectively. Utilising datasets with depth and multidirectional information can enhance the accuracy and robustness of image classification. Among the models, the CNN served as a baseline model, VGG accuracy was 12.3% higher than that of CNN, DenseNet had a smaller gap between the average accuracy and the optimal result, and ResNet performed the best in classification tasks.
... MaxEnt is frequently used to simulate species distribution probability utilizing environmental data from several "background" information in addition to confirmed presence locations [60,61]. In addition to species distribution prediction, some scholars have applied it to one-class classification [62], such as the simulation of tree species distribution [63], tree species classification [64], and extraction of fire-burned land [65]. Permafrost has special environmental attributes, and it proliferates or degenerates with changes in climate and environment [66]. ...
High-resolution permafrost mapping is an important direction in permafrost research. Arxan is a typical area with permafrost degradation and is situated on the southern boundary of the permafrost region in Northeast China. With the help of Google Earth Engine (GEE), the maximum entropy classifier (MaxEnt) is used for permafrost mapping using the land surface temperature (LST) of different seasons, deviation from mean elevation (DEV), solar radiation (SR), normalized difference vegetation index (NDVI), and normalized difference water index (NDWI) as the characteristic variables. The prior data of permafrost distribution were primarily based on 201 borehole data and field investigation data. A permafrost probability (PP) distribution map with a resolution of 30 m was obtained. The receiver operating characteristic (ROC) curve was used to test the distribution results, with an area under the curve (AUC) value of 0.986. The results characterize the distribution of permafrost at a high resolution. Permafrost is mainly distributed in the Greater Khingan Mountains (GKM) in the research area, which run from the northeast to the southwest, followed by low-altitude area in the northwest. According to topographic distribution, permafrost is primarily found on slope surfaces, with minor amounts present in peaks, ridges, and valleys. The employed PP distribution mapping method offers a suggestion for high-resolution permafrost mapping in permafrost degradation areas.
... It is based on the geographical association between the occupancy of the species' locations and climate variables. SDM is useful for documenting biodiversity in geographical space, predicting the potential distributions of the vulnerable species and assuming the effects of climate in contrast to anthropogenic changes (Loiselle et al. 2003;Dale et al. 2001;Franklin 2010;Saatchi et al. 2008). Nowadays, critical conservation decisions are being taken up based on suitability thresholds given by any such statistical and machine learning distribution models (Sharma et al. 2021). ...
Climate change has signifcantly afected the potential distribution and altitudinal shift of several plant species. Amentotaxus
assamicus being one of the critically endangered gymnosperms under the family Taxaceac is restricted only to a few pockets
of Arunachal Pradesh with low population size. The current study aims to analyze the current distribution of A. assamica in
the state using key environmental parameters and to predict the potential suitable habitat in accordance with two IPCC representative concentration pathway (RCP) scenarios. The future potential distribution was projected for two possible climate
scenarios (RCP 4.5 and RCP 8.5) given by three various global climate models (GCMs), viz., BC_CSM 1.1 (BC), CCSM4
(CC), and CNRM-CM5 (CN). A total of 36 independent localities of A. assamica were used to model the current species
distribution along with 23 environmental variables, including bioclimatic parameters, elevation, global land cover, and soil
data. To run the future simulations, IPCC AR5 scenarios were used for 19 bioclimatic variables. Maxent modeling was used
for the current distribution of A. assamica in Arunachal Pradesh, India, through 10 duplicate runs which showed the test AUC
average of 0.905 as well as a standard deviation of 0.057. Soil available nitrogen at 15 cm depth was found to contribute the
maximum in the model accounting for 38.2% followed by soil nitrogen at 5 cm depth (21.8%). Bio 4, Bio 6, Bio 7, and Bio
19 were the key variables that contributed to varying extent in all the three GCMs consisting of two scenarios each. Under
the high suitability zone, the optimistic scenario (RCP 4.5; 3618.25 km2
) represented the maximum area followed by RCP
8.5 (3269.89 km2
) whereas the lowest in the current distribution model revealed as 2909.64 km2
. Furthermore, the high suitability distribution range in terms of altitudinal regime shifted from 270 msl of lowest elevation in the current distribution
to the 966 msl in the RCP 4.5 scenario and 894 msl in the RCP 8.5 scenario. The altitudinal shift of the distribution found
in the futuristic model is signifcant, and the species’ lower range of altitudinal distribution has clearly shifted upward. The
fndings of this study would be useful in determining quantifed future climate space for the species and allow the conservation managers to formulate appropriate conservation strategies.
... Potentially suitable areas for wild radish were predicted based on 152 global occurrence records and 10 environmental variables using the MaxEnt model. The AUC value offers a threshold-independent measure of the overall accuracy of the model and is an important model quality indicator for evaluating the accuracy of the applied model [40,41]. The AUC value of this study under each scenario framework, i.e., LGM, current, and future, is much greater than 0.9, suggesting that the prediction model exhibits an excellent fitting ability. ...
Climate change can exert a considerable influence on the geographic distribution of many taxa, including coastal plants and populations of some plant species closely related to those used as agricultural crops. East Asian wild radish, Raphanus raphanistrum subsp. sativus, is an annual coastal plant that is a wild relative of the cultivated radish (R. sativus). It has served as source of genetic material that has been helpful to develop and improve the quality and yield of radish crops. To assess the impact of climate change on wild radish in East Asia, we analyzed its distribution at different periods using the maximum entropy model (MaxEnt). The results indicated that the precipitation of the driest month (bio14) and precipitation seasonality (bio15) were the two most dominant environmental factors that affected the geographical distribution of wild radish in East Asia. The total potential area suitable for wild radish is 102.5574 × 104 km2, mainly located along the seacoasts of southern China, Korea, and the Japanese archipelago. Compared with its current distribution regions, the potentially suitable areas for wild radish in the 2070s will further increase and expand northwards in Japan, especially on the sand beach habitats of Hokkaido. This research reveals the spatiotemporal changes for the coastal plant wild radish under global warming and simultaneously provides a vital scientific basis for effective utilization and germplasm innovation for radish cultivars to achieve sustainable agriculture development.
... For forest AGB estimation using remotely sensed data, the environmental constraint information can be derived from the spectral information of remote sensing image data (e.g., MODIS, ALOS, shuttle radar topography mission, SRTM, Landsat). Furthermore, the ME model could contain many attributes and thus is highly suitable for large-scale forest AGB mapping [212]. Saatchi et al. [213] successfully developed a global, tropical forest biomass (aboveground and belowground) distribution map using an ME algorithm based on fourteen remotely sensed variables (five MODIS NDVI, three MODIS LAI, four quick scatter meter, QSCAT, and two SRTM derived metrics). ...
Quantifying forest aboveground biomass (AGB) is essential for elucidating the global carbon cycle and the response of forest ecosystems to climate change. Over the past five decades, remote-sensing techniques have played a vital role in forest AGB estimation at different scales. Here, we present an overview of the progress in remote sensing-based forest AGB estimation. More in detail, we first describe the principles of remote sensing techniques in forest AGB estimation: that is, the construction and use of parameters associated with AGB (rather than the direct measurement of AGB values). Second, we review forest AGB remotely sensed data sources (including passive optical, microwave, and LiDAR) and methods (e.g., empirical, physical, mechanistic, and comprehensive models) alongside their limitations and advantages. Third, we discuss possible sources of uncertainty in resultant forest AGB estimates, including those associated with remote sensing imagery, sample plot survey data, stand structure, and statistical models. Finally, we offer forward-looking perspectives and insights on prospective research directions for remote sensing-based forest AGB estimation. Remote sensing is anticipated to play an increasingly important role in future forest AGB estimation and carbon cycle studies. Overall, this comprehensive review may (1) benefit the research communities focused on carbon cycle, remote sensing, and climate change elucidation, (2) provide a theoretical basis for the study of the carbon cycle and global climate change, (3) inform forest ecosystems and carbon management, and (4) aid in the elucidation of forest feedbacks to climate change.
... Ces modèles constituent un outil de prise de décisions par leur capacité à contribuer à définir les zones prioritaires où les espèces pourraient être réintroduites permettant ainsi la compréhension de la biologie et de l'écologie de celles-ci. Il est aussi essentiel pour prendre en compte l'effet du changement climatique et/ou des pressions anthropiques dans la dynamique paysagère (Saatchi et al., 2008). ...
RESUME Les variabilités climatiques pourraient compromettre les services écosystémiques fournis par les espèces ligneuses alimentaires en milieu naturel. Cette étude conduite dans la région de Zinder visait à modéliser la distribution actuelle de B. senegalensis dans les écosystèmes du Niger par l'approche du Maximum d'Entropie. Au total, 669 points d'occurrence distants d'au moins 1 km ont été combinés aux variables bioclimatiques de WorldClim 2.1 et de l'altitude d'une part et celles d'AfriClim 3.0 d'autre part après des analyses de corrélations de Spearman et de détermination du Facteur d'Inflation de la Variance réalisés avec le logiciel R. Ces prédicteurs rendent compte de la disponibilité en eau et du gradient d'aridité. WorldClim 2.1 projette la variation annuelle de température (Bio_7), la saisonnalité de la température (Bio_4) et l'élévation (elev) comme principales variables prédictives et AfriClim 3.0 suggère l'indice d'aridité du trimestre le plus humide (mimq), la saisonnalité de la température (Bio 4) et la durée de la plus longue saison sèche (llds). Les modèles prédisent les plus fortes probabilités de distribution de l'espèce essentiellement dans des zones qu'elle occupe actuellement. B. senegalensis peut être une espèce candidate pour reboiser les écosystèmes dégradés dans les zones prédites favorables à sa distribution spatiale. ABSTRACT Climate variability could compromise the ecosystem services provided by woody food species in the wild. This study conducted in the Zinder region aimed to model the current distribution of B. senegalensis in Niger ecosystems using the Maximum Entropy Approach. A total of 669 points of occurrence at least 1 km apart were combined with bioclimatic variables from WorldClim 2.1 and altitude on the one hand and AfriClim I. D. MOUSSA et al. / Int. J. Biol. Chem. Sci. 16(5): 2053-2069, 2022 2054 3.0 on the other hand after Spearman correlation and Variance Inflation Factor analyses performed with R software. These predictors account for water availability and the aridity gradient. WorldClim 2.1 projects annual temperature variation (Bio_7), temperature seasonality (Bio_4) and elevation (elev) as the main predictors and AfriClim 3.0 suggests the wettest quarter aridity index (mimq), temperature seasonality (Bio 4) and the duration of the longest dry season (llds). The models predict the highest probability of distribution of the species primarily in areas it currently occupies. B. senegalensis may be a candidate species to reforest degraded ecosystems in predicted areas favorable to its spatial distribution.
... In addition, it is less sensitive to the multicollinearity problem (Phillips, Anderson, and Schapire 2006). It showed the best predictive capacity and was the most precise (Wang et al. 2007;Saatchi et al. 2008). The model requires occurrence points and environmental variables as inputs. ...
Climate change is expected to alter the growing conditions of agricultural crops. With increasing surface temperature, future suitable areas for crop production will see an altitude shift. Such shift is an adaptation response of crops to climate change. However, in the study area there are a limited number of studies that have dealt with geographical shifts of crops caused by climate change. This study was conducted with the aim of assessing impacts of climate change on altitudinal migration of crops and length of growing period (LGP). The climate and crop modeling study were carried out using ArcGIS, Diva GIS and MaxEnt using 30 years of climate data for the period 1980 to 2009. Results showed that wheat (Triticum aestivum) and barley (Hordeum vulgare L.) would migrate upward along the altitudinal gradients in the coming 80 years. However, areas under these crops are expected to drop by 16-100%. Highly impacted areas are expected to increase, whereas low impacted and new suitable areas are expected to decline significantly. Suitable areas for sorghum (Sorghum bicolor) and teff (Eragrostis tef Zucc.) production are expected to increase. While wheat and barley are projected to be highly affected by future climate change, sorghum and teff should be relatively stable. No significant difference was observed in LGP between the considered RCP 2.6 and RCP 8.5 climate scenarios. Therefore, this study concluded that upward movement of crops was one mechanism to adapt to climate change, and new varieties resilient to future climate change needs to be developed. ARTICLE HISTORY
... Another advantage of MaxEnt is that it calculates species occurrence probability (i.e., a continuous value from 0 to 1) instead of deriving a binarized (0 or 1) distribution result (Phillips et al., 2006). The MaxEnt model has been successfully applied to address diverse issues regarding the potential geographical distribution of species, such as estimating the potential distribution of invasive plants and evaluating the impact of climate change on species distributions (Garcia et al., 2013;Li et al., 2019a;Ndlovu et al., 2018;Saatchi et al., 2008;Yue et al., 2019;Zhang et al., 2018). Thus, the MaxEnt model holds great potential to estimate the possible planting areas of ratoon rice at a large scale. ...
Ratoon rice has emerged as a promising rice cropping system to improve grain production and reduce labor costs compared with traditional single/double rice in China. However, the potential planting areas of ratoon rice in China remain unclear. This research investigated the potential northern limits and promotion extent of ratoon rice in China by considering its climatic suitability based on the optimized maximum entropy (MaxEnt) model as well as terrain and land use conditions. The MaxEnt model derived by all environmental variables yielded a good performance, with average AUC (area under the curve) and TSS (true skill statistic) over the validation dataset of 0.940 and 0.825, respectively. The comparison with field samples and previous studies revealed the reliability of the derived potential promotion areas. Potential northern limits contained a closed curve surrounding the Sichuan Basin, and the other curve ran from Yunnan Province to Jiangsu Province. Safe promotion areas of ratoon rice in China were 472,003 km², mainly located in Sichuan, Hubei, Guangxi and Hunan. Risky promotion areas were 74,150 km², which were dominant in Henan, Anhui and Yunnan. Our study provides crucial information for rice planting pattern adjustment to alleviate national food insecurity caused by the loss of double rice.
... In the past few decades, ENMs have been widely applied to study the distribution of species. Many studies have demonstrated that the MaxEnt model has certain advantages in terms of prediction accuracy, particularly in the case of fewer target species distribution data (Phillips et al., 2006;Saatchi et al., 2008;Yi et al., 2017). Zhang et al. (2016) compared the prediction accuracy of 4 commonly used niche models, and the results showed that MaxEnt model had better prediction effect. ...
