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Using species distribution modelling to disentangle realised versus potential distributions for rare species conservation
... Following Marcer et al. (2013), the 19 bioclimatic variables at a resolution of 30 seconds available from the WorldClim database (Hijmans et al., 2005) were used to modelling species environmental niches. In this work, the environmental information considered when modelling species' niches was only climatic because climate has a strong influence on invertebrate physiology and distribution ranges (Gullan & Cranston, 2010). ...
... In this work, the environmental information considered when modelling species' niches was only climatic because climate has a strong influence on invertebrate physiology and distribution ranges (Gullan & Cranston, 2010). We decided not to test variable collinearity because the aim of this study was predictive, and predictive performance is not strongly affected by collinearity with MaxEnt modelling (Feng et al., 2019;Marcer et al., 2013). ...
... To construct PBM, we estimated the potential area of occupancy (pAOO; see Marcer et al., 2013) for species with presence records higher than 4 (see Pearson et al., 2007). To this end, for each species was calculated the minimum convex polygon based on its spatial occurrences using the freely available software DIVA-GIS (www.diva-gis.org). ...
1. Potential biodiversity maps (PBMs) allow the identification of areas with different potential for conservation, to support political decisions about the management and protection of biodiversity. 2. As these maps are seldom constructed for inconspicuous species, we proposed to develop PBMs for species belonging to the Geadephaga (Coleoptera), which is a group of beetles that contributes as predators for pest suppression and other ecosystem services in forest ecosystems. Given that human activities are reducing forest integrity, we consider that it is crucial to recognize how diversity patterns of Geadephaga are related to degraded forests. 3. We developed these maps for the Geadephaga associated with subantarctic forests considering diversity measures of species richness, specificity, and rarity to establish spatial relationships between each diversity measure and different levels of forest integrity, and to identify potential hotspots and suggest conservation priorities. 4. Results showed a latitudinal pattern of decrease in scores on richness and specific-ity from north to south, but a patchy pattern of species rarity across the region. Outcomes also show that areas with high scores of diversity measures are overlapped with degraded forest, and that hotspots have a low spatial overlap between them. 5. In this work, we provide for the first time regional PBMs at a relatively high spatial resolution of three different diversity measures for Geadephaga that inhabit
... Además, han ayudado en la evaluación del estado de conservación (Ortega-Andrade et al., 2013;Ortega-Andrade et al., 2015), al descubrimiento de especies nuevas (Raxworthy et al., 2003) y a la detección de poblaciones de especies consideradas crípticas o raras (Raxworthy et al., 2003;Rebelo & Jones, 2010;Udyawer et al., 2020). De igual manera, los MDEs han demostrado ser procedimientos sistemáticos con resultados más representativos que las tradicionales técnicas usadas por la UICN para calcular la extensión de la ocurrencia (EOO, por sus siglas en inglés) (De Castro-Pena et al., 2014;Marcer et al., 2013;Syfert et al., 2014). ...
... Nos enfocamos en especies de murciélagos presentes en la región tumbesina con alguna categoría de amenaza: Vulnerable (VU), En Peligro (EN) o En Peligro Crítico (CR), ya sea a nivel nacional (Ecuador, Perú o ambos) (SERFOR, 2018;Tirira, 2011) o global (IUCN, 2023. Se analizaron ocho de estas especies basándonos en el cumplimiento de los siguientes criterios: (1) la especie se resuelve taxonómicamente, evitando registros que sean confundidos con otra especie (Cayuela et al., 2009;Marcer et al., 2013) o entre especies similares (Guisan et al., 2007); (2) la especie posee más de cinco registros independientes (≥ 10 km de distancia) dentro de su distribución global para la obtención de modelos confiables (Boria et al., 2014;Pearson et al., 2007); y (3) al menos un 20 % de la distribución global de la especie (reportada por mapas de la UICN 2014-2016) está incluida en la región tumbesina. En el caso de que una especie de murciélago no haya cumplido con el primer criterio, no se evaluaron los criterios posteriores, es decir, los registros de presencia no fueron descargados, ni tampoco fue calculada su área de distribución dentro de la región tumbesina. ...
... Identificar el área de distribución de una especie es importante para comprender su estado de conservación (Marcer et al., 2013;Ortega-Andrade et al., 2013;Ortega-Andrade et al., 2015;Syfert et al., 2014) y definir acciones efectivas de conservación. En los trópicos la gran diversidad y la baja abundancia de los registros de presencia dificultan conocer la distribución geográfica de las especies, siendo los MDEs una importante herramienta para predecir su distribución (Cayuela et al., 2009). ...
Title: Species distribution models and conservation status of threatened bats in the Tumbesian region of Ecuador and Perú
Introduction: Biodiversity is being lost at an accelerating rate because of global change. Tools such as species distribution models (SDMs) have been widely used to improve knowledge about species' conservation status and help develop management strategies to mitigate biodiversity loss. SDMs are especially important for species with restricted distributions, such as endemic species.
Objective: To determine how potential distribution predicted by SDMs for eight threatened bat species differed from the distribution maps reported by the IUCN. Also, to infer the area of distribution and state of endemism of each specie, and to evaluate the importance of the Tumbesian region for their conservation.
Methods: Based on presence records across the species' entire ranges, we used SDMs to assess the conservation status of these eight species in the Tumbesian region of Ecuador and Peru.
Results: The areas estimated by SDMs were 35-78 % smaller for four species (Eptesicus innoxius, Lophostoma occidentale, Platalina genovensium and Lonchophylla hesperia) and 26-1600 % larger for three species (Amorphochilus schnablii, Promops davisoni and Rhogeessa velilla) than those reported by the IUCN. For Tomopeas ravus, the area estimated by the SDM and IUCN was similar but differed in spatial distribution. SDMs coincided with areas of endemism reported by previous authors for E. innoxius, R. velilla, and T. ravus, but were different for A. schnablii, P. genovensium, P. davisoni, and L. hesperia, due in part to projected distributions for these latter species in dry inter-Andean valleys according to the SDMs.
Conclusions: The Tumbesian region represents a significant portion (40-96 %) of the predicted distribution of seven of the eight species studied, underscoring the importance of this region for bat conservation. Our results show likely distributions for these species and provide an important basis for identifying research gaps and developing conservation measures for threatened bats in the Tumbes biodiversity hotspot.
https://revistas.ucr.ac.cr/index.php/rbt/article/view/54459
... Species that are rare due to either a restricted geographic range, habitat specificity or small population size are at greater risk of extinction because their populations may not be as resilient to perturbations in the environment (Rabinowitz, Cairns, & Dillon, 1986;Gaston, 1994;Jeliazkov et al., 2022). Therefore, quantifying the two key biological parameters of range extent and population size is fundamental for directing conservation action for threatened rare taxa (Jones et al., 1995;Marcer et al., 2013;Syfert et al., 2014). Habitat loss and fragmentation are the two primary threats to biodiversity globally (Díaz et al., 2019), particularly for tropical biodiversity hotspots (Brooks et al., 2002). ...
... SDMs are predictive spatial models that infer species-habitat associations by correlating species presence points with habitat covariates that represent the focal species optimal conditions and resources (Guisan, Thuiller, & Zimmermann, 2017;Matthiopoulos, Fieberg, & Aarts, 2020). Indeed, SDMs can inform IUCN species range metrics and predict habitat in areas that may lack occurrence data for inclusion in Red List assessments (Marcer et al., 2013;Pena et al., 2014;Syfert et al., 2014;Breiner et al., 2017). Using interpolated model predictions, range metrics such as AOH, EOO and AOO can then be calculated based on inferred or predicted habitat following IUCN Red List guidelines (IUCN, 2019). ...
... We validated our models in conjunction with expert judgement because this approach gives most benefit to conservation risk assessments (Marcer et al., 2013;Syfert et al., 2014). Following modelling protocols established by Velásquez-Tibatá et al. (2019), we assessed a range of four binary thresholds for biological realism (median, 75% upper quantile, maximising the sum of sensitivity and specificity (maxTSS) and Cohen's Kappa), using expert critical feedback to assess the predictive ability of our models ( Figure S3, boxes 4b,c). ...
Many range‐restricted taxa are experiencing population declines, yet we lack fundamental information regarding their distribution and population size. Establishing baseline estimates for both of these key biological parameters is however critical for directing conservation planning for at‐risk range‐restricted species. The International Union for the Conservation of Nature (IUCN) Red List uses three range metrics that define species distributions and inform extinction risk assessments: extent of occurrence (EOO), area of occupancy (AOO) and area of habitat (AOH). However, calculating all three metrics using standard IUCN approaches relies on a geographically representative sample of locations, which for rare species is often spatially biased. Here, we apply model‐based interpolation using Species Distribution Models (SDMs), correlating occurrences with remote‐sensing covariates, to calculate IUCN range metrics, protected area coverage and a global population estimate for the Critically Endangered Philippine Eagle (Pithecophaga jefferyi). Our final range wide continuous SDM had high predictive accuracy (continuous Boyce Index = 0.934) and when converted to a binary model estimated an AOH as 28 624 km², a maximum EOO as 617 957 km², and a minimum EOO as 275 459 km², with an AOO as 53 867 km². Based on inferred habitat from the AOH metric, we estimate a global population of 392 breeding pairs (range: 318–447 pairs), or 784 mature individuals, across the Philippine Eagle global range. Protected areas covered 32% of AOH, 13% less than the target representation, with the continuous model identifying key habitat as priority conservation areas. We demonstrate that even when occurrences are geographically biased, robust habitat models can quantify baseline IUCN range metrics, protected area coverage and a population size estimate. In the absence of adequate location data for many rare and threatened taxa, our method is a promising spatial modelling tool with widespread applications, particularly for island endemics facing high extinction risk.
... Meanwhile, Prevéy et al (2020) used the MaxEnt model to predict the potential fitness zone and phenology of Vaccinium membranaceum (American lingonberry) and found that the simulated results were in good agreement with observations [46]. The MaxEnt model is an ecological niche model based on the theory of maximum entropy [47], and is the preferred model for predicting the potential distributions of critically endangered species because of its simplicity, high degree of accuracy, low sample size requirement, and high stability compared to other modeling methods [48][49][50]. The MaxEnt model has many advantages compared to other prediction models. ...
... The bioclimatic data were obtained from the WorldClim database (http://www.worldclim.com (accessed on Oct. 25, 2022)) at a resolution of 2.5 arc-min [48,55]. The data contain four sections: the LIG period (120,000-14,000 years ago), the LGM period (22,000 years ago), the current period (1970-2000), and two future ...
