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The effect of the extent of the study region on GIS models of species geographic distributions and estimates of niche evolution: Preliminary tests with montane rodents (genus Nephelomys) in Venezuela
Journal of Biogeography
Abstract
Aim Various techniques model a species’ niche and potential distribution by comparing the environmental conditions of occurrence localities with those of the overall study region (via a background or pseudoabsence sample). Here, we examine how changes in the extent of the study region (ignored or under‐appreciated in most studies) affect models of two rodents, Nephelomys caracolus and Nephelomys meridensis .
Location North‐central South America.
Methods We used M axent to model the species' potential distributions via two methods of defining the study region. In Method 1 (typical of most studies to date), we calibrated the model in a large study region that included the ranges of both species. In Method 2, we calibrated the model using a smaller study region surrounding the localities of the focal species, and then applied it to the larger region. Because the study region of Method 1 is likely to include areas of suitable conditions that are unoccupied because of dispersal limitations and/or biotic interactions, this approach is prone to overfitting to conditions found near the occupied localities. In contrast, Method 2 should avoid such problems but may require further assumptions (‘clamping’ in M axent ) to make predictions for areas with environmental conditions beyond those found in the smaller study region. For each method, we calculated several measures of geographic interpredictivity between predictions for the species (cross‐species AUC, cross‐species omission rate, and proportional geographic overlap).
Results Compared with Method 1, Method 2 revealed a larger predicted area for each species, less concentrated around known localities (especially for N. caracolus ). It also led to higher cross‐species AUC values, lower cross‐species omission rates and higher proportions of geographic overlap. Clamping was minimal and occurred primarily in regions unlikely to be suitable.
Main conclusions Method 2 led to more realistic predictions and higher estimates of niche conservatism. Conclusions reached by many studies depend on the selection of an appropriate study region. Although detailed information regarding dispersal limitations and/or biotic interactions will typically be difficult to obtain, consideration of coarse distributional patterns, topography and vegetational zones often should permit delimitation of a much more reasonable study region than the extremely large ones currently in common use.
... Despite their potential utility for addressing several questions, SDMs can be sensitive to input decisions made during model generation (Elith et al. 2011;Merow et al. 2013;Araújo et al. 2019). For presence-only models, one decision that is known to dramatically impact SDMs when projected across broad areas (e.g. when modeling species' global ranges) is the extent used to sample background points relative to presences of the species (VanDerWal et al. 2009;Anderson and Raza 2010;Giovanelli et al. 2010;Amaro et al. 2023). However, SDMs are also used to make predictions at smaller scales, for example within management jurisdictions or protected areas (e.g. ...
... For each extent, both the input presence and background points for a given species were restricted to come from within the intersection of the species range and the specified study extent. Thus, our study differs from others where input presences are held constant and background points are varied (VanDerWal et al. 2009;Anderson and Raza 2010;Giovanelli et al. 2010;Amaro et al. 2023). As the three extents for each species are nested ( Fig. 3; Fig. S1), this study design amounts to changing the total extent around the focal protected area when developing models for a given species (Fig. 2). ...
... We know of only two other studies that have specifically explored how varying both the input localities and background points (as opposed to just background points: VanDerWal et al. 2009;Anderson and Raza 2010;Anderson and Gonzalez 2011;Searcy and Shaffer 2014;Walker 2018;Connor et al. 2019;Schmidt et al. 2020) around a focal area of application influences SDM predictions and performance at the range limits of otherwise widespread species. Trumbo et al. (2011) found that predictions were substantially different between models developed using state borders versus range-wide study extents for four amphibian species at the western edge of their ranges in Missouri, USA. ...
Species distribution models (SDMs) are often generated to inform conservation plans. When developed for use in spatially-restricted areas, such as protected areas, investigators often make arbitrary decisions as to the geographic extent from which locality data to inform the model are drawn (i.e. the “study extent” of the model). However, there has been little attention to the impacts of this decision on model predictions. Here we explore the impacts of study extent on SDM predictions of (i) suitable habitat for or (ii) the actual occurrence of individual species, as well as on (iii) the identification of sites that could support multiple species (i.e. from stacked-SDMs). Focusing on six amphibian species of conservation concern at the edge of their range in western Canada, we generated SDMs using range-wide, ecoregion, and political study extents and compared the alternative predictions for each species in one of two national parks of interest. Differences in model predictions were substantial, with precent agreement among models developed with different extents as low as 10% for one of the species. Study extent also influenced the ability of models to predict independent occurrence at the edge of the range, although most models performed poorly in this regard (AUC < 0.7). Finally, study extent influenced stacked predictions, suggesting that uncertainty in individual species predictions muddies interpretation of SDMs at the community-level. Importantly, results varied across species and region, precluding simple recommendations for choosing a study extent; Instead, uncertainty arising from this decision should be quantified before using SDMs in conservation planning.
... Mobility (M) is the area accessible by species related to their distribution over periods of time (the 'accessible area' [5]). Selecting the extent of species' accessible areas, including buffer zones, impacts model prediction results [5,6]. ...
... The accessible area refers to the parts of the world accessible to species via dispersal over time [5]. The extent of the accessible area and the inclusion of a buffer zone have an important effect on ENM performance [5,6]. We used two accessible area sizes to delimitate our modelling extent (figure 1). ...
... For example, the wild water buffalo No MSDM predicted potentially suitable habitat around the Sre Pok Wildlife Sanctuary in Cambodia where the species is distributed [76], but after the spatial restriction (MSDM), this potential habitat was excluded as we lack occurrence data in Cambodia. Although our study showed slightly different TSS values between two different accessible area extents, we encourage testing the different accessible areas as it affects the model results [6]. Moreover, model performance varied with accessible area sizes and spatial restrictions, emphasizing the need for careful accessible area definition in ecological modelling [5]. ...
Wild bovids provide important ecosystem functions as seed dispersers and vegetation modifiers. Five wild bovids remain in Thailand: gaur ( Bos gaurus ), banteng ( Bos javanicus ), wild water buffalo ( Bubalus arnee ), mainland serow ( Capricornis sumatraensis ) and Chinese goral ( Naemorhedus griseus ). Their populations and habitats have declined substantially and become fragmented by land-use change. We use ecological niche models to quantify how much potential suitable habitat for these species remains within protected areas in Asia and then specifically Thailand. We combined species occurrence data from several sources (e.g. mainly camera traps and direct observation) with environmental variables and species-specific and single, large accessible areas in ensemble models to generate suitability maps, using out-of-sample predictions to validate model performance against new independent data. Gaur, banteng and buffalo models showed reasonable model accuracy throughout the entire distribution (greater than or equal to 62%) and in Thailand (greater than or equal to 80%), whereas serow and goral models performed poorly for the entire distribution and in Thailand, though 5 km movement buffers markedly improved the performance for serow. Large suitable areas were identified in Thailand and India for gaur, Cambodia and Thailand for banteng and India for buffalo. Over 50% of suitable habitat is located outside protected areas, highlighting the need for habitat management and conflict mitigation outside protected areas.
... Estimating ecological niches using correlative methods usually requires linking the environmental characteristics of known species occurrences with the conditions associated with the environmental background over a calibration area (CA) (Anderson & Raza, 2010;Barve et al., 2011;Phillips et al., 2006). This allows finding the environmental space that is similar to the conditions in which the species has been observed (Saupe et al., 2012). ...
... we can better address such shortfalls and improve our understanding of biodiversity patterns. It is crucial to establish the CA when we use ENM/SDM (Allouche et al., 2008;Anderson & Raza, 2010;Barve et al., 2011;Cardador et al., 2014;Feng, 2023;Godsoe, 2010;Holloway et al., 2016;Sullivan et al., 2012;Van Der Wal et al., 2009). ...
... For example, in 64 of the 129 analysed studies, researchers implicitly or explicitly referenced the general concept of CA through extensions that circumscribed the species' presence (with RE, PD and distance restriction methods, i.e. simple and spatial constraints approaches). We believe that explicitly describing a CA is a conscious decision to acknowledging the importance of the area extent and the included environments, as has been suggested (Anderson & Raza, 2010;Barve et al., 2011;Cardador et al., 2014;Holloway et al., 2016;Saupe et al., 2012). ...
Aim
The calibration area (CA) corresponds to the geographic region used by different algorithms that estimate the species' environmental preferences and delimit its geographic distribution. This study intended to identify, test and compare current literature's most commonly employed approaches and methods for CA creation, highlighting the differences with the accessible area (M), a frequently misapplied concept.
Location
Global.
Taxon
Arthropods, amphibians, reptiles, birds and mammals.
Methods
We conducted a literature review and analysed 129 recent articles on species distribution that use correlative models to identify the methods used to establish the CA and their frequency. We also evaluated seven of the most widely used methods for 31 species from different taxa.
Results
We found that the most frequently used methods in literature corresponded to biogeographic entities (BE). Moreover, according to our evaluation, those methods that seek to establish the CA through the accessible area approach (including BE and ‘grinnell’) were the best evaluated. Finally, we highlight the advantages and disadvantages of the analysed methods in selecting CA.
Main Conclusions
Although we cannot fail to recognize the usefulness and validity of the different methods to establish CAs, we suggest calibrating ecological niche and species distribution models in light of explicit a priori hypotheses regarding the extent of accessible areas (M) as a delimitation of the CA, which theoretically includes the species' dispersal ability and its barriers. We recommend using the BE method, which is simple to establish and highly operational.
... In presence-only models, the selection of background data significantly influences the performance of the model, affecting the accuracy of habitat suitability estimations (Anderson and Raza 2010;Jarnevich et al. 2017;Amaro et al. 2023). Ideally, selecting the extent of background data for species distribution models should be based on the species' ecological characteristics, specifically their dispersal limitations. ...
... Ideally, selecting the extent of background data for species distribution models should be based on the species' ecological characteristics, specifically their dispersal limitations. However, such data are often not available for most species (Anderson and Raza 2010;Barve et al. 2011). Since sampling bias in datasets is often unknown (Fourcade et al. 2014), some studies have resorted to using climate classifications correlated with species occurrences to inform background data selection (e.g., Hill and Terblanche 2014;Hill et al. 2017). ...
Controlling background data selection in presence-only models is crucial for addressing sampling biases and enhancing model performance. While numerous studies have evaluated the impact of various background data selection techniques across different taxa, research remains limited on how spatially restricted background areas and employing random and biased distribution methods, influence model performance for Rattus species predictions. These species often present challenging collection conditions and low trap success rates, potentially leading to spatial biases in the occurrence records that may affect the accuracy of model predictions. Thus, this study examined methods to assess model accuracy variability for Rattus species by applying spatial background restrictions within the study area. These restrictions were defined by four main criteria: (1) areas within islands with documented species occurrences, (2) areas within the species' extent of occurrence according to IUCN range maps, (3) defined road distance, and (4) varying buffer areas around recorded species occurrences. To further assess the effects of spatial background restrictions on model performance, we used two methods to distribute the background sampling points: random and biased (bias file) method. Our findings demonstrated that the selection of spatial background restrictions and the distribution methods for background sampling points play a critical role in influencing model performance and the accuracy of predicted habitat suitability for Rattus species. Our findings highlight that defining a specific spatial restriction, such as restricting background selection to within 5 km of a road, improves model performance. However, overly narrow or restrictive buffer sizes, such as the 20 km buffer size used in this study, fail to capture the full environmental variability of the species, which can diminish model accuracy. Furthermore, the method used to distribute background sampling points whether random or biased affects species predictive outcomes. To ensure reliable predictions, we recommend a systematic evaluation of different spatial restriction methods and distribution approaches, along with a thorough analysis of their impacts on model performance. This approach not only reveals how outcomes vary across different modeling scenarios but also provides a strong basis for determining the most reliable predictions. By carefully assessing these factors, researchers can refine and optimize habitat suitability models for Rattus species, ultimately enhancing predic-tive accuracy and ensuring more consistent and dependable results.
... In the independent validation, models trained with local data only showed a better AUC than the others, and the patterns of performance were more complex. Our results highlight the distinctive characteristics of different scales of response and predictor variables (Anderson and Raza, 2010;Mateo et al., 2019). Fine-scale models can better capture local conditions, benefiting from dynamically downscaled RCM climate data and finer spatial resolution Fig. 6. ...
... This result underscores the difficulty of aligning meaningful spatial scales of ecological processes with available climate data, a major challenge especially in long-lasting humandominated mountain systems such as the Alps, where human activities have profoundly altered ecosystem spatial patterns (Batzing et al., 1996;Plieninger et al., 2016;Zanon et al., 2018). Coarse-scale models or data pooling approaches, on the other hand, can be trained over broader geographic extents, covering a larger portion of a species' niche (Anderson and Raza, 2010;Sánchez-Fernández et al., 2011). We should still note that SDMs usually refer to the species level, but many adaptation strategies take place at the level of genotypes and populations, which are defined at local spatial scales. ...
To cope with climate change-induced alterations, forest ecosystems' conservation and restoration require models that are both capable to incorporate current local-scale dynamics but also to anticipate future changes. These requirements may be fulfilled by robust assessments of response (i.e., species data such as forest inventories) and predictor (e.g., climate) variables. The aim of this study is to predict current and future probability of occurrence for 22 tree species comparing inventory and climate data at different spatial scales and test for model performance , reliability, and niche truncation. We built species distribution models (SDMs) for 22 tree species of Piedmont, an Alpine administrative region of northwestern Italy. We compared (i) a fine-scale model calibrated with a local forest inventory with a 250-m spatial resolution at the extent of Piedmont and a regional climate model calibrated on the Italian extent versus (ii) coarse-scale model calibrated with a pan-European forest inventory (EU-Forest) at 1-km resolution and a global climate dataset (CHELSA v1.2). Moreover, (iii) we developed a data pooling method by combining the species data and using CHELSA. We evaluated models using spatial-block cross-validation and external validation through several metrics. We predicted the probability of occurrence for current and future under RCP4.5 and RCP8.5 climate scenarios. Models built with local species data performed better for the future than those incorporating broad species data and their current predictions reflected the realized distribution of species but they suffered from niche truncation while extrapolated to the future. Indeed, models calibrated at the local scale predicted greater magnitude of changes for future scenarios compared to coarse-scale models. Integrating species data at different extents and resolutions is a valid approach when both are available.
... According to several studies, either too constrained or overly expansive calibration areas may compromise the accuracy of model predictions (VanDerWal et al. 2009, Acevedo et al. 2012. Other studies have found that smaller calibration areas may yield superior model accuracy, as they mitigate the risk of overfitting to conditions near occupied localities or exclude regions with suitable conditions that remain unoccupied due to dispersal limitations and biotic interactions (Anderson and Raza 2010). Furthermore, selecting a background from larger areas leads to changes in variable importance, resulting in models becoming increasingly simplified and dominated primarily by just a few variables (VanDerWal et al. 2009). ...