The wasp Scleroderma guani is an important parasitic natural enemy of a variety of stem borers such as longicorn beetles. Studying and clarifying the suitable area of this wasp plays an important role in controlling stem borers. Based on information about the actual distribution of S. guani and on a set of environmental variables, MaxEnt niche model and ArcGIS were exploited to predict the potential distribution of this insect in China. This work simulated the geographical distribution of potential climatic suitability of S. guani in China at present and in different periods in the future. Combining the relative percent contribution score of environmental factors and the Jackknife test, the dominant environmental variables and their appropriate values restricting the potential geographical distribution of S. guani were screened. The results showed that the prediction of the MaxEnt model was highly in line with the actual distribution under current climate conditions, and the simulation accuracy was very high. The distribution of S. guani is mainly affected by bio18 (Precipitation of Warmest Quarter), bio11 (Mean Temperature of Coldest Quarter), bio13 (Precipitation of Wettest Month), and bio3 (Isothermality). The suitable habitat of S. guani in China is mainly distributed in the Northeast China Plain, North China Plain, middle-lower Yangtze Plain, and Sichuan Basin, with total suitable area of 547.05 × 104 km2, accounting for 56.85% of China's territory. Furthermore, under the RCP2.6, RCP4.5, and RCP8.5 climate change scenarios in the 2050s and 2090s, the areas of high, medium, and low suitability showed different degrees of change compared to nowadays, exhibiting expansion trend in the future. This work provides theoretical support for related research on pest control and ecological protection.
... Maximum entropy species distribution modeling (Maxent) was first used to simulate the geographical distribution of species [45]. It is a general machine learning algorithm [46], suitable for different species such as birds, terrestrial plants and bats [47][48][49], for example. However, the implication of Maxent in grasshopper occurrence is not well-docu-mented. ...
Grasshoppers mainly threaten natural grassland vegetation and crops. Therefore, it is of great significance to understand the relationship between environmental factors and grasshopper occurrence. This paper studies the spatial distribution and key factors of grasshopper occurrence in two grass types by integrating a machine learning model (Maxent) and remote sensing data within the major grasshopper occurrence areas of Inner Mongolia, China. The modelling results demonstrate that the typical steppe has larger suitable area and more proportion for grasshopper living than meadow steppe. The soil type, above biomass, altitude and temperature mainly determine the grasshopper occurrence in typical steppe and meadow steppe. However, the contribution of these factors in the two grass types is significantly different. In addition, related vegetation and meteorological factors affect the different growing stages of grasshoppers between the two grass types. This study clearly defines the different effects of key environmental factors (meteorology, vegetation, soil and topography) for grasshopper occurrence in typical steppe and meadow steppe. It also provides a methodology to guide early warning and precautions for grasshopper pest prevention. The findings of this study will be helpful for future management measures, to ensure grass ecological environment security and the sustainable development of grassland.
... To date, the GBIF data portal provides free access to more than two billion species occurrence records. This growing number of digitized and georeferenced species occurrence records has created an opportunity to monitor species diversity and distribution patterns at large spatial scales over extended time periods [29,30]. However, there are major concerns about the use of such datasets, as the scientific community acknowledges that these species occurrence records result from years of different researchers working with different aims and methodologies are often biased due to variation in sampling efforts in space and time [27,31,32]. ...
Fungi are a hyper-diverse kingdom that contributes significantly to the regulation of the global carbon and nutrient cycle. However, our understanding of the distribution of fungal diversity is often hindered by a lack of data, especially on a large spatial scale. Open biodiversity data may provide a solution, but concerns about the potential spatial and temporal bias in species occurrence data arising from different observers and sampling protocols challenge their utility. The theory of species accumulation curves predicts that the cumulative number of species reaches an asymptote when the sampling effort is sufficiently large. Thus, we hypothesize that open biodiversity data could be used to reveal large-scale macrofungal diversity patterns if these datasets are accumulated long enough. Here, we tested our hypothesis with 50 years of macrofungal occurrence records in Norway and Sweden that were downloaded from the Global Biodiversity Information Facility (GBIF). We first grouped the data into five temporal subsamples with different cumulative sampling efforts (i.e., accumulation of data for 10, 20, 30, 40 and 50 years). We then predicted the macrofungal diversity and distribution at each subsample using the maximum entropy (MaxEnt) species distribution model. The results revealed that the cumulative number of macrofungal species stabilized into distinct distribution patterns with localized hotspots of predicted macrofungal diversity with sampling efforts greater than approximately 30 years. Our research demonstrates the utility and importance of the long-term accumulated open biodiversity data in studying macrofungal diversity and distribution at the national level.
... Both CD and HW species are defined using parsimonious response functions, with annual precipitation and annual mean temperature as independent variables. Still, the geographical patterns obtained through our virtual species are consistent with reported empirical patterns, such as for birds (e.g., Eberhard and Bermingham, 2004, Echarri et al., 2009), mammals (e.g., Poo-Muñoz et al., 2014, Da Rocha et al., 2015 and trees (e.g., Nicola et al., 2014;Saatchi et al., 2008). Moreover, the emergent dynamics of species ranges between 0 and 22 kyr BP is both biologically plausible (Araújo and Pearson, 2005;Clark, 1998;Higgins et al., 2003;Pearson, 2006; Tingley et al., 2009) and reliable in the context of Neotropical paleobiogeography (Ingenloff and Peterson, 2015, Cabanne et al., 2016, Varela et al., 2017, Eduardo et al., 2018a, 2018b. ...
Species distribution models (SDMs), the most prominent tool in modern biogeography, rely on the assumptions that (i) species distribution is in equilibrium with the environment and (ii) that climatic niche has been conserved throughout recent geological time. These issues affect the spatial and temporal transferability of SDMs, limiting their reliability for applications such as when studying effects of past climate change on species distribution and extinctions. The integration of paleontological and neontological data for a multitemporal calibration and validation of SDMs has been suggested for improving SDMs flexibility. Here, we provide an empirical test for a multitemporal calibration, employing virtual species (i.e., with perfectly-known distributions) and comparing them directly with monotemporal SDMs (i.e., SDM calibrated in a single time layer). We used 1kyr-interval scenarios throughout the last 22 kyr BP for two ecologically different species in South America (a “hot and wet” species and a “cold and dry” species). Models with multitemporal calibration performed similarly to models with monotemporal calibration, regardless of species, sample sizes, and time frame. However, multitemporal calibration performed better when dealing with non-analogous climates among time layers. By improving the temporal SDMs transferability, multitemporal calibration opens new avenues for integrating fossil and recent occurrence data, which may substantially benefit biogeography and paleoecology.
... The maximum entropy model (MaxEnt) is a modeling method based on presence-only species records associated to a package of environments of a region of interest (Phillips et al. 2006;Phillips & Dudik 2008). Some studies have demonstrated that this modeling tend to be more accurate than other models considering incomplete species distribution records (Saatchi et al. 2008;Yi et al. 2017), although its results should be treated only as estimates of relative suitability (Guillera-Arroita et al. 2014;Zurell et al. 2020). The prediction recovered here suggests that O. moreleti has a plenty of climatically suitable areas in South America with high potential of invasion. ...
The Portuguese millipede Ommatoiulus moreleti (Lucas, 1860) is widespread by commerce in Atlantic Islands, Australia, and South Africa, besides being regarded as a pest infesting plantations of vegetables and fruits. To date, the species has never been recorded in South America. In this study, we report for the first time the occurrence of O. moreleti in Brazil, with adults and immatures collected in the municipality of Campos do Jordão, São Paulo state. A maximum entropy model was used to explore the predicted distribution of the species in South America. The results showed a highly suitable area in the continent (AUC = 0.987), mainly in the Atlantic Forest, and regions of Argentina, Chile, and Uruguay. Considering percent contribution, the annual mean temperature (= 34%) and the temperature seasonality (= 33.4%) were the main variables which influenced the modeling. The potential risks of invasion of O. moreleti must be highlighted, including possible competition with native millipedes, its being a significant agricultural pest, and its successful invasion into new habitat without natural enemies.
... Habitat suitability models, which can signifi cantly improve the understanding on species niche requirements, have been widely used to predict the potential distributions of species by using distribution points and environmental variables (Peterson et al., 2002;Hirzel et al., 2006;Xavier et al., 2016). Implementation of habitat suitability started with terrestrial species, with increasing numbers of publications each year (Phillips et al., 2006;Saatchi et al., 2008;Robinson et al., 2011;Melo-Merino et al., 2020). In the Southern Ocean, Bombosch et al. (2014) modelled habitat suitability of humpback and Antarctic minke whales in the Southern Ocean, they produced daily basinwide/circumpolar prediction maps of habitat suitability. ...
Antarctic krill is the key species of ecological system in the Amundsen Sea. At present, the suitable distribution is unobtainable by scientific surveys or data from the fishery. In this paper, the maximum entropy algorithm (Maxent) was used to obtain the potential distribution of adult Antarctic krill in order to provide useful information and reasonable reference for the policy on protecting potential krill habitats around the Amundsen Sea. Occurrence points and 17 environmental variables were used to simulate the distributions. Results show that the high and moderate suitable habitats lie between 65°S and 72°S in the Amundsen Sea. The high suitable habitat accounts for 8.1% of the total area of the Amundsen Sea. The sea ice persistence (ICE), total phytoplankton (PHYC), and the minimum value of dissolved iron (Fe_min) are the three dominant contributors to the model. Results from the response curves show that Antarctic krill preferred habitats with ICE of 0.42–0.93, PHYC of 2.48–2.77 mmol/m3 and Fe_min of (7.10×10−5)−(9.45×10−5) mmol/m3. Positive trends existed in the PHYC of the high and moderate suitable habitat, and a positive trend existed in the Fe_min of moderate suitable habitat. However, the probability of presence of Antarctic krill will decrease if the increase of the PHYC and Fe_min continues.
... It is an excellent choice to use the maximum entropy (MaxEnt) model to forecast the potential geographical distribution of species according to the existing species distribution information and various environmental data (Elith & Graham, 2009;Phillips, Anderson, et al., 2006;Phillips & Dudík, 2008. MaxEnt, based on the maximum entropy theory, has good accuracy even when the information on species distribution is insufficient (Saatchi et al., 2008;Yi et al., 2017). The model takes the climatic variables of the existing distribution points of species as the constraint conditions, supposing that the species will appear in all regions with suitable climatic conditions, but not in any regions not suitable for climatic conditions; the greater the entropy of species, the closer the probability distribution of species is to reality (Phillips, Anderson, et al., 2006). ...
Understanding the impacts and constraints of climate change on the geographical distribution of wild Akebia trifoliata is crucial for its sustainable management and economic development as a medicinal material or fruit. In this study, according to the first‐hand information obtained from field investigation, the distribution and response to climate change of A. trifoliata were studied by the MaxEnt model and ArcGIS. The genetic diversity and population structure of 21 natural populations of A. trifoliata were studied by simple sequence repeat (SSR) markers. The results showed that the most important bioclimatic variable limiting the distribution of A. trifoliata was the Mean Temperature of Coldest Quarter (bio11). Under the scenarios SSP1‐2.6 and SSP2‐4.5, the suitable area of A. trifoliata in the world will remain stable, and the suitable area will increase significantly under the scenarios of SSP3‐7.0 and SSP5‐8.5. Under the current climate scenario, the suitable growth regions of A. trifoliata in China were 79.9–122.7°E and 21.5–37.5°N. Under the four emission scenarios in the future, the geometric center of the suitable distribution regions of Akebia trifoliata in China will move to the north. The clustering results of 21 populations of A. trifoliata analyzed by SSR markers showed that they had a trend of evolution from south to north. Precipitation and temperature were the two most important climatic factors that restrict the geographic distribution of Akebia trifoliata. Under the current climate scenario, the suitable growth regions of A. trifoliata in China were 91.7–121.9°E and 21.6–37.5°N. Combined with the evolutionary relationship and prediction results, 21 populations of A. trifoliata tended to migrate to the north. The distribution center of A. trifoliata in China will shift to high latitude regions with the increase of temperature in the future.
... Furthermore, vegetation phenology variables reduce overfitting of species distribution prediction models, since bioclimatic variables are coarser at the grain level (1 km spatial resolution) and only offer interpolated climatic information, which depict homogeneity at large spatial extents. On the other hand, vegetation phenology data is finer at grain level, offering higher spatial resolution (250 m), hence have more power to incorporate environmental heterogeneity on the ground (Saatchi et al. 2008). In this regard, remotely sensed vegetation phenology data captures adverse environmental conditions, which limit the spatial distribution of stingless bees within homogenous climatic pockets. ...
Stingless/meliponine bees are eusocial insects whose polylactic nature enables interaction with a wide variety of wild plants and crops that enhance pollination and, hence, support ecosystem services. However, their true potential regarding pollination services and honey production is yet to be fully recognized. Worldwide, there are over 800 species of meliponine bees, with over 20 species documented on the African continent. Out of these, only 12 species have been well documented in Kenya. Moreover, interest on meliponine bees has increased amid climate change, agricultural intensification, and other anthropogenic effects. Generally, stingless bees are under-researched, with no previous documented evidence of their ecological niche (EN) distribution in most African countries. Hence, this study sought to establish the influence of bioclimatic, topographic, and vegetation phenology on their spatial distribution and change patterns. Stingless response variables from 490 sample points were collected and used in conjunction with 11 non-conflating features to build stingless ecological niche models. Six machine learning-based EN models were used to predict the distribution of seven stingless bees’ species combined. The results from the EN models showed that annual precipitation was the most influential variable to stingless bee distribution (contributing 43.09% logit), while potential evapotranspiration and temperature seasonality contributed 21.18% of the information needed to predict the spatial distribution of stingless bees. Vegetation phenology (21.36%) and topography (14.36%) had moderate effect on stingless bees’ distribution. On the other hand, high seasonality in precipitation and temperature indicated high stingless niche variability in the future (i.e. 2055). The performance of six EN algorithms used to predict distribution of stingless bees was found to be “excellent” for random forest (true skills statistics (TSS) = 0.91) and ranger (TSS = 0.90) and “good” for generalized additive models (TSS = 0.87), multivariate adaptive regression spline (TSS = 0.80), and boosted regression trees (TSS = 0.80), while they were “fair” for recursive portioning and regression trees (TSS = 0.79). These EN models could be utilized to inform stingless bee farming and insects pollinated crops by highlighting regions that provide highly suitable conditions for stingless bees. Additionally, the findings could be harnessed to increase both bee and agricultural productivity and forest conservation efforts through supplementary pollination services.
... By comparing the true skill statistics, we found that the value of MaxEnt was the closest to 1, indicating that this model had the best accuracy [51]. In the absence of species distribution data, MaxEnt still performed well in generating accurate predictions [52][53][54]. This model has been widely used in conservation biology, invasion biology and other fields [49,[54][55][56][57][58]. ...