Global climate change, dominated by climate warming, is seriously affecting the balance of global ecosystems, but the risk of species extinction is particularly high in low-altitude mountain areas. To clarify the response of the endemic and critically endangered species Ilex nanchuanensis to climate change, this study used the MaxEnt model to simulate and predict the potential habitat of I. nanchuanensis during the Last Interglacial, Last Glacial Maximum, the current period, and two future periods (the 2050 s and 2070 s). The results showed that the hottest monthly minimum temperature is the most important climatic factor affecting the geographical distribution of I. nanchuanensis. Furthermore, I. nanchuanensis will be at risk of population shrinkage and extinction in the future, with the center of mass moving further northwest as concentrations of greenhouse gases increase, especially in the 2070 s, when its geographical distribution shrinks the most under the RCP6 scenario. Therefore, to actively respond to the impacts of climate change, protected areas should be established around the geographical distribution centers of species, and core, buffer, and experimental areas should be scientifically and rationally delineated for the conservation and cultivation of germplasm resources.
... It has also been defined as a presence only model that uses predictive data sets to discriminate crop occurrence records [39,40]. Although the underlying prediction of those areas has been systematically sampled from most existing lands, the MaxEnt model is often constructed from spatially based occurrence records [41]. MaxEnt is one of the most popular niche-based methods for modeling geographical crop distributions [42]. ...
... We calculated the suitable habitat area and proportion for each climate scenario in four different periods to further analyze the impact of climate change on the land suitability of wheat, rice, and corn under different scenarios (Table 5). A prerequisite understanding of species distribution is important for species utilization and restoration in ecosystems [38,41]. Under the SSP585 climate scenarios for the study area in 2040, 2060, 2080, and 2100, the suitable high, medium, and low habitats for wheat will significantly increase, while the unsuitable area will significantly decrease. ...
Accurately predicting changes in the potential distribution of crops resulting from climate change has great significance for adapting to and mitigating the impacts of climate change and ensuring food security. After understanding the spatial and temporal suitability of wheat (Triticum aestivum), rice (Oryza sativa), and maize (Zea mays), as well as the main bioclimatic variables affecting crop growth, we used the MaxEnt model. The accuracy of the MaxEnt was extremely significant, with mean AUC (area under curve) values ranging from 0.876 to 0.916 for all models evaluated. The results showed that for wheat, annual mean temperature (Bio-1) and mean temperature of the coldest quarter (Bio-11) contributed 39.2% and 13.4%, respctively; for rice, precipitation of the warmest quarter (Bio-18) and elevation contributed 34.9% and 19.9%, respectively; and for maize, Bio-1 and precipitation of the driest quarter (Bio-17) contributed 36.3% and 14.3%, respectively. The map drawn indicates that the suitability of wheat, rice, and corn in South Asia may change in the future. Understanding the future distribution of crops can help develop transformative climate change adaptation strategies that consider future crop suitability. The study showed an average significant improvement in high-suitable areas of 8.7%, 30.9%, and 13.1%, for wheat, rice, and maize, respectively; moderate-suitable area increases of 3.9% and 8.6% for wheat and rice, respectively; and a decrease of −8.3% for maize as compared with the current values. The change in the unsuitable areas significantly decreases by −2.5%, −13.5%, and −1.7% for wheat, rice, and maize, respectively, compared to current land suitability. The results of this study are crucial for South Asia as they provide policy-makers with an opportunity to develop appropriate adaptation and mitigation strategies to sustain wheat, rice, and corn production in future climate scenarios.
... The use of geospatial analysis is one of the main strengths of this study. Geospatial analysis in combination with bio-climatic modelling has been used productively in several studies (see: Yang et al. 2005;Baldwin 2009;Rebelo and Jones 2010;Sardà-Palomera et al. 2012;Marcer et al. 2013); using location data in modelling and mapping disease distribution and hotspots. ...
... MaxEnt, for example,Pearson et al. (2007),Wisz et al. (2008),Baldwin (2009),Rebelo and Jones (2010),Sardà-Palomera et al. (2012),Marcer et al. (2013). MaxEnt has also been used for modelling of TBDsSage et al. 2017;Zannou et al. 2021). ...
Understanding the spatial and temporal distribution of Bovine anaplasmosis is crucial for identifying areas of high prevalence for targeted disease control. This research was aimed at modelling and mapping the B. anaplasmosis potential distribution, and identify hotspots as well as significant variables explaining the occurrence of the disease. The Getis Ord Gi* statistic for Hotspot analysis was used as well as MaxEnt ecological niche modelling. The effects of time, land-use, and agro-ecological regions on B. anaplasmosis occurrence were tested using Analysis of Variance (ANOVA). Results showed that several districts in Zimbabwe are suitable for the occurence of the disease for example Binga, Seke, Buhera, Kwekwe, Gweru, Mhondoro, Chegutu, Sanyati, and in the North: Mbire, Muzarabani, Mt Darwin, Shamva, Bindura, Zvimba and Makonde. Morbidity and mortality hotspots were detected in Gokwe-south, Kwekwe, and Chirumhanzu districts. Binga, Gokwe-south, Gutu, Hurungwe, Mazoe, Nkayi, Shamva, and Kwekwe districts also experienced high disease incidences. Temperature seasonality, precipitation seasonality, mean diurnal range, and isothermality were the most important variables in explaining 93% of B. anaplasmosis distribution. Unlike land-use and agro-ecological regions, time (months) had a significant effect on B. anaplasmosis occurrence with July and September having significantly (p < 0.05) higher cases and deaths than the rest of the months. The results of this study provide insights into the management strategies and control of B. anaplasmosis in Zimbabwe. It is thus concluded that geo-spatial techniques, combined with ecological niche modelling can provide useful insights into disease prevalence and distribution and hence can contribute to effective management and control of B. anaplasmosis in Zimbabwe.
... The SDMs and the ecological niche models -ENMs are popular and effective tools for conservation actions such as: (1) to produce environmental suitability maps (Guisan and Thuiller 2005); (2) to understand spatial patterns of biological diversity Cayuela et al. 2009); (3) to assess the effectiveness of reserve networks (Araújo et al. 2011;Marcer et al. 2013;Fagundes et al. 2018); and (4) to efficiently propose protection areas (Nóbrega and De Marco Jr 2011;Sodré et al. 2012). SDM relies on associations between environmental conditions and information about species records to identify areas that are critical for maintaining species populations (Peterson et al. 1999;Warren et al. 2010), reducing the uncertainty in distribution predictions (Elith et al. 2006) and producing low-cost accurate predictive potential habitat maps which, in turn, provide helpful information for biodiversity conservation strategies (Araújo et al. 2008;Cayuela et al. 2009). ...
... Overall, our results suggest that species distribution modelling can lead to an accurate conservation status assessment by applying dispersal constraints to ecological niche models. This approach resonates with other researches, reducing overprediction and allowing a better estimate of the extent of occurrence for rare and endangered species (Jetz et al. 2008;Marcer et al. 2013) compared to the minimum polygon convex (Herzog et al. 2012;Ramesh et al. 2017), even when little data is available (Pena et al. 2014). At first, a 31% reduction in estimated distribution when using SDM instead of MCP may seem small and raises questions about cost-effectiveness. ...
Most assessments of the conservation status of Brazilian turtles use the IUCN geographic range criteria performed by the Minimum Convex Polygon (MCP). This technique often leads to over- or under-estimating the geographic distribution of rare, vulnerable, or endangered species. We aimed to demonstrate that using Species Distribution Models (SDM) on the geographic range assessment of turtles could be more accurate than using the minimum polygon convex. We reduced overestimation of species’ extent of occurrence by adding dispersal constraints, which avoids under- or over-estimating the impact of threatening events. The extent of occurrence derived from MCP was 31% higher than SDM on average, ranging from 4 to 311% higher. Using remaining habitat variables, we found that habitat loss within the predicted extent of occurrence increased by 79% from 1985 to 2019, and inferred population fragmentation increased by 161%. The distribution of turtles Acanthochelys radiolata, Acanthochelys spixii, Hydromedusa maximiliani, Hydromedusa tectifera, Mesoclemmys vanderhaegei, Phrynops williamsi, and Ranacephala hogei is severely fragmented, with most of their extent of occurrence being split into patches that are unavailable to the species persistence. Our findings highlight the importance of using SDM combined with dispersal constraints, which may further benefit from future information about the dispersal capacity of turtles. Furthermore, adding environmental layers to this combination makes it possible to discuss processes affected by habitat fragmentation, such as the fragmentation of species populations, an aspect essential to evaluate population viability and local extinctions.
... The Wallacean Shortfall contributes to a second knowledge deficit where, if the current range of a species is unknown or not fully described, it is not possible to determine whether and when a species is in decline or possibly gone extinct. Thus, the environmental factors that limit the distribution and abundance of many threatened species are still poorly understood (Marcer et al. 2013). ...
... First, we calculated the Extent of Occurrence, fitting a minimum convex polygon around the furthest boundaries of the smoothed model AOH polygon following IUCN guidelines (IUCN 2018). We calculated both a maximum Extent of Occurrence, including all the area with the minimum convex polygon, and a minimum Extent of Occurrence, masking out the areas that could either not be occupied, or are unlikely to be, within the minimum convex polygon, in our case over the ocean and outside the moist tropical forest ecoregions (Marcer et al. 2013). Second, we calculated the Area of Occupancy as the number of raster pixels predicted to be occupied, scaled to a 2 Â 2 km grid (4-km 2 cells) following IUCN guidelines (IUCN 2018). ...
Knowledge gaps regarding distribution, habitat associations, and population size for rare and threatened range-restricted taxa lead to uncertainty in directing conservation action. Quantifying range metrics and species–habitat associations using Species Distribution Models (SDMs) with remote-sensing habitat data can overcome these setbacks by establishing baseline estimates for biological parameters critical for conservation assessments. Area of Habitat (AOH) is a new range metric recently developed by the International Union for Conservation of Nature (IUCN) Red List. AOH seeks to quantify inferred habitat within a species’ range to inform extinction risk assessments. Here, we used SDMs correlating occurrences with remote-sensing covariates to calculate a first estimate of AOH for the Endangered Madagascar Serpent-eagle Eutriorchis astur , and then updated additional IUCN range metrics and the current global population estimate. From these baselines we then conducted a gap analysis assessing protected area coverage. Our continuous SDM had robust predictive performance (Continuous Boyce Index = 0.835) and when reclassified to a binary model estimated an AOH = 30,121 km ² , 13% less than the current IUCN range map. We estimated a global population of 533 mature individuals derived from the Madagascar Serpent-eagle AOH metric, which was within current IUCN population estimates. The current protected area network covered 95% of AOH, with the binary model identifying three additional key habitat areas as new protected area designations to fully protect Madagascar Serpent-eagle habitat. Our results demonstrated that correlating presence-only occurrences with remote-sensing habitat covariates can fill knowledge gaps useful for informing conservation action. Applying this spatial information to conservation planning would ensure almost full protected area coverage for this endangered raptor. For tropical forest habitat specialists, we recommend that potential predictors derived from remote sensing, such as vegetation indices and biophysical measures, are considered as covariates, along with other variables including climate and topography.