... Consequently, the discrimination accuracy of the model may increase due to the ease to parameterize models with good discrimination capacity but that are low in useful information (Barve et al. 2011;Acevedo et al. 2012). This could be the result of larger calibration areas covering places with appropriate environmental conditions that are unoccupied because of biotic interactions and/or dispersal constrains, which could induce overfitting to conditions close to the occupied localities (Anderson and Raza 2010). On the other hand, the importance of coarse-scale factors such as climate may be underestimated at small calibration areas (Barve et al. 2011;Acevedo et al. 2012). ...
Species distribution models (SDM) are widely used tools in ecology and conservation aimed at predicting the potential distribution of a species based on its environmental requirements and occurrence data. SDM face many challenges and uncertainties that influence their accuracy. Selecting the ideal calibration area is one of these difficulties. This study analyzes the influence of the extent of the calibration area on the accuracy of SDM through simulations with virtual species. Using bioclimatic variables, 100 virtual species were generated. Occurrence probabilities were determined based on environmental suitability, spatial sampling bias, and accessible areas. SDM were built using MaxEnt, varying size of calibration area, spatial filtering of occurrence records, predictor collinearity treatment, and regularization parameter. Model performance was assessed in terms of functional accuracy (true model accuracy) and discrimination accuracy (model ability to separate occurrence from random sites). Results show that the extent of the calibration area was the most influential factor (explaining 50% of the variance in functional accuracy), while regularization multiplier, predictor collinearity, and spatial thinning had minimal impact (about 4% of explained variance combined). Overall, larger calibration areas generally led to higher functional accuracy, although it varies across species. The correlation between functional and discrimination accuracy was relatively low, indicating that models performing well in one metric may not excel in the other. In conclusion, this research advances the discussion on calibration area selection, providing insights on its substantial effects on model accuracy. Our findings demonstrate that the size of the calibration area is one of the most critical factors affecting the accuracy of models, surpassing the influence of other factors. These insights highlight the importance of select appropriate calibration areas to improve model predictions and ensure more reliable applications of the models.
... careful selection of the extent to which a species distribution is modelled must be made (Anderson & Raza, 2010). While this study has shown the significance of modelling from a buffered extent (from the EOO) to provide insights into range expansion, choosing an extent that is too large, and which includes areas where dispersal may be limited (because of geographic barriers or biotic interactions), may result in reduced performance of correlative ENMs (Anderson & Raza, 2010). ...
... careful selection of the extent to which a species distribution is modelled must be made (Anderson & Raza, 2010). While this study has shown the significance of modelling from a buffered extent (from the EOO) to provide insights into range expansion, choosing an extent that is too large, and which includes areas where dispersal may be limited (because of geographic barriers or biotic interactions), may result in reduced performance of correlative ENMs (Anderson & Raza, 2010). Given the good performance of the models in this study, the buffered extent used appeared to be reasonable, and provided valuable insights into how sungazers might respond to climate change. ...
The sungazer (Smaug giganteus) is a strict grassland specialist lizard endemic to South Africa's highveld grasslands. It is currently listed as Vulnerable (IUCN) and is primarily threatened by anthropogenic activities. Because sungazers are habitat specialists, climate change may be detrimental to the species, considering their life-history traits, and the area of available suitable habitat. We assessed how climate change may impact the sungazers' geographic range by first producing an ecological niche model (ENM) for the species within a buffered region of its extent of occurrence (buffered EOO). The ENM was then projected to 2040, 2060, 2080 and 2100 under two climate change scenarios using Shared Socioeconomic Pathways (SSP); SSP245 (moderate-case) and SSP585 (worst-case). A mean ensemble of three global circulation models for each time period and scenario was used to create habitat suitability maps which were refined using a natural grassland variable overlay. Resulting maps were clipped to the sungazers' EOO and interpreted distribution. Within the interpreted distribution, models predicted an area of 10 198 km2 of current suitable habitat. At this scale, future habitat suitability is predicted to remain relatively stable (area: 9910 km2; 3% decline) under SSP245 by 2100. However, a 24% decline (area: 7705 km2) in habitat suitability was predicted under SSP585. Within the buffered EOO, habitat suitability increased in south-western regions, which was more prominent under SSP585. Although this finding suggests that sungazers could track favourable conditions, their life history and low dispersal ability makes climate tracking unlikely. Because sungazers only occur in primary grasslands, regions dominated by agricultural activities, further land use developments are likely to affect the species survival. Thus, careful conservation management is essential, and we recommend the establishment of protected areas with cognizance of our predictions for current and future suitable habitat within the sungazers' interpreted distribution.
... Several methods have been proposed to not use target-group information. For example, the restricted background method confines background points to locations within a specific radius distance of the presence points (Anderson and Raza, 2010;Senay et al., 2013), focusing on the habitat available near the sampling location to mitigate sampling bias (VanDerWal et al., 2009). Similar to the "bias file" in Maxent software (Elith et al., 2010), the background thickening method sets the relative probability of sampling background points as the number of presence points within the radius length of each location (Vollering et al., 2019), which has been proven to alleviate the negative impact of spatial autocorrelated sampling. ...
... Not suitable for small sample sizes (Varela et al., 2014) Environmental filtering Environmental variability in species distributions is simplified (Zhang and Zhu, 2020) Representativenessoriented Need sampling effort information outside the target species Adjust background data (Barber et al., 2022) Target group background Often unavailable Subjective, correcting bias from geospatial only (Anderson and Raza, 2010;Senay et al., 2013) Restricted background Restricted distance is difficult to determine (Vollering et al., 2019) Thickening background Invalid for bias not caused by point spatial autocorrelation (Monsarrat et al., 2019) Weaken bias covariates May not be applicable and challenging to capture Subjective, correcting bias from the environment only (Moua et al., 2020) Similarity background Need to determine parameters that constrain environmental proximity sample sizes. Our approach utilizes kernel density estimation (KDE) to quantify sampling bias across geographic and environmental dimensions. ...
Correcting sampling bias in species distribution models (SDMs) is challenging. The difficulty lies in accurately identifying and quantifying bias and the scarcity of samples, which greatly impedes the implementation of bias correction. Current methods often adjust the distribution of presence or background points within geographic or environmental spaces to correct the sampling bias in probability estimation within SDMs. However, these methods may lead to information loss, rely on subjective assumptions, and often separate geography and environment when correcting for bias. This study proposes a novel and easily implementable method termed “aggregation background.” This method selects background data based on the aggregation degree of presence points in the geographic and environmental feature space, thereby approximating the representation and correction of sampling bias in the presence samples. We compared this new method with other prevalent sampling bias correction methods in the existing literature by analyzing ecological authenticity. Under varying biases and sample sizes, the aggregation background and geographic filtering methods achieved more accurate species distribution predictions compared to the target group background and other methods. Notably, when the sample size was small (≤70), the aggregation background was superior to that obtained using the geographic filtering method. These findings underscore the effectiveness of the aggregation background in improving bias correction using limited available presence sample data, without relying on assumptions about sampling bias. Our method provides a new approach for correcting complex unknown biases in SDMs.
... Using grasshopper distribution data and environmental variables to model the ecological niche of grasshoppers and assess habitat quality in grasshopper suitable areas can help develop scientifically sound pest control measures [22,23]. Currently, widely used species distribution models (SDMs) include random forest (RF) [24], logistic regression model [25], generalized linear model (GLM) [26], ecological niche factor analysis (ENFA) [27], Bioclimate Analysis and Prediction System (BIOCLM) [28], and maximum entropy (MaxEnt) [29,30]. ...
... The fitness zone of A. rhodopa decreased in size under SSP126, increased in size under both SSP245 and SSP370, and did not change in size under SSP585. Myrmeleotettix palpalis had the largest suitable area, which was reduced under SSP126, SSP245, SSP370, and SSP585, accounting for 22.2%, 22.0%,22.1%, and 22.3% of the grassland area, respectively, with the largest reduction under SSP245, which was reduced by 0.5 × 10 4 km 2(Figure 2). ...
Simple Summary
Grasshoppers are the most widely distributed pests in the natural grasslands of the Hexi Corridor in Gansu, northwest China. We clarified the distribution of the grasshopper suitable areas and the main environmental variables affecting the distribution of the grasshopper suitable areas, which will provide a basis for monitoring and forecasting grasshoppers in grassland. Therefore, based on the MaxEnt model, this study predicted the distribution of the four grasshoppers in their suitable areas by combining five environmental variables, namely climate, vegetation, soil, topography, and human footprint, and analyzed the main influencing factors affecting the distribution of the suitable areas. Mean annual precipitation was the main environmental variable affecting the distribution of grasshopper habitats, and the extent of the habitat of four species of grasshoppers either increased or decreased in future.
Abstract
Angaracris rhodopa (Fischer et Walheim), Calliptamus abbreviatus (Ikonnikov), Myrmeleotettix palpalis (Zubowsky), and Oedaleus decorus asiaticus (Bey-Bienko) are the main grasshoppers that harm the natural grassland in the Hexi Corridor in Gansu, northwest China. In this study, the MaxEnt model was employed to identify the key environmental factors affecting the distribution of the four grasshoppers’ habitats and to assess their distribution under current and future climate conditions. The aim was to provide a basis for grasshopper monitoring, prediction, and precise control. In this study, distribution of suitable habitats for A. rhodopa, C. abbreviates, M. palpalis, O. decorus asiaticus were predicted under current and future climatic scenarios using the Maxent model. The average AUC (area under the ROC curve) and TSS (true skill statistic) values of the four grasshoppers were greater than 0.9, and the simulation results were excellent and highly reliable. The mean annual precipitation was the main factor limiting the current range of suitable areas for these four species. Under the current climate, A. rhodopa, C. abbreviatus, and O. decorus asiaticus were mainly distributed in the central and eastern parts of the Hexi Corridor, and M. palpalis was distributed throughout the Hexi Corridor, with a suitable area of 1.29 × 10⁴, 1.43 × 10⁴, 1.44 × 10⁴, and 2.12 × 10⁴ km², accounting for 13.7%, 15.2%, 15.3%, and 22.5% of the total area of the grasslands in the Hexi Corridor, respectively. The highly suitable areas of A. rhodopa, C. abbreviatus, and O. decorus asiaticus were mainly distributed in the eastern-central part of Zhangye City, the western part of Wuwei City, and the western and southern parts of Jinchang City, with areas of 0.20 × 10⁴, 0.29 × 10⁴, and 0.35 × 10⁴ km², accounting for 2.2%, 3%, and 3.7% of the grassland area, respectively. The high habitat of M. palpalis was mainly distributed in the southeast of Jiuquan City, the west, middle, and east of Zhangye City, the west of Wuwei City, and the west and south of Jinchang City, with an area of 0.32 × 10⁴ km², accounting for 3.4% of the grassland area. In the 2030s, the range of A. rhodopa, C. abbreviatus, and O. decorus asiaticus was predicted to increase; the range of M. palpalis will decrease. The results of this study could provide a theoretical basis for the precise monitoring and control of key areas of grasshoppers in the Hexi Corridor.
... The number of occurrence records of other fossil species of Arbacia was too low for running robust paleoecological models. To anticipate potential modeling extrapolation, it was decided to split the spatial extent of some modeled species into bioprovinces and to restrain projections to plausible depths (Anderson & Raza, 2010;Guillaumot et al., 2021). Models were generated with 24 to 411 presence-only records (Table S2) along with multiple sets of background records randomly sampled in each bioprovince to properly represent the variation in the environmental covariates (Valavi et al., 2021; Table S2). ...
Past biogeographic events and environmental changes, along with ecological niche evolution are determining factors of species diversity and distribution. Studying species niche evolution can help improve our understanding of determining factors underpinning species evolution with regards to past biogeographic events and infer speciation processes at the origin of clades. In the present work, the ecological niche of all extant species of the echinoid Arbacia Gray, 1835 and of the related fossil species of the Pliocene, Arbacia improcera (Conrad, 1843) was characterized using Ecological Niche Modeling that enable a comprehensive representation of the species fundamental niche. Unlike many other echinoids, species of Arbacia are distributed in both tropical and temperate seas and show highly contrasting distribution patterns making the genus an interesting case study of ecological niche evolution. Comparison of ecological niches between closely related species provided insight on the importance of ecological niche evolution with regards to the genus phylogeny and the fossil record. Main results highlighted the importance of niche differentiation between species, but also between genetic units within a same species. This result holds true when comparing with their Pliocene relative and classic biogeographic scenarios. Résumé : Evolution de la niche écologique du genre d'échinide Arbacia Gray, 1835 (Echinoidea : Arbacioida). Les événements biogéographiques passés et les changements environnementaux, ainsi que l'évolution des niches écologiques, sont des facteurs déterminants de la diversité et de la distribution des espèces. L'étude de l'évolution des niches écologiques peut contribuer à améliorer notre compréhension des facteurs déterminants sous-tendant l'évolution des espèces par rapport aux événements biogéographiques passés et à déduire les processus de spéciation à l'origine des clades. Dans le présent travail, la niche écologique des espèces actuelles du genre d'échinide Arbacia Gray, 1835 et d'une espèce fossile du Pliocène, Arbacia improcera (Conrad, 1843) a été caractérisée à l'aide de modèles de niches écologiques, permettant de représenter la niche fondamentale des espèces. Contrairement à de nombreux autres échinides, les espèces d'Arbacia sont réparties dans les mers tropicales et tempérées et présentent des schémas de distribution très contrastés, en faisant un cas d'étude intéressant pour l'évolution des niches écologiques. La comparaison des niches entre espèces étroitement apparentées a permis de comprendre l'importance de l'évolution des niches écologiques par rapport à la
... To reduce spatial bias, we used SDMtoolbox v2.5 [54] in ArcGIS to filter the dataset, ensuring that no two localities were closer than 10 km, resulting in 56 localities. The study area was defined by a minimum bounding rectangle around the localities with a 5 • buffer [55,56], covering the known range of P. vlangalii from 76 to 107 • E and from 28 to 44 • N. ...
The Kunlun, Arjin, and Qilian mountain ranges mark the northern edge of the Qinghai–Tibet Plateau (QTP), where rapid uplift and Quaternary glacial cycles have shaped a unique cold desert ecosystem and species distribution. Despite sampling challenges, phylogeographic studies are crucial for understanding reptile populations such as the Qinghai toad-headed agama (Phrynocephalus vlangalii), a viviparous lizard with limited dispersal and multiple subspecies in the northeastern QTP. Our fieldwork identified populations of P. vlangalii on the northern slope of the Kunlun–Arjin Mountains, similar to the controversial subspecies P. v. lidskii. We analyzed 130 individuals from the northern slope of the Kunlun–Arjin–Qilian Mountains and 253 individuals from GenBank, using three mitochondrial genes and two nuclear genes to assess intraspecific differentiation and demographic history. We found high haplotype diversity and low nucleotide diversity in P. vlangalii, with phylogenetic analyses revealing six distinct clades. Clade VI, confirmed as P. v. lidskii, and Clade IV, a new genetic lineage, were identified alongside three recognized subspecies. Genetic variation was largely attributed to clade splitting, indicating significant divergence. The Mantel test indicated that geographical and environmental factors drove population differentiation. Bayesian molecular clock analysis suggested that the most recent common ancestor of P. vlangalii lived 2.55 million years ago, influenced by the Qinghai–Tibet Movement and glacial cycles. Demographic history and ecological niche modeling (ENM) indicated no population decline during the Last Glacial Maximum, supporting the glacial maximum expansion model, with ENM predicting future habitat expansion for P. vlangalii. In addition, morphological data from 13 meristic and 15 metric characters confirmed clade differences. Our findings significantly advance our understanding of P. vlangalii diversification, population dynamics and response to geological and climatic changes in the QTP.