As an important Tibetan medicine and a secondary protected plant in China, Pomatosace filicula is endemic to the country and is mainly distributed in the Qinghai–Tibet Plateau (QTP). However, global climate change caused by greenhouse gas emissions might lead to the extinction of P. filicula. To understand the potential spatial distribution of P. filicula in future global warming scenarios, we used the MaxEnt model to simulate changes in its suitable habitat that would occur by 2050 and 2070 using four representative concentration pathway (RCP) scenarios and five global climate models. The results showed that the QTP currently contains a suitable habitat for P. filicula and will continue to do so in the future. Under the RCP2.6 scenario, the suitable habitat area would increase by 2050 but shrink slightly by 2070, with an average reduction of 2.7%. However, under the RCP8.5 scenario, the area of unsuitable habitat would expand by an average of 54.65% and 68.20% by 2050 and 2070, respectively. The changes in the area of suitable habitat under the RCP4.5 and RCP6.0 scenarios were similar, with the unsuitable area increasing by approximately 20% by 2050 and 2070. Under these two moderate RCPs, the total suitable area in 2070 would be greater than that in 2050. The top three environmental factors impacting the habitat distribution were altitude, annual precipitation (BIO12) and annual temperature range (BIO7). The cumulative contribution rate of these three factors was as high as 82.8%, indicating that they were the key factors affecting the distribution and adaptability of P. filicula, P. filicula grows well in damp and cold environments. Due to global warming, the QTP will become warmer and drier; thus, the growing area of P. filicula will move toward higher elevations and areas that are humid and cold. These areas are mainly found near the Three-River Region. Future climate change will aggravate the deterioration of the P. filicula habitat and increase the species’ survival risk. This study describes the distribution of P. filicula and provides a basis for the protection of endangered plants in the QTP.
... Several studies have already pointed out that the use of variables derived from satellite images can be useful to improve species distribution models (Bradley & Fleishman 2008;Wilson et al. 2013;Oke & Thompson 2015). This is especially true for studies based on few points of data collection for interpolation, which is usually the case for the Amazonian region (Hopkins 2007;Saatchi et al. 2008;Cayuela et al. 2009;Percequillo et al. 2017;Prado et al. 2019). Our best model recovered that intermediate altitudes (500-1500 m) are the most favorable for the occurrence of G. peruanus, something expected since other species of Gracilinanus inhabit the lowlands areas in the Amazonia and Central Brazil (Creighton & Gardner 2008;Voss et al. 2009;Semedo et al. 2015). ...
2022): Distribution limits, natural history and conservation status of the poorly known Peruvian gracile mouse opossum (Didelphimorphia: Didelphidae), Studies on Neotropical Fauna and Environment, ABSTRACT The Peruvian gracile mouse opossum (Gracilinanus peruanus) is known from scattered localities. Very little information is available on its natural history, habitat requirements and geographic distribution. Also, its conservation status has never been evaluated. We provide a new hypothesis on its distribution limits using species distribution models (SDM), and evaluate for the first time the conservation status of the species. The SDM suggested high suitability for G. peruanus in the Brazilian Shield, indicating continuous suitable areas for the species between the disjunct areas of western Brazil and Bolivia/Peru. Voucher records show that Teles Pires River is not a geographic barrier and the eastern limits still need to be delimited. To the south, the species appears to be limited by the Pantanal wetlands and the Chaco biome, and to the west by the Andes mountains. We suggest that G. peruanus should be globally classified as 'Least Concern.' Nevertheless, the species may be considered as Near Threatened in a near future in Brazil, mainly because it occurs in an area with high rates of deforestation and habitat loss in the Cerrado of western Brazil, thus reinforcing the importance of protecting gallery forests to forest dweller species. ARTICLE HISTORY
... It has the advantages of small sample size, fast running speed, and stable operation (Phillips et al., 2006a;Estes et al., 2013;Li J. et al., 2020). Even in the case of insufficient species distribution information, it also has good accuracy and can test the accuracy of prediction results (Saatchi et al., 2008;Yi et al., 2017). Therefore, it is widely used in many aspects of species distribution analysis (Yang et al., 2013;Qin et al., 2017;Zhang et al., 2019). ...
Akebia quinata, also known as chocolate vine, is a creeping woody vine which is used as Chinese herbal medicine, and found widely distributed in East Asia. At present, its wild resources are being constantly destroyed. This study aims to provide a theoretical basis for the resource protection of this plant species by analyzing the possible changes in its geographic distribution pattern and its response to climate factors. It is the first time maximum entropy modeling (MaxEnt) and ArcGIS software have been used to predict the distribution of A. quinata in the past, the present, and the future (four greenhouse gas emission scenarios, namely, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). Through the prediction results, the impact of climate change on the distribution of A. quinata and the response of A. quinata to climate factors were analyzed. The results showed that the most significant climatic factor affecting the distribution pattern of A. quinata was the annual precipitation. At present, the suitable distribution regions of A. quinata are mainly in the temperate zone, and a few suitable distribution regions are in the tropical zone. The medium and high suitable regions are mainly located in East Asia, accounting for 51.1 and 81.7% of the worldwide medium and high suitable regions, respectively. The migration of the geometric center of the distribution regions of A. quinata in East Asia is mainly affected by the change of distribution regions in China, and the average migration rate of the geometric center in each climate scenario is positively correlated with the level of greenhouse gas emission scenario.
... Although the normalized difference vegetation index (NDVI) is an effective substitute for forage availability that can increase the elephant occurrence [81,82] we used EVI, which has shown better saturation in high biomass regions, apart from being modified for aerosol effect and controlled for noise from soil background [19,[83][84][85]. Elephants face difficulties in thermoregulation when temperatures exceed their core body temperature [86], so their occupancy will be lesser in the regions with high temperatures. ...
Land development has impacted natural landforms extensively, causing a decline in resources and negative consequences to elephant populations, habitats, and gene flow. Often, elephants seek to fulfill basic needs by wandering into nearby human communities, which leads to human–elephant conflict (HEC), a serious threat to conserving this endangered species. Understanding elephant space use and connectivity among their habitats can offset barriers to ecological flow among fragmented populations. We focused on the Keonjhar Forest Division in Eastern India, where HEC has resulted in the deaths of ~300 people and several hundred elephants, and damaged ~4100 houses and ~12,700 acres of cropland between 2001 and 2018. Our objectives were to (1) analyze elephant space use based on their occupancy; (2) map connectivity by considering the land structure and HEC occurrences; (3) assess the quality of mapped connectivity and identify potential bottlenecks. We found that (1) the study area has the potential to sustain a significant elephant population by providing safe connectivity; (2) variables like forests, precipitation, rural built-up areas, cropland, and transportation networks were responsible for predicting elephant presence (0.407, SE = 0.098); (3) five habitat cores, interconnected by seven corridors were identified, of which three habitat cores were vital for maintaining connectivity; (4) landscape features, such as cropland, rural built-up, mining, and transportation networks created bottlenecks that could funnel elephant movement. Our findings also indicate that overlooking HEC in connectivity assessments could lead to overestimation of functionality. The study outcomes can be utilized as a preliminary tool for decision making and early planning during development projects.
... In this study, 410 species location data and 9 environmental variables were analyzed by MaxEnt to predict the distribution of tea in mainland China. Area under the curve (AUC) is an important model quality index to evaluate the model accuracy, because it provides a single measure of the accuracy of the overall model independent of selection threshold [65,66]. Generally, models with an AUC value greater than 0.75 might show good performance for niche model [23,67]. ...
Climate change has dramatic impacts on the growth and the geographical distribution of tea (Camellia sinensis L.). Assessing the potential distribution of tea will help decision makers to formulate appropriate adaptation measures to use the altered climatic resources and avoid the damage from climate hazards. The objective in this study is to model the current and future distribution of tea species based on the four SSPs scenarios using the MaxEnt model in China. For the modeling procedure, tea growth records in 410 sites and 9 climate variables were used in this paper. The area under the receiver operating characteristic (ROC) curve (AUC) was used to evaluate the performance of the model. The AUC value was over 0.9 in this study, showing the excellent simulation result of the model. In relation to the current distribution, areas of 82.01 × 104 km2 (8.51% of total land area in China), 115.97 × 104 km2 (12.03% of total land area in China), and 67.14 × 104 km2 (6.97% of total land area in China) were recognized as Marginal, Medium, and Optimal climate suitable habitats for tea over China. Compared to the current distribution, most of the Optimal suitability areas in southeast China would be lost in four scenarios. The area of Marginal and Medium suitable habitats would expand in SSP370 and SSP585, especially in 2041–2061 and 2081–2100. The suitable area of tea would expand northwards and westwards, suggesting that additional new suitable habitats could be created for tea production with the future climate change, especially in Shandong, Henan, Guizhou, and Yunnan Provinces. This research would provide vital scientific understanding for policy making on tea production, tea garden site chosen and adopyion of adaptation methods in the future.
... Advances in Remote Sensing (RS) and Geographic Information System (GIS) techniques facilitate effective evaluation for PAs using landscape assessments (Peres et al., 2006;Joppa et al., 2008;Ren et al., 2008;Barber et al., 2012;Nagendra et al., 2013). Landscape indicators can serve as broad-scale surrogates to perceive and analyze changes in species and their habitats (Saatchi et al., 2008;Sowinska-Swierkosz and Soszynski, 2014;Heiskanen et al., 2017), because a landscape pattern is determined and co-shaped by synergies of species diversity, ecological functions and anthropogenic disturbance (Uuemaa et al., 2009;Amici et al., 2015;Viciani et al., 2018). For example, the loss and disintegration of habitats can be characterized by the fragmentation of habitat landscapes (Ludeke et al., 1990), and other anthropogenic threats can be quantified with the percentages of various human-induced landscape patches (Brown and Vivas, 2005). ...
The establishment of protected areas (PAs) is an effective and prevailing tool for conserving wetlands globally, however, strong evidence indicates that not all PAs can deliver adequate conservation to wetland ecosystems due to the impacts of natural and anthropogenic factors. Many studies have used multiple indices to assess the conservation effectiveness of PAs, but those complicated indices are usually difficult to be employed at large-scale. Therefore, this study aims to develop a rapid assessment methodology for conservation effectiveness of wetland PAs across the Chinese mainland. We combined the area and pattern-based landscape indices (i.e., Landuse Dynamic Index and Fragmentation Dynamic Index) to measure wetland landscape changes between 1990 and 2008, based on which we construct the Area-Pattern Quadrant Analysis (APQA) system to facilitate the conservation assessment of wetland national nature reserves. Four typical modes of landscape changes were generalized to reveal and compare the conservation effectiveness associated with their cause-effect drivers among 92 wetland national nature reserves within ten major river basins across the Chinese mainland. In addition, 210 subcatchments containing the reserves were also used as baselines to assess and discriminate the natural and human-induced effects on the performance of those reserves. The areal percentages among the four modes indicated that the wetland landscape changes were dominated by spatially heterogeneous drivers, and reserves showed relatively low conservation effectiveness, particularly in those densely populated and socioeconomically developed regions such as the Yangtze River Basin, the Yellow River Basin and the Pearl River Basin. Only 16 reserves were excellently rated in conservation effectiveness, in comparison with 31 reserves poorly rated due to greater anthropogenic encroachment. Our research demonstrated that APQA-based landscape assessment can be used as an easily-used early warning indicator to capture wetland landscape changes in relation to their underlying natural and anthropogenic drivers, and to provide knowledge-based management guidance by using standardized and rapid assessment techniques.
... Variabel lingkungan tersebut dimodelkan salah satunya dengan menggunakan metode maksimum entropi atau Maxent. Saatchi et al. (2008) menjelaskan bahwa variabel data pengindraan jauh dan iklim yang diolah bersama dapat menghasilkan model distribusi terbaik. | 95 ...
Dicksonia blumei (Kunze) T.Moore merupakan salah satu jenis paku pohon yang diprioritaskan untuk dikonservasi sebagaimana yang diamanatkan di dalam CITES appendix II. Salah satu sebaran alaminya adalah Kepulauan Sunda Kecil dimana tercatat ada sepuluh spesimen D. blumei di Bali (Batukaru dan Bedugul). Penelitian ini bertujuan mendapatkan informasi mengenai kesesuaian habitat dan arahan lokasi untuk reintroduksi jenis D. blumei di Bukit Tapak, Bedugul, Bali. Permodelan dilakukan dengan metode maksimum entropi (Maxent). Data yang digunakan dalam penelitian adalah topografi, iklim dan tanah dimana tersebar titik D. blumei di Bali. Data tersebut kemudian digabungkan dengan data keberadaan Alsophila latebrosa sebagai salah satu inang tumbuh dari D. blumei di alam. Performa model menunjukkan hasil yang luar biasa dengan nilai training data Area Under the Curve (AUC) sebesar 0,997 dan nilai test data AUC sebesar 0,967. Variabel iklim yang paling dominan adalah b10 (rerata suhu pada quartal terpanas) yaitu 25,8%. Zonasi kesesuaian habitat D. blumei juga cukup luas yaitu ± 15 km2 pada kawasan Bedugul (Kabupaten Tabanan dan Buleleng). Detail titik lokasi untuk arahan reintroduksi/restorasi didapatkan dengan menggunakan interpretasi citra Pleaides melalui proses perhitungan statistik spectral library dari kanopi A. latebrosa. Hasil deteksi dengan pendekatan interpretasi citra Pleiades diperoleh akurasi sebesar 88%. Hasil penggabungan informasi kesesuaian habitat D. blumei dan titik sebaran A. latebrosa menunjukkan 28 titik lokasi di bagian barat daya hingga barat laut Bukit Tapak yang diprediksi sesuai sebagai lokasi reintroduksi D. blumei.
... The output is a thematic map reflecting the suitability of the relative distribution of species 29,30 . Maxent model has been widely used in the field of species distribution research 31,32 , such as the potential distribution of Haloxylon persicum in Central Asia under global warming 33 , the prediction of the suitable growth zone of Rhinopithecus roxellana due to the sharp reduction of human disturbance 34 , and the distribution of five economic tree species in the Amazon River basin combined with remote sensing technology 35 . Besides, Maxent niche models have also been used to study the potential distribution of invasive species in recent years, such as Solidago canadensis, Ageratina adenophora, Mimosa pigra, Flaveria bidentis, Solenopsis invicta and Ambrosia artemisiifolia [36][37][38][39][40][41] . ...