... Such information is nevertheless limited for most species and nonexistent for most rare taxa. Hence, strategies to better guide research and conservation endeavors for poorly known species are necessary, even in light of very limited data (Marcer et al., 2013). ...
... These methods can be critically applied using raw occurrence (presence-only) data, often the only available information for poorly known species. Nevertheless, additional data can be integrated to increase model performance (Franklin, 2013;Villero et al., 2016;Baker et al., 2020;Frans et al., 2021) and the results can be later refined when new studies become available (Marcer et al., 2013;McShea, 2014). ...
Conservation of poorly known species is challenging as lack of knowledge on their specific requirements may hamper effective strategies. Here, by integrating biogeographical and landscape analyses, we show that informed actions can be delineated for species with limited presence-only data available. We combine species distribution and connectivity models with temporal land cover changes to define priority areas for conservation of the endemic Brazilian three-banded armadillo, one of the most threatened xenarthrans that was once considered extinct in the wild. We revealed that areas of savanna and grassland are the most suitable habitats for the species and that uplands in the Caatinga ecoregion have a greater likelihood for dispersal. The few remnant armadillo populations are spatially associated with core areas of natural vegetation remnants. Worrisomely, 76% of natural core areas were lost in the past 30 years, mirroring the species’ severe population decline. Preserving the remnant core natural areas should be a high priority to ensure the species’ survival. We highlight key areas for proactive and reactive conservation actions for the three-banded armadillo that will benefit other threatened sympatric species. Our integrative framework provides a set of valuable information for guided conservation management that can be replicated for other poorly known species.
... Our current model predicting suitable habitat of B. occidentalis provides a foundation for monitoring this declining species. Maxent models often over-predict suitable habitat [46,47]; therefore, our results should be considered conservatively. Species distribution models are a useful tool to guide survey efforts and identify potentially limiting variables; however, they only predict areas of suitable habitat and not the range of the species. ...
Monitoring declining species is crucial to inform conservation but is challenging for rare species with limited information. The Western Bumble Bee (Bombus occidentalis) was previously common in the western United States but has drastically declined. Despite documented populations in the Intermountain West, many areas remain under-sampled. Species distribution models (SDM) can guide sampling efforts in large areas by predicting where the highest probability of suitable habitat may occur. We developed a sampling SDM using historical observations (1910–2010) in Wyoming to predict suitable habitat in the past. Using the model, we selected sampling sites that ranged from low to high predicted habitat suitability and we revisited historical locations where B. occidentalis were observed. Using all data (historical and current), we selected the predictors that explained the most variance, and created separate historical and current (2017–2018) SDM using the same variables to assess how predicted habitat suitability changed. We detected B. occidentalis at 30% of the revisited historical sites and 25% of all sites sampled. Areas predicted to be highly suitable for B. occidentalis in Wyoming declined by 5%; a small decrease compared to declines in the western portion of their range. Predicted habitat suitability increased the most in foothill areas. Creating SDM with landscape and climatic variables can bolster models and identify highly contributing variables. Regional SDM complement range-wide SDM by focusing on a portion of their range and assessing how predicted habitat changed.
... Complementarily, AOO denotes the area actually occupied by a species, typically estimated by mapping known or projected distribution points onto a 2×2 km grid and counting the number of occupied cells (IUCN, 2024). These metrics are not only vital for well-documented species in the IUCN Red List but are also integral to evaluating regional endemic, rare, keystone, and flagship species, highlighting their critical role in extinction risk assessments (Braby et al., 2018;Chakona et al., 2022;Dong et al., 2023;Gaston & Fuller, 2009;Marcer et al., 2013). ...
Assessing the threat status of species in response to global change is critical for biodiversity monitoring and conservation efforts. However, current frameworks, even the IUCN Red List, often neglect critical factors such as genetic diversity and the impacts of climate and land-use changes, hindering effective conservation planning. To address these limitations, we developed an enhanced extinction risk assessment framework using Diploderma lizards as a model. This framework incorporates long-term field surveys, environmental data, and land-use information to predict distributional changes for 10 recently described Diploderma species on the Qinghai-Xizang Plateau, which hold ecological significance but remain underassessed in conservation assessment. By integrating the distribution data and genetically inferred effective population sizes (Ne), we conducted scenario analyses and used a rank-sum approach to calculate Risk ranking scores (RRS) for each species. This approach revealed significant discrepancies with the IUCN Red List assessments. Notably, D. yangi and D. qilin were identified as facing the highest extinction risk. Furthermore, D. vela, D. batangense, D. flaviceps, D. dymondi, D. yulongense, and D. laeviventre, currently classified as “Least Concern”, were found to warrant reclassification as “Vulnerable” due to considerable threat from projected range contractions. Exploring the relationship between morphology and RRS revealed that traits such as snout-vent length and relative tail length could serve as potential predictors of extinction risk, offering preliminary metrics for assessing species vulnerability when comprehensive data are unavailable. This study enhances the precision of extinction risk assessment frameworks and demonstrates their capacity to refine and update risk assessments, especially for lesser-known taxa.
... This method produces absence data artificially using the cell values in the background and creates the distribution model by calculating the possibilities based on these cells (Phillips et al., 2006). In particular, it provides better results when creating habitat suitability models in wildlife studies, when determining the distribution areas of endemic or rare, endangered species, and identifying the present and future impacts of climate change on the distribution of species (Ray et al., 2011;Babar et al., 2012;Clements et al., 2012;Kumar, 2012;Marcer et al., 2013;Garcia et al., 2013;Ma et al., 2014;Süel, 2019;Acarer, 2024). Since it is difficult to validate the absence data of the target species in habitat suitability modelling studies (Mateo et al., 2010), presenceonly data is suitable for such studies. ...
The purpose of this work was to elucidate the fundamental characteristics and application areas of various modelling techniques that are widely employed in current ecological modelling research. Using five distinct distribution modelling techniques, possible species distribution modelling and mapping of the Brutian pine species in the Gölhisar district were conducted. The data was collected from Brutian pine species in 400 sampling plots in the area. The variables used in the models were elevation, slope, aspect, radiation index, heat index, topographic position index and bedrock types. Logistic regression, classification tree, random forest, generalized additive model and maximum entropy were used as the species distribution modelling methods. Receiver Operating Characteristics (ROC) curves were created and the performance of the species distribution models was evaluated with the Area Under the ROC curve (AUC). The statistical analyses revealed that the best models were generalized additive model, random forest, classification tree, maximum entropy and logistic regression, respectively. Elevation and bedrock types had the highest contribution to the Brutian pine distribution models. The outputs of the generalized additive model technique that had the highest AUC value were mapped. Some ecological and statistical differences were found between the models and their reasons were presented. Compared to the methods commonly used in species distribution modelling studies, generalized additive model technique has a specific smoothing function which ensures both fittings between the envirenvironmental changes and explanatory curves and more accurate ecological interpretation of the models obtained.
... Each method is unique and comes with pros and cons of its own. Machine learning is one of the most popular and successful technologies for modelling species distribution, among other things [11]. ...
... Interactions between climate change and non-climate threats, and uncertainty and variability in future climate trajectories, further compound the difficulty faced by managers trying to address climate threats (Lawler et al. 2015). The incorporation of climate-change considerations into conservation action plans is required because management decisions made now will affect the persistence of species into the future (Marcer et al. 2013). ...
Context
Global climate is changing rapidly, necessitating timely development of specific, actionable species conservation strategies that incorporate climate-change adaptation. Yet, detailed climate-change adaptation planning is noticeably absent from species management plans. This is problematic for restricted species, which often have greater extinction risk.
Aims
Focusing on the restricted and endangered Tarengo leek orchid (Prasophyllum petilum), we aimed to adapt and test a framework for producing strategies for its management under climate change.
Methods
We used expert elicitation to estimate the severity of threats and assess potential management actions to mitigate threat impacts. We created a conceptual model detailing ecology, threats and likely impacts of climate change on the species, including the interactions between components.
Key results
Although climate change-related threats will affect the species, the most severe threats were non-climate threats including grazing, weeds, and habitat degradation. Fire management was deemed highly beneficial but had low feasibility for some populations. Without management, experts estimated up to a 100% decrease in one P. petilum population, and up to 50% decrease if management remained unchanged.
Conclusions
Management actions with the highest benefit and feasibility addressed the non-climate threats, which, in turn, can give the species the best opportunity to withstand climate-change impacts. Experts highlighted the difficulty of addressing climate threats with such limited knowledge; therefore, further research was recommended.
Implications
This adapted framework enabled a structured analysis of threats, and informed selection of priority adaptation options. We recommend its use for other restricted species for efficient and robust decision-making in climate-change management.
... Assessing population persistence may require a more holistic approach, considering not only species adaptations but also the condition of dependent species and related habitats (e.g., [32,33]). Along with population-level studies evaluating declines, modeling species distributions under varying climate scenarios can help identify potential locations for conservation and guide future research [34,35]. One method for estimating species distributions utilizes the maximum entropy model (MaxEnt). ...
Wild bees are vital for the pollination of native plants and crops, providing essential ecosystem services. Climate change is known to impact biodiversity and species distributions, but insects adapted to desert ecosystems may exhibit unique physiological, behavioral, and evolutionary responses. The desert pallid bee (C. pallida), a solitary bee native to the arid southwestern United States and northern Mexico, primarily forages on yellow palo verde (P. microphylla), blue palo verde (P. florida), and desert ironwood (O. tesota). This study used MaxEnt to estimate the current and projected geographical overlap of suitable habitats for C. pallida and its host plants. Here, we used MaxEnt to estimate the current and forecasted overlapping geographically suitable habitat of C. pallida with all three host plants. We forecasted potential environmentally suitable areas for each species to the year 2040 using the current distribution model and climate projections with moderate CO2 levels. We found a continued spatial alignment in the suitable area of the bee and its host plants with a 70% increase in the range overlap area, though shifted to higher average altitudes and a slight northern expansion. These findings may provide insight to stakeholders on the conservation needs of desert-dwelling pollinators.
... These data are vital to developing global conservation strategies (Boitani et al., 2011), and form the basis on which species are assessed and classified on the IUCN Red List (Hoffmann et al., 2008;Cazalis et al., 2022). These data can be supplemented with additional information on the species' ecology, behaviour and habitat usage to form predictions of realised niche area and areas of occupancy (Marcer et al., 2013;Breiner et al., 2017), however such data are limited for many understudied cryptic species. The second application of ENMs and SDMs is to guide the future search for cryptic species (Fois et al., 2018). ...