... This created the final set of six records for S. devorator and 11 records for P. tamdaoensis as the input data for the Max-Ent model. We used 19 bioclimatic variables at 30-arcsec resolution available at the WorldClim 2.1 database (Fick and Hijmans 2017) and restricted the extent using a two-degree buffer around the minimum convex polygon of the occurrence localities (Anderson and Raza 2010). ...
The Vietnam Skink (Plestiodon tamdaoensis) was described from Tam Dao National Park, Vietnam, in 1937, and the species is currently known only from northern Vietnam and Hong Kong of China, whereas the Devouring Forest Skink (Scincella devorator) was described from Yen Tu Nature Reserve, Vietnam, in 2004, and the species is endemic to northern Vietnam. As a result of our field surveys in 2023 and 2024 in Ba Vi National Park, Vietnam, we reported new distribution records, morphological data, and natural history of Plestiodon tamdaoensis and Scincella devorator. In addition, we used species distribution modeling to predict the potential distribution of these species. The model showed that the potential distribution of P. tamdaoensis is approximately 110,000 km² and that of S. devorator is approximately 130,000 km², covering northern Vietnam, southern China, and northern Laos, significantly expanding its known range compared to the IUCN range map.
... Using the "ENMevaluate" function of the ENMeval R package [40], we built and evaluated 135 models by combining nine regularization multiplier values (ranging from 0.5 to 4.5 in 0.5 increments) with all 15 possible combinations of the four feature classes (Linear = L, Quadratic = Q, Product = P, and Hinge = H). We defined the calibration area for model fitting by establishing a 1.5˚(~155 km 2 ) buffer around a Minimum Convex Polygon (MCP) formed by all occurrence points of the target species [36,41,42]. We assumed this area is potentially accessible to the species and encompasses sufficient environmental heterogeneity to estimate the species' environmental or niche preferences. ...
Knowledge of the potential distribution and locations of poorly known threatened species is crucial for guiding conservation strategies and new field surveys. The painted tree-rat (Callistomys pictus) is a monospecific, rare, and endangered echimyid rodent endemic to the southern Bahia Atlantic Forest in Brazil. There have been no records of the species published in the last 20 years, and the region has experienced significant forest loss and degradation. According to the IUCN, only 13 specimens had been previously reported, with 12 found in the north of Ilhéus and adjacent municipalities, and one recorded approximately 200 km away from this region, suggesting that its distribution might be wider. We aimed to search for unpublished and more recent records of the C. pictus, by consulting the gray literature (including Environmental Impact Study (EIA) reports, Brazilian Red Lists, and management plans of protected areas), scientific collections, online databases, and mastozoologists working in the region. We estimated the species’ potential distribution using Ecological Niche Modeling to identify regions, municipalities, and protected areas most likely to support this species, based on factors such as climate suitability and forest cover. We reported three new sightings of the species, including the first within a protected area. We estimated suitable climate conditions across 23,151 km², of which 9,225 km² has a high potential for harboring the species. The area between Itacaré and Valença needs more extensive survey efforts as it has high habitat suitability and only one record has been confirmed there so far. Meanwhile, the region between Una and Ilhéus urgently requires habitat conservation initiatives. While the species may have a broader distribution than previously thought, its known occurrences are limited to a few locations, and suitable habitats are underrepresented in protected areas. Additionally, the rarity of sightings continues to indicate a concerning conservation status.
... The calibration area was delineated in 3 stages: (1) defining the known distribution of S. cinnamomea (BirdLife International 2024), (2) mapping occurrences from citizen science databases and geolocator data and applying the minimum convex polygon method (Anderson and Raza 2010), and (3) applying a 200-km buffer (Gomes et al. 2018) to ensure model accuracy without abrupt boundary effects. QGIS 3.14.15-Pi ...
The movement ecology of migratory Sporophila seedeaters in the Neotropics, particularly their migratory routes and nonbreeding areas, remains poorly understood. This study aimed to fill this gap by providing a detailed description of these migratory routes, stopovers, and nonbreeding areas using light-level geolocators on Sporophila cinnamomea (Chestnut Seedeater), a vulnerable species that breeds in southern South America. Species distribution modelling was employed to estimate the probability of occurrence at distribution extremes. Over 3 breeding seasons (October to March 2018–2021), geolocators were affixed to 14 adult males. The overall device recovery rate was 64% (n = 9), and these 8 devices operated, on average, for 301 days. Autumn migration commenced on 24 February, lasting 46 days and spanning 1,679 km, with an average of 2.38 ± 0.92 stopovers—twice the number observed during spring migration (1.40 ± 0.89). We identified 8 nonbreeding areas utilized by the birds for an average of 145 days, primarily located between the northern and northeastern regions of the Paraná and Paraguay Rivers and the southern Tocantins River. Spring migration began in early September, lasted ~58 days, and covered 2,940 km. In both migrations, birds followed routes along the Paraná-Paraguay River valleys, with no significant difference in the number of stopovers detected between seasons. The nonbreeding period model indicated high habitat suitability in 2 regions within the Cerrado biome, particularly a larger area predominantly in the southern and then in the central regions of the Tocantins River. In the southern limit, the breeding model highlighted the most representative area, which is located in the central-eastern region of the Uruguay River. Our study offers valuable insights into the migratory patterns of S. cinnamomea. These findings should be used to inform the planning and establishment of protected areas aimed at conserving grassland species.
... To account for potential spatial biases and oversampling in presence point data collection, we spatially thinned remaining occurrences to a radius of 10 km between each locality point. We used the distance of 10 km due to the high spatial heterogeneity of the geographic ranges of these three species, and previous literature indicates that this distance is optimal under heterogenous conditions (Anderson & Raza, 2010;Boria et al., 2014;Pearson et al., 2007). Predictor variables used consisted of all 19 WorldClim bioclimatic variables (bioclims) relating to temperature, precipitation, and seasonality characteristics (Table I) at a resolution of 30 arcsecs (~ 1 km 2 at the equator). ...
Cryptic species complexes consist of geographically confluent, closely related species that were once classified as a single species. The diversification mechanisms of cryptic species complexes often are mediated by environmental factors, which in some cases lead to ecological speciation. Niche-based distribution modeling can be an important tool in characterizing the extent of ecological divergence between species that may have resulted from environmentally driven speciation scenarios. We used climatic niche modeling to examine the degree of ecological divergence within the Paragalago zanzibaricus species complex in East Africa. We expected parapa-trically distributed P. cocos and P. zanzibaricus to display a significant degree of climatic niche distinction and allopatrically distributed P. zanzibaricus and P. granti to exhibit a degree of niche conservatism. The extent of niche overlap between the three species was assessed by using a Niche Similarity Analysis (NSA) on biocli-matic values. Selected models for all three species exhibited good predictive ability , although the model for P. cocos was most optimal and appeared most consistent with its known range. NSA showed that P. cocos and P. zanzibaricus were statistically more similar than predicted from null distributional values. Results for NSA between the other two species pairings appear to be within the null distribution. The extent of niche overlap between all three species is consistent with the expectations of allopatric speciation processes. Future studies should examine alternative hypotheses for speciation within this group, including the role of sensory drive, interspe-cific competition, and the impact of Plio-Pleistocene climatic cycles.
... 222 km) buffer around them. This ensures that Maxent selects the bioclimatic data from "background" pixels from a region where known records are more likely to form a representative sample of the suitable climatic conditions for the species (Anderson and Raza 2010). These ecoregions might act as boundaries that have repeatedly constrained the distributional potential of the species (Barve et al. 2011). ...
Coendou quichua is a widely distributed trans-Andean species in Colombia, Ecuador, and Panama. However, analysis of the cytochrome b (Cytb) gene suggests the presence of cryptic diversity. Recent reviews found that morphological variation within this taxon is mainly associated with elevation. Still, mitochondrial divergence values between some populations are similar to those reported between well-diagnosable sister species in the genus. Here, we provide new Cytb sequences from Colombian and Ecuadorian specimens and morphological observations from specimens collected in different natural regions to show that C. quichua is indeed a species complex. Coendou quichua complex contains 3 separate lineages: (i) the typical C. quichua from the Andes of Ecuador; (ii) a sister lineage from the Chocó-Darién ecoregion; and (iii) an undescribed new species from wet and dry forests of the Magdalena inter-Andean valley and the Caribbean regions of Colombia. Based on morphological, ecological niche modeling, and geographical analyses, the lineage from Chocó-Darién in Colombia and Ecuador is here treated as a different species for which the name C. rothschildi is available. The lineage involving samples from the wet and dry forests of the Magdalena inter-Andean Valley and the Caribbean regions represents an unnamed taxon described herein as Coendou vossi sp. nov., endemic to Colombia.
Revisión del complejo del puercoespín andino Coendou quichua (Rodentia: Erethizontidae) con la descripción de una nueva especie de Colombia Resumen Coendou quichua se considera una especie trasandina de amplia distribución que se encuentra en Colombia, Ecuador y Panamá. Sin embargo, análisis del gen citocromo b (Cytb) sugieren la presencia de diversidad críptica. Revisiones recientes encontraron una variación morfológica dentro de este taxón asociada principalmente a la elevación, mientras que los valores de divergencia mitocondrial entre algunas poblaciones son similares a los reportados entre especies hermanas del género. Proporcionamos nuevas secuencias de Cytb de especímenes colombianos y ecuatorianos, así como observaciones morfológicas de especímenes recolectados en diferentes regiones naturales para mostrar que C. quichua es de hecho un complejo de especies. El complejo C. quichua posee tres linajes: (i) el típico C. quichua de los Andes de Ecuador; (ii) un linaje hermano de la ecorregión de Chocó-Darién; y (iii) una nueva especie no descrita de bosques húme-dos y secos del valle interandino del Magdalena y la región Caribe de Colombia. Con base en análisis morfológicos, y geográficos, junto a modelos de nicho ecológico, el linaje del Chocó-Darién es tratado como una especie diferente para la cual el nombre C. rothschildi está disponible. De manera similar, el linaje que involucra muestras de los bosques húmedos y secos del Valle interandino del Magdalena y del Caribe representa un taxón sin nombre disponible el cual describimos aquí como Coendou vossi sp. nov., endémico de Colombia.
... The size of the calibration area affects the model's performance metrics. The models' discrimination ability (i.e., the ability to correctly distinguish between presence and absence localities), for example, usually increases with the size of the calibration area (Anderson and Raza 2010;Barbet-Massin et al. 2012;Amaro et al. 2023). This mainly happens because larger areas tend to include absences that are ecologically more distant from presences, making them easier to distinguish (Lobo et al. 2008;Vanderwal et al. 2009). ...
Watermelon mosaic virus (WMV) frequently infects crops in the Cucurbitaceae family, posing a significant challenge in their production. Managing viruses in crops remain a challenge, primarily due to the limited number of available strategies. The most effective strategy for controlling WMV is to prevent its introduction into regions currently free of the disease. To achieve this, it is necessary to map the locations where the WMV is present and identify areas at risk of invasion. This can be achieved through maximum entropy modeling (Maxent). This study aimed to map the countries with potential distribution for WMV and determine the environmental factors related to its ecological niche. The generated model was robust and reliable according to the 21 metrics used to evaluate it. The response curves of the selected variables revealed that the survival of WMV is directly linked to specific conditions of temperature, precipitation, and altitude, with the virus having a higher probability of survival in warm regions, at altitudes below 1000 m, and with good rainfall availability. The suitability map showed that 46.08% of the planet presents some probability of WMV survival, with the areas of highest probability located in countries in southern Europe, as well as in the United States, Brazil, Argentina, China, Turkey, and Iran. Additionally, the climate zoning map indicated that WMV occurs most frequently in areas classified as Cfa (humid subtropical), Csa (Mediterranean), Aw (tropical savanna), and BSk (cold semi-arid) according to the Köppen-Geiger classification.
... This limited buffer only included areas where the species were most likely to be searched for and reported, which was particularly important in the invaded African region as the environments greatly vary across the continent and many countries do not or cannot report pest locations. Additionally, limiting the background extent to where the species is likely at equilibrium with its environment and to areas that do not cross dispersal barriers increases the precision of the MaxEnt model by limiting false negative signals (Pearson 2007, VanDerWal et al., 2009, Anderson and Raza 2010. Additionally, the greater number of background points from the MaxEnt default of 10,000 was used due to the large background extent, which incorporated several countries. ...
... To evaluate the performance of spatially segregated localities, we split the landscape into four regions and calibrated our models using one evaluation record and k−1 calibration records (where k is the total number of occurrence records). Because model performance can be affected by the extent of the background sampling area (Anderson 2012;Anderson and Raza 2010), model feature class, and regularization multipliers (Phillips and Dudík 2008;Shcheglovitova and Anderson 2013;Radosavljevic and Anderson 2014), we implemented best practices into our modeling exercise. To avoid overprediction, we restricted the background area to a minimum convex polygon based on the extent of the training region (i.e., based on known occurrence data), buffered by 100 km. ...
We combined mitochondrial DNA sequence data and paleoclimatic distribution models to document phylogeographic patterns and investigate the historical demography of two manakins, Ceratopipra rubrocapilla and Pseudopipra pipra, as well as to explore connections between Amazonia and the Atlantic Forest. ND2 sequences of C. rubrocapilla (75 individuals, 24 sites) and P. pipra (196, 77) were used in Bayesian inference and maximum likelihood analyses. We estimated mitochondrial nucleotide diversity, employed statistical tests to detect deviations from neutral evolution and constant population sizes, and used species distribution modeling to infer the location of suitable climate for both species under present‐day conditions, the Last Glacial Maximum (LGM), and the Last Interglacial Maximum (LIG). Mitochondrial sequence data from C. rubrocapilla indicate one Amazonian and one Atlantic Forest haplogroup. In P. pipra, we recovered a highly supported and differentiated Atlantic Forest haplogroup embedded within a large Southern Amazonian clade. Genetic and taxonomic structure in Amazonia differs widely between these two species; older P. pipra has a more marked genetic structure and taxonomic differentiation relative to the younger C. rubrocapilla. Both species have similar genetic patterns in the Atlantic Forest. Paleoclimatic distribution models suggest connections between southwestern Amazonia and the southern Atlantic Forest during the LIG, but not between eastern Amazonia and the northeastern Atlantic Forest, as suggested by previous studies. This indicates that multiple corridors, and at different locations, may have been available over the Pliocene and Pleistocene between these two regions.