Alien invasive plants pose a threat to global biodiversity and the cost of control continues to rise. Early detection and prediction of potential risk areas are essential to minimize ecological and socio-economic costs. In this study, the Maxent model was used to predict current and future climatic conditions to estimate the potential global distribution of the invasive plant Xanthium italicum . The model consists of 366 occurrence records (10 repeats, 75% for calibration and 25% for verification) and 10 climate prediction variables. According to the model forecast, the distribution of X. italicum was expected to shrink in future climate scenarios with human intervention, which may be mainly caused by the rise in global average annual temperature. The ROC curve showed that the AUC values of the training set and the test set are 0.965 and 0.906, respectively, indicating that the prediction result of this model was excellent. The contribution rates of annual mean temperature, monthly mean diurnal temperature range, standard deviation of temperature seasonal change and annual average precipitation to the geographical distribution of X. italicum were 65.3%, 11.2%, 9.0%, and 7.7%, respectively, and the total contribution rate was 93.2%. These four variables are the dominant environmental factors affecting the potential distribution of X. italicum , and the influence of temperature is greater than that of precipitation. Through our study on the potential distribution prediction of X. italicum under the future climatic conditions, it has contribution for all countries to strengthen its monitoring, prevention and control, including early warning.
... Remote sensing products (RS-products) have been increasingly used to derive metrics that allow tracking biodiversity from space 18,25,26 , monitoring the state of human impacts 3,27 , as predictors for describing large patterns of species diversity 28-30 or to derive Essential Biodiversity Variables, i.e., measures that allow the detection and quantification of biodiversity changes 12,31,32 . Despite their high spatial and temporal resolution, quasi-global coverage and range of data products (e.g., precipitation, plant productivity, biophysical variables, land cover), RS-products have been rarely used as predictors for biodiversity models 17,30,33 . RS-products have been dubbed important "next-generation" environmental predictors in biodiversity models 34 , given that remote sensing continuously captures an increasing range of Earth's biophysical features at global scale 35,36 , avoiding the uncertainty associated with environmental predictors derived from traditional climatic data. ...
Biodiversity is rapidly changing due to changes in the climate and human related activities; thus, the accurate predictions of species composition and diversity are critical to developing conservation actions and management strategies. In this paper, using satellite remote sensing products as covariates, we constructed stacked species distribution models (S-SDMs) under a Bayesian framework to build next-generation biodiversity models. Model performance of these models was assessed using oak assemblages distributed across the continental United States obtained from the National Ecological Observatory Network (NEON). This study represents an attempt to evaluate the integrated predictions of biodiversity models—including assemblage diversity and composition—obtained by stacking next-generation SDMs. We found that applying constraints to assemblage predictions, such as using the probability ranking rule, does not improve biodiversity prediction models. Furthermore, we found that independent of the stacking procedure (bS-SDM versus pS-SDM versus cS-SDM), these kinds of next-generation biodiversity models do not accurately recover the observed species composition at the plot level or ecological-community scales (NEON plots are 400 m2). However, these models do return reasonable predictions at macroecological scales, i.e., moderately to highly correct assignments of species identities at the scale of NEON sites (mean area ~ 27 km2). Our results provide insights for advancing the accuracy of prediction of assemblage diversity and composition at different spatial scales globally. An important task for future studies is to evaluate the reliability of combining S-SDMs with direct detection of species using image spectroscopy to build a new generation of biodiversity models that accurately predict and monitor ecological assemblages through time and space.
... In the validation procedure of the IDF equations, the coefficient of determination of the regression (r²) was used and the residues produced were evaluated. It is observed that the equations obtained by the study present a wide applicability in engineering works and, especially, in -GÃO et al., 2008;SAATCHI et al., 2008;MALHA-DO et al., 2012;ZHONG et al., 2012) Por outro lado, não houve estudos que aferissem a acurácia da base de dados do CHIRPS em gerar curvas IDF para a região. Assim, o presente estudo visa suprir essa lacuna do conhecimento prospectando a comparação entre curvas IDF geradas com séries históricas do CHIRPS e curvas modeladas com dados de pluviômetros para a parte brasileira da bacia do rio Madeira, que representa um dos principais afluentes do rio Amazonas. ...
The work proposes to evaluate the use of Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS)-
type satellite data for the generation of intensity-duration-frequency curves (IDF) in the Madeira River basin. IDF
curves were generated simultaneously for data observed in 37 pluviometers distributed in the basin and for the CHIRPS pixels that geographically cover these observational stations in situ. Initially, the data were analyzed by
adjusting the Gumbel distribution for return periods ranging from 2 to 100 years and rain duration of 5 minutes
to 24 hours. In the validation procedure of the IDF equations, the coefficient of determination of the regression
(r²) was used and the residues produced were evaluated. It is observed that the equations obtained by the study
present a wide applicability in engineering works and, especially, in activities that require hydrological support,
due to the diversity of durations and return periods used, in addition, the analysis of errors and the estimation
of the coefficients of determination of the IDF functions, which were close to 0.70, highlight their use in the representation of extreme events. The comparison of the observational and satellite databases allowed to establish
the reliability of the use of CHIRPS satellite data in the construction of IDF relations for situations where local
rainfall information is not available, although the discrepancies between the bases tend to increase and be directly proportional to the return period.
... There are multiple SDM approaches models, including GARP, BIOCLIM, logistic regression, and MaxEnt. Especially among these models, the MaxEnt model (Maximum Entropy models) has been generally used for estimating species niches and distributions based on the maximum-entropy (Wei et al. 2018;Venne and Currie 2021).The biggest advantage of the MaxEnt model is that it provides more accurate results with less data than other methods working (Saatchi et al. 2008;Yi et al. 2017). Many studies have intensively focused on simulating the spatial distribution of species (Byeon et al. 2018;Qin et al. 2020;Yan et al. 2020;Korbel 2021;Meeussen and Hof 2021;Norallahi and Kaboli 2021a, b;Saputra and Lee 2021). ...
Global warming has become the center of worldwide environmental concerns, especially in recent years. One of the ways to deal with global warming that causes climate change is to adopt the renewable energy power technique. Different renewable energy sources such as solar, wind, hydro, ocean, geothermal, and bioenergy are currently the backbone of green and sustainable economic growth. However, renewable energy sites are directly or indirectly dependent on environmental, social, and technical criteria.The main objective of this paper is to identify potential best renewable energy site alternatives using the maximum entropy model (MaxEnt) and Geographical Information systems (GIS). Thus, the framework formed by the findings will guide investors in the renewable energy sector. The results showed that suitable areas for solar and wind were mainly located in the Hatay and Mersin of the Eastern Mediterranean Region in Turkey. The energy suitability site maps indicate that 8% (3.42 km2) and 3.39% (1554 km2) of the total study area have suitability and very suitability for solar and wind energy respectively. Moreover, it is seen that 44.82% (20,689km2) of the regions are the same when suitable and very suitable regions are overlaid for the installation of solar and wind energy sites. The receiver operating characteristic (ROC) curve was used to evaluate model performance. The area under the curve (AUC) values are calculated 0.87 and 0.95 for solar and wind energy, respectively. Relying on realistic data, this paper proposes an innovative method to identify suitable areas for solar and wind power plants. The maps obtained to contribute to renewable energy production will be useful for creating future strategies in the Mediterranean region.
... Remote sensing products (RS-products) have been increasingly used to derive metrics that allow tracking biodiversity from space 18,25,26 , monitoring the state of human impacts 3,27 , as predictors for describing large patterns of species diversity [28][29][30] or to derive Essential Biodiversity Variables, i.e., measures that allow the detection and quanti cation of biodiversity changes 12,31 . Despite their high spatial and temporal resolution, quasi-global coverage and range of data products (e.g., precipitation, plant productivity, biophysical variables, land cover), RS-products have been rarely used as predictors for biodiversity models 17,30,32 . RS-products have been dubbed important "next-generation" environmental predictors in biodiversity models 33 , given that remote sensing continuously captures an increasing range of Earth's biophysical features at global scale 34,35 , avoiding the uncertainty associated with environmental predictors derived from traditional climatic data. ...
Biodiversity is rapidly changing due to changes in the climate and human related activities; thus, the accurate predictions of species composition and diversity are critical to developing conservation actions and management strategies. In this paper, using oak assemblages distributed across the continental United States obtained from the National Ecological Observatory Network (NEON), we assessed the performance of stacked species distribution models (S-SDMs), constructed using satellite remote sensing as covariates and under a Bayesian framework, in order to build the next-generation of biodiversity models. This study represents an attempt to evaluate the integrated predictions of biodiversity models—including assemblage diversity and composition—obtained by stacking next-generation SDMs. We found three main results. First, environmental predictors derived entirely from satellite remote sensing represent adequate covariates for biodiversity modeling. Second, applying constraints to assemblage predictions, such as imposing the probability ranking rule, not necessarily results in more accurate species diversity predictions. Third, independent of the stacking procedure (bS-SDM versus pS-SDM versus cS-SDM), this kind of biodiversity models do not accurately recover the observed species composition at plot level or ecological scales (NEON plots), however, they do return reasonable predictions at macroecological scales, i.e., mid to high correct assignment of species identities at the scale of NEON sites. Our results provide insights for the prediction of assemblage diversity and composition at different spatial scales. An important task for future studies is to evaluate the reliability of combining S-SDMs with direct detection of species using image spectroscopy to build a new generation of biodiversity models to accurately predict and monitor ecological assemblages through time and space.
... To explore the spatial probability distribution of the solar features of the climate in terms of heliotherapy, we used the Maximum Entropy Method (MaxEnt) [74,75]. Max-Ent using a presence-only algorithm is suitability for research on habitat suitability and environmental modeling [76,77]. The preprocessing analysis was performed using the SAGA. ...
Global solar radiation is an important atmospheric stimulus affecting the human body
and has been used in heliotherapy for years. In addition to environmental factors, the effectiveness of global solar radiation is increasingly influenced by human activity. This research was based on the use of heliographic and actinometric data (1996–2015) and the model distribution of global solar radiation to determine the possibility of heliotherapy with the example of two health resorts: Cieplice and Kołobrzeg (Poland). The solar features of health resorts (sunshine duration and global solar radiation) were characterized, and they were correlated with the spatial distribution of global
solar radiation data obtained with the use of remote sensing techniques (System for Automated Geoscientific Analyzes-SAGA), including COoRdination and INformation on the Environment (CORINE) land cover (CLC) data. Using the maximum entropy model (MaxEnt), a qualitative and quantitative relationship between morphometric parameters and solar climate features was demonstrated for individual land cover types. Studies have shown that the period of late spring and summer, due to the climate’s solar features, is advisable for the use of heliotherapy. The human activity that determines the land cover is the main element influencing the spatial differentiation of the possibilities of using this form of health treatment. It also affects topographic indicators shown as significant in the MaxEnt predictive model. In general, areas with high openness were shown as predisposed for health treatment using global solar radiation, which is not consistent with areas commonly used for heliotherapy. The conducted research has shown the need for an interdisciplinary approach to the issue of heliotherapy, which will contribute to the optimization of the use of this form of health treatment from the perspective of climate change and human pressure.
... They are being destroyed by degradation and conversion to other land uses. Under pressure to make informed management decisions rapidly, conservation practitioners must increasingly rely on predictive models to provide them with information on species distributions (Loiselle et al. 2003;Saatchi et al. 2000). The most accurate ways to collect biographical data on species distributions are intensive ground surveys or inventories of species in the field. ...
Kalbi S, Fallah A, Hojjati SM. 2014. Using and comparing two nonparametric methods (CART and RF) and SPOT-HRG satellite data to predictive tree diversity distribution. Nusantara Bioscience 6: 57-62. The prediction of spatial distributions of tree species by means of survey data has recently been used for conservation planning. Numerous methods have been developed for building species habitat suitability models. The present study was carried out to find the possible proper relationships between tree species diversity indices and SPOT-HRG reflectance values in Hyrcanian forests, North of Iran. Two different modeling techniques, Classification and Regression Trees (CART) and Random Forest (RF), were fitted to the data in order to find the most successfully model. Simpson, Shannon diversity and the reciprocal of Simpson indices were used for estimating tree diversity. After collecting terrestrial information on trees in the 100 samples, the tree diversity indices were calculated in each plot. RF with determinate coefficient and RMSE from 56.3 to 63.9 and RMSE from 0.15 to 0.84 has better results than CART algorithms with determinate coefficient 42.3 to 63.3 and RMSE from 0.188 to 0.88. Overall the results showed that the SPOT-HRG satellite data and nonparametric regression could be useful for estimating tree diversity in Hyrcanian forests, North of Iran.
Microclimate ecology is attracting renewed attention because of its fundamental importance in understanding how organisms respond to climate change. Many hot issues can be investigated in desert ecosystems, including the relationship between species distribution and environmental gradients (e.g., elevation, slope, topographic convergence index, and solar insolation). Species Distribution Models (SDMs) can be used to understand these relationships. We used data acquired from the important desert plant Nitraria tangutorum Bobr. communities and desert topographic factors extracted from LiDAR (Light Detection and Ranging) data of one square kilometer in the inner Mongolia region of China to develop SDMs. We evaluated the performance of SDMs developed with a variety of both the parametric and nonparametric algorithms (Bioclimatic Modelling (BIOCLIM), Domain, Mahalanobi, Generalized Linear Model, Generalized Additive Model, Random Forest (RF), and Support Vector Machine). The area under the receiver operating characteristic curve was used to evaluate these algorithms. The SDMs developed with RF showed the best performance based on the area under curve (0.7733). We also produced the Nitraria tangutorum Bobr. distribution maps with the best SDM and suitable habitat area of the Domain model. Based on the suitability map, we conclude that Nitraria tangutorum Bobr. is more suited to southern part with 0–20 degree slopes at an elevation of approximately 1010 m. This is the first attempt of modelling the effects of topographic heterogeneity on the desert species distribution on a small scale. The presented SDMs can have important applications for predicting species distribution and will be useful for preparing conservation and management strategies for desert ecosystems on a small scale.
Exploring the development of species distribution patterns under climate change is the basis of biogeography and macroecology. However, under the background of global climate change, few studies focus on how the distribution pattern and the range of insects have or will change in response to long-term climate change. An old but small, Northern-Hemisphere-distributed beetle group Osphya is an ideal subject to conduct the study in this aspect. Here, based on a comprehensive geographic dataset, we analyzed the global distribution pattern of Osphya using ArcGIS techniques, which declared a discontinuous and uneven distribution pattern across the USA, Europe, and Asia. Furthermore, we predicted the suitable habitats of Osphya under different climate scenarios via the MaxEnt model. The results showed that the high suitability areas were always concentrated in the European Mediterranean and the western coast of USA, while a low suitability exhibited in Asia. Moreover, by integrating the analyses of biogeography and habitat suitability, we inferred that the Osphya species conservatively prefer a warm, stable, and rainy climate, and they tend to expand towards higher latitude in response to the climate warming from the past to future. These results are helpful in exploring the species diversity and protection of Osphya.