Cryptic species are groups of two or more taxa that were previously classified as single nominal species. Being almost morphologically indistinguishable, cryptic species have historically been hard to detect. Only through modern morphometric, genetic, and molecular analyses has the hidden biodiversity of cryptic species complexes been revealed. Cryptic diversity is now widely acknowledged, but unlike more recognisable, charismatic species, scientists face additional challenges when studying cryptic taxa and protecting their wild populations. Demographical and ecological data are vital to facilitate and inform successful conservation actions, particularly at the individual species level, yet this information is lacking for many cryptic species due to their recent taxonomic description and lack of research attention. The first part of this article summarises cryptic speciation and diversity, and explores the numerous barriers and considerations that conservation biologists must navigate to detect, study and manage cryptic species populations effectively. The second part of the article seeks to address how we can overcome the challenges associated with efficiently and non-invasively detecting cryptic species in-situ, and filling vital knowledge gaps that are currently inhibiting applied conservation. The final section discusses future directions, and suggests that large-scale, holistic, and collaborative approaches that build upon successful existing applications will be vital for cryptic species conservation. This article also acknowledges that sufficient data to implement effective species-specific conservation will be difficult to attain for many cryptic animals, and protected area networks will be vital for their conservation in the short term.
... SDMs have been widely acknowledged as useful methods in several restoration evaluations to confront significant conservation issues (Franklin, 2010). Improved understanding of the actual distribution of species provides important ecological information and excellent forecasting abilities and may be used to define species distribution and evaluate sampling tactics for rare and threatened species (Guisan et al., 2006;Marcer et al., 2013), to create networks of natural reserves and identify conservation target regions (Fajardo et al., 2014;Hermoso et al., 2015), or to direct efforts towards ecological restoration and species conservation (Angelieri et al., 2016;Villero et al., 2017). Additionally, SDMs can be extended to different geographical or temporal instances as part of risk evaluations of invasive species (Roura-Pascual et al., 2009;Jimenez-Valverde et al., 2011), as well as to hindcast or forecast possible impacts of climate change (Martin et al., 2013;Runge et al., 2016;Regos et al., 2016). ...
Plant species, particularly those growing in mountains, are extremely susceptible to climatic alterations, as their distribution is predominantly determined by climatic factors. Changes in the climatic conditions influence the habitat suitability of plant species, and the species may adapt to it by changing their habitats in order to better match pre-existing climatic conditions. The Himalayan region, which symbolizes the greatest bioclimatic altitudinal gradient and provides the highest altitudinal boundaries for vascular plant species in the world, is extremely susceptible to climate change because of predominant topographic inclinations. In the region, suitable habitat areas of many plant species are dwindling due to warming and changes in precipitation regimes. It is anticipated that the currently suitable habitats of such species will become unsuitable and vice versa in the near future. Furthermore, many high-elevation plant species that are susceptible to high temperatures are relocating to higher altitudes. To comprehend the behavior of the species and make plans for the protection of biodiversity, it is imperative to monitor the distribution and habitat suitability of plant species with respect to climate change. For that purpose, the species distribution modeling (SDM) technique is quite helpful. This chapter explores the impact of climate change on the habitat suitability of plant species growing in the Himalayan region and the role of SDM in forecasting species’ response to climate change. Additionally, implications for the conservation and management of plant species in the region are discussed.
... This is particularly true for rare or scarce species, where records often only indicate occurrence 57 . Such incomplete distribution information poses challenges for evaluation and assessment 58 . Partial data simulation is necessary to model species distributions under such conditions of limited data 6 . ...
As an important fishery resource and endangered species, studying the habitat of Coilia nasus (C. nasus) is highly significant. This study used fishery survey data from southern Zhejiang coastal waters from 2016 to 2020, employing a maximum entropy model (MaxEnt) to map the habitat distribution of C. nasus. Model performance was evaluated using two metrics: the area under the curve (AUC) of the receiver operating characteristic curve for the training and test sets and true skill statistics (TSS). This study aimed to predict the habitat distribution of C. nasus and explore how environmental variables influence habitat suitability. The results indicated that the models for each season had strong predictive performance, with AUC values above 0.8 and TSS values exceeding 0.6, indicating that they could accurately predict the presence of C. nasus. In the study area, C. nasus was primarily found in brackish or marine waters near bays and coastal islands. Among all environmental factors, salinity (S) and bottom temperature (BOT) had the highest correlations with habitat distribution, although these correlations varied across seasons. The findings of this study provide empirical evidence and a reference for the conservation and management of C. nasus and for the designation of its protected areas.
... Also, orientation was not important for ENM construction, but some species are unique to the north sides of mountains (e.g. Hippocrepis balearica) while others are found on the south side (Lomelosia cretica) emphasizing the importance of considering species-specific characteristics to assess particular cases (García et al., 2020;Marcer et al., 2013). In fact, this study acts as a first step in the future to analyze species-specific ecological niche models. ...
Cliff ecology has been studied for decades, providing information about its high biodiversity values and their vulnerability to climate change. Also, insular ecosystems present biodiversity hotspots with high endemicity, but they are also severely affected by anthropogenic effects. Together, insular cliff communities combine both biodiversity uniqueness and high vulnerability to global change, but few studies have evaluated these particular ecosystems. Our aim was to provide information on the spatial distribution of insular cliff-specific vegetation assessing which environmental and climatic variables contribute to the definition of cliff habitat conditions. Ecological niche modelling for cliff populations in Balearic Islands has been calculated with presences of 20 plant species and climatic and geographical variables using a Random Forest model. The most important climatic variables for the model generation were mean temperature of the driest quarter and precipitation of the coldest quarter. The map obtained showed that mountain ranges from all islands provide highly suitable conditions for rupicolous species. Both the pessimistic and optimistic models showed that the habitat suitability of cliff vegetation in the mountain ranges would decrease, while they are close to zero in lowlands for the period 2021–2040. This study emphasizes the vulnerability of cliff habitats to climate change due to their limited dispersal capacity and distribution and the strict requirements for habitat suitability. From this work, future studies can focus on single-species analysis to evaluate if any cliff specialist species can be at risk of extinction due to climate change.
... The Maximum Entropy (MaxEnt) model was chosen for this study as it has performed well compared with alternative modelling methods, such as GARP, DOMAIN and ENFA (Elith et al. 2006), and it is an appropriate model choice with small sample sizes or few locality records (Pearson et al. 2007;Wisz et al. 2008;Gogol-Prokurat 2011;Jackson and Robertson 2011;Razgour et al. 2011;Chunco et al. 2013;Marcer et al. 2013;Fois et al. 2015). ...
This study evaluates how a modelling approach to determine areas of suitable
habitat for the Critically Endangered Albany cycad Encephalartos latifrons can assist
in systematic conservation planning for this and other rare and threatened cycads. A map distinguishing suitable from unsuitable habitat for E. latifrons was produced and important environmental predictors (climate, geology, topography and vegetation) influencing the suitable habitat were estimated. The maximum entropy (MaxEnt) modelling technique was chosen for this study as it has consistently performed well compared with alternative modelling methods and is also an appropriate model choice when the sample size is small and locality records are relatively few. Predicted habitat suitability showed that some locations chosen for translocation and restoration of E. latifrons specimens are not suitable.
This revealed that modelling suitable habitat can guide relocation and regeneration of E. latifrons and perhaps other threatened cycads with restricted distributions and few locality records. The species distribution model constructed for E. latifrons is the first reported habitat model for a Critically Endangered cycad in South Africa. The results may be incorporated into conservation planning and structured decision-making about translocations and restoration programmes involving vulnerable cycads, which are among the most threatened organisms globally.
... In order to reduce biodiversity losses under climate change, predicting the potential geographic distribution range of a species in predicted scenarios of climate in the future is a significant conservation action (Martinez-Meyer, 2005). However, distribution data of threatened and endangered species are often spare and clustered, which causes challenges to model each its suitable habitat using commonly used modelling approaches (Marcer et al., 2013;Qin et al., 2017;Arar et al., 2020). In recent years, species distribution models (SDMs) such as Maximum Entropy (MaxEnt) have been a popular and effective tool to predict the potential distribution pattern of plant species in general, especially threatened and endangered species (Phillips et al., 2023) because this model only requires species occurrence data and the surrounding environmental variables (i.e. ...
... This model simulates a twofold increment of CO2 concentration in the atmosphere, providing insights into future climate scenarios (Chen et al. 2003). For modeling species distribution and habitat suitability, various approaches exist, including statistical liner models like Generalized Additive Model (GAM) and the Generalized Linear Model (GLM), geographical analysisbased models such as domain and biomapper, and more recent Maximum Entropy (MaxEnt), in particular, offers several advantages in terms of capacity, data requirements, accuracy, and the ability to discriminate environmental variables (Marcer et al. 2013;Stephenson et al. 2022). ...
Modeling climate change impacts under future CCM3 scenario on sorghum (Sorghum bicolor) as an drought resilient crop in tropical arid Lombok Island, Indonesia. Intl J Trop Drylands 8: 35-43. The arid ecosystems and drought conditions exacerbated by climate change and rising CO2 levels necessitate the identification of alternative drought-tolerant crops. Sorghum bicolor L. has emerged as a promising option due to its resilience to drought. However, there is dearth of information regarding its future potential distribution, particularly in arid regions like Lombok Island, Indonesia, where sorghum is being considered as a viable alternative to ensure food security. This study employs Maximum Entropy (MaxEnt) modeling, incorporating environmental and bioclimatic variables, along with the National Center for Atmospheric Research (NCAR) Community Climate Model (CCM3) scenario reflecting doubled CO2 levels, to model the future potential distribution of S. bicolor. The model projects a total suitable habitat area of 1,875 km 2 , constituting 39.56% of Lombok Island's land area. Notably, very high-suitability areas of 175 km 2 , and high-suitability areas of 200 km 2 encompass 3.69% and 4.22% of the island's territory, respectively, predominantly concentrated in the southern region of the island and characterized by low precipitation and high temperatures, particularly at altitudes ranging from 0 to 1,000 meters. The model's performance, evaluated using the Area Under the Curve (AUC), yields a score of 0.725, indicating a good level of accuracy. Key factors influencing sorghum distribution include annual precipitation (68.69%), isothermality (9.56%), temperature seasonality (9.56%), precipitation seasonality (8.69%), and annual mean temperature (3.47%). The CCM3 model forecasts an expansion of sorghum distribution toward the north, occupying approximately 6.25% of Lombok's total area. These findings highlight sorghum's adaptability and resilience to future climate changes, positioning it as a valuable resource for sustainable agriculture in arid environments.