... This approach captures habitat the species can reasonably disperse to without encroaching deep into the Cerrado. This reduces the chance of bias imposed by sampling suitable environments that are unreachable due to the species' dispersal abilities (Anderson and Raza 2010). The area within the polygon was used as the background environment for FIGURE 3 | Full analytical workflow for each species, from creating species distribution models (SDMs) to generating genetic diversity maps. ...
In integrative distributional, demographic and coalescent (iDDC) modelling, a critical component is the statistical relationship between habitat suitability and local population sizes. This study explores this relationship in two Enyalius lizard species from the Brazilian Atlantic Forest: the high‐elevation E. iheringii and low‐elevation E. catenatus and how this transformation affects spatiotemporal demographic inference. Most previous iDDC studies assumed a linear relationship, but this study hypothesises that the relationship may be nonlinear, especially for high‐elevation species with broader environmental tolerances. We test two key hypotheses: (1) The habitat suitability to population size relationship is nonlinear for E. iheringii (high‐elevation) and linear for E. catenatus (low‐elevation); and (2) E. iheringii exhibits higher effective migration across populations than E. catenatus . Our findings provide clear support for hypothesis (2), but mixed support for hypothesis (1), with strong model support for a nonlinear transformation in the high‐elevation E. iheringii and some (albeit weak) support for a nonlinear transformation also in E. catenatus . The iDDC models allow us to generate landscape‐wide maps of predicted genetic diversity for both species, revealing that genetic diversity predictions for the high‐elevation E. iheringii align with estimated patterns of historical range stability, whereas predictions for low‐elevation E. catenatus are distinct from range‐wide stability predictions. This research highlights the importance of accurately modelling the habitat suitability to population size relationship in iDDC studies, contributing to our understanding of species' demographic responses to environmental changes.
... In accordance with this recommendation, the background for this study, involving an expansive area and a large number of occurrence records, was set at 30,000 for L. rigidum and 50,000 for L. multiflorum. Model performance can be further improved by restricting the occurrence of background points to fractions containing occurrence points (Phillips 2008;Anderson and Raza 2010). Therefore, we limited the occurrence of background points to locations within a radius of 500 km from the occurrence points of Lolium species. ...
Invasive alien plants cause severe global problems; therefore, determining the factors that lead to the success or failure of invasion is a critical question in the field of invasion ecology. In this study, we aimed to determine the factors underlying differences in the distribution range of alien plants in Japan by investigating why Lolium multiflorum thrives in a wide range of habitats while L. rigidum is mainly distributed on sandy beaches. We initially evaluated environmental niche suitability through species distribution modelling and subsequently examined whether species traits influence the differences in range expansion between the two species. We used MaxEnt modelling to identify potential environmental niches for both species. The analysis revealed that L. rigidum was considerably less suited to the Japanese climate compared to L. multiflorum, with high summer precipitation in Japan identified as one of the climatic factors limiting the distribution of L. rigidum. Given that these winter annual plants remain dormant as seeds during summer, in subsequent experiments, we buried seeds in paddy field soil and sandy beach sand during summer and evaluated their survival rate in autumn. The survival rate of L. rigidum seeds was significantly lower than that of L. multiflorum, particularly in paddy soil. Factors contributing to seed mortality may include the decay or early germination of L. rigidum seeds under Japan’s high rainfall conditions. This study emphasises the importance of considering local environmental factors alongside climate niche modelling in the risk assessment of invasive species. Moreover, the integration of species distribution modelling for large-scale evaluations and manipulation experiments for fine-scale assessments proved effective in identifying climatic conditions and species traits influencing the success or failure of alien species invasion.
... Study from a larger spatial extent means possibility of a good proportion of background points with less information (Barbet-Massin et al. 2012), thus leading the issue of model overfitting. In species distribution models, the selection of geographic backdrop locations and the decrease of spatial correlation have both seen significant progress in recent years (Anderson and Raza 2010;Shcheglovitova and Anderson 2013;Radosavljevic and Anderson 2014;Boria et al. 2014). A biased file is an example of one of these developments. ...
... The study area was reduced to (49.12°N-11.13°S, 64.19-131.44°E) in order to improve the models 46,47 . ...
Considering the global biodiversity crisis and the growing demand for medicinal plants, it is crucial to preserve therapeutically useful herbs. From a conservation management perspective under climate change, identifying areas that enable valuable natural resources to persist in the future is crucial. Machine learning-based models are commonly used to estimate the locations of climate refugia, which are critical for the effective species conservation. The aim of this study was to assess the impact of global warming on the epiphytic medicinal orchid—Bulbophyllum odoratissimum. Given how the long-term survival of plants inhabiting shrubs and trees depends on the availability of suitable phorophyets, in this research potential range changes in reported orchid plant hosts were evaluated. According to conducted analyses, global warming will cause a decline in the coverage of the suitable niches for B. odoratissimum and its main phorophyte. The most significant habitat loss in the case of the studied orchid and Pistacia weinmannifolia will be observed in the southern part of their geographical ranges and some new niches will simultaneously become available for these plants in the northern part. Climate change will significantly increase the overlap of geographical ranges of P. weinmannifolia and the orchid. In the SSP5-8.5 scenario trees will be available for more than 56% of the orchid population. Other analyzed phorophytes, will be available for B. odoratissimum to a very reduced extent, as orchids will only utilize these species as habitats only occasionally. This study provides data on the distribution of climatic refugia of B. odoratissimum under global warming. Moreover, this is the first evaluation of the future geographical ranges for its phorophytes. According to the conducted analyses, only one of the previously reported tree species which are inhabited by B. odoratissimum, P. weinmannifolia, can serve as a phorophyte for this orchid in the future. In this study, the areas designated as suitable for the occurrence of both orchids and their phorophytes should be considered priority conservation areas for the studied medicinal plants.
... A spatially appropriate study area is needed to (1) obtain adequate representation of the range of environmental variation available to the WTGW and (2) prevent models from overfitting to the bias in occurrence points (Anderson and Raza 2010). The ~32,000 km 2 region that incorporates the entire known distribution (and potential habitat outside of the known distribution) of the WTGW is managed or comanaged by a range of organisations, including Warddeken Land Management, Jawoyn Association Aboriginal Moore et al. 2019;von Takach et al. 2020). ...
Context
Many Australian threatened species occur on Indigenous-owned and/or managed lands, often in vast, remote areas that are difficult and expensive to access. One such species is the white-throated grasswren (WTGW, Amytornis woodwardi), a rare ground-dwelling bird found in rocky spinifex-covered escarpment habitats of northern Australia. To make surveying rare species more tractable, we can predict habitat suitability by associating occurrence points with environmental covariates that may influence the species’ distribution.
Aims
Here, we combine western and Indigenous knowledge and approaches to better quantify the habitat associations and distribution of the WTGW.
Methods
We modelled habitat suitability across the region using historical occurrence records and applicable environmental variables with input from Traditional ecological knowledge. We then used this habitat-suitability map as a visual tool for participatory mapping and planning sessions with Traditional Custodians to select on-ground survey sites. Collaborative surveys were then undertaken to target WTGWs at 39 sites across the Arnhem Plateau by using several methods, including bioacoustic audio recorders (BARs), call-playback (CPB) surveys, and motion-detection cameras.
Key results
Collaboration between Traditional Custodians and scientists at all stages helped make this project a success. Our model suggests that WTGWs typically occupy habitat patches that have lower distance-to-unburnt (fire extent) values, lower proportion-of-area-burnt values, lower vegetation-cover values, and higher time-since-fire values. On-ground surveys detected WTGWs at six sites with BARs and at one of these six sites with CPB and camera-trapping, suggesting that BARs were the most effective detection method.
Conclusions
Our results provided key ecological information for use by land managers in the region and highlighted the importance of effective fire management for the persistence of WTGW populations. The success of the cross-cultural collaboration across several Indigenous organisations relied on the expertise of Traditional Custodians and Indigenous rangers. With Traditional Custodians and Indigenous rangers leading the fieldwork, co-benefits of the program included connecting people with Country and supporting the transfer of intergenerational knowledge surrounding the WTGW.
Implications
Whereas fire management in the region over the past decade has led to broadscale reductions in the frequency, extent and intensity of fires, strategic imposition of fire regimes that retain sufficient unburnt refugia at habitat scales appears necessary for viable populations of species such as the WTGW to persist.
... This approach captures habitat the species can reasonably disperse to without encroaching deep into the Cerrado. This reduces the chance of bias imposed by sampling suitable environments that are unreachable due to the species' dispersal abilities (Anderson and Raza 2010). The area within the polygon was used as the background environment for FIGURE 3 | Full analytical workflow for each species, from creating species distribution models (SDMs) to generating genetic diversity maps. ...
... Additional distribution data were sourced from historical literature and news reports. To avoid the spatial autocorrelation of the species distribution points [27], the Spatially Rarefy Occurrence Data for Species Distribution Models (SDMs) tool [28] in ArcGIS 10.8 was used, resulting in 133 validated M. berezovskii distribution points ( Figure 1). These filtered data were converted to the CSV format for subsequent model calculations. ...
The forest musk deer (Moschus berezovskii) is a national Class I protected wild animal in China, and the IUCN Red list classifies it as globally endangered. It has significant value in traditional Chinese medicine and spices. However, wild M. berezovskii has faced a severe population decline due to human hunting, habitat loss, and fragmentation. Thus, studying its population size and distribution pattern is of great importance to develop effective conservation measures. Here, we determined the optimal MaxEnt model and used stratified sampling and the fecal pile counting method to predict the population size and potential habitat distribution of wild M. berezovskii in Chongqing using 133 species distribution points and 28 environmental variables. The results were as follows: (1) When the optimal model parameters were RM = 3.5 and FC = LQHPT, it had high model prediction accuracy (AUC = 0.909 ± 0.010, TSS = 0.663). (2) Under various climatic, topographic, vegetation, and anthropogenic disturbance scenarios, M. berezovskii was primarily distributed in northern, eastern, southwestern regions of Chongqing, covering an area of approximately 5562.80 km². (3) The key environmental factors affecting the potential habitat distribution of M. berezovskii were elevation (36.5%), normalized difference vegetation index (NDVI, 16.6%), slope (11.8%), and land-use type (7.6%), whereas climate and anthropogenic disturbance factors had relatively little influence. (4) A population estimation for M. berezovskii identified approximately 928 ± 109 individuals in Chongqing. We recommend prioritizing the preservation of high-altitude habitats and native vegetation to mitigate human interference and minimize road damage. In summary, our results can enhance the understanding of M. berezovskii distribution and provide a basis for effective conservation and management initiatives.
... Cabe a ressalva para as introduções biológicas por ações antropogênicas, que transcendem as barreiras biogeográficas e história evolutiva da espécie (Soberón & Peterson, 2005). Ao considerar a M, deve-se ter em mente que a espécie em questão não teria visitado regiões fora da M por razões não relacionadas aos fatores abióticos A (Anderson & Raza, 2010;Barve et al., 2011). De maneira mais sucinta, a região M é a região geográfica acessível para a espécie dado um período relevante (Soberón et al., 2005;Barve et al., 2011). ...
The imminence of the impacts of anthropogenic climate change threatens biodiversity as a whole, with worrying prospects for the coming decades. The concern extends to the bees, a group recognized for their role in pollination in natural and agricultural environments. The main aim of this study was to investigate the potential impacts of climate change on the distribution of bees, especially the range-shift to the south, focusing on one of the most diverse regions in the world - South and Southeast South America. For this purpose, ecological niche modeling (ENM) has been used to access the areas of suitability in the present and in two climate change scenarios (one optimistic and one pessimistic) for 2050. A sample of 20 bee species (19 genera) was selected from different taxonomic and functional groups, with representatives of the five subfamilies that occur on the continent. For six selected genera, another six close-related species (of the same genus, but with a more northern distribution) were selected for pairwise comparisons of changes in distribution and geographical overlapping. For the 20 southern species, the projections were quantified in terms of suitable stability, losses and gains of suitability. Also, the projections were classified into range-shift classes: (i) displaced. (ii) reduced, (iii) expanded and (iv) unchanged. Overall, the models point to predominance in suitable stable areas (~60%), however with rather expressive values of losses (~20%) and slightly lower gains (~16%). Displaced distributions were the most effect for the species in the future scenarios (35%), followed by reduced (30-35%) and expanded (25%) distributions. In addition, the models indicate drastic richness declines in the region in climate change scenarios. For the functional groups, it was possible to identify higher susceptibility to losses of suitable areas for the solitary species, those with below ground nesting and the floral resources specialists. On the other hand, eusocial species, those with above ground nesting and generalists tended to be less susceptible, with more frequent tendencies to displacement or expansion of distribution range. The models predicted reductions in the overlapping areas of species from the same genus, with a tendency to shift the overlapping areas towards the south. For five of the six genera (Augochlora, Bombus, Paroxystoglossa, Tetraglossula, and Thectochlora), species with more Northern distribution tend to be more susceptible to loss of suitability than those with Southern distribution. In contrast, for Rhinocorynura was predicted the expansion of the northern species and reduction of the southern species. Finally, it is highlighted the importance of considering species from different taxonomic and functional groups in efforts to measure impacts of climate change on species distribution in order to better evaluate the different potential responses and provide support for robust decision-making to mitigate impacts on biodiversity.
... We constructed models using the Maximum Entropy (MaxEnt) algorithm (Phillips and Dudík 2008). To calibrate the models, we selected different areas according to each assessed scenario, and delimitation of these areas was conducted following the criterion proposed by Anderson and Raza (2010). The occurrence data were divided into training and test sets by randomly selecting 25% of the total records for the test set. ...
The evolutionary history and taxonomy of the Leopardus tigrinus species complex have been studied based on several approaches, mostly employing genetic and morphological data, leading to distinct classification schemes. We approached this problem from an ecological perspective, with 2 main goals: (i) to evaluate ecological niche differences among regional L. tigrinus populations to determine the extent of ecological divergence among them; and (ii) to identify environmental barriers to historical dispersal that could have driven differentiation among the proposed groups. We modeled the ecological niche of all taxonomic/geographic groups proposed so far to comprise the L. tigrinus complex using the Maximum Entropy algorithm, and evaluated geographic and ecological niche differences among them. Furthermore, we investigated possible environmental barriers to historical dispersal that could have driven differentiation among regional groups. We evaluated 4 hypothetical barriers across 3 time periods to assess their potential historical effect. We found high ecological divergence between northeastern tigrina populations and the northern Andean and Central American tigrinas. Other groups within the L. tigrinus complex are less divergent. In addition, the Guiana Shield tigrina, where the type locality of the species is located, seems to be ecologically similar to populations from northeastern Brazil while also showing some overlap with Andean populations. The Panama center, the Llanos of Colombia and Venezuela, and the Amazon region were identified as historical barriers for tigrina dispersal across all time periods. The inferred historical barriers and ecological divergence observed in this study contribute to the inference of evolutionary differentiation among geographic groups comprising the L. tigrinus complex, revealing areas of consistently low habitat suitability that have likely contributed to divergence among regional populations.