Today, climate change affects all living things on earth. It also leads to serious losses in terms of biodiversity, ecosystem services, and human welfare. In this context, Laurus nobilis L. is a very important species for Turkey, and the Mediterranean countries. This research aimed to simulate the current distribution of the suitable habitat for L. nobilis in Turkey and to predict its possible range shifts in future climate scenarios. To predict the geographical distribution of L. nobilis, the study used the maximum -entropy algorithm-based MaxEnt 3.4.1 with seven bioclimatic variables created using the Community Climate System Model 4.0 (CCSM4) and the prediction models RCP4.5-8.5 for the years 2050-2070. The results indicated that the most important bioclimatic variables that shape the distribution of L. nobilis are BIO11-mean temperature of coldest quarter, and BIO7-annual temperature range. Two climate change scenarios predicted that the geographical distribution of L. nobilis would increase slightly and then decrease in the future. However, the spatial change analysis showed that the general geographical distribution area of L. nobilis did not change significantly , but the "moderate," "high," and "very high" suitable habitats changed towards "low" suitable habitats. These changes were particularly effective in Turkey's Mediterranean region, which shows that climate change is instrumental in determining the future of the Mediterranean ecosystem. Therefore, suitabil-ity mapping and change analysis of potential future bioclimatic habitats can help in planning for land use, conservation, and ecological restoration of L. nobilis.
The AnisoVeg product consists of monthly 1 km composites of anisotropy (ANI) and nadir-normalized (NAD) surface reflectance layers obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor over the entire South American continent. The satellite data were preprocessed using the multi-angle implementation atmospheric correction (MAIAC). The AnisoVeg product spans 22 years of observations (2000 to 2021) and includes the reflectance of MODIS bands 1 to 8 and two vegetation indices (VIs), namely the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI). While the NAD layers reduce the data variability added by bidirectional effects on the reflectance and VI time series, the unique ANI layers allow the use of this multi-angular data variability as a source of information for vegetation studies. The AnisoVeg product has been generated using daily MODIS MAIAC data from both Terra and Aqua satellites, normalized for a fixed solar zenith angle (SZA = 45∘), modeled for three sensor view directions (nadir, forward, and backward scattering), and aggregated to monthly composites. The anisotropy was calculated by the subtraction of modeled backward and forward scattering surface reflectance. The release of the ANI data for open usage is novel, and the NAD data are at an advanced processing level. We demonstrate the use of such data for vegetation studies using three types of forests in the eastern Amazon with distinct gradients of vegetation structure and aboveground biomass (AGB). The gradient of AGB was positively associated with ANI, while NAD values were related to different canopy structural characteristics. This was further illustrated by the strong and significant relationship between EVIANI and forest height observations from the Global Ecosystem Dynamics Investigation (GEDI) lidar sensor considering a simple linear model (R2=0.55). Overall, the time series of the AnisoVeg product (NAD and ANI) provide distinct information for various applications aiming at understanding vegetation structure, dynamics, and disturbance patterns. All data, processing codes, and results are made publicly available to enable research and the extension of AnisoVeg products for other regions outside of South America. The code can be found at https://doi.org/10.5281/zenodo.6561351 (Dalagnol and Wagner, 2022), EVIANI and EVINAD can be found as assets in the Google Earth Engine (GEE; described in the data availability section), and the full dataset is available from the open repository https://doi.org/10.5281/zenodo.3878879 (Dalagnol et al., 2022).
We mapped and modelled the potential areas vulnerable to Lantana camara ( L . camara ) invasion in semi‐arid savannah ecosystems in the communal lands of Bushbuckridge and Kruger National Park, South Africa. Specifically, we modelled potentially vulnerable areas based on remotely sensed data and environmental variables. The Maximal Entropy (Maxent) algorithm was used to model the vulnerable area. The reliability of the modelled results was assessed using Skills Statistic (TSS), Area Under Curve (AUC) and Kappa statistics. According to the results, Bushbuckridge communal lands are more susceptible to L . camara invasions than Kruger National Park. The risk of L . camara invasion in the study site was modelled with high accuracy (AUC score of 0.95) using the best model (Model 7), which is a composite of all model variables (remote sensing and environmental variables). The spatial distribution maps derived from Maxent showed that L . camara was more likely to invade communal lands than protected areas. Using remotely sensed spectral indices as standalone model variables (Model 4) showed the lowest accuracy, with an AUC score of 0.85. Overall, model input variables such as elevation had a significant influence on the spatial distribution of L . camara in the study area.
In Canadian boreal forests, bryophytes represent an essential component of biodiversity and play a significant role in ecosystem functioning. Despite their ecological importance and sensitivity to disturbances, bryophytes are overlooked in conservation strategies due to knowledge gaps on their distribution, which is known as the Wallacean shortfall. Rare species deserve priority attention in conservation as they are at a high risk of extinction. This study aims to elaborate predictive models of rare bryophyte species in Canadian boreal forests using remote sensing-derived predictors in an Ensemble of Small Models (ESMs) framework. We hypothesize that high ESMs-based prediction accuracy can be achieved for rare bryophyte species despite their low number of occurrences. We also assess if there is a spatial correspondence between rare and overall bryophyte richness patterns. The study area is located in western Quebec and covers 72,292 km2. We selected 52 bryophyte species with
Modelos de Distribuição de Espécies são úteis na descoberta de padrões de distribuição das espécies, e das variáveis que estão influenciando. O objetivo deste trabalho foi descrever a distribuição potencial de Vouacapoua americana para o território amazônico, bem como verificar pressão antropogênica nos locais de ocorrência. Foram utilizados os dados de ocorrência da espécie obtidos por registros em herbários e variáveis climáticas derivadas de precipitação e temperatura. Modelos foram gerados através do algoritmo de máxima entropia e validados a partir de matrizes de confusão e da área sob a Curva. Dados secundárias de uso do solo, desflorestamento, pastagens e queimadas de diferentes plataformas foram processados no open gis QGIS 3.14. Constatou-se ocorrência da espécie ao norte da América do Sul. Na Amazônia brasileira, no arco de ocupação consolidada, foram identificados 294 focos de calor e 98,521 km² de pastagens. Estudos com essa abordagem podem subsidiar políticas públicas para conservação da biodiversidade. Potential distribution of Vouacapoua americana Aubl. in the Brazilian Amazon and the impact of the change in land useA B S T R A C TPredictive modelling has been used to analyze species geographic distribution based on conditioning variables. The objective of this work was to describe a potential distribution of Vouacapoua americana to the Amazonian territory, checking if the species is under anthropogenic pressure in the occurrence sites. Data on the occurrence of the species were used, using records in herbariums and climatic variables derived from precipitation and temperature. Models were generated using the maximum entropy algorithm and validated from confusion matrices and the area under the curve. Secondary data on land use, deforestation, pastures and forest fires were processed in the open gis QGIS 3.14. The species was found in northern South America. A total of 294 foci of calories and 98,521 km² of pastures were found in endemic areas of the Vouacapoua americana in the Brazilian Amazon. The result is that the species receives anthropogenic pressure, making it necessary for public policies to conserve biodiversity.
Habitat factors including topography and soil nutrients affect the formation of understory plant diversity patterns on a small spatial scale. Assessing the combination of suitable habitat factors in areas with abundant understory plant diversity is significant for the management and improvement of plant diversity in the plantations. This study used the maximum entropy model (MaxEnt) to simulate the geographical distribution of the area with abundant understory plant diversity and analyzed the contribution of ten habitat factors to the existence probability of the area with abundant understory plant diversity. The results showed that the regions with medium altitude (292–500 m), gentle slope (13–23 degrees), and high soil organic matter content (>2.3 g/kg) were more likely to breed abundant understory plant diversity. Topographic factors had a dominant effect on the spatial distribution of the understory plant diversity, with the importance of 64.9%. Although the effect of soil nutrient factors on understory plant diversity was less than that of topographic factors, it still made up a large proportion (35.1%). Promoting soil biochemical cycles could be an effective way to increase understory plant diversity. By changing soil organic matter content to 2.3 g/kg, soil available nitrogen to 300 mg/kg, and soil available potassium to 120 mg/kg, the average existence probability of the area with abundant understory plant diversity increased from 0.34 to 0.63. We conclude specific measures including introducing the native broad-leaved tree species such as Cotinus coggygria Scop. and Acer truncatum Bunge into the Platycladus orientalis plantation, logging residue management, and litter management are critical for promoting soil biochemical cycles and thereby increasing understory plant diversity.
Climate change acts as a major cause of species extinction by impacting the distribution and abundance of species. The impact of global climate change on ecosystems, especially at the species level, is already being observed across the world. Species distribution models can help to assess the potential effects of climate change on the spatial redistribution of species under different climate scenarios which is crucial for the management of habitat for endangered and critically endangered flora and fauna. Considering this, the present study aimed to predict the effect of climate change on two ecologically important species viz. Ficus benghalensis and Ficus hispida in Chattogram, Cox's Bazar and Bandarban Districts of Bangladesh using Maximum Entropy platform. Under the RCP2.6 and RCP8.5 scenario, the model predicted the future suitable habitat by 2050 and 2070. The study predicted insignificant changes in terms of suitable habitat for the selected species under climate change scenarios indicating higher climate resilience of both Ficus benghalensis and Ficus hispida. The findings of the study may contribute to the policymaking regarding wildlife conservation and forest management as both these species are crucial for a range of fauna.
Grasslands play an important ecological role and are the dominant landscape in the whole extent of the Andes and high elevation regions of South America. Climate change models indicate a strong impact on mountain systems. The climatic conditions of mountain systems are greatly influenced by topography and elevation. Poa scaberula Hook. f. is an Andean grass and its habitat may be threatened by climate change and human practices. Species distribution models, using bioclimatic variables, are often used to predict the future distribution and environmental requirements of species. In this study we prepared two bioclimatic models for P. scaberula using the Maxent algorithm, the first based on the inclusion of the variable elevation (E +) and the second on the exclusion of elevation (E-) to identify the main environmental variables for habitat suitability shift, to examine changes in the extent of the area of suitable habitat under current and future climate scenarios, and to project and quantify the spatial pattern of shifts in the areal extent of suitable habitat under future climate conditions. We then compared both projections and evaluate the contribution of the elevation variable in the performance of the models. We observed that the inclusion of elevation decreased the performance of the models. In models with omission of the elevation mean cold hardiness, temperature seasonality and annual mean temperature emerge as the critical factors shaping habitat suitability for P. scaberula. Under the low and higher concentration greenhouse gas emissions scenario, the range of the species may decrease as global warming intensifies. The information gained from this study should be an useful reference for development biogeographic studies and the impact of global warming on regions of high elevations, and contribute to implement conservation and management strategies for Andean grassland and high elevation grasslands.
Sinopse Trata este trabalho principalmente do estudo botânico, silvicultural e tecnológico de uma essência de grandes possibilidades comerciais, conhecida na Amazônia brasileira por "ucuuba de várzea" (Virola surinamensis (Rol.) Warb.)
The rain forests at Bajo Calima, Colombia are described for woody plant composition. Two upland plots, 1.0 and 0.5 ha in size, were selected and all trees $\geq$ 10 cm dbh were measured and identified. Trees $\geq$ 2.5 cm and $\leq$ 9.9 cm dbh were sampled in 0.1 ha subplots. Biomass was estimated using allometric equations. Biomass levels are low, at 210 tons/ha, and tree canopy heights rarely exceed 30-35 m. Few trees over 100 cm dbh were found. Forests at Bajo Calima are among the most species-rich in the world, with over 250 tree species $\geq$ 10 cm dbh per ha. Palms are numerically abundant in the overstory, with Jessenia bataua being most common. Free-climbing lianas are uncommon. Only 11 species had more than 8 individuals $\geq$ 10 cm dbh per ha. Measures of soil nutrients indicate low fertility and possible aluminum toxicity The pluvial rain forests at Bajo Calima lend support to previous findings that high diversity is correlated with both high rainfall and low nutrient levels.
Soil waterlogging and the subsequent reduction in the amount of oxygen available for the respiration of the root system selected, along the evolutive process, plants able to thrive in seasonally or permanently flooded areas. In neotropical plants there are many types of adaptations to flooding. In this paper we present the results of the work carried out with seeds and seedlings of C brasiliense subjected to hypoxia during germination and early development. C brasiliense seeds are not photoblastic and survive up to three months burried in a water saturated substrate, but germination only takes place in well-drained soils. Soil waterlogging does not inhibit seedling growth and there are no apparent morphological changes of the aerial part of flooded plants. New and aerated roots that make plant survival possible replace old and spoiled roots. In contrast to many typical species of flood-prone areas where growth is inhibited by oxygen stress. C. brasiliense seedlings seem to be well adapted to their waterlogged environment. Seed dispersion, the absence of photoblastic response as well as seed and seedling capacity of surviving and growing in waterlogged soils contribute to the wide geographic distribution of C. brasiliense always associated with areas subjected to soil waterlogging.
Large-scale patterns of Amazonian biodiversity have until now been obscured by a sparse and scattered inventory record. Here we present the first comprehensive spatial model of tree alpha-diversity and -density in Amazonian rainforests, based on the largest-yet compilation of forest inventories and bolstered by a spatial interpolation technique that allows us to estimate diversity and density in areas that have never been inventoried. These data were then compared to continent-wide patterns of rainfall seasonality. We find that dry season length, while only weakly correlated with average tree alpha-diversity, is a strong predictor of tree density and of maximum tree alpha-diversity. The most diverse forests in any given dry season length are concentrated in a narrow latitudinal band just south of the equator, while the least diverse forests in any given dry season length are found in the Guayana Shield and Amazonian Bolivia. Denser forests are more diverse than sparser forests, even when we used a measure of diversity that corrects for sample size. We propose that rainfall seasonality regulates tree alpha-diversity and -density by affecting shade tolerance and subsequently the number of different functional types of trees that can persist in an area.