... Each machine-learning technique is unique and has advantages and disadvantages therein. Marcer et al. (2013) claim that SDM is one of the most effective and widely used strategies for modeling the habitat suitability of certain species. SDM is performed in R environments, and R has advantages for developing SDM algorithms. ...
Wibowo AA, Meylani V, Pratiwi NA, Febriani DN, Suryawati NN. 2024. Modeling the future distributions of Centropus bengalensis (Lesser coucal) in Muara Gembong Wetlands, West Java, Indonesia, related to CMIP5 climate change scenarios. Intl J Bonorowo Wetlands 14: 49-56. Wetlands and their water birds have been threatened recently due to climate change. In West Java, Muara Gembong is a threatened wetland along with Lesser coucal (Centropus bengalensis, Gmelin 1788). This study aimed to model and forecast the distribution of Lesser coucal in the remaining wetland habitats to support species conservation. The novelty of this study is that it uses future Species Distribution Modeling (SDM) based on climate change scenarios. Modeling was performed based on SDM using R platforms incorporating 19 bioclimatic variables. The climate change scenarios used trajectories based on the 5 th Coupled Model Intercomparison Project (CMIP) using RCP 2.6 and RCP 8.5 trajectories for 2050 and 2070. A multicollinearity test was performed, and the coucal occurrences were recorded at five sampling points. The results show climate change scenarios will significantly alter the suitable habitats for coucal, and the Area Under the Curve (AUC) is 0.75. The distribution of the species is mostly affected by isothermality (Bio 3), temperature annual range (Bio 7), and precipitation seasonality (Bio 15). In the low emission scenario, or RCP 2.6, from 2050 to 2070, it is predicted that the suitable habitats for coucals will be increased and expanded to the east and the north in coastal areas. Habitats classified in 2050 as less suitable will become moderately suitable in 2070 under the RCP 2.6 scen ario. This condition is contrary to the high emission scenario under RCP 8.5. In this scenario, the habitats with high suitability only increased slightly. At the same time, and opposite to the low emission scenario, the RCP 8.5 scenario will cause moderately suitable habitats to become less suitable or have low suitability. This study provides empirical evidence of how a climate change scenario with high emissions can impact the water birds living in the wetlands.
... Each tool is unique, with its own set of advantages and disadvantages. According to Marcer et al. (2013), several advantages of SDM include the need for only species presence data, the capacity to run with a limited quantity of data, the high accuracy of prediction results, the high reproducibility, and the ability to predict the most discriminating bioclimatic factors (Fois et al., 2018). ...
Artocarpus altilis, commonly known as breadfruit, is a potential crop adapted to a wide variety of climates and widely spread, including in Indonesia. However, information on how this species can adapt to climate change, in particular in Central Java, is still limited. In Indonesia, Central Java is the center for cultivation areas for many crop species to support the 145 million people living on Java Island. One of the potential crops being developed in Central Java is breadfruit. To assess the suitable cultivation areas for breadfruit, species distribution modeling (SDM) was used to predict the current and future (2050–2070) distribution of breadfruit. Two climate change scenarios, including optimistic RCP2.6 and pessimistic RCP8.5 models, were considered to represent future climate change impacts. Based on the results for both optimistic and pessimistic scenarios, the breadfruit’s suitable cultivation areas will expand eastward. Implementing a mitigation climate change scenario and limiting the temperature increase to only 1°C under RCP2.6 will provide 270.967 km² more of suitable cultivation areas for breadfruit in 2050 and 133.296 km² in 2070. To conclude, this study provides important information on the status and potential cultivation areas for breadfruit, mainly in the Southeast Asia region. The identification of suitable areas will guide land conservation for breadfruit to support food security in this region.
... The results showed that precipitation of the driest month (bio14) was the primary bioclimatic variable affecting the presence of O. microphylla, with optimum conditions at 30 to 40 mm. As a plant growth prerequisite, precipitation is the primary limiting factor for almost all species37,38 . Variations in precipitation and consequent changes in temperature and humidity disrupt the balance of soil moisture and most physiological plant functions36,39 . ...
Climate change has significantly influenced the growth and distribution of plant species, particularly those with a narrow ecological niche. Understanding climate change impacts on the distribution and spatial pattern of endangered species can improve conservation strategies. The MaxEnt model is widely applied to predict species distribution and environmental tolerance based on occurrence data. This study investigated the suitable habitats of the endangered Ormosia microphylla in China and evaluated the importance of bioclimatic factors in shaping its distribution. Occurrence data and environmental variables were gleaned to construct the MaxEnt model, and the resulting suitable habitat maps were evaluated for accuracy. The results showed that the MaxEnt model had an excellent simulation quality (AUC = 0.962). The major environmental factors predicting the current distribution of O. microphylla were the mean diurnal range (bio2) and precipitation of the driest month (bio14). The current core potential distribution areas were concentrated in Guangxi, Fujian, Guizhou, Guangdong, and Hunan provinces in south China, demonstrating significant differences in their distribution areas. Our findings contribute to developing effective conservation and management measures for O. microphylla, addressing the critical need for reliable prediction of unfavorable impacts on the potential suitable habitats of the endangered species.
... Each tool is unique, with its own set of pros and downsides. According to Marcer et al. (2013), among other things, MaxEnt is one of the best and is most often used habitat suitability modeling tools. Several advantages of MaxEnt include the need for only species presence data, the capacity to run with a limited quantity of data, the high accuracy of prediction results, the high reproducibility, and the ability to predict the most discriminating environmental factors (Fois et al., 2018). ...
... Such uncertainties can arise from biological assumptions (i.e., population dynamics, migration and dispersal, biotic interactions) (Guisan and Thuiller 2005) as well as model building criteria (i.e., predictor selection, parameterization, model evaluation) (Araújo and Guisan 2006). As these and other challenges are improved upon and addressed, the utility of niche based SDMs has continued to provide beneficial applications to the field of ecological conservation (Marcer et al. 2013;Porfirio et al. 2014;Chucholl 2017). ...
The spread of ecosystem modifying invasive plant (EMIP) species is one of the largest threats to native ecosystems in Hawaiʻi. However, differences in niche characteristics between Hawaiʻi’s isolated insular environment and the wider global distribution of these species have not been carefully examined. We used species distribution modeling (SDM) methods to assess similarities and differences in niche characteristics between global and regional scales for 17 EMIPs present in Hawaiʻi. With a clearer understanding of the global context of regional plant invasion, we combined two SDM methods to better understand the potential future regional spread: (1) a nested modeling approach to integrate global and regional invasive species distribution projections; and (2) integrating all available agency and citizen science data to minimize the effect of monitoring gaps and biases. Our results show there are multiple similarities in niche characteristics across regional and global scales for most species, such as similar sets of climatic determinants of distribution, similar responses along environmental gradients, and moderate to high niche overlap between global and regional models. However, some differences were apparent and likely due to several factors including incomplete regional spread, community assembly or diversity effects. Invaders that established earlier showed a higher degree of niche overlap and similar environmental gradient responses when comparing global and regional models. This pattern, coupled with the tendency for regionally-based projections to predict narrower distributions than global projections, indicates a potential for continued spread of several invasive species across the Hawaiian landscape. Our study has broader implications for understanding the distribution and spread of invasive species in other regions, as similar analyses and models, including a novel way to characterize environmental gradient response differences across regions or scales, can likely provide valuable information for conservation and management efforts.
... Developing conservation priorities in light of environmental degradation requires sound knowledge of the distribution of plant biodiversity (Kandziora et al. 2013;Wulff et al. 2013; Baral et al. 2014), the current state of populations of endemic species and the critical habitats of highly endangered species (Volis and Tojibaev 2021). The main point to pay attention to when preserving rare species in nature is to know the territory occupied by the species (Marcer et al. 2013). This type of information is of great importance in creating a map in the format of GIS (Geo-Information System) to inform the distribution of the species, assess the risk of its extinction, predict the areas of potential distribution in the future, and determine conservation measures (Rakhimova et al. 2020;Daminova et al. 2023). ...
Daminova N, Nosirov S, Akbarov F, Tojibaev K, Temirov E. 2024. Distribution and conservation of the narrow sub-endemic shrub, Lonicera paradoxa in Pamir-Alay, Central Asia. Biodiversitas 25: 439-448. Lonicera paradoxa Pojark. (Lonicera ser. Alpigenae Pojark. ex A.Byalt) is a shrub with a geographically limited range. This species is included in the national red book lists of Uzbekistan, Kyrgyzstan, and Tajikistan and is considered one of the rare species in danger of extinction in nature. The species grows in a significant elevation from 1600 to 3200 m on the Northern slopes of the Alay Range, Central Asia. In this study, we investigated the geographic distribution of L. paradoxa based on scientific literature, herbarium data collections, and field surveys. We then modeled the current and potential future geographic distribution using Maxent. We also conducted a propagation study for its conservation under laboratory and greenhouse conditions. Our fieldwork in 2021-2022 found new records of L. paradoxa in Uzbekistan and Kyrgyzstan. We calculated the Extent of Occurrence (EOO) and Area of Occupancy (AOO) of the species, which were 5.08 km 2 (VU) and 28 km 2 (EN), respectively. Therefore, based on the IUCN Red List and Criteria, we recommend the conservation status of L. paradoxa as endangered (EN) in categories B2 ab (ii, iii, iv) + D. Our modeling prediction suggested that compared to the current, suitable habitat of L. paradoxa to reduce significantly in the future with very suitable habitats are expected to expand to the south and high mountain areas. For the first time, the conservation measures of L. paradoxa were carried out at the Tashkent Botanical Garden. The conducted research made it possible to preserve L. paradoxa in ex-situ conditions. The results of this research can be helpful in determining and planning measures for the protection of L. paradoxa in the territory of the Kyrgyz and Tajikistan Republics, both now and in the future.
... The lack of similar calculations in the native habitat of T. natans is a major omission in the field of aquatic habitat conservation, but this is due to inadequate knowledge of the distribution of this species. Adequate knowledge of the species' distribution is critical to properly determining its conservation status (Marcer et al., 2013). ...