... Furthermore, different approaches to building SDMs often produce contrasting patterns (Qiao et al. 2015, Steen et al. 2017. The choices made during model construction, such as the selection of modeling algorithms (Qiao et al. 2019), the specific climate models employed for hindcasting or forecasting (Fitzpatrick et al. 2018), and the criteria for selecting background points (Anderson and Raza 2010), can all influence the resulting habitat suitability predictions. Thus, different inferences may be drawn due to the variety of approaches to distribution modeling. ...
Climate change poses a threat to biodiversity, and it is unclear whether species can adapt to or tolerate new conditions, or migrate to areas with suitable habitats. Reconstructions of range shifts that occurred in response to environmental changes since the last glacial maximum (LGM) from species distribution models (SDMs) can provide useful data to inform conservation efforts. However, different SDM algorithms and climate reconstructions often produce contrasting patterns, and validation methods typically focus on accuracy in recreating current distributions, limiting their relevance for assessing predictions to the past or future. We modeled historically suitable habitat for the threatened North American tree green ash Fraxinus pennsylvanica using 24 SDMs built using two climate models, three calibration regions, and four modeling algorithms. We evaluated the SDMs using contemporary data with spatial block cross‐validation and compared the relative support for alternative models using a novel integrative method based on coupled demographic‐genetic simulations. We simulated genomic datasets using habitat suitability of each of the 24 SDMs in a spatially‐explicit model. Approximate Bayesian computation (ABC) was then used to evaluate the support for alternative SDMs through comparisons to an empirical population genomic dataset. Models had very similar performance when assessed with contemporary occurrences using spatial cross‐validation, but ABC model selection analyses consistently supported SDMs based on the CCSM climate model, an intermediate calibration extent, and the generalized linear modeling algorithm. Finally, we projected the future range of green ash under four climate change scenarios. Future projections using the SDMs selected via ABC suggest only minor shifts in suitable habitat for this species, while some of those that were rejected predicted dramatic changes. Our results highlight the different inferences that may result from the application of alternative distribution modeling algorithms and provide a novel approach for selecting among a set of competing SDMs with independent data.
... The geographical extent of the study was determined by making a 500-km buffer around all the occurrence points in QGIS 3.16 after spatial thinning. This extent is large enough to encompass the known historical range of S. dekayi, yet small enough to exclude areas that are unrealistic for future S. dekayi colonization to avoid overfitting due to modelling on an excessively large spatial scale (Anderson and Raza 2010). ...
Human activities in the Anthropocene contribute to natural resource and climatic pattern shifts, which in turn affect the distributions of organisms.
Currently, research efforts to understand organismal distribution patterns mainly focus on rare species, leaving common taxa to receive less
attention. We constructed ecological niche models using MaxEnt to evaluate the distributions of a common urban reptile, Dekay’s brown snake
(Storeria dekayi). We investigate the importance of human disturbance for the current S. dekayi distribution, the niche differentiation of the two
genetic lineages of S. dekayi separated by the Mississippi River, and the potential future range shift of S. dekayi. We found no significant effect of
human disturbance on current S. dekayi distribution. There was no significant evidence of niche differentiation between two lineages (eastern
and western), which can possibly be attributed to their shallow genetic divergence. We also predicted a range expansion for S. dekayi in the future
with anthropogenic climate change. Understanding the distribution patterns of common species is important because of the pivotal role they
play in ecosystem functioning and services. We encourage more work to be conducted in the future on common and urban-dwelling species’
distributions to conserve and promote urban biodiversity and biodiversity overall.
... The choice of a proper geographic area for sampling background points varies depending on the species and the goals of the study (Santini et al., 2021). This is important for the development of niche models that rely on presence-only data, such as Maxent models (VanDerWal et al., 2009;Anderson and Raza, 2010;Barbet-Massin et al., 2012;Khosravi et al., 2016;Cooper and Soberón, 2018;Machado-Stredel et al., 2021;Amaro et al., 2023). It is also crucial to make sure the sample size is large enough to adequately represent all environments (Renner et al., 2015). ...
The impact of invasive species on biodiversity, food security and economy is increasingly noticeable in various regions of the globe as a consequence of climate change. Yet, there is limited research on how climate change affects the distribution of the invasive Asian citrus psyllid Diaphorina citri Kuwayama (Hemiptera:Liviidae) in Ghana. Using maxnet package to fit the Maxent model in R software, we answered the following questions; (i) what are the main drivers for D. citri distribution, (ii) what are the D. citri-specific habitat requirements and (iii) how well do the risk maps fit with what we know to be correctly based on the available evidence?. We found that temperature seasonality (Bio04), mean temperature of warmest quarter (Bio10), precipitation of driest quarter (Bio17), moderate resolution imaging spectro-radiometer land cover and precipitation seasonality (Bio15), were the most important drivers of D. citri distribution. The results follow the known distribution records of the pest with potential expansion of habitat suitability in the future. Because many invasive species, including D. citri, can adapt to the changing climates, our findings can serve as a guide for surveillance , tracking and prevention of D. citri spread in Ghana.
... Thus, data on 985 locations for G. japonicus and 64 locations for G. swinhonis were obtained. We used only one coordinate within a 10 km radius of each location data to reduce the spatial autocorrelation and bias of location data (Boone and Krohn 1999;Anderson and Raza 2010). This resulted in 309 location data for G. japonicus and 51 location data for G. swinhonis used in downstream analyses (Table 1). ...
The introduction of exotic species and the associated impacts are increasing worldwide due to the development and increase in transportation and international trade. As new populations of the non-native Gekko japonicus and G. swinhonis have recently been discovered in South Korea, this study was conducted to investigate the distribution of these species, evaluate the establishment potential of non-native populations and infer their routes of introduction. The study comprised targeted field surveys around the major international ports, generation of ecological niche models (ENMs), based on the known distributions and comparison of the ecological niches of the two species. The results suggest that G. japonicus and G. swinhonis are primarily distributed in the humid subtropical climate (Cfa) and the monsoon-influenced hot-summer humid continental climate (Dwa), respectively. According to the ENMs predicted across South Korea, suitable habitats for G. japonicus were located along the western and southern coasts of the country, whereas those for G. swinhonis were predicted along the western coastal regions and several major cities inland. These distributional patterns may be attributed to adaptations of the two gecko congeners to distinct climatic conditions leading to divergent ecological niches. Considering the known distributions of the two species in South Korea, the most likely routes of introduction are accidental translocations through international trade and the inland populations most likely originated from human-mediated dispersals along the major rivers. This study highlights the need to implement quarantine procedures for international cargo arriving in South Korea. Additional field surveys are further recommended to focus on urban areas adjacent to international ports and major rivers to curtail further introductions of non-native geckos.
... The extent of the study region affects the model results (Anderson & Raza 2010). Therefore, a polygon area covering all the presence-absence data of the species was created as a "minimum enclosing circle area" and used for current, past, and future distribution estimates in model cali-bration and projection ( Fig. 1). ...
... Only one distribution point was retained in each 5 km × 5 km grid, thus reducing the influence of sampling bias and data redundancy on the prediction results. Finally, 844 distribution points were retained for model training and verification [43]. (Fig. 1-A, Table S1). ...
Bursaphelenchus xylophilus (Steiner&Buhrer) Nickle is a global quarantine pest that causes devastating mortality in pine species. The rapid and uncontrollable parasitic spread of this organism results in substantial economic losses to pine forests annually. In this study, we used the MaxEnt model and GIS software ArcGIS10.8 to predict the distribution of B. xylophilus based on collected distribution points and 19 environmental variables (with a correlation coefficient of|R| > 0.8) for the contemporary period (1970–2000), 2041–2060 (2050s), 2061–2080 (2070s), and 2081–2100 (2090s) under four shared socioeconomic pathways (SSPs). We conducted a comprehensive analysis of the key environmental factors affecting the geographical distribution of B. xylophilus and suitable distribution areas. Our results indicate that in current prediction maps B. xylophilus had potential suitable habitats in all continents except Antarctica, with East Asia being the region with the most highly suitable areas and the most serious epidemic area currently. Precipitation of the warmest quarter, temperature seasonality, precipitation of the wettest month, and maximum temperature of the warmest month were identified as key environmental variables that determine the distribution of B. xylophilus. Under future climatic conditions, the potential geographic distribution of B. xylophilus will expand relative to current conditions. In particular, under the SSP5-8.5 scenario in 2081–2100, suitable areas will expand to higher latitudes, and there will be significant changes in suitable areas in Europe, East Asia, and North America. These findings are crucial for future prevention and control management and monitoring.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12862-024-02234-1.
... maculatus were obtained from GBIF. For all occurrence data sets, we eliminated duplicates and reduced effects of spatial autocorrelation by thinning records within a distance of 5 km (Anderson & Raza, 2010;Phillips, 2008;Radosavljevic & Anderson, 2014). Twenty-nine variables (predictors) were compiled from three environmental data sets (Tables S2 and S3 (Table S3; Figure S3). ...
Ecological processes that are behind distributions of species that inhabit isolated localities, complex disjunct distributions, remain poorly understood. Traditionally, vicariance and dispersion have been proposed as explanatory mechanisms that drive such distributions. However, to date, our understanding of the ecological processes driving evolution of ecological niches associated with disjunct distributions remains rudimentary.
Here, we propose a framework to deconstruct drivers of such distribution using World's most widespread freshwater fish Galaxias maculatus as a model and integrating marine and freshwater environments where its life cycle may occur. Specifically, we assessed ecological and historical factors (Gondwanan vicariance, marine dispersion) and potential dispersion (niche‐tracking) that explain its distribution in the Southern Hemisphere.
Estimated distribution was consistent with previously reported distribution and mainly driven by temperature and topography in freshwater environments and by primary productivity and nitrate in marine environments. Niche dynamics of G. maculatus provided evidence of synergy between vicariance and marine dispersion as explanatory mechanisms of its disjunct distribution, suggesting that its ecological niche was conserved since approximately 30 Ma ago.
This integrated assessment of ecological niche in marine and freshwater environments serves as a generic framework that may be applied to understand processes underpinning complex distributions of diadromous species.
... During this process, models with optimal performance were evaluated by cross-validation using the area under the curve (AUC; Guo & Liu, 2010) and the true skill statistics (TSS). AUC and TSS values range between 0.0 and 1.0, although these differ in the values considered ideal for SDMs; AUC values ≥0.9, while TSS values ≥0.8 (Anderson & Raza, 2010;Coetzee et al., 2009;Guisan & Zimmermann, 2000;Toranza et al., 2016). Since we used two indicators to evaluate the models' performance and only used presence data, we followed Vorsino et al. (2014) for the cut-off values: AUC ≥0.85 and TSS ≥0.70. ...
Species Distribution Models have emerged in recent decades as a powerful tool for biodiversity research, as they allow, for example, assessing the status of species in their whole distributions. These models have been particularly helpful in demonstrating that amphibian populations are at high risk, with many declining and others already extinct. Here, we assessed the potential distribution of the helmeted water toad (Calyptocephalella gayi), an endemic Chilean amphibian considered a living fossil and cataloged as vulnerable, which requires significant conservation efforts. We modeled the species' potential distribution using the Maximum Entropy (Maxent) approach and determined the overlap with national protected areas. In addition, we conducted a geospatial risk analysis to estimate the threat level to which this toad is being subjected. The potential distribution of C. gayi ranges from 28 S to 44 S, mainly explained by altitude, mean diurnal temperature range, slope, and distance to water bodies. Protected areas only cover 3.55% of the species' potential geographic distribution, which is of concern, considering that the geospatial risk analysis showed that 60.61% of C. gayi's distribution is subjected to extreme and high risks. We discuss how these results are relevant to focusing and directing efficient protection and conservation efforts for this species shortly. K E Y W O R D S amphibian conservation, Calyptocephalella gayi, endemism, geospatial risk analysis
The Montane Shrew, Sorex monticola, is a common and wide-ranging mammal throughout western North America. Previous studies identified multiple mitochondrial lineages, but limited geographic sampling constrained our understanding of distributional limits, phylogeographic variation, and biogeographic history. We used range-wide sampling and multi-model phylogenetic analyses to examine mitochondrial phylogeographic variation, evaluate niche differentiation, and test historical biogeographic hypotheses. We examined cytochrome b gene sequences from 462 individuals and 277 localities across the distribution of S. monticola and related species, including the first specimens from the Sierra Nevada (California, United States) and Sierra Madre Occidental (Durango and Chihuahua, Mexico). Estimated genealogical relationships, divergence times, and delimitation approaches identified 3 well-supported, deeply divergent, geographically structured clades consistent with previous estimates (Coastal, Southern, Northern). Sorex monticola was paraphyletic with S. sonomae and all species of North American water shrews. We also identified minimal divergence between Coastal S. monticola and 2 nominal species, S. pacificus and S. bairdii, that are sympatric in the Pacific Northwest. Demographic tests indicated that some lineages represent stable and isolated island and montane populations, while others represent populations that experienced demographic expansion since the Last Glacial Maximum. Niche differentiation tests revealed that each clade occupies distinctive environmental conditions, with projections of future conditions suggesting that populations isolated in southern mountains may face extirpation associated with warming climate and aridification. This range-wide assessment of geographic genetic variation lays a foundation for selecting samples from key populations for expanded genome-level investigations into evolutionary relationships and taxonomic limits, enabling tests of hypotheses related to Pleistocene climatic drivers of biotic diversification processes across western North America.
The Annamite striped rabbit (Nesolagus timminsi) is an Endangered lagomorph endemic to the Annamite Mountain Range in Vietnam and Laos, with much unknown about its distribution range. In this study, we used previously known records of the Annamite striped rabbit to construct distribution models using Maxent, a modeling approach that has been proven to be robust in identifying potential areas of undiscovered populations of targeted species. Using the optimal model results as a guideline to select the field site, we conducted a systematic camera trap survey in Dak Long Protection Forest, Kon Tum Province, Vietnam. We recorded the species in five events at two locations of the study site, and to the best of our knowledge this finding represents a new population of the rabbit in this region. Our discovery significantly expands the known range of the striped rabbit, and we discuss the implications of this finding for N. timminsi conservation. We also suggest several protected areas in the South of our discovery site that, according to our optimal model results, may harbor unknown populations of the Annamite striped rabbit, and hence they should be prioritized for future field surveys. Our finding also highlights the importance of modeling tools in biodiversity surveys, especially for elusive and poorly studied species.