This note is intended to serve primarily as a reference guide to users wishing to make use of the Tropical Rainfall Measuring Mission data. It covers each of the three primary rainfall instruments: the passive microwave radiometer, the precipitation radar, and the Visible and Infrared Radiometer System on board the spacecraft. Radiometric characteristics, scanning geometry, calibration procedures, and data products are described for each of these three sensors. 1. TRMM overview The atmosphere gets three-fourths of its heat energy from the release of latent heat by precipitation, and an estimated two-thirds of this precipitation falls in the Tropics. Differences in large-scale rainfall patterns and their associated energy release in the Tropics, in turn, affect the entire global circulation, as manifested in El Nino events, to name just one example. The most im- portant impact of rain and its variability is on the bio- sphere, including humans. The ''average'' rainfall is rarely observed. Instead, several seasons of drought and starvation are often followed by a year or two of tor- rential downpours and disastrous floods. Quantitative estimates of tropical precipitation, unfortunately, still vary by as much as 100%, depending upon the esti- mates. These differences are due to both the lack of good direct measurements of rainfall, as well as the highly variable nature of the parameters both spatially and temporally. Cloud and rain processes are now simulated fairly well on the scale of cloud ensembles (50-100 km). However, global models for prediction of weather and climate have much coarser resolution, therefore they must ''parameterize'' cloud processes. Most of these
Further studies on growth and reproduction of many tree species are needed to know the regeneration patterns of tropical forests (Clark & Clark 1987, Heideman 1989). Thus, the need for studies on the c. 3% of Atlantic forest that remains in Brazil is acute, particularly in the rarely studied swamp habitats (Scarano et al. 1997). We studied the canopy tree species Calophyllum brasiliense Camb. (Clusiaceae) in flooded and unflooded habitats of a coastal lowland rain forest, in order to describe demography, sexual expression, phenology and flower and fruit production. We report the differences of such traits among three contiguous habitats found in the lowlands - unflooded forest, freshwater-flooded forest and mangrove.
Effects of mammalian herbivory and seasonal drought were studied for Virola surinamensis (Myristicaceae) juveniles on Barro Colorado Island, Panama. Seedlings were planted at three months of age and the juveniles were monitored for two years; Treatments included: intact plants protected from mammals by cages, defoliated plants similarly protected, and unprotected plants, each planted in treefall gaps, on gap edges, and in the shaded understorey.
Juveniles planted in treefall gaps survived seasonal drought far better than those planted on gap edges or in shaded understorey. Two years after establishment, juveniles protected from mammalian herbivory showed a 78% survival in gaps (mean 6.8% skylight), 50% survival on gap edges (mean 3.0% skylight), and 33% survival in shaded understorey (1.4% skylight). This advantage was due to accelerated growth in gaps. Juveniles in gaps increased 616% in height, 1075% in leaf number, and 1800% in total leaf area. Comparable numbers in edges were 247%, 378% and 690%; in understorey 33%, 222% and 289%. Accelerated growth in gaps permitted yearlings to survive drought that killed suppressed yearlings in understorey. Mean light differentials as small as 0.6% and 0.3% skylight significantly influenced survival on edges and in shaded understorey, respectively.
Mammalian herbivory killed juveniles directly, and defoliation by mammals strongly accentuated drought mortality by suppressing root development. Natural defoliation was not attributable to gap conditions. Demographic projections from experimental data suggest that mammalian herbivory kills at least 48% of the juveniles of this species over two years, and contributes to the death of 32% more that actually die of drought stress. These projections suggest that 14% of the juveniles of this species die of drought mortality, independent of herbivory, during the first two years. Herbivory most strongly affects plants < 0.5 m in height, and is a continuing source of mortality among suppressed juveniles in the understorey. Steep slopes and large seed size each enhanced juvenile growth and survival in the intermediate conditions of gap edges, but not under the extreme conditions of gaps or shaded understorey.
The context of establishment determines the ‘shade tolerance’ of this conspicuous canopy tree. Without serious mammalian herbivory or extreme dry seasons, V. surinamensis can easily recruit as a shade tolerant plant in the understorey. Under present conditions on Barro Colorado Island, it cannot. Persistence involves both the chances of arrival in different microhabitats, and survival therein. Projections that include both the forest area represented by gaps, gap edges, and understorey and the experimental results from this study indicate that juvenile V. surinamensis can survive for two years in gaps, edges, and understorey, but that the higher proportions of vigorous individuals survive in edges, gaps and understorey, respectively.
This review paper evaluates the potential of remote sensing for assessing species diversity, an increasingly urgent task. Existing studies of species distribution patterns using remote sensing can be essentially categorized into three types. The rst involves direct mapping of individual plants or associations of single species in relatively large, spatially contiguous units. The second tech-nique involves habitat mapping using remotely sensed data, and predictions of species distribution based on habitat requirements. Finally, establishment of direct relationships between spectral radiance values recorded from remote sensors and species distribution patterns recorded from eld observations may assist in assessing species diversity. Direct mapping is applicable over smaller extents, for detailed information on the distribution of certain canopy tree species or associ-ations. Estimations of relationships between spectral values and species distribu-tions may be useful for the limited purpose of indicating areas with higher levels of species diversity, and can be applied over spatial extents of hundreds of square kilometres. Habitat maps appear most capable of providing information on the distributions of large numbers of species in a wider variety of habitat types. This is strongly limited by variation in species composition, and best applied over limited spatial extents of tens of square kilometres.
Rock outcrops are considered as habitat or ecological islands discordant from the adjacent matrix. The floras of 24 aggregated outcrop regions within the New England Batholith of eastern Australia were sampled and investigations made into species range differences. A measure is developed to describe differences in species range sizes across floras (range saturation: RS). Range sizes increased in areas with higher incident radiation (higher available energy) and concordantly in regions with a greater proportion of hemi-parasites, epiphytes and herbs (which were demonstrated to have large range sizes). Differences in species’ range sizes of granite outcrop occurring species on the New England Batholith of eastern Australia at different scales and extents are regressed against selected environmental variables and against local species richness and abundance. Although species’ range size has been linked in a number of systems with increased species richness and local species abundance, such correlations were not obtained in this investigation. Analyses of species’ range sizes could not be used to infer directly on processes that maintain species richness or abundance within the granitic outcrop flora of the New England Batholith.
Scatterometers have provided continuous synoptic microwave radar coverage of the Earth from space for nearly a decade. NASA launched three scatterometers: the current SeaWinds scatterometer onboard QuikSCAT (QSCAT, 13.4 GHz) launched in 1999; the NASA scatterometer (NSCAT, 14.0 GHz), which flew on the Japanese Space Agency's ADEOS‐1 platform during 1996–1997; and the Seasat‐A scatterometer system (SASS, 14.6 GHz), which flew in 1978. The European Space Agency's (ESA) 5.3‐GHz scatterometer (ESCAT) has been carried onboard both the ERS‐1 and ERS‐2 satellites since 1991.
properties, including the phase state, of a particular surface type. Varying response from the surface also results from different polarizations, viewing angles and orientations, and radar frequencies. The wide swath of scatterometers provides near daily global coverage at intrinsic sensor resolutions that are generally between 25–50 km.
We examined the effect of range size in commonly applied macroecological analyses using continental distribution data for all 550 Neotropical palm species (Arecaceae) at varying grain sizes from 0.5° to 5°. First, we evaluated the relative contribution of range-restricted and widespread species on the patterns of species richness and endemism. Second, we analysed the impact of range size on the predictive value of commonly used predictor variables. Species sequences were produced arranging species according to their range size in ascending, descending, and random order. Correlations between the cumulative species richness patterns of these sequences and environmental predictors were performed in order to analyse the effect of range size. Despite the high proportion of rare species, patterns of species richness were found to be dominated by a minority of widespread species (∼20%) which contained 80% of the spatial information. Climatic factors related to energy and water availability and productivity accounted for much of the spatial variation of species richness of widespread species. In contrast, species richness of range-restricted species was to a larger extent determined by topographical complexity. However, this effect was much more difficult to detect due to a dominant influence of widespread species. Although the strength of different environmental predictors changed with spatial scale, the general patterns and trends proved to be relatively stabile at the examined grain sizes. Our results highlight the difficulties to approximate causal explanations for the occurrence of a majority of species and to distinguish between contemporary climatic factors and history.
Prediction of species’ distributions is central to diverse applications in ecology, evolution and conservation science. There is increasing electronic access to vast sets of occurrence records in museums and herbaria, yet little effective guidance on how best to use this information in the context of numerous approaches for modelling distributions. To meet this need, we compared 16 modelling methods over 226 species from 6 regions of the world, creating the most comprehensive set of model comparisons to date. We used presence-only data to fit models, and independent presence-absence data to evaluate the predictions. Along with well-established modelling methods such as generalised additive models and GARP and BIOCLIM, we explored methods that either have been developed recently or have rarely been applied to modelling species’ distributions. These include machine-learning methods and community models, both of which have features that may make them particularly well suited to noisy or sparse information, as is typical of species’ occurrence data. Presence-only data were effective for modelling species’ distributions for many species and regions. The novel methods consistently outperformed more established methods. The results of our analysis are promising for the use of data from museums and herbaria, especially as methods suited to the noise inherent in such data improve.
The amount and spatial distribution of forest biomass in the Amazon basin is a major source of uncertainty in estimating the flux of carbon released from land-cover and land-use change. Direct measurements of aboveground live biomass (AGLB) are limited to small areas of forest inventory plots and site-specific allometric equations that cannot be readily generalized for the entire basin. Furthermore, there is no spaceborne remote sensing instrument that can measure tropical forest biomass directly. To determine the spatial distribution of forest biomass of the Amazon basin, we report a method based on remote sensing metrics representing various forest structural parameters and environmental variables, and more than 500 plot measurements of forest biomass distributed over the basin. A decision tree approach was used to develop the spatial distribution of AGLB for seven distinct biomass classes of lowland old-growth forests with more than 80% accuracy. AGLB for other vegetation types, such as the woody and herbaceous savanna and secondary forests, was directly estimated with a regression based on satellite data. Results show that AGLB is highest in Central Amazonia and in regions to the east and north, including the Guyanas. Biomass is generally above 300 Mg ha−1 here except in areas of intense logging or open floodplains. In Western Amazonia, from the lowlands of Peru, Ecuador, and Colombia to the Andean mountains, biomass ranges from 150 to 300 Mg ha−1. Most transitional and seasonal forests at the southern and northwestern edges of the basin have biomass ranging from 100 to 200 Mg ha−1. The AGLB distribution has a significant correlation with the length of the dry season. We estimate that the total carbon in forest biomass of the Amazon basin, including the dead and belowground biomass, is 86 Pg C with ±20% uncertainty.
Hypotheses for divergence and speciation in rainforests generally fall into two categories: those emphasizing the role of geographic isolation and those emphasizing the role of divergent selection along gradients. While a majority of studies have attempted to infer mechanisms based on the pattern of species richness and congruence of geographic boundaries, relatively few have tried to simultaneously test alternative hypotheses for diversification. Here we discuss four examples, taken from our work on diversification of tropical rainforest vertebrates, in which we examine patterns of genetic and morphological variation within and between biogeographic regions to address two alternative hypotheses. By estimating morphological divergence between geographically contiguous and isolated populations under similar and different ecological conditions, we attempt to evaluate the relative roles of geographic isolation and natural selection in population divergence. Results suggest that natural selection, even in the presence of appreciable gene flow, can result in morphological divergence that is greater than that found between populations isolated for millions of years and, in some cases, even greater than that found between congeneric, but distinct, species. The relatively small phenotypic divergence that occurs among long-term geographic isolates in similar habitats suggests that morphological divergence via drift may be negligible and/or that selection is acting to produce similar phenotypes in populations occupying similar habitats. Our results demonstrate that significant phenotypic divergence: (1) is not necessarily coupled with divergence in neutral molecular markers; and (2) can occur without geographic isolation in the presence of gene flow.
Factors affecting seedling Virola surinamensis (Myristicaceae) survival and growth were investigated on Barro Colorado Island, Panama. Seedlings planted 3 months after germination were monitored in treefall gaps and understory using 2.25 ha irrigated and control plots through the first dry season. During the dry season, irrigated plants in gaps increased total leaf area significantly more than did irrigated plants in the shaded understory. Over the same dry season, control plants in gaps and in the shaded understory lost similar amounts of leaf area. Seedlings in understory were suppressed in stem height and biomass in both irrigated and control plots; these measures were greater in gaps and greatest in irrigated gaps (height). Roots were similar in length in all treatments, but greater in biomass in gaps than understory due to greater proliferation of secondary roots in control and irrigated gaps than in control and irrigated understory. This experiment demonstrates both water and light limitation during the first dry season after germination. V. surinamensis seedlings are capable of survival and modest growth of leaf area in the deep shade of the understory in moist locations; they are severely disadvantaged in shaded understory subject to drought, where most seeds fall and most seedlings establish. The broken canopy of a gap allows shoot and consequently root growth that permits seedlings to survive seasonal drought.
Tree size, density, and species richness were established for three one-hectare plots of terra firme forest in central Amazonian Brazil. In the three hectares, 1916 individual trees with DBH 10 cm were sampled. A total of 58 families, 181 genera, and 513 species were determined. Hectare A had 285 species, 138 genera, and 47 families; hectare B 280 species, 123 genera, and 48 families; and hectare C 280 species, 125 genera, and 44 families. Comparably high species richness in Amazonia has heretofore only been reported from western Amazonia. This dispels the idea that high species richness can only develop in areas with rich soils and relatively high rainfall. It is suggested that such high species richness is the result of a combination of habitat heterogeneity and geological history. These high diversity forests, because they occur on nutrient poor soils, can be protected with little or no impact on development in the region because the soils are essentially useless for agriculture and for supporting long-term cattle pasture.
We developed interpolated climate surfaces for global land areas (excluding Antarctica) at a spatial resolution of 30 arc s (often referred to as 1-km spatial resolution). The climate elements considered were monthly precipitation and mean, minimum, and maximum temperature. Input data were gathered from a variety of sources and, where possible, were restricted to records from the 1950-2000 period. We used the thin-plate smoothing spline algorithm implemented in the ANUSPLIN package for interpolation, using latitude, longitude, and elevation as independent variables. We quantified uncertainty arising from the input data and the interpolation by mapping weather station density, elevation bias in the weather stations, and elevation variation within grid cells and through data partitioning and cross validation. Elevation bias tended to be negative (stations lower than expected) at high latitudes but positive in the tropics. Uncertainty is highest in mountainous and in poorly sampled areas. Data partitioning showed high uncertainty of the surfaces on isolated islands, e.g. in the Pacific. Aggregating the elevation and climate data to 10 arc min resolution showed an enormous variation within grid cells, illustrating the value of high-resolution surfaces. A comparison with an existing data set at 10 arc min resolution showed overall agreement, but with significant variation in some regions. A comparison with two high-resolution data sets for the United States also identified areas with large local differences, particularly in mountainous areas. Compared to previous global climatologies, ours has the following advantages: the data are at a higher spatial resolution (400 times greater or more); more weather station records were used; improved elevation data were used; and more information about spatial patterns of uncertainty in the data is available. Owing to the overall low density of available climate stations, our surfaces do not capture of all variation that may occur at a resolution of 1 km, particularly of precipitation in mountainous areas. In future work, such variation might be captured through knowledge-based methods and inclusion of additional co-variates, particularly layers obtained through remote sensing.