Global changes are responsible for the movement of species. While many research emphasize the spread of alien or invasive alien species, the rapid spread of rare, native species is poorly study. In our studies, we focused on Trapa natans, a species that is considered a strictly protected plant species in Europe, but is considered an alien invasive species in North America and Australia. The aim of the study was to analyze the historical and current occurrence of T. natans at the northern range of this species (Poland, Central Europe) and, based on future climate projections (wordClim), to determine its potential spread in Europe by modeling the occupation area of available habitats in Europe. We found a rapid spread of T. natans in Poland associated with increasing temperatures. Statistical analyzes showed that the mean temperature of the warmest quarter and precipitation of the driest month are the most important climatic variables determining habitat suitability for T. natans. The model for 2021-2040 showed an expansion of habitats suitable for the species to the north (Great Britain and Ireland, Scandinavia), to the east (Germany and Central Europe), to the northeast (Eastern Europe, e.g., Lithuania, Latvia), and to the south (Italy and Southern Europe). In the next two time periods (2041-2060 and 2061-2080), the models showed that the entire European area is suitable for colonization by the species, with the exception of the high mountain regions and Spain. T. natans is a representative species whose distribution and recent range changes allow us to track aquatic species feedbacks to climate change in the species' home range and is a good ecological indicator of global warming. The message for conservationists is that the status of species classified as rare needs to be urgently reviewed.
... However, obtaining reliable information on habitat suitability, especially for endangered species, can be challenging because of limited data availability at broad spatial scales (e.g., regions, countries, or continents; Imron et al., 2016;Marcer et al., 2013;Sodik et al., 2020). ...
Elephants were once widely distributed across the Indonesian island of Sumatra but now exist in small, isolated populations. Using the best data available on elephant occurrence, we aimed to (a) predict potential habitat suitability for elephants ( Elephas maximus sumatranus ) across the island of Sumatra and (b) model landscape connectivity among the extant elephant populations. We used direct sightings and indirect observations of elephant signs, as well as six remotely sensed proxies of surface ruggedness, vegetation productivity and structure, and human land use and disturbance, to model habitat suitability in a Google Earth Engine (GEE) environment. We validated the habitat suitability prediction using 10‐fold spatial block cross validation and by calculating the area under the precision‐recall curve (AUC‐PR), sensitivity, and specificity for each model iteration. We also used a geolocation dataset collected from global positioning system (GPS) collars fitted on elephants as an independent validation dataset. Models showed good predictive performance with a mean AUC‐PR of 0.73, sensitivity of 0.76, and specificity of 0.68. Greater than 83% of the independent GPS collar geolocations were located in predicted suitable habitat. We found human modification, surface ruggedness, and normalized difference vegetation index to be the most important variables for predicting suitable elephant habitat. Thirty‐two percent, or 135,646 km ² , of Sumatra's land area was predicted to be suitable habitat, with 43 patches of suitable habitat located across Sumatra. Areas with high connectivity were concentrated in the Riau and North Sumatra provinces. Though our analysis highlights the need to improve the quality of data collected on Sumatran elephants, more suitable habitat remains on Sumatra than is used by known populations. Targeted habitat conservation, especially of the suitable habitat in and around the Lamno, Balai Raja, Tesso Tenggara, Tesso Utara, Bukit Tigapuluh, Seblat, Padang Sugihan, and Bukit Barisan Selatan ranges, may improve the long‐term viability of this critically endangered species.
... SDM provides insights into species distribution-environment relationships and can be used to estimate the bioclimatic niche of a species by correlating species occurrence or abundance records with climatic data [21,22]. SDM has been widely used in ecology, biogeography, conservation biology, and wildlife management as a tool to predict the potential distributions of a species using projected scenarios based on the likelihood of the existence of a targeted species in response to various environmental factors [22][23][24][25]. SDM has also proven to be useful in predicting how species may respond to changes in climate conditions [26]. ...
Climate change is affecting freshwater ecosystems globally, particularly those in semi-arid and arid regions. The Central Anatolian Ecoregion (CAE) in Türkiye has a semi-arid climate and is home to numerous endemic fish species. We used species distribution modelling to elucidate the distribution of sixteen endemic fish species in CAE and predicted their potential distributions for 2041–2060 and 2081–2100 based on the CMIP6 climate model. Half of the species are predicted to experience a significant loss of climatically suitable areas. Anatolichthys fontinalis, Gobio gymnostethus, Gobio hettitorum, and Pseudophoxinus burduricus will face a complete loss of suitable areas by 2081–2100 under a high emissions climate scenario, whereas Cobitis bilseli, Egirdira nigra, Gobio intermedius, and Squalius anatolicus will experience a significant loss. The other eight species can potentially benefit from climate warming if all other stressors remain equal. Anthropogenic stressors, such as water abstraction for irrigation, pollution, invasive species introductions, and dam construction, are already putting endemic fish populations in CAE under extreme pressure. Climate change is expected to exacerbate these threats. Regular monitoring of freshwater ecosystems and fish fauna in the CAE and protecting the region from key anthropogenic stressors are recommended to successfully conserve these endemic freshwater fishes under climate change.
... Thus, we can e ectively target conservation strategies. However, occurrence data tend to be very sparse for the vast majority of species, especially the rare ones (Alaoui et al., 2021a,b;Marcer et al., 2013). Model simulations could help depict potentially suitable areas and evaluate the risk of extinction possibilities based on a variety of climate scenarios . ...
The increasing temperatures and decreasing rainfall are expected to have negative effects on ecosystem services causing significant shrinkage or shift in forest distributions particularly in the Mediterranean basin. In this study, we aimed to determine the distribution of Atlas cedar (Cedrus atlantica Manetti), modeling the current and potential future distributions in Morocco with Maximum Entropy (MaxEnt) approach. Modeling was performed using all bioclimatic variables that show a significant relationship to the current distribution of Atlas cedar and that were specifically preferred in the literature by several similar studies. Prediction of warmer future scenarios showed that populations in the potential area would decrease by 21% for RCP 4.5 (2050), by 23% for RCP 4.5 (2070), by 35% for RCP 8.5 (2050), and 41% for RCP 8.5 (2070) and that there would be an impact in all ranges including the Cedar Biosphere Reserve in Morocco. Similarly, the Atlas cedar would lose its isolated-marginal populations in its southern and western extents. The results underline the importance of a genetic conservation program for cedar populations in Morocco. Otherwise, gene pools seem to turn extinct due to climate change. Furthermore, this study is intended to provide a starting point for continuous monitoring of Atlas cedars distributions while observing its climatic migration. Species distribution modeling generates valuable information for conservation management strategies for this endemic, rare, and threatened relict tree species. The results can be used to identify high-priority areas for Atlas cedar restoration and conservation against the expected impact of climate change.
... Species Distribution Models (SDMs) are widely used to predict the geographic range of a species, given data on the presence and environmental variables that affect its distribution (Wilson et al., 2011). The MaxEnt method has proved to be the most effective of the many algorithms for modelling species distribution and for predicting range dynamics under the global climate change (Marcer et al., 2013). The maximum entropy model (MaxEnt) was used in this study (www.cs.princeton.edu/wschapire/MaxEnt), because it has been shown to perform better for modelling the spatial distribution of species in the present and for predicting future changes under the influence of the global climate change (Chen et al., 2022). ...
Curly mallow (Malva verticillata L.) is a promising species for cultivation to obtain valuable compounds for the application in medicine, and this species can be used in the bioenergy system to provide industry with alternative energy sources. For the highest economic efficiency, the practical use of this species requires the development of complex measures related to both agrotechnologies and selective breeding. Such measures require resources and there is an urgent problem of assessing the prospects of such investments taking into account the global climate change. Therefore, the problem that we aimed to solve was the assessment of how the global climate change would impact the curly mallow in general in the global context, as well as in the conditions of Ukraine in the next 50–70 years. The database of the Global Biodiversity Information Facility (GBIF) contains 2,104 records of curly-leaved mallow. This species is found on all the continents except Antarctica. Asia accounts for 39.1% of the species’ range, Europe – 53.3%, Africa – 3.6%, North America – 3.2%, South America – 0.1%, Australia – 0.8%. The modelling of M. verticillata response to the climatic factors showed that the best response models were V (in 31.6% of cases) and VII (in 36.8% of cases). Model V characterizes unimodal bell-shaped asymmetric response, and model VII – bimodal asymmetric response. The species response to the mean annual temperature is asymmetric bell-shaped with a shift to the right. The optimal average annual temperature for this species is 9.1 °C. Comparing the distribution of available resources and their use is the basis for identifying the features of the ecological niche of the species. The MaxEnt approach indicates that Southeast Asia and Europe have the most favourable conditions for the existence of this species. Changes in the climatic conditions over the next 50–70 years will make the conditions for the life of M. verticillata in the southern hemisphere unfavourable, and the favourable conditions for it in the northern hemisphere will shift significantly to the north. At the same time, conditions in the autochthonous range of the species will become unfavourable. Obviously, if not for the significant potential of the species to disperse, it would have died out as a result of the significant climate change. The area where favourable conditions for the species will remain unchanged is Central Europe. Conditions in Eastern Europe, including Ukraine, will moderately improve. The results indicate the perspective of the cultivation of curly mallow in Ukraine in the future.
... Recently, one of the proposed solutions to solve problems related to EOO and AOO estimates for threatened species has been the application of species distribution modelling (see: Jiménez-Alfaro et al. 2012;Marcer et al. 2013;Pena et al. 2014;Syfert et al. 2014;Silva et al. 2020), an approach that may provide more accurate predictive maps and still has the advantage of being low-costwhen compared to genetic and or demographic studies (Araújo et al. 2002;Guisan and Thuiller 2005;Cayuela et al. 2009). Thus, a promising approach is the use of species distribution modeling combined with the EOO polygons. ...
Primary data, such as geographical records of species, are base-line for conservation status assessments. For many years, data on Brazil’s threatened butterflies (58 taxa (based on Brazilian Red List Fauna of 2014)) have been in need of improvement and the present paper provides a careful curation of their geographical distribution data as well as an update of extent of occurrence (EOO) and area of occupancy (AOO). EOO and AOO were estimated using two scenarios, one named “previous estimate” using all only published geographical records, and other named “current estimate” using both, published records (from literature) plus all newly obtained “unpublished records” (not published in literature). In total, ~ 6,700 records were compiled from several sources; 1,053 records are non duplicated geographical data. Of these 1,053 records, 566 (69%) come from surveyed literature (published records), 258 (31%) are unpublished records, and 229 (22%) were found to contain errors after data curation.
Comparing “previous” to “current” estimates of both, EOO and AOO, changes in geographical range were reported for 48 taxa (83%). Based on current data (applying the thresholds of IUCN criterion B (geographic range data)), there is a potential for changes in conservation status categories for 51 taxa (88%). Importantly, approximately half of unpublished records are from scientific collections and the remainder were provided by citizen scientists (via personal communication), showing the importance of both data sources. The present updates of geographical records based on new records and curated data (and consequently, EOO and AOO) of threatened Brazilian butterflies may aid future conservation status assessments and also reinforce the importance of data curation.