The Black Sea basin has a strategic geographic location bridging Asia and Europe and depends on traditional livestock practices for their local economies. Anthrax, a zoonotic bacterial disease caused by Bacillus anthracis, poses a significant global threat impacting public health, food security, pastoralist communities, and national economies. The disease is endemic or sporadic in the Black Sea basin, however, the study of its distribution has seldom been addressed, despite its burden and the presence of historical B. anthracis burial sites in the region. The viability of B. anthracis in a particular region is going to be influenced by multiple environmental factors, such as soil composition, climate, vegetation, and host abundance. For characterizing the potential distribution of B. anthracis in the Black Sea basin and assessing the potential for anthrax outbreaks, we applied an ecological niche modelling framework using the Maxent algorithm. This machine-learning algorithm models species distributions based on presence data and background information from a specified calibration region. We analyzed multiple variable combinations and proposed a novel approach for interpreting in-risk anthrax areas. Our findings underscored the importance of host abundance to the anthrax dynamics in the region. We identified anthrax-suitable areas spanning central and eastern Türkiye, Armenia, southern Georgia, southern Russia, Bulgaria, southern and eastern Romania, Hungary, Moldova, and southern Ukraine, which align with findings from previous global and regional studies on the potential suitability of anthrax. The insights gained from our research may help to develop targeted interventions, such as awareness and educational campaigns about anthrax, supervision of anthrax-infected carcasses disposal, and the promotion of livestock vaccination in high-risk areas. Additionally, these results can inform policies to mitigate the spread of anthrax in pastoralist communities in the Black Sea basin and foster collaboration between veterinary and public health entities on anthrax control.
Aim
Recent literature is unveiling more complex responses of biodiversity to Pleistocenic climatic oscillations than those derived from initial models based solely on southern glacial macrorefugia. Here, we zoom in to delve into the genetic diversity and population structure of Podarcis lusitanicus , a lizard species native to the north‐western Iberian Peninsula.
Location
North‐western Iberian Peninsula.
Taxon
Podarcis lusitanicus .
Methods
Utilising a comprehensive multilocus approach, we explored the species' phylogeography and demographic history at a very fine spatial scale and timeframe. Concurrently, we evaluated both current and historical habitat suitability for the species, linking it to the observed demographic trends. We examined DNA sequences from two mitochondrial (cytb and ND4) and three nuclear genes (ACM4, MC1R and PDC) in 230 specimens collected from 21 populations spanning most of the species' distribution range.
Results
Our findings corroborate cytonuclear discordances in Podarcis , with nuclear genes failing to identify the mitochondrial clades in P . lusitanicus . Despite its separation from other Podarcis species approximately 4 million years ago, its diversification occurred much more recently, around 0.4 million years ago, resulting in three monophyletic lineages that are geographically coherent. This pattern suggests that some lineages of P . lusitanicus might have gone extinct after the split from the common ancestor. Pleistocene climatic oscillations profoundly influenced the demographic history of the species. During warmer periods, such as the Last Interglacial, populations expanded and came into secondary contact, whereas during the Last Glacial Maximum, populations contracted and became isolated in multiple micro‐refugia. The subsequent Mid‐Holocene warming facilitated population recovery, range expansion and lineage admixture.
Main Conclusions
This study underscores the intricate interplay between isolation and dispersal at a microscale, driven by Pleistocene climatic fluctuations, which have profoundly shaped the genetic structure and diversity of Iberian terrestrial biodiversity and may remain overlooked by biogeographic studies at coarser scales.
The Sparreboom's Crocodile Newt (Tylototriton sparreboomi) is a recently discovered species of crocodile newts from Vietnam and it is currently known only from Sin Ho District in Lai Chau Province. Due to the limited information available on its population status and distribution, the species has been classified as Data Deficient in the IUCN Red List. As a result of our field surveys in 2021 and 2022 in north-western Vietnam, novel data on distribution and population status of and threats to this poorly-known species were collected. We combined the newly-discovered locations with previous occurrence records and used the data as input for modelling the potential distribution of the Sparreboom's Crocodile Newt. The results showed that the Sparreboom's Crocodile Newt’s potential distribution encompasses areas in Lai Chau, Dien Bien and Son La Provinces, Vietnam, as well as a section in Jinping County, Yunnan Province, China. Based on the findings, we suggest that protected areas in the eastern side of Hoang Lien Mountain Range, such as Che Tao, Muong La, Hoang Lien – Van Ban and Bat Xat Nature Reserves and Hoang Lien National Park in Vietnam and Jinping Feishuling Nature Reserve in China be priority sites for the species conservation. Future research and conservation initiatives should prioritise efforts in such areas in an effort to find and protect new populations of the newt.
Antecedentes y Objetivos: El hongo Fusarium oxysporum f. cubense afecta fuertemente al plátano. Debido a los cambios climáticos que se prevén, particularmente en Ecuador, la expansión de este patógeno se podría ver favorecida y provocar pérdidas económicas considerables para la agricultura. Por ello, en esta investigación se evaluaron los cambios en la distribución geográfica potencial de F. oxysporum f. cubense en Ecuador continental ante varios escenarios de cambio climático.
Métodos: Se utilizaron 77 registros de F. oxysporum f. cubense extraídos de bases de datos internacionales y de la literatura científica. Las variables bioclimáticas se tomaron de Worldclim, para la actualidad y para 2050 y 2070, bajo diferentes escenarios de cambio climático. Con el algoritmo de Máxima Entropía se obtuvo el modelo de nicho climático de la especie y se proyectó a un escenario de emisiones de gases mitigador (SSP-126), uno cauteloso (SSP-245) y otro extremo (SSP-585).
Resultados clave: El modelo tuvo buen rendimiento y predice adecuadamente las zonas con condiciones ambientales similares a los sitios con registros de presencia de F. oxysporum f. cubense. Las condiciones favorables aparecen principalmente en la región Sierra y Costa. Bajo los escenarios climáticos, la mayor expansión de condiciones favorables se dará en la región Costa, en el escenario de mitigación (SSP-126) para 2050, con un aumento de 18,550 km2 respecto a la distribución potencial actual.
Conclusiones: Si las emisiones de gases de efecto invernadero generan un clima en 2050 como el simulado en el SSP-126, las condiciones favorables para este hongo superarán a las actuales y se expandirá dentro de la Costa hacia las zonas de producción de banano. Esto provocará un impacto
negativo en la economía de los productores bananeros.
Coral reefs represent a source of ecosystem services relevant for the permanence of coastal human communities. Given the importance of zooxanthellate scleractinian corals (ZSC) in these ecosystems, information on the presence and distribution of this group is valuable for studying the biodiversity and establishing management strategies to ensure their permanence over time. Despite being a highly studied biological group in the Western Atlantic, records of ZSC presence are heterogeneous and are distributed mainly in the Caribbean Sea. One way to analyze the distribution of biota at large spatial scales is through biogeographic regionalizations. To identify the distribution patterns of ZSC in the Western Atlantic and propose a biogeographic regionalization based in this biological group, we performed a Parsimony Analysis of Endemism (PAE) at ecoregion scale using Species Distribution Models (SDM) to fill the gaps in data on the distribution of these corals. Through these analyses, we identified the presence of two main biogeographic provinces: Northwestern Atlantic Tropical Province and Tropical Southwestern Atlantic. According to Jaccard Index, this regionalization is highly similar to those performed with multitaxa by other authors, which demonstrates that for these purposes this taxa can be considered as a surrogate group. In terms of management, this study highlights the importance of establishing preservation and conservation measures that address this biological group, in order to guarantee both the permanence of other taxa and the ecosystem goods and services they provide.
Species Distribution Models (SDMs) have been used extensively to understand species-habitat relationships and design conservation strategies. The ability to train these models using a wide variety of datasets and modelling algorithms has led to their wide applicability across systems. However, the ease of modelling also leads to their use as off-the-shelf models without a detailed investigation of the data and their suitable end-use application. The effect of various modelling parameters on inferences has been explored, however, their interaction with training data type is limited. We used country-wide data for four sympatric Indian small cat species to understand the sensitivity of SDMs to data types, sampling extents and their interaction. Our results reveal the non-stationarity of models with varying modelling parameters. The extent of the training dataset had major implications on the inferences and interacted strongly with the type of dataset used. The divergent distribution of the target species revealed that the effect of sampling extent was more pronounced for species that have limited distribution within the predictive extent. Lastly, our results highlight the significance of sampled environmental space in explaining the non-stationarity of the model outputs.
Species are distributed in predictable ways in geographic spaces. The three principal factors that determine geographic distributions of species are biotic interactions ( B ), abiotic conditions ( A ), and dispersal ability or mobility ( M ). A species is expected to be present in areas that are accessible to it and that contain suitable sets of abiotic and biotic conditions for it to persist. A species' probability of presence can be quantified as a combination of responses to B , A , and M via ecological niche modeling (ENM; also frequently referred to as species distribution modeling or SDM). This analytical approach has been used broadly in ecology and biogeography, as well as in conservation planning and decision‐making, but commonly in the context of ‘natural’ settings. However, it is increasingly recognized that human impacts, including changes in climate, land cover, and ecosystem function, greatly influence species' geographic ranges. In this light, historical distinctions between natural and anthropogenic factors have become blurred, and a coupled human–natural landscape is recognized as the new norm. Therefore, B , A , and M (BAM) factors need to be reconsidered to understand and quantify species' distributions in a world with a pervasive signature of human impacts. Here, we present a framework, termed human‐influenced BAM (Hi‐BAM, for distributional ecology that ( i ) conceptualizes human impacts in the form of six drivers, and ( ii ) synthesizes previous studies to show how each driver modifies the natural BAM and species' distributions. Given the importance and prevalence of human impacts on species distributions globally, we also discuss implications of this framework for ENM/SDM methods, and explore strategies by which to incorporate increasing human impacts in the methodology. Human impacts are redefining biogeographic patterns; as such, future studies should incorporate signals of human impacts integrally in modeling and forecasting species' distributions.
Information from natural history collections (NHCs) about the diversity, taxonomy and historical distributions of species worldwide is becoming increasingly available over the Internet. In light of this relatively new and rapidly increasing resource, we critically review its utility and limitations for addressing a diverse array of applications. When integrated with spatial environmental data, NHC data can be used to study a broad range of topics, from aspects of ecological and evolutionary theory, to applications in conservation, agriculture and human health. There are challenges inherent to using NHC data, such as taxonomic inaccuracies and biases in the spatial coverage of data, which require consideration. Promising research frontiers include the integration of NHC data with information from comparative genomics and phylogenetics, and stronger connections between the environmental analysis of NHC data and experimental and field-based tests of hypotheses.
The area under the receiver operating characteristic (ROC) curve, known as the AUC, is currently considered to be the standard method to assess the accuracy of predictive distribution models. It avoids the supposed subjectivity in the threshold selection process, when continuous probability derived scores are converted to a binary presence–absence variable, by summarizing overall model performance over all possible thresholds. In this manuscript we review some of the features of this measure and bring into question its reliability as a comparative measure of accuracy between model results. We do not recommend using AUC for five reasons: (1) it ignores the predicted probability values and the goodness-of-fit of the model; (2) it summarises the test performance over regions of the ROC space in which one would rarely operate; (3) it weights omission and commission errors equally; (4) it does not give information about the spatial distribution of model errors; and, most importantly, (5) the total extent to which models are carried out highly influences the rate of well-predicted absences and the AUC scores.
Theory predicts low niche differentiation between species over evolutionary time scales, but little empirical evidence is available. Reciprocal geographic predictions based on ecological niche models of sister taxon pairs of birds, mammals, and butterflies in southern Mexico indicate niche conservatism over several million years of independent evolution (between putative sister taxon pairs) but little conservatism at the level of families. Niche conservatism over such time scales indicates that speciation takes place in geographic, not ecological, dimensions and that ecological differences evolve later.
PERSPECTIVES scious processing in humans. Moreover, several studies have reported residual local and specific brain activation patterns in vegetative state patients, whereas long-range neural integration observed during conscious processing was lacking (4, 5). ...
Heteromys anomalus is widely distributed along the Caribbean coast of South America and had been considered the only spiny pocket mouse present in Venezuela for several decades. Recent taxonomic studies in western Venezuela, however, have documented the presence of H. australis from the Cordillera de Mérida and described H. oasicus from the Península de Paraguaná . Here, we revise the genus Heteromys in central and eastern Venezuela as well as in Trinidad and Tobago. Morphological comparisons with samples of H. anomalus from throughout its known range in Venezuela and with representatives of all other recognized species of the genus indicate the existence of a species new to science, which we describe as Heteromys catopterius (the Overlook Spiny Pocket Mouse). This species ranges from 350 to 2425 m in elevation in the Cordillera de la Costa in northern Venezuela, with the vast majority of records above ca. 700 m. In central and eastern Venezuela, H. anomalus is found in the surrounding lowlands and lower slopes of the Cordillera. Externally, H. catopterius is darker than H. anomalus and lacks the distinctly rounded ears of that species. The new species averages larger than H. anomalus for most external and cranial measurements. Cranially, the two species have differences in proportion; the skulls of H. catopterius are proportionately wider (less elongated) than those of H. anomalus, with a relatively wider interorbital constriction, wider interparietal, and more inflated braincase. Heteromys catopterius occurs in wet montane forests (especially cloud forests) in four highland areas of the Cordillera de la Costa: the main Aragua-Carabobo chain, the El vila massif, the Serranía del Interior, and the Macizo Oriental. Presumably, the warm, dry lowlands of the Depresió n de Unare represent a barrier for the species, separating populations in the Macizo Oriental from the more westerly populations. Large areas of the known ranges of both H. catopterius and H. anomalus are protected in the Venezuelan network of protected areas.
No-analog communities (communities that are compositionally unlike any found today) occurred frequently in the past and will develop in the greenhouse world of the future. The well documented no-analog plant communities of late-glacial North America are closely linked to "novel" climates also lacking modern analogs, characterized by high seasonality of temperature. In climate simulations for the Intergovernmental Panel on Climate Change A2 and B1 emission scenarios, novel climates arise by 2100 AD, primarily in tropical and subtropical regions. These future novel climates are wanner than any present climates globally, with spatially variable shifts in precipitation, and increase the risk of species reshuffling into future no-analog communities and other ecological surprises. Most ecological models are at least partially parameterized from modern observations and so may fail to accurately predict ecological responses to these novel climates. There is an urgent need to test the robustness of ecological models to climate conditions outside modern experience.
Heteromys anomalus is widely distributed along the Caribbean coast of South America and was generally considered the only spiny pocket mouse present in Venezuela until H. australis was reported recently from the Cordillera de Mérida. Here, I revise the spiny pocket mice of western Venezuela and compare specimens from the semi-insular Península de Paraguaná with samples from throughout the distributional area of H. anomalus. Morphological comparisons with all other species of the genus suggest that the Heteromys from Paraguaná are closely related to H. anomalus, but univariate and multivariate analyses show that they are much smaller than samples of H. anomalus. In addition, adults from Paraguaná display differences in cranial shape when compared with adults of H. anomalus. Hence, specimens from Paraguaná are here described as a new species, Heteromys oasicus, and taxonomic treatments of H. anomalus and H. australis in western Venezuela are also provided. Although the original geographic context of this case of dwarfism in body size is unknown, H. oasicus currently appears to be isolated in mesic vegetation on the low Cerro Santa Ana and Fila de Monte Cano, which lie in a matrix of tropical thorn forest. The new species represents one of the few vertebrates considered endemic to Paraguaná, but basic inventories and taxonomic studies are not yet complete in the region.