Remote-sensing systems typically produce imagery that averages information over tens or even hundreds of square meters – far too coarse to detect most organisms – so the remote sensing of biodiversity would appear to be a fool's errand. However, advances in the spatial and spectral resolutions of sensors now available to ecologists are making the direct remote sensing of certain aspects of biodiversity increasingly feasible; for example, distinguishing species assemblages or even identifying species of individual trees. In cases where direct detection of individual organisms or assemblages is still beyond our grasp, indirect approaches offer valuable information about diversity patterns. Such approaches derive meaningful environmental parameters from biophysical characteristics that are revealed by remote sensing.
An algorithm based on the physics of radiative transfer in vegetation canopies for the retrieval of vegetation green leaf area index (LAI) and fraction of absorbed photosynthetically active radiation (FPAR) from surface reflectances was developed and implemented for operational processing prior to the launch of the moderate resolution imaging spectroradiometer (MODIS) aboard the TERRA platform in December of 1999. The performance of the algorithm has been extensively tested in prototyping activities prior to operational production. Considerable attention was paid to characterizing the quality of the product and this information is available to the users as quality assessment (QA) accompanying the product. The MODIS LAI/FPAR product has been operationally produced from day one of science data processing from MODIS and is available free of charge to the users from the Earth Resources Observation System (EROS) Data Center Distributed Active Archive Center. Current and planned validation activities are aimed at evaluating the product at several field sites representative of the six structural biomes. Example results illustrating the physics and performance of the algorithm are presented together with initial QA and validation results. Potential users of the product are advised of the provisional nature of the product in view of changes to calibration, geolocation, cloud screening, atmospheric correction and ongoing validation activities.
Comparative, quantitative biogepgraphic studies are revealing empirical patterns of interspecific variation in the sizes, sahpes, boundaries, and internal structures of geographic ranges; these patterns promise to contribute to understanding the historical and eoclogical processes that influence the distributions of species. This review focuses on characteristics of ranges that appear to reflect the influences environmental limiting factors and dispersal. Among organisms as a whole, range size varies by more than 12 orders of magnitude. Within genera, families, orders, and classes of plants and animals, range size often varies by several orders of magnitude, and this variation is associated with variation in body size, population density, dispersal mode, latitude, elevation, and depth (in marine systems). The shapes of ranges and the dynamic changes in range boundaries reflect the interacting influences of limiting environmental conditions(niche variables) and dispersal/extinction dynamics. These processes also presumably account for most of the internal structure of ranges: the spatial patterns and order-of-magnitude of variation in the abundance of species among sites within their ranges. The results of this kind of "ecological biogeography" need to be integrated with the results of phylogenetic and palaeoenvironmental approaches to "historical biogeography" so we can better understand the processes that have determined the geographic distributions of organisms.
Large-scale patterns of Amazonian biodiversity have until now been obscured by a sparse and scattered inventory record. Here we present the first comprehensive spatial model of tree a-diversity and tree density in Amazonian rainforests, based on the largest-yet compilation of forest inventories and bolstered by a spatial interpolation technique that allows us to estimate diversity and density in areas that have never been inventoried. These data were then compared to continent-wide patterns of rainfall
seasonality. We find that dry season length, while only weakly correlated with average tree a-diversity, is a strong predictor of tree density and of maximum tree a-diversity. The most diverse forests for any given DSL are concentrated in a narrow latitudinal band just south of the equator, while the least diverse forests for any given DSL are found in the Guayana Shield and Amazonian Bolivia. Denser forests are more diverse than sparser forests, even when we used a measure of diversity that corrects for sample size. We propose that rainfall seasonality regulates treea-diversity and tree density by affecting shade
tolerance and subsequently the number of different functional types of trees that can persist in an area.
To study the impact of the annual long-term flooding (flood- pulse) on seasonal tree development in Amazonian floodplains, the phenology and growth in stem diameter of various tree species with different leaf-change patterns were observed over a period of 2 y. The trees of the functional ecotypes, evergreen, brevi-deciduous, deciduous and stem-succulent showed a periodic behaviour mainly triggered by the flood-pulse. Trees have high increment during the terrestrial phase. Flooding causes a shedding of some or all leaves leading to a cambial dormancy of about 2 mo and the formation of an annual ring. Studies carried out in tropical dry forests verify a strong relationship between the phenological development and the water status of the trees, strongly affected by seasonal drought. The comparison of the phenology and the diameter growth of the corresponding ecotypes in floodplain forest and a semi- deciduous forest in Venezuela shows a displacement of at least 2 mo in the periodicity, except for stem-succulent tree species. For stem-succulent trees it remains unclear which factors influence phenology and stem diameter growth.
Aim Maps of species richness are the basis for applied research and conservation planning as well as for theoretical research investigating patterns of richness and the processes shaping these patterns. The method used to create a richness map could influence the results of such studies, but differences between these methods have been insufficiently evaluated. We investigate how different methods of mapping species ranges can influence patterns of richness, at three spatial resolutions. Location California, USA. Methods We created richness maps by overlaying individual species range maps for terrestrial amphibians and reptiles. The methods we used to create ranges included: point-to-grid maps, obtained by overlaying point observations of species occurrences with a grid and determining presence or absence for each cell; expert-drawn maps; and maps obtained through species distribution modelling. We also used a hybrid method that incorporated data from all three methods. We assessed the correlation and similarity of the spatial patterns of richness maps created with each of these four methods at three different resolutions. Results Richness maps created with different methods were more correlated at lower spatial resolutions than at higher resolutions. At all resolutions, point-to-grid richness maps estimated the lowest species richness and those derived from species distribution models the highest. Expert-drawn maps and hybrid maps showed intermediate levels of richness but had different spatial patterns of species richness from those derived with the other methods. Main conclusions Even in relatively well-studied areas such as California, different data sources can lead to rather dissimilar maps of species richness. Evaluating the strengths and weaknesses of different methods for creating a richness map can provide guidance for selecting the approach that is most appropriate for a given application and region.
The distribution of vascular epiphytes was analyzed on three emergent trees (Hura crepitans, Ceiba pentandra, and Couratari stellata). A total number of 77 species, including 46 orchids, in 17 families was recorded. Number of species was highest in the center of the tree crowns while cover was highest in the middle third of the crown or equally distributed. This leads to the assumption that succession does not always reach equilibrium on old branches towards crown center. Distribution patterns of single species do not necessarily correspond to that of the total epiphyte mass. The entire epiphyte cover is highest on the top of branches while certain species, that could be discussed as specialists on the basis of particular life-form characteristics, are equally abundant on the bottom. There was no clear distribution pattern with respect to branch inclination. Distribution patterns are partly explained by the life-form of the plants.
Recent collections from wet forest areas of Panama include several species of Myristicaceae new to science or to the North American continent. The two new species described below are of special interest as having the largest fruits in their respective genera.
With the rise of new powerful statistical techniques and GIS tools, the development of predictive habitat distribution models has rapidly increased in ecology. Such models are static and probabilistic in nature, since they statistically relate the geographical distribution of species or communities to their present environment. A wide array of models has been developed to cover aspects as diverse as biogeography, conservation biology, climate change research, and habitat or species management. In this paper, we present a review of predictive habitat distribution modeling. The variety of statistical techniques used is growing. Ordinary multiple regression and its generalized form (GLM) are very popular and are often used for modeling species distributions. Other methods include neural networks, ordination and classification methods, Bayesian models, locally weighted approaches (e.g. GAM), environmental envelopes or even combinations of these models. The selection of an appropriate method should not depend solely on statistical considerations. Some models are better suited to reflect theoretical findings on the shape and nature of the species’ response (or realized niche). Conceptual considerations include e.g. the trade-off between optimizing accuracy versus optimizing generality. In the field of static distribution modeling, the latter is mostly related to selecting appropriate predictor variables and to designing an appropriate procedure for model selection. New methods, including threshold-independent measures (e.g. receiver operating characteristic (ROC)-plots) and resampling techniques (e.g. bootstrap, cross-validation) have been introduced in ecology for testing the accuracy of predictive models. The choice of an evaluation measure should be driven primarily by the goals of the study. This may possibly lead to the attribution of different weights to the various types of prediction errors (e.g. omission, commission or confusion). Testing the model in a wider range of situations (in space and time) will permit one to define the range of applications for which the model predictions are suitable. In turn, the qualification of the model depends primarily on the goals of the study that define the qualification criteria and on the usability of the model, rather than on statistics alone.
HERBARIUM specimen collecting in the Brazilian Amazon has been concentrated in widely scattered collecting centres associated with some proposed centres of substrate-independent endemism13, suggesting that these may be sampling artefacts. Furthermore, many Amazonian plant species are uncommon, so the more intensely a local flora is studied, the more it will seem to be unique. This weakens the botanical argument for a dry Pleistocene Amazon4 and the associated forest refuge theory for the origin of Amazonian plant diversity13, because modern endemism centres are used as evidence to define past isolated forest patchessites of allopatric speciation in a supposedly dry climate (Fig. 1). With a detailed map of botanical collection density, it is possible to recognize the true concentrations of plant endemism, which is important for selecting priority conservation areas to guarantee preservation of unique species.
Given a resource gradient (e.g. light intensity, prey size) in a community, species evolve to use different parts of this gradient; competition between them is thereby reduced. Species relationships in the community may be conceived in terms of a multidimensional coordinate system, the axes of which are the various resource gradients (and other aspects of species relationships to space, time, and one another in the community). This coordinate system defines a hyperspace, and the range of the space that a given species occupies is its niche hypervolume, as an abstract characterization of its intra‐community position, or niche. Species evolve toward difference in niche, and consequently toward difference in location of their hypervolumes in the niche hyperspace. Through evolutionary time additional species can fit into the community in niche hypervolumes different from those of other species, and the niche hyperspace can become increasingly complex. Its complexity relates to the community's richness in species, its alpha diversity.
Species differ in the proportions of the niche hyperspace they are able to occupy and the share of the community's resources they utilize. The share of resources utilized is expressed in species' productivities, and when species are ranked by relative productivity (or some other measurement) from most to least important, importance‐value or dominance‐diversity curves are formed. Three types of curves may represent manners in which resources are divided among species: (a) niche pre‐emption with strong dominance, expressed in a geometric series, (b) random boundaries between niches, expressed in the MacArthur distribution, and (c) determination of relative importance by many factors, so that species form a frequency distribution on a logarithmic base of importance values, a lognormal distribution. The forms of importance‐value curves do not permit strong inference about resource division, but are of interest for their expression of species relationships and bearing on measurement of diversity.
The continuous fields Moderate Resolution Imaging Spectroradiometer (MODIS) land cover products are 500-m sub-pixel representations of basic vegetation characteristics including tree, herbaceous and bare ground cover. Our previous approach to deriving continuous fields used a linear mixture model based on spectral endmembers of forest, grassland and bare ground training. We present here a new approach for estimating percent tree cover employing continuous training data over the whole range of tree cover. The continuous training data set is derived by aggregating high-resolution tree cover to coarse scales and is used with multi-temporal metrics based on a full year of coarse resolution satellite data. A regression tree algorithm is used to predict the dependent variable of tree cover based on signatures from the multi-temporal metrics. The automated algorithm was tested globally using Advanced Very High Resolution Radiometer (AVHRR) data, as a full year of MODIS data has not yet been collected. A root mean square error (rmse) of 9.06% tree cover was found from the global training data set. Preliminary MODIS products are also presented, including a 250-m map of the lower 48 United States and 500-m maps of tree cover and leaf type for North America. Results show that the new approach used with MODIS data offers an improved characterization of land cover.
Museum records have great potential to provide valuable insights into the vulnerability, historic distribution, and conservation of species, especially when coupled with species-distribution models used to predict species' ranges. Yet, the increasing dependence on species-distribution models in identifying conservation priorities calls for a more critical evaluation of model robustness. We used 11 bird species of conservation concern in Brazil's highly fragmented Atlantic Forest and data on environmental conditions in the region to predict species distributions. These predictions were repeated for five different model types for each of the 11 bird species. We then combined these species distributions for each model separately and applied a reserve-selection algorithm to identify priority sites. We compared the potential outcomes from the reserve selection among the models. Although similarity in identification of conservation reserve networks occurred among models, models differed markedly in geographic scope and flexibility of reserve networks. It is essential for planners to evaluate the conservation implications of false-positive and false-negative errors for their specific management scenario before beginning the modeling process. Reserve networks selected by models that minimized false-positive errors provided a better match with priority areas identified by specialists. Thus, we urge caution in the use of models that overestimate species' occurrences because they may misdirect conservation action. Our approach further demonstrates the great potential value of museum records to biodiversity studies and the utility of species-distribution models to conservation decision-making. Our results also demonstrate, however, that these models must be applied critically and cautiously.
We compared the seed fate of two animal-dispersed, large-seeded timber species (Dipteryx panamensis [Fabaceae] and Carapa guianensis [Meliaceae]) in logged and fragmented forests with that for continuous forest in northeastern Costa Rica. For both species, we quantified rates of seed removal (an index of vertebrate predation) and the fate of dispersed seeds (those carried away from their original location that either germinated or were not subsequently removed within three months). We predicted that (1) fewer seeds would be dispersed by vertebrates in fragmented forest than in continuous forest due to low population abundances after hunting and/or loss of suitable habitat, and (2) seed predation rates would be higher in forest fragments than in continuous forest due to high abundance of small-bodied seed consumers. We compared three forest fragments currently managed for timber (140–350 ha) and a large reserve of continuous forest (La Selva, 1500 ha and connected to a national park). An exclusion experiment was performed (seeds placed in the open vs. seeds within semipermeable wire cages; 5 cm mesh size) to evaluate the relative roles of large and small animals on seed removal. Seed germination capacity did not differ among all four sites for both species. Removal of Dipteryx seeds was higher in forest fragments (50% removal within 10 days and related to the activity of small rodents) compared to La Selva (50% removal after 50 days). Also, more Dipteryx seeds were dispersed at La Selva than in fragmented forests. Contrary to our predictions, removal of Carapa seeds was equally high among all four sites, and there was a trend for more seeds of Carapa to be dispersed in fragments than in La Selva. Our results suggest that fragmentation effects on tree seed fate may be specific to species in question and contingent on the animal biota involved, and that management strategies for timber production based on regeneration from seed may differ between forest patches and extensive forests.