... Each tool is unique with particular advantages and disadvantages. Marcer et al. (2013) stated that MaxEnt can be considered one of the best and extensively used habitat suitability modeling tools among others. Several advantages of MaxEnt include the requirement of only presence data of species, the ability to run with a small amount of data, the high accuracy of prediction results, the high reproducibility, as well as the ability to predict the most discriminant environmental factors (Fois et al. 2018). ...
Gunawan, Sulistijorini, Chikmawati T, Sobir. 2021. Predicting suitable areas for Baccaurea angulata in Kalimantan, Indonesia Using MaxEnt Modelling. Biodiversitas 22: 2646-2653. Baccaurea angulata Merr. or ‘Belimbing Dayak’ is an underutilized fruit indigenous in Kalimantan. This species potentially used as edible fruit and medicinal plant. Unfortunately, the forest conversion to oil palm and rubber plantations causes decreasing the habitat of B. angulata. However, little is known about the occurrences and suitable habitat of B. angulata in Kalimantan. This investigation is might be the first study report on predicting the distribution of B. angulata in Kalimantan using MaxEnt (Maximum Entropy). The results show that four variables namely solar radiation in October, altitude, precipitation of warmest quarter, and gloslope are significant factors determining B.angulata’s suitable habitat. The location of suitable habitat for B. angulata is accordant with the real present distribution. The extent of potentially suitable area was significantly larger than the present occurrence of B. angulata in Kalimantan. The highest suitable areas identified in this study covered West Kalimantan and South Kalimantan. They included parts of SB (Sambas), LD (Landak), SG (Sanggau), SK (Sekadau) and BK (Bengkayang) of West Kalimantan Provinces, and TL (Tanah Laut), BN (Banjar) of South Kalimantan Provinces. The MaxEnt model performed better than random method with an Area Under Curve (AUC) value of 0.937 and it was statistically significant. It indicated that MaxEnt model was highly accurate and informative for habitat suitability of B. angulata. The predicted model of suitable areas can be used for management, monitoring, cultivation and future conservation of B. angulata.
... The IUCN guidelines suggest a power law method for downscaling coarse-grain AOO estimates to finer grains (IUCN 2019), although more sophisticated methods are now available (Groom et al. 2018). Alternatively, multiple variations on utilising SDMs have been proposed to predict which unsampled cells are likely to be occupied (Harris and Pimm 2008;Jetz et al. 2008;Boitani et al. 2008;Marcer et al. 2013;Ocampo-Peñuela et al. 2016;Breiner et al. 2017). ...
Geographic range size is the most commonly implemented criterion of species’ extinction risk used in IUCN Red List assessments, especially for poorly-recorded species. IUCN applies two contrasting range size measures to capture different facets of a species’ distribution: Extent of Occurrence (EOO; Criterion B1) is the area bounding all known occurrences and is a proxy for the spatial autocorrelation of risk, while the Area of Occupancy (AOO; Criterion B2) is the area occupied within this boundary and is related to population size at finer grains. Various methods have been proposed to measure both EOO and AOO. We evaluate the impact of applying four methods for each of Criterion B1 and of B2, as well as key parameter choices, on the Red List status of 227 poorly-recorded neotropical pteridophyte species. Between 2 and 100% of species would be considered threatened depending on methodology. The minimum convex polygon method of estimating EOO was relatively robust to sampling effort for all but the least-recorded species. The IUCN-recommended method for estimating AOO of summing occupied 2 × 2 km grid cells was very strongly correlated with the total number of records. It is likely that only a small fraction of species can be adequately assessed using this method, and we recommend caution applying the method to poorly-recorded species in particular, where models predicting occupancy in unsampled areas (e.g. species distribution models) may provide more accurate assessments. It is vital that methodological information is retained with assessments, and comparisons should only be made between assessments utilising equivalent methods.
... Protecting this species' habitat throughout the country will result in the preservation of a significant portion of the Philippines' remaining tropical dipterocarp forests (Tabaranza 1997). It is imperative that these forests over limestone in Samar must be managed and protected in a sustainable manner to sustain the population of the critically endangered Philippine Eagle (Marcer et al. 2013;Syfert et al. 2014;McClure et al. 2018;Buechley et al. 2019). Likewise, the other 3 forests over limestone (Table 4) need to have extra protection as they host keystone endemics which are on various threatened statuses per IUCN (Bellard et al. 2016;Blackburn et al. 2019). ...
Villanueva ELC, Obeña RDR, Origenes MG, Buot IE Jr. 2022. Vegetation structure of forests over limestone and its influencing environmental factors in Samar Island Natural Park, Philippines. Biodiversitas 23: 6492-6512. Samar Island National Park (SINP) forests over limestone, a nominee to the UNESCO World Natural Heritage List, needs a comprehensive study of its vegetation structure as the baseline for monitoring and effective conservation. Thus, an inventory was conducted in SINP inland forests over limestone with the following objectives: 1) to discuss the woody vegetation structure in the forests of SINP, and 2), to determine the environmental factors affecting the characteristic physiognomy and ecology of Paranas and Taft forests. Standard vegetation techniques in a total of eighteen 20m x 20m plots, classification and ordination analyses were conducted. A total of 3,578 tree individuals in 72 species, under 46 genera belonging to 35 families, were recorded from Paranas and Taft. Cluster analysis revealed 3 vegetation clusters named after the dominant species: 1) Shorea-Manilkara-Walaceodendron Cluster, 2) Shorea and Dracaena Cluster, and 3) Shorea-Manilkara-Hancea Cluster. Canonical correspondence analysis identified temperature as the most important environmental variable influencing the vegetation of SINP. Other environmental variables, despite their high rate of change and little effect on other species, may have multiple effects and indirect influences on other factors influencing the structure of vegetation in SINP. Thus, the development of appropriate conservation strategies is a must.
... Rare species are more likely to become extinct due to smaller geographical ranges, low abundances, and greater sensitivity to environmental changes (Pimm et al. 1995;Lavergne et al. 2005;Broennimann et al. 2005;Lomba et al. 2010). Incomplete information about their distribution, which has long been collected and with limited spatial accuracy, makes the assessment of these species particularly challenging (Engler et al. 2004;Lomba et al. 2010;Gogol-Prokurat 2011;Marcer et al. 2013). Consequently, following the guidelines of the International Union for Conservation of Nature (IUCN), estimating the scope of species distribution is at the heart of most evaluation projects (IUCN 2001). ...
The Earth is rich in biodiversity, rich in valuable flora and fauna. Even so, many destructions are happening through time. Plants, for example, despite their valuable services to mankind, are being ruthlessly destroyed due to development projects and increased dependence. Although many species are threatened by anthropogenic pressure, many are threatened by invasive alien species and climate changes. Thus, many plants are threatened with extinction and are included in the RET group by IUCN. Before taking scientific measures to ensure their conservation and cultivation, it is essential to study their natural distribution and their demographic status. There are a number of strategies adopted by the government and various organizations to protect them. The ex situ and in situ formulas are mostly applicable for RET plant conservation. But there are so many limitations to each of them, and the strategies are specific to plants including in the threatened category. Botanical gardens and seed banks have a major role in this conservation.
Keywords: RET plants- Conservation strategies- Ex situ -In situ-Challenges-GSPC-BRAHMS
... In particular, Maximum Entropy (MaxEnt) has been proved to be more effective in niche modelling compared to models such as GARP, DOMAIN and ENFA [11]. MaxEnt is also suitable for modelling species that have small numbers of distribution records [29,38,14,19,32,5,25,12] and it has been used in modelling cycads in some studies [23,3]. Analyses were conducted using R version 3.0.3 ...
In recent decades climate change has emerged as one of the major forces driving biodiversity loss and species extinction. Cycads are highly threatened species and previous studies have revealed they are also being affected by climate change. In this study we tested the possible response to climate change of four rare cycad taxa (Encephalartos species) endemic to South Africa, displaying a low but reasonable number of natural occurrences. Maximum Entropy model (MaxEnt) was used in carrying out the predictions based on eight environmental variables. Our results revealed no range contraction but a slight spread in the distribution of these taxa. Temperature seasonality, vegetation types and landforms are by far the most important predictors of the species modelled. On the contrary, the mean annual temperature and precipitations showed very low contributions in all models. We conclude that climate change may not determine a reduction in range size of the Encephalartos species studied. Possible decline in South African cycads may still occur through anthropogenic influences.
... These studies consistently demonstrate that species-specific conservation management initiatives require fine-tuned information for the predicted spatial distribution of threatened species. Indeed, this allows the optimization of the allocation of highly limited financial and human resources, which can consider present and future predictions of the presence or absence ranges calculated for the key management areas [21,52]. Our study indicates a significant reduction in the core area of the range of the Great Bustard projected for near-future time periods, which is of key conservation importance; this result suggests that species-specific conservation management initiatives targeting the protection of the Great Bustard should focus on the central part of the Great Plain, which affects the design of conservation area classifications and the spatial distribution of bustard-compatible agricultural planning initiatives. ...
(1) The intercorrelated effects of climatic processes and anthropogenic land use changes have been shown to govern the population declines in several bird species, which have led to global extinctions. Ground-nesting birds are especially sensitive to modifications in spatial as well as temporal patterns of climatic change. The Great Bustard (Otis tarda) is one of the most endangered species, which has suffered considerable range contractions and population declines in extensive areas of its historical distribution. (2) Here, we aim to (i) identify the key climatic predictors governing the historical distribution of the Great Bustard within the Carpathian Basin during the past three decades, (ii) provide spatial predictions for the historical range of the study species, and (iii) identify areas where species-specific conservation planning initiatives need to focus on by predicting the distribution of the Great Bustard for future time periods. To do so, here we apply bioclimatic niche modeling implemented in the MaxEnt software package, which is fitted on historical occurrence locations as a function of potential bioclimatic predictors. (3) We show that (i) the most important bioclimatic predictors governing the distribution of the Great Bustard are the annual mean temperature, mean temperatures of the wettest and driest quarters, as well as the annual precipitation; (ii) all lowland areas of the Carpathian Basin were suitable for the Great Bustard during historical time periods; (iii) the SDM predictions show the historical suitability of the Muntenia and Dobrodgea regions and the Upper Thracian Plain; and (iv) the future projections show a substantial decrease in the core distribution area, whereas the boundary areas are expected to remain stable. In summary, our study emphasizes that the distribution modeling of endangered taxa using historical records can strongly support species-specific conservation planning initiatives.