Geographic studies addressing the role of competition in determining species' macrodistributions have been limited by only simple or subjective means of identify- ing regions of suitable habitat. Now, ecological-niche models of species' potential distributions present a possible approach to testing for the geographic patterns predicted under competitive exclusion and competitive release. Previously, we mod- eled the potential distributions of two spiny pocket mice (Heteromys australis and H. anomalus) in northwestern South America using specimen localities, environmental data, and the Genetic Algorithm for Rule-Set Prediction (GARP). Here we superim- pose the models to examine known distributional records in areas of potential sympatry between the two species, thus testing the geographic predictions of compet- itive exclusion. In addition, we examine environmental characteristics of known localities, testing for data consistent with competitive release. Areas of potential sympatry are minimal, lying in regions of intermediate water balance. Only records of H. australis are known from areas of potential sympatry in regions where the species' ranges meet, consistent with exclusion of H. anomalus by H. australis. Heteromys anomalus inhabits areas ecologically suitable for both species only in the isolated Sierra Nevada de Santa Marta, in which H. australis is not present (most likely for historical reasons). Furthermore, environmental characteristics of localities of H. anomalus in biogeographic regions where H. australis is absent fit the pattern predicted under competitive release. In contrast, localities of H. australis show no indication of competitive release. Although the results of present analyses do not conclusively demonstrate competitive exclusion or release, they provide directional hypotheses that can now be tested in experimental field and laboratory studies.
Aim
Predictive models of species’ distributions use occurrence records and environmental data to produce a model of the species’ requirements and a map of its potential distribution. To determine regions of suitable environmental conditions and assess biogeographical questions regarding their ranges, we modelled the potential geographical distributions of two spiny pocket mice (Rodentia: Heteromyidae) in northwestern South America.
Location
North-western South America.
Methods
We used the Genetic Algorithm for Rule-Set Prediction (GARP), environmental data from GIS maps and georeferenced collection localities from a recent systematic review of Heteromys australis and H. anomalus to produce the models.
Results
GARP models indicate the potential presence of H. australis throughout mesic montane regions of north-western South America, as well as in some lowland regions of moderately
high precipitation. In contrast, H. anomalus is predicted to occur primarily in drier areas of the Caribbean coast and rain-shadowed valleys of the Andes.
Conclusions
The models support the disjunct status of the population of H. australis in the Cordillera de Mérida, but
predict a continuous distribution between known populations of H. anomalus in the upper Magdalena Valley and the Caribbean coast. Regions of suitable environmental conditions exist disjunct from known distributional areas for both species, suggesting possible historical restrictions to their ranges. This technique holds wide application to other study systems.
This paper demonstrates the use of a bioclimatic model mapped over geographical regions as a tool for spatially refined risk assessment for the establishment of non-indigenous plants with invasive behaviour. Drawing on the relationship between plant distribution and climate, the approach uses gridded spatial interpolated monthly means of temperature and precipitation linked with accurate maps of general native distribution ranges to predict the long-term potential of a plant species to invade a certain region. The ascertained potential for establishment is illustrated by the example of garlic mustard (Alliaria petiolata[M. Bieb.] Cavara & Grande) in North America. The first step is to calculate and visualize the number of populated grid cells along climatic gradients in frequency diagrams for the general native distribution range. Interpretations of the response curves recorded are used for assessing apparent climatic range boundaries. Modelling was gradually optimized based on the results of experience-based interpretations and by examining omission and over-representation errors. The obtained climatic model of the range of A. petiolata shows considerable congruencies with its mapped, native Eurasian range. Degrees of climatic similarity between North America and the native range of A. petiolata were calculated with the help of GIS methodology and were used to assess the regionally different likelihood of establishment in North America of the invasive species under consideration.
Aim Techniques that predict species potential distributions by combining observed occurrence records with environmental variables show much potential for application across a range of biogeographical analyses. Some of the most promising applications relate to species for which occurrence records are scarce, due to cryptic habits, locally restricted distributions or low sampling effort. However, the minimum sample sizes required to yield useful predictions remain difficult to determine. Here we developed and tested a novel jackknife validation approach to assess the ability to predict species occurrence when fewer than 25 occurrence records are available.
Location Madagascar.
Methods Models were developed and evaluated for 13 species of secretive leaf‐tailed geckos ( Uroplatus spp.) that are endemic to Madagascar, for which available sample sizes range from 4 to 23 occurrence localities (at 1 km ² grid resolution). Predictions were based on 20 environmental data layers and were generated using two modelling approaches: a method based on the principle of maximum entropy (Maxent) and a genetic algorithm (GARP).
Results We found high success rates and statistical significance in jackknife tests with sample sizes as low as five when the Maxent model was applied. Results for GARP at very low sample sizes (less than c. 10) were less good. When sample sizes were experimentally reduced for those species with the most records, variability among predictions using different combinations of localities demonstrated that models were greatly influenced by exactly which observations were included.
Main conclusions We emphasize that models developed using this approach with small sample sizes should be interpreted as identifying regions that have similar environmental conditions to where the species is known to occur, and not as predicting actual limits to the range of a species. The jackknife validation approach proposed here enables assessment of the predictive ability of models built using very small sample sizes, although use of this test with larger sample sizes may lead to overoptimistic estimates of predictive power. Our analyses demonstrate that geographical predictions developed from small numbers of occurrence records may be of great value, for example in targeting field surveys to accelerate the discovery of unknown populations and species.
Increasing concern over the implications of climate change for biodiversity has led to the use of species–climate envelope models to project species extinction risk under climate-change scenarios. However, recent studies have demonstrated significant variability in model predictions and there remains a pressing need to validate models and to reduce uncertainties. Model validation is problematic as predictions are made for events that have not yet occurred. Resubstituition and data partitioning of present-day data sets are, therefore, commonly used to test the predictive performance of models. However, these approaches suffer from the problems of spatial and temporal autocorrelation in the calibration and validation sets. Using observed distribution shifts among 116 British breeding-bird species over the past ∼20 years, we are able to provide a first independent validation of four envelope modelling techniques under climate change. Results showed good to fair predictive performance on independent validation, although rules used to assess model performance are difficult to interpret in a decision-planning context. We also showed that measures of performance on nonindependent data provided optimistic estimates of models' predictive ability on independent data. Artificial neural networks and generalized additive models provided generally more accurate predictions of species range shifts than generalized linear models or classification tree analysis. Data for independent model validation and replication of this study are rare and we argue that perfect validation may not in fact be conceptually possible. We also note that usefulness of models is contingent on both the questions being asked and the techniques used. Implementations of species–climate envelope models for testing hypotheses and predicting future events may prove wrong, while being potentially useful if put into appropriate context.
Modelling strategies for predicting the potential impacts of climate change on the natural distribution of species have often focused on the characterization of a species’ bioclimate envelope. A number of recent critiques have questioned the validity of this approach by pointing to the many factors other than climate that play an important part in determining species distributions and the dynamics of distribution changes. Such factors include biotic interactions, evolutionary change and dispersal ability. This paper reviews and evaluates criticisms of bioclimate envelope models and discusses the implications of these criticisms for the different modelling strategies employed. It is proposed that, although the complexity of the natural system presents fundamental limits to predictive modelling, the bioclimate envelope approach can provide a useful first approximation as to the potentially dramatic impact of climate change on biodiversity. However, it is stressed that the spatial scale at which these models are applied is of fundamental importance, and that model results should not be interpreted without due consideration of the limitations involved. A hierarchical modelling framework is proposed through which some of these limitations can be addressed within a broader, scale-dependent context.
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.
This paper briefly reviews some limitations associated with the application of existing modelling procedures to conservation ecology, and describes a new procedure (DOMAIN) which avoids these problems. The procedure computes potential distributions based on a range-standardized, point-to-point similarity metric and provides a simple, robust method for modelling potential distributions of plant and animal species. DOMAIN offers advantages over similar methods in its ability to operate effectively using only presence records and a limited number of biophysical attributes. The use of a continuous similarity function gives DOMAIN increased flexibility as an heuristic tool, suitable for application in survey design, reserve selection and potential mapping of rare and common species. Potential distributions were computed for two Australian marsupial bettong species (Aepyprymnus rufescens Gray and Bettongia tropica Wakefield) using DOMAIN and two alternative models. Of the three procedures, the DOMAIN model produced distribution patterns that were most consistent with the known ecology of the species, and most appropriate for survey design.
In the last two decades, interest in species distribution models (SDMs) of plants and animals has grown dramatically. Recent advances in SDMs allow us to potentially forecast anthropogenic effects on patterns of biodiversity at different spatial scales. However, some limitations still preclude the use of SDMs in many theoretical and practical applications. Here, we provide an overview of recent advances in this field, discuss the ecological principles and assumptions underpinning SDMs, and highlight critical limitations and decisions inherent in the construction and evaluation of SDMs. Particular emphasis is given to the use of SDMs for the assessment of climate change impacts and conservation management issues. We suggest new avenues for incorporating species migration, population dynamics, biotic interactions and community ecology into SDMs at multiple spatial scales. Addressing all these issues requires a better integration of SDMs with ecological theory.
Species distribution modelling is central to both fundamental and applied research in biogeography. Despite widespread use of models, there are still important conceptual ambiguities as well as biotic and algorithmic uncertainties that need to be investigated in order to increase confidence in model results. We identify and discuss five areas of enquiry that are of high importance for species distribution modelling: (1) clarification of the niche concept; (2) improved designs for sampling data for building models; (3) improved parameterization; (4) improved model selection and predictor contribution; and (5) improved model evaluation. The challenges discussed in this essay do not preclude the need for developments of other areas of research in this field. However, they are critical for allowing the science of species distribution modelling to move forward.
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.
Heteromys anomalus is widely distributed along the Caribbean coast of South America and was generally considered the only spiny pocket mouse present in Venezuela until H. australis was reported recently from the Cordillera de Merida. Here, I revise the spiny pocket mice of western Venezuela and compare specimens from the semi-insular Peninsula de Paraguand with samples from throughout the distributional area of H. anomalus. Morphological comparisons with all other species of the genus suggest that the Heteromys from Paraguana are closely related to H. anomalus, but univariate and multivariate analyses show that they are much smaller than samples of H. anomalus. In addition, adults from Paraguana display differences in cranial shape when compared with adults of H. anomalus. Hence, specimens from Paraguana are here described as a new species, Heteromys oasicus, and taxonomic treatments of H. anomalus and H. australis in western Venezuela are also provided. Although the original geographic context of this case of dwarfism in body size is unknown, H. oasicus currently appears to be isolated in mesic vegetation on the low Cerro Santa Ana and Fila de Monte Cano, which lie in a matrix of tropical thorn forest. The new species represents one of the few vertebrates considered endemic to Paraguand, but basic inventories and taxonomic studies are not yet complete in the region.
The composite nature of the species Oryzomys albigularis Tomes, 1860 has long been recognized from the karyological heterogeneity of diploid and fundamental numbers, with at least two sets of populations in the eastern and northern Andes with 2n = 66 and 2n = 80, respectively. The aim of this study was to test for differences among populations within the species using cranial morphometrics. Populations for the multivariate analyses were defined according to differences in autosomal arm numbers within a single modal diploid number from six locations in two mountain systems of Venezuela, in northern South America. Principal component and canonical variate analyses clearly discriminated among the karyomorphs, the variation at the interorbital, palatal, and incisive foramen regions being the most informative. These traits explained most of the total variance, after adjusting for size effects. We found no evidence for congruence between the patterns of morphometric variation and geographical distance among karyomorphs, but similarity patterns among samples resulted in congruence when morphometric and karyological data were considered. Since variation of these last two datasets can be considered independent of each other, the observed congruence is suggestive of a phylogenetic structure in the data. Our results are consistent with a composite nature for the species, as most of the observed cranial variation appears to be associated with major karyotypic differences. Given the theoretical relevance of the implied karyotypic changes as a means of reproductive isolation, morphometric evidence is used to support the splitting of the 'albigularis' form in at least two distinct species.
The geographical limits of Nothofagus cunninghamii are highly correlated with climate and appear to be more or less in equilibrium with the climate of the present century in all but one of the areas of its present range. Suitable climates for the species occur in the highlands of NE Victoria and S New South Wales, beyond its present range, and it is possible that it occurred within the predicted area prior to the last ice age. Populations along the NE edge of its present range in the Central Highlands of Victoria may be migrating NE along a narrow corridor of apparently suitable climate to re-occupy the postulated former range. Rate of migration would be extremely slow because of the poor dispersal ability of the species and the adverse impact of recurrent fires.-from Author
Live-trapping of Heteromys anomalus melanoleucus in a mosaic environment in the mountains of northern Venezuela in April-May, 1960, indicated a preference for primary forest and Heliconia thickets. This species was largely segregated from six other species of small terrestrial mammals captured in the same grid of traps. The number of independent young trapped increased greatly in early May, suggesting a surge of mating in the nonrainy season of early March. Signs of breeding in some adults of both sexes were regressing in May, when seasonal rains were approaching peak intensity. The animals were not diurnal and were weakly, if at all, territorial. Home range size was of the order of 600-2500 m2. One female, captured repeatedly, moved 87 m in one 24-hr period. Several rats carried palm nuts in their cheek pouches. Some had fly eggs in their dorsal hair. This species appeared unwary, phlegmatic, and disoriented in comparison with common North American rats and mice.
Patterns of habitat partitioning have been widely described, but how the patterns are formed is as yet incompletely understood. To document and describe the different types of community organization that can produce patterns of partitioning, I surveyed the literature to find examples of habitat partitioning. I then identified, characterized, and tabulated occurrences of the different types of community organization. In ten years of Ecology, there were 66 instances of habitat partitioning accompanied by experimental demonstrations of how the patterns were formed. Shared preferences and distinct preferences were most commonly cited as producing patterns of partitioning (34 and 17 times, respectively), but there were also 8 examples of one-sided preferences, 4 examples of centrifugal organization, and 3 instances of niche contraction. This indicates that a variety of community organizations can produce patterns of partitioning and that shared preferences may occur more frequently in nature than generally thought. The presence of particular types of organization was influenced by the kind of study organism, with shared preferences occurring more frequently than distinct preferences among autotrophs and among different-aged individuals of the same species. Distinct preferences occurred when species were partitioned on different hosts rather than along gradients, suggesting that the kinds of resources that species use in a habitat may also influence how patterns of partitioning are formed.
A field study, designed to test the hypothesis that habitat segregation in sympatric populations of Microtus pennsylvanicus and M. montanus is due to competition, was conducted on the National Bison Range in western Montana between June 1961 and May 1962. Experimental reduction of the numbers of M. pennsylvanicus induced movements of M. montanus into the vacated habitat, forming the basis for acceptance of the hypothesis. The nature of the movements plus reciprocal avoidance behavior of both species of voles during normal circumstances suggest that the voles conform to the principle of competitive exclusion. The significance of niche overlap is stressed and its adaptive qualities are discussed.