En bosque continue y en un paisaje de bosque fragmentado bajo manejo para producción maderera y en el noreste de Costa Rica, se evaluó el destino de las semillas de dos especies de árboles comerciales con semillas grandes que son dispersadas por animates (Dipteryx panamensis [Fabaceae] y Carapa guianensis [Meliaceae]). Se compararon las tasas de remoción (un indicador de la depredación por vertebrados) como de dispersión (aquellas semillas removidas de su ubicación original y que germinaron o no fueron removidas posteriormente durante tres meses) entre estas dos especies. Nuestras predicciones fueron que (1) los vertebrados dispersarían menos semillas en los fragmentos que en el bosque continuo y que (2) las tasas de depredación serían mayores en los fragmentos que en el bosque contínuo debido a una aha abundancia de consumidores. Se compararon tres fragmentos de bosque manejados (140-350 ha) y una reserva de bosque continuo de gran tamaño (La Selva, 1500 ha y conectada a un parque nacional). Se realizó un experimento de exclusion (semillas colocadas sobre el suelo vs. semillas dentro de jaulas semipermeables con una malla de 5 cm) para evaluar el impacto relative de vertebrados grandes y pequeños en las tasas de remoción. La capacidad de germinación de las semillas de ambas especies no varió entre los cuatro sitios de estudio. La tasa de remoción de las semillas de Dipteryx fue mucho mas alta en los tres fragmentos (50% de remoción en 10 días relacionado con la actividad de roedores pequeños) que en La Selva (50% de remoción luego de 50 días). Además, en comparación con los fragmentos, un mayor número de semillas de Dipteryx fue dispersado en La Selva. Contrario a nuestras predicciones, la remoción de semillas de Carapa fue uniformemente alta en los cuatro sitios de estudio y hubo una tendencia en las semillas de Carapa a ser dispersadas con más frecuencia en los fragmentos que en La Selva. Estos resultados sugieren que los efectos de la fragmentación sobre el destino final de las semillas de árboles pueden ser específicos para cada especie y dependen de la biota animal involucrada en el proceso. Asimismo, las estrategias de manejo para la producción de madera basadas en la regeneración a partir de semillas pueden diferir entre los fragmentos de bosque y las áreas boscosas extensas.
Aim Maps of species richness are the basis for applied research and conservation planning as well as for theoretical research investigating patterns of richness and the processes shaping these patterns. The method used to create a richness map could influence the results of such studies, but differences between these methods have been insufficiently evaluated. We investigate how different methods of mapping species ranges can influence patterns of richness, at three spatial resolutions.
Location California, USA.
Methods We created richness maps by overlaying individual species range maps for terrestrial amphibians and reptiles. The methods we used to create ranges included: point-to-grid maps, obtained by overlaying point observations of species occurrences with a grid and determining presence or absence for each cell; expert-drawn maps; and maps obtained through species distribution modelling. We also used a hybrid method that incorporated data from all three methods. We assessed the correlation and similarity of the spatial patterns of richness maps created with each of these four methods at three different resolutions.
Results Richness maps created with different methods were more correlated at lower spatial resolutions than at higher resolutions. At all resolutions, point-to-grid richness maps estimated the lowest species richness and those derived from species distribution models the highest. Expert-drawn maps and hybrid maps showed intermediate levels of richness but had different spatial patterns of species richness from those derived with the other methods.
Main conclusions Even in relatively well-studied areas such as California, different data sources can lead to rather dissimilar maps of species richness. Evaluating the strengths and weaknesses of different methods for creating a richness map can provide guidance for selecting the approach that is most appropriate for a given application and region.
Deciduousness is an important functional attribute of tropical trees, reflecting climatic conditions. Precisely quantifying and mapping deciduousness in tropical forests will be necessary for calibrating remote sensing images which attempt to assess canopy properties such as carbon cycling, productivity, or chlorophyll content. We thus set out to assess the degree of canopy deciduousness in three moist, semi-deciduous tropical forests in central Panama. One site is a 6-ha research plot near the Atlantic coast of Panama, where rainfall is 2830 mm/yr. The second site is a 50-ha plot on Barro Colorado Island, near the center of the isthmus of Panama, where rainfall is 2570 mm/yr, and the final site is a 4-ha plot near the Pacific coast of Panama, where rainfall is 2060 mm/yr. At each site, a random sample of trees from all canopy species (those with individuals ≥ 30 cm DBH) were visited and scored for deciduousness three times during the 1997 dry season. The estimated peak fraction of deciduous individuals in the canopy at the wetter site was 4.8%, at the intermediate site, 6.3%, and at the drier site, 24.3%. The estimated fraction of crown area deciduous peaked at 3.6%, 9.7%, and 19.1% at the wet, medium, and dry sites respectively. The percentage of canopy species that was deciduous –14%, 28%, and 41%–was much higher than the percentage of deciduous individuals, because not all individuals of deciduous species were deciduous. During the 1999 dry season, every individual of all the deciduous species was visited at the two drier sites, and the total number of deciduous trees observed closely matched the estimated numbers based on the smaller 1997 samples.
Although levels of biological diversity may seem to be equivalent in different areas, diversity is created and maintained by a range of different ]processes: overlap of habitat on gradients; a dynamic mosaic of communities; and accumulation and evolution of taxa in extremely stable areas. These different communities will respond in very different ways to disturbance. The most fragile are those whose component taxa are genetically adapted to the stability of a predictable environment. These areas are often under pressure from local rural populations and require intensive local conservation management actions. In other areas, where diversity is adapted to dynamism, communities are more resilient to disturbance and conservation can be best effected by policy instruments.
Plant species richness and range-size rarity in Africa south of the Sahara is concentrated in centres of plant diversity and endemism. Distribution patterns of plants mapped in the Distributiones Plantarum Africanum series and selected taxonomic monographs are analysed using the computer programme WORLDMAP. The plants are divided into four groups: herbaceous geophytes, mesophytic herbs, light-demanding shrubs and woody genera. Each group has peaks of species richness and range-size rarity at locations different to the other groups. Herbaceous geophytes and mesophytic herbs have their peaks of species richness and range-size rarity in the same location, the western Cape for geophytes and the Crystal Mountain for mesophytic herbs, whereas light-demanding shrubs and woody genera have peaks in different places. The results are discussed in relation to possible factors determining species richness and endemism and their likely conservation significance.
The Genetic Algorithm for Rule-Set Prediction (GARP) is one of several current approaches to modeling species’ distributions using occurrence records and environmental data. Because of stochastic elements in the algorithm and underdetermination of the system (multiple solutions with the same value for the optimization criterion), no unique solution is produced. Furthermore, current implementations of GARP utilize only presence data—rather than both presence and absence, the more general case. Hence, variability among GARP models, which is typical of genetic algorithms, and complications in interpreting results based on asymmetrical (presence-only) input data make model selection critical. Generally, some locality records are randomly selected to build a distributional model, with others set aside to evaluate it. Here, we use intrinsic and extrinsic measures of model performance to determine whether optimal models can be identified based on objective intrinsic criteria, without resorting to an independent test data set. We modeled potential distributions of two rodents (Heteromys anomalus and Microryzomys minutus) and one passerine bird (Carpodacus mexicanus), creating 20 models for each species. For each model, we calculated intrinsic and extrinsic measures of omission and commission error, as well as composite indices of overall error. Although intrinsic and extrinsic composite measures of overall model performance were sometimes loosely related to each other, none was consistently associated with expert-judged model quality. In contrast, intrinsic and extrinsic measures were highly correlated for both omission and commission in the two widespread species (H. anomalus and C. mexicanus). Furthermore, a clear inverse relationship existed between omission and commission there, and the best models were consistently found at low levels of omission and moderate-to-high commission values. In contrast, all models for M. minutus showed low values of both omission and commission. Because models are based only on presence data (and not all areas are adequately sampled), the commission index reflects not only true commission error but also a component that results from undersampled areas that the species actually inhabits. We here propose an operational procedure for determining an optimal region of the omission/commission relationship and thus selecting high-quality GARP models. Our implementation of this technique for H. anomalus gave a much more reasonable estimation of the species’ potential distribution than did the original suite of models. These findings are relevant to evaluation of other distributional-modeling techniques based on presence-only data and should also be considered with other machine-learning applications modified for use with asymmetrical input data.
Species distribution models were developed for three high economic value timber trees (Calophyllum brasiliensis, Carapa guianensis and Virola surinamensis) that are heavily harvested in the Amazon Basin. A combination of habitat measurements extracted from remote sensing data (MODIS, QSCAT and SRTM) and bioclimatic surfaces was examined to ascertain the most influential factors determining the occurrence of these tree species. The prediction of species’ occurrence rates was tested separately for each species distribution model and the results were examined for their ability to accurately map the spatial distribution of these tree species. By evaluating the omission and commission rates we concluded that species distribution models based on remote sensing data contributed significantly in quantifying environmental properties used to summarize the ecological niche of each tree species. Specific vegetation characteristics (such as percentage of tree cover, vegetation moisture and roughness, annual NDVI and mean LAI during the dry LAI) showed the dependence of these species’ occurrence in more densely vegetated forests. Areas with high leaf area (even during the dry months) and areas with high vegetation moisture were predicted as potential species habitat for C. brasiliensis. The density vegetation during the dry season and vegetation phenology were strongly correlated with climate differences, such as variations in air temperature and precipitation seasonality for V. surinamensis. Lower elevation areas with more exuberant vegetation and a high greenness index were among the most important factors accounting for the geographical distribution of C. guianensis. Species distribution models are increasingly important in many fields of research and conservation. The potential of remotely sensed data to monitor environmental changes in tropical areas, along with the understanding of ecosystem function, are both critical for conservation of biodiversity and the long-term process of sustaining ecosystems.
Predicting the potential effects of future climatic change and human disturbances on natural vegetation distribution requires large-scale biogeographical models. There have been two basic approaches to modelling vegetation response to changing climates: static (time-independent) or dynamic (time-dependent) biogeographical models. This paper reviews and compares two major types of static biogeographical models, climate–vegetation classification and plant functional type models, and the first generation of Dynamic Global Vegetation Models (DGVMs). These models have been widely used to simulate the potential response of vegetation to past and future climate change. Advantage and disadvantage of each type of model are discussed. Global vegetation modelling for investigations of climate change effects has progressed from empirical modelling to process-based equilibrium modelling to the first generation of DGVMs. Some DGVMs are able to capture the responses of potential natural vegetation to climate change with a strong orientation towards population processes. Nevertheless, the uncertainty around the quantitative simulated results indicates that DGVMs are still in the early stages of development. Validating and capturing disturbance-related effects are major challenges facing the developers of the next generation of DGVMs. In future, DGVMs will become an important tool for assessing the effects of climate change on potential vegetation dynamics and terrestrial carbon storage and for managing terrestrial ecosystem sustainability.
Part 1. It is shown that in a large collection of Lepidoptera captured in Malaya the frequency of the number of species represented by different numbers of individuals fitted somewhat closely to a hyperbola type of curve, so long as only the rarer species were considered. The data for the commoner species was not so strictly `randomized', but the whole series could be closely fitted by a series of the logarithmic type as described by Fisher in Part 3. Other data for random collections of insects in the field were also shown to fit fairly well to this series. Part 2. Extensive data on the capture of about 1500 Macrolepidoptera of about 240 species in a light-trap at Harpenden is analysed in relation to Fisher's mathematical theory and is shown to fit extremely closely to the calculations. The calculations are applied first to the frequency of occurrence of species represented by different numbers of individuals--and secondly to the number of species in samples of different sizes from the same population. The parameter ` alpha ', which it is suggested should be called the `index of diversity', is shown to have a regular seasonal change in the case of the Macrolepidoptera in the trap. In addition, samples from two traps which overlooked somewhat different vegetation are shown to have ` alpha ' values which are significantly different. It is shown that, provided the samples are not small, ` alpha ' is the increase in the number of species obtained by increasing the size of a sample by e (2.718). A diagram is given (Fig. 8) from which any one of the values, total number of species, total number of individuals and index of diversity (alpha), can be obtained approximately if the other two are known. The standard error of alpha is also indicated on the same diagram. Part 3. A theoretical distribution is developed which appears to be suitable for the frequencies with which different species occur in a random collection, in the common case in which many species are so rare that their chance of inclusion is small. The relationships of the new distribution with the negative binomial and the Poisson series are established. Numerical processes are exhibited for fitting the series to observations containing given numbers of species and individuals, and for estimating the parameter alpha representing the richness in species of the material sampled; secondly, for calculating the standard error of alpha, and thirdly, for testing whether the series exhibits a significant deviation from the limiting form used. Special tables are presented for facilitating these calculations.
Ecologists, although they acknowledge the problems involved, generally conduct their research on too few species, in too small an area, over too short a period of time. In The Balance of Nature?, a work sure to stir controversy, the distinguished theoretical ecologist Stuart L. Pimm argues that ecology therefore fails in many ways to address the enormous ecological problems now facing our planet. Ecologists describing phenomena on larger scales often use terms like "stability," "balance of nature," and "fragility," and Pimm begins by considering the various specific meanings of these terms. He addresses five kinds of ecological stabilityâstability in the strict sense, resilience, variability, persistence, and resistanceâand shows how they provide ways of comparing natural populations and communities as well as theories about them. Each type of stability depends on characteristics of the species studied and also on the structure of the food web in which the species is embedded and the physical features of the environment. The Balance of Nature? provides theoretical ecology with a rich array of questionsâquestions that also underpin pressing problems in practical conservation biology. Pimm calls for nothing less than new approaches to ecology and a new alliance between theoretical and empirical studies.
One basic summary of the spatial pattern of biodiversity across the surface of the Earth is provided by a species-range size distribution, the frequency distribution of the numbers of species exhibiting geographic ranges of different sizes. Although widely considered to be approximately lognormal, increasingly it appears that across a variety of groups of organisms this distribution systematically departs from such a form. Whatever its detailed shape, however, the distribution must arise as a product of three processes, speciation, extinction and transformation (the temporal dynamics of the range sizes of species during their life times). Considering the role potentially played by each of these processes necessitates drawing on information from a diverse array of research fields, and highlights the possible role of geographic range size as a common currency uniting them.