A wide range of modelling algorithms is used by ecologists, conservation practitioners, and others to predict species ranges from point locality data. Unfortunately, the amount of data available is limited for many taxa and regions, making it essential to quantify the sensitivity of these algorithms to sample size. This is the first study to address this need by rigorously evaluating a broad suite of algorithms with independent presence-absence data from multiple species and regions. We evaluated predictions from 12 algorithms for 46 species (from six different regions of the world) at three sample sizes (100, 30, and 10 records). We used data from natural history collections to run the models, and evaluated the quality of model predictions with area under the receiver operating characteristic curve (AUC). With decreasing sample size, model accuracy decreased and variability increased across species and between models. Novel modelling methods that incorporate both interactions between predictor variables and complex response shapes (i.e. GBM, MARS-INT, BRUTO) performed better than most methods at large sample sizes but not at the smallest sample sizes. Other algorithms were much less sensitive to sample size, including an algorithm based on maximum entropy (MAXENT) that had among the best predictive power across all sample sizes. Relative to other algorithms, a distance metric algorithm (DOMAIN) and a genetic algorithm (OM-GARP) had intermediate performance at the largest sample size and among the best performance at the lowest sample size. No algorithm predicted consistently well with small sample size (n < 30) and this should encourage highly conservative use of predictions based on small sample size and restrict their use to exploratory modelling.
The area under the receiver operating characteristic (ROC) curve, known as the AUC, is currently considered to be the standard method to assess the accuracy of predictive distribution models. It avoids the supposed subjectivity in the threshold selection process, when continuous probability derived scores are converted to a binary presence-absence variable, by summarizing overall model performance over all possible thresholds. In this manuscript we review some of the features of this measure and bring into question its reliability as a comparative measure of accuracy between model results. We do not recommend using AUC for five reasons: (1) it ignores the predicted probability values and the goodness-of-fit of the model; (2) it summarises the test performance over regions of the ROC space in which one would rarely operate; (3) it weights omission and commission errors equally; (4) it does not give information about the spatial distribution of model errors; and, most importantly, (5) the total extent to which models are carried out highly influences the rate of well-predicted absences and the AUC scores.
It is a common assumption that species’ ranges are limited by their physiological tolerances to climatic factors. Biotic factors, such as competition, are rarely considered. We investigated the distributions of Ulexminor and U. gallii at three spatial scales – from geographic ranges to individual heaths – to examine whether the species are negatively associated, as predicted by the hypothesis that the ranges of the species are limited by competition with each other. Distribution maps for the British Isles and France (100–400 km2 survey units) show the two species have largely separated, but slightly overlapping ranges. A region of range overlap on the heaths of Dorset, southern England was mapped using 4 ha survey squares. There was strong negative association between the species, and the heaths could be divided into zones where one species was dominant. There was some indication of edaphic differences between the U. minor-dominated zones and the U. gallii zones. The few heaths where the species co-occurred were surveyed using 4 m2 quadrats placed along transects. Usually one species was widespread over the heath, while the other occurred in patches. The species were strongly negatively associated in all transects. Therefore, the two species showed strong negative associations at three mapping scales. Apparent co-occurrences detected at one spatial scale largely disappeared when species were mapped at finer scales, emphasising the fractal nature of distributions. This provides evidence that the distributions of the two species are not independent and that they cannot coexist, and therefore that their ranges are limited by competition. Over their ranges, competitive superiority is probably determined by the climate. At the range boundaries in the region of overlap, climate is not important, but other physical factors such as edaphic conditions may determine the outcome of competition.
The terms ‘habitat’, ‘environment’ and ‘niche’ are used inconsistently, and with some confusion, within the ecological literature on species distribution and abundance modelling. Here I suggest interrelated working definitions of these terms whereby the concept of habitat remains associated with descriptive/correlative analyses of the environments of organisms, while the niche concept is reserved for mechanistic analyses. To model the niche mechanistically, it is necessary to understand the way an organism's morphology, physiology, and especially behaviour, determine the kinds of environment it experiences when living in a particular habitat, and it is also necessary to understand how those environmental conditions affect fitness (growth, survival and reproduction). While distributions can potentially be predicted by modelling descriptions or correlations between organisms and habitat components, we must model an organism's niche mechanistically if we are to fully explain distribution limits. A mechanistic understanding of the niche is also critical when we want to predict an organism's distribution under novel circumstances such as a species introduction or climate change.
The geographical limits of Nothofagus cunninghamii are highly correlated with climate and appear to be more or less in equilibrium with the climate of the present century in all but one of the areas of its present range. It is suggested that suitable climates for the species occur in the highlands of northeastern Victoria and southern New South Wales, beyond its present range, and it is possible that it occurred within the predicted area prior to the last ice age. It is suggested that populations of N. cunninghamii along the northeastern edge of its present range in the Central Highlands of Victoria may be migrating northeast along a narrow corridor of apparently suitable climate to re-occupy the postulated former range. The rate of migration would be expected to be extremely slow because of the poor dispersal ability of the species and the adverse impact of recurrent fires.
Climate envelope models (CEMs) have been used to predict the distribution of species under current, past, and future climatic conditions by inferring a species' environmental requirements from localities where it is currently known to occur. CEMs can be evaluated for their ability to predict current species distributions but it is unclear whether models that are successful in predicting current distributions are equally successful in predicting distributions under different climates (i.e. different regions or time periods). We evaluated the ability of CEMs to predict species distributions under different climates by comparing their predictions with those obtained with a mechanistic model (MM). In an MM the distribution of a species is modeled based on knowledge of a species' physiology. The potential distributions of 100 plant species were modeled with an MM for current conditions, a past climate reconstruction (21 000 years before present) and a future climate projection (double preindustrial CO2 conditions). Point localities extracted from the currently suitable area according to the MM were used to predict current, future, and past distributions with four CEMs covering a broad range of statistical approaches: Bioclim (percentile distributions), Domain (distance metric), GAM (general additive modeling), and Maxent (maximum entropy). Domain performed very poorly, strongly underestimating range sizes for past or future conditions. Maxent and GAM performed as well under current climates as under past and future climates. Bioclim slightly underestimated range sizes but the predicted ranges overlapped more with the ranges predicted with the MM than those predicted with GAM did. Ranges predicted with Maxent overlapped most with those produced with the MMs, but compared with the ranges predicted with GAM they were more variable and sometimes much too large. Our results suggest that some CEMs can indeed be used to predict species distributions under climate change, but individual modeling approaches should be validated for this purpose, and model choice could be made dependent on the purpose of a particular study.
Accurate modeling of geographic distributions of species is crucial to various applications in ecology and conservation. The best performing techniques often require some parameter tuning, which may be prohibitively time-consuming to do separately for each species, or unreliable for small or biased datasets. Additionally, even with the abundance of good quality data, users interested in the application of species models need not have the statistical knowledge required for detailed tuning. In such cases, it is desirable to use ‘‘default settings’’, tuned and validated on diverse datasets. Maxent is a recently introduced modeling technique, achieving high predictive accuracy and enjoying several additional attractive properties. The performance of Maxent is influenced by a moderate number of parameters. The first contribution of this paper is the empirical tuning of these parameters. Since many datasets lack information about species absence, we present a tuning method that uses presence-only data. We evaluate our method on independently collected high-quality presenceabsence data. In addition to tuning, we introduce several concepts that improve the predictive accuracy and running time of Maxent. We introduce ‘‘hinge features’ ’ that model more complex relationships in the training data; we describe a new logistic output format that gives an estimate of probability of presence; finally we explore ‘‘background sampling’’ strategies that cope with sample selection bias and decrease model-building time. Our evaluation, based on a diverse dataset of 226 species from 6 regions, shows: 1) default settings tuned on presence-only data achieve performance which is almost as good as if they had been tuned on the evaluation data itself; 2) hinge features substantially improve model
Abstract Recent speciation research has generally focused on how lineages that originate in allopatry evolve intrinsic reproductive isolation, or how ecological divergence promotes nonallopatric speciation. However, the ecological basis of allopatric isolation, which underlies the most common geographic mode of speciation, remains poorly understood and largely unstudied. Here, we explore the ecological and evolutionary factors that promote speciation in Desmognathus and Plethodon salamanders from temperate eastern North America. Based on published molecular phylogenetic estimates and the degree of geographic range overlap among extant species, we find strong evidence for a role for geographic isolation in speciation. We then examine the relationship between climatic variation and speciation in 16 sister-taxon pairs using geographic information system maps of climatic variables, new methods for modeling species' potential geographic distributions, and data on geographic patterns of genetic variation. In contrast to recent studies in tropical montane regions, we found no evidence for parapatric speciation along climatic gradients. Instead, many montane sister taxa in the Appalachian Highlands inhabit similar climatic niches and seemingly are allopatric because they are unable to tolerate the climatic conditions in the intervening lowlands. This temporal and spatial-ecological pattern suggests that niche conservatism, rather than niche divergence, plays the primary role in promoting allopatric speciation and montane endemism in this species-rich group of vertebrates. Our results demonstrate that even the relatively subtle climatic differences between montane and lowland habitats in eastern North America may play a key role in the origin of new species.
Aim Distribution modelling relates sparse data on species occurrence or abundance to environmental information to predict the population of a species at any point in space. Recently, the importance of spatial autocorrelation in distributions has been recognized. Spatial autocorrelation can be categorized as exogenous (stemming from autocorrelation in the underlying variables) or endogenous (stemming from activities of the organism itself, such as dispersal). Typically, one asks whether spatial models explain additional variability (endogenous) in comparison to a fully specified habitat model. We turned this question around and asked: can habitat models explain additional variation when spatial structure is accounted for in a fully specified spatially explicit model? The aim was to find out to what degree habitat models may be inadvertently capturing spatial structure rather than true explanatory mechanisms.
Location We used data from 190 species of the North American Breeding Bird Survey covering the conterminous United States and southern Canada.
Methods We built 13 different models on 190 bird species using regression trees. Our habitat‐based models used climate and landcover variables as independent variables. We also used random variables and simulated ranges to validate our results. The two spatially explicit models included only geographical coordinates or a contagion term as independent variables. As another angle on the question of mechanism vs. spatial structure we pitted a model using related bird species as predictors against a model using randomly selected bird species.
Results The spatially explicit models outperformed the traditional habitat models and the random predictor species outperformed the related predictor species. In addition, environmental variables produced a substantial R ² in predicting artificial ranges.
Main conclusions We conclude that many explanatory variables with suitable spatial structure can work well in species distribution models. The predictive power of environmental variables is not necessarily mechanistic, and spatial interpolation can outperform environmental explanatory variables.
It is well known that biodiversity data from historical inventories presents important geographic and taxonomic biases. Due to this, current knowledge on the distribution of most species could be incomplete and biased. We assess how the biases in historical biodiversity data might affect the description of the environmental niche of the species, using exhaustive data on the distribution of dung beetles in Madrid as a case study. We describe the historical process of survey and compare such historical data with the results of an exhaustive survey, identifying the environmental biases in the historical surveys during different periods, and assessing the completeness of the environmental niche of the species provided by historical data through time. Events like the Spanish Civil War affect the tempo and spread of surveys, but the exhaustive work since 1970 provides a good, though incomplete, coverage of the region by 1998. In spite of this, the biases in historical data result in a limited knowledge about the niche of an important number of species. Although nearly a half of the species had the 100% of their niche covered by data in 1998, roughly a third had less than 75%, nearly a fourth less than 50%, and 18 species had to be excluded from the analyses due to the lack of data. Our results point out that data from non-standardized inventories often provide an incomplete description of the environmental responses of most species. Due to this, we highlight that currently predictive models of species distributions present some limitations, since the results of models based in partial information about the environmental niche of the species will be compromised. Therefore, the biases in the available data must be evaluated before constructing predictive maps of species distributions, and taken into account when drawing conclusions or conservation strategies from these maps.
Species distribution models should provide conservation practioners with estimates of the spatial distributions of species requiring attention. These species are often rare and have limited known occurrences, posing challenges for creating accurate species distribution models. We tested four modeling methods (Bioclim, Domain, GARP, and Maxent) across 18 species with different levels of ecological specialization using six different sample size treatments and three different evaluation measures. Our assessment revealed that Maxent was the most capable of the four modeling methods in producing useful results with sample sizes as small as 5, 10 and 25 occurrences. The other methods compensated reasonably well (Domain and GARP) to poorly (Bioclim) when presented with datasets of small sample sizes. We show that multiple evaluation measures are necessary to determine accuracy of models produced with presence-only data. Further, we found that accuracy of models is greater for species with small geographic ranges and limited environmental tolerance, ecological characteristics of many rare species. Our results indicate that reasonable models can be made for some rare species, a result that should encourage conservationists to add distribution modeling to their toolbox.
Aim Numerous geographical information system (GIS)‐based techniques for estimating a species’ potential geographical distribution now exist. While a species’ potential distribution is more extensive than its documented range, the lack of records from some suitable regions may simply derive from inadequate sampling there. Using occurrence records of both the study species and the more inclusive overall target group, I propose a progression of statistical models to evaluate apparent absences in species distributions.
Location Northern Venezuela.
Methods Employing data from the Smithsonian Venezuelan Project (a large set of standardized mammalian inventories undertaken across Venezuela), I tested distributional hypotheses for the sigmodontine rodent Oryzomys albigularis ( Tomes, 1860 ). Those inventories collected O. albigularis in two of the five major montane regions of northern Venezuela (the Cordillera de Mérida/Macizo de El Tamá and Cordillera de la Costa Central). I used the Genetic Algorithm for Rule‐Set Prediction (GARP) to estimate the species’ potential distribution in northern Venezuela. Then, based on all collection localities from the Smithsonian Venezuelan Project, I determined the probability that the absence of O. albigularis from the three regions of potential presence where it was not documented (the Serranía de Perijá, Lara–Falcón highlands, and Cordillera de la Costa Oriental) could be the result of inadequate sampling.
Results and main conclusions All statistical models indicated that the sampling efforts of the Smithsonian Venezuelan Project were insufficient to demonstrate conclusively the absence of O. albigularis from any of the three regions lacking records. Indeed, a subsequent compilation of specimens from ten natural history museums confirmed its presence in the Serranía de Perijá and the Lara–Falcón highlands. Tests using empirical sampling effort and taking human modification of the landscape into account most closely fulfilled the assumptions required for the tests. By providing a framework for bringing additional quantitative rigour to studies of species distributions, these methods will probably prove of wide applicability to other systems.
We compared predictive success in two common algorithms for modeling species’ ecological niches, GARP and Maxent, in a situation that challenged the algorithms to be general – that is, to be able to predict the species’ distributions in broad unsampled regions, here termed transferability. The results were strikingly different between the two algorithms – Maxent models reconstructed the overall distributions of the species at low thresholds, but higher predictive levels of Maxent predictions reflected overfitting to the input data; GARP models, on the other hand, succeeded in anticipating most of the species’ distributional potential, at the cost of increased (apparent, at least) commission error. Receiver operating characteristic (ROC) tests were weak in discerning models able to predict into broad unsampled areas from those that were not. Such transferability is clearly a novel challenge for modeling algorithms, and requires different qualities than does predicting within densely sampled landscapes – in this case, Maxent was transferable only at very low thresholds, and biases and gaps in input data may frequently affect results based on higher Maxent thresholds, requiring careful interpretation of model results.