... SDMs can be helpful in deducing the potential habitat distribution and the bioclimatic factors (Raxworthy et al. 2003, Anderson and Martinez-Meyer 2004, Franklin and Miller 2009, Thorn et al. 2009, Peterson et al. 2011, Wilson et al. 2011 shaping such distribution and habitat suitability, especially for rare and cryptic species (Santos et al. 2006) like T. verrucosus. Among many SDM algorithms, MaxEnt (Maximum Entropy, Phillips 2004) is mostly relied upon, especially in the cases of rare organisms as it relies on presence-only data collection method of species occurrence , Elith et al. 2006, Pearson et al. 2007, Wisz et al. 2008, Rebelo and Jones 2010, Elith et al. 2011, Sardà-Palomera et al. 2012, Garcia et al. 2013, Marcer et al. 2013. Thus the practice of MaxEnt modeling since the past few decades seems quite obvious to investigate the shifts in species range, calculate possible threats under present and future Climates and fluctuation in the species diversity (Franklin 2009, Peterson et al. 2002, Duckett et al. 2013, Gallagher et al. 2013. ...
This account presents information on all aspects of the biology of Trapa natans L. (water caltrop and water chestnut) that are relevant to understanding its ecological characteristics and behaviour. The main topics are presented within the standard framework of the International Biological Flora: distribution, habitat, communities, responses to biotic factors, responses to environment, structure and physiology, phenology, floral and seed characters, herbivores and disease, history, conservation and global heterogeneity.
The water caltrop is an annual herbaceous hydrophyte rooted in the sediment of water bodies, forming flexuous underwater stems that create a buoyant, light‐capturing leaf rosette at the water surface. The submerged stem nodes additionally bear linear leaves. These are replaced by photosynthetically active, pinnately branched structures and unbranched adventitious roots early on, which complement previously established roots on the hypocotyl, altogether facilitating anchorage, nutrient and water absorption, aeration and capture of subsurface irradiance. Solitary flowers pollinated primarily through autogamy and incidentally through entomophily give rise to a fully developed edible single‐seeded drupe with two to four barbed horns. Fruits are dispersed with the help of hydrochory, epizoochory and anthropochory.
Throughout its lowland, global temperate, subtropical and tropical distribution in Eurasia and Africa, the thermophilic macrophyte is found in shallow, sun‐exposed, nutrient‐rich freshwater bodies with low‐velocity flows and steady water levels. These offer slightly acidic to mildly basic conditions. The accompanying soft substrate is usually characterized by a high organic matter content. Regularly co‐occurring with other macrophytes, some of which are also of conservation concern, such as those in rare stands of the association Trapetum natantis in Europe, the water caltrop has at times been outcompeted, though it may form monodominant stands, due to several competitive features.
Formerly widespread in Europe, T. natans is today recognized as a rare, strictly protected macrophyte. It has been introduced to Australia and North America; on the latter continent, its naturalization, spread and aggressive overgrowth have led to extensive control efforts. Having been used as a crop since Neolithic times, it is still exploited in Asia for means of food production, phytoremediation, ornamental purposes, medication and alternative uses.
La provincia de Entre Ríos se caracteriza por su gran riqueza de aves, cuya protección e inventarios presentan grandes sesgos de representación geográfica y composición de especies. Actualmente, la avifauna de la provincia es poco conocida y sus áreas protegidas han sido establecidas con escasa planificación. Lo que generó profundos sesgos en la representatividad de la biodiversidadprovincial. Se elaboró un inventario exhaustivo de las aves de Entre Ríos con 80.000 registros. Luego se clasificaron las especies bajo algún grado de amenaza y aquellas consideradas raras por su escasez de registros. Se fraccionó la provincia en 39 celdas de 0,5° lat-long para analizar la riqueza de las aves raras y amenazadas y encontrar las áreas prioritarias para la conservación mediante complementariedad. Se comparó el sistema de Áreas Protegidas (AP) con las áreas complementarias para detectar vacíos de conservación. Entre Ríos posee 394 especies, requiriendo diez celdas (26 %) para poder contener y proteger todas las especies raras o aves amenazadas de la provincia. Las áreas prioritarias mayormente incluyeron celdas sobre los ríos Paraná y Uruguay. Estas áreas prioritarias se superponen deficientemente con las APs existentes. Se recomienda la creación de nuevas AP o cambiar las categorías de protección de algunas AP ubicadas sobre las áreas prioritarias.
Weed species have the potential to alter the structure and functions of the ecosystem and besides their antagonistic ecological relationships with main crops, simultaneously they are also valued for their secondary metabolites of pharmaceutical and nutraceutical values. Climate and community-associated changes may alter the presence of such species as well as the concentration and quality of their active chemical constituents. In the present study, we carried out a comparative study to assess the proportional performance of different algorithms (both regression and machine learning based) for the assessment of habitat suitability of Tribulus terrestris within Indian arid and semi-arid areas. Furthermore, the impact of niche modeling on the Extent of Occurrence (EOO) and Area of Occupancy (AOO) of this species with three bioclimatic timeframe projections was also quantified. We hypothesized that these objectives will enable us to identify the major bioclimatic and community predictors that determine the habitat suitability of T. terrestris and also give projected area cover with this species under different suitability classes. For the above objectives, we implemented the ensemble techniques in which different algorithms (General linear model; GLM), (Generalized additive model; GAM), (Classification tree analysis; CTA), (Artificial neural network; ANN), (Support vector machine; SVM), (Multivariate adaptive spline; MARS), (Random forest; RF), and (Maximum entropy; MAXENT) were utilized and their prediction performance was assessed by using Kappa statistic, Area Under the receiver operating characteristic Curve (AUC), sensitivity, specificity, and True Skill Statistic (TSS). Niche overlap was carried out to visualize the amount of area retained by this species under different predictions. Comparative evaluation of different approaches revealed the best performance of random forest among all other algorithms that produced excellent model qualities for all three studied bioclimatic variables while good model quality for Habitat Heterogeneity Indices (HHI). Our results also revealed that HHI are less dynamic for species distribution modeling (SDM) of this species as compared to bioclimatic variables. Precipitation of Coldest Quarter (BC-19), Precipitation Seasonality (BC-15), and Annual Precipitation (BC-12) were the most significant variables that affect the SDM of this species. With current climatic conditions, we observed that optimum areas are located in the northern region of the arid and semi-arid areas of India covering 92,400 km2 areas. While during 2050 projection area under this class increases up to 100,800 km2 which suggests a 9.09% increase. While during 2070, this class covers 91,900 km2 which showed −8.83% area decreases with respect to the previously projected timeframe and only 0.54% decrease compared to the current BC. With HHI variables, we found the disintegration of different classes in small patches as compared to bioclimatic variables. Overall, 111.25 km centroid shifting will be anticipated from the current to 2070-time era. In this analysis, we also find a significant negative pattern between EOO and AOO (R2 = 0.87). Our results can be used to enhance ecologically (regarded as weed species) as well as economic (regarded as medicinally most important species) management in order to curb this or for harvesting the higher biomass (standing state) for its important secondary metabolites.
The Sardinian grass snake, Natrix helvetica cetti, is an endangered endemic snake subspecies with a restricted and highly fragmented geographic distribution. Information on its ecology and detailed geographic distribution are scarce and may negatively impact on its conservation status. Therefore, a literature review on its taxonomy, morphology, ecology, and conservation is presented here. Moreover, field records from the authors, citizen science and the existing literature provide an updated geographic distribution highlighting its presence within 13 new and 7 historic 10 × 10 km cells. Bioclimatic niche modelling was then applied to explore patterns of habitat suitability and phenotypic variation within N. h. cetti. The geographic distribution of the species was found to be positively correlated with altitude and precipitation values, whereas temperature showed a negative correlation. Taken together, these outcomes may explain the snake’s presence, particularly in eastern Sardinia. In addition, analysis of distribution overlap with the competing viperine snake (N. maura) and the urodeles as possible overlooked trophic resources (Speleomantes spp. and Euproctus platycephalus) showed overlaps of 66% and 79%, respectively. Finally, geographical or bioclimatic correlations did not explain phenotypic variation patterns observed in this highly polymorphic taxon. Perspectives on future research to investigate N. h. cetti’s decline and support effective conservation measures are discussed.
Stream habitat is distributed discontinuously, which restricts the dispersal of animals among streams. Freshwater decapod crustaceans have poor dispersal ability and thus may suffer high extinction risk in the face of habitat destruction and other forces. Here, the ecological characteristics of two freshwater decapods, the threatened crayfish Cambaroides japonicus (de Haan, 1841) and the widespread crab Geothelphusa dehaani (White, 1847), were compared. The objective of the study was to determine which characteristics are most likely to limit the distribution of C. japonicus .
Environmental DNA sampling was conducted to detect C. japonicus and G. dehaani populations, and environmental characteristics were measured to identify suitable stream habitat. Phylogenetic divergence and genetic differentiation among populations were examined. Using species distribution modelling, the future distributions of both species under climate change were predicted.
Stream habitats harbouring C. japonicus tended to be supplied with more beech leaves and fewer cedar leaves, whereas those of G. dehaani were relatively wide, indicating that C. japonicus favours upstream areas in natural broad‐leaved forest. The results also showed greater genetic divergence among populations of C. japonicus than those of G. dehaani . Modelling indicated that most areas within the current distribution of C. japonicus were predicted to be areas with low distribution probability under a future climate scenario.
To protect C. japonicus , further loss of local populations should be prevented to allow for the maintenance of high genetic diversity among populations, which may provide the evolutionary capability of surviving under future climate conditions. To prevent further loss of C. japonicus populations, natural deciduous forests in mountainous areas need to be preserved. C. japonicus can be the indicator species within the freshwater environment in these forests. Conservation measures for C. japonicus should also be effective for the conservation of the other freshwater invertebrates in upstream areas.
Background The aim of this study is to examine the efects of four diferent bioclimatic predictors (current, 2050,
2070, and 2090 under Shared Socioeconomic Pathways SSP2-4.5) and non-bioclimatic variables (soil, habitat heterogeneity index, land use, slope, and aspect) on the habitat suitability and niche dimensions of the critically endangered
plant species Commiphora wightii in India. We also evaluate how niche modelling afects its extent of occurrence
(EOO) and area of occupancy (AOO).
Results The area under the receiver operating curve (AUC) values produced by the maximum entropy (Maxent) under various bioclimatic time frames were more than 0.94, indicating excellent model accuracy. Non-bioclimatic
characteristics, with the exception of terrain slope and aspect, decreased the accuracy of our model. Additionally,
Maxent accuracy was the lowest across all combinations of bioclimatic and non-bioclimatic variables (AUC=0.75 to
0.78). With current, 2050, and 2070 bioclimatic projections, our modelling revealed the signifcance of water availability parameters (BC-12 to BC-19, i.e. annual and seasonal precipitation as well as precipitation of wettest, driest, and
coldest months and quarters) on habitat suitability for this species. However, with 2090 projection, energy variables
such as mean temperature of wettest quarter (BC-8) and isothermality (BC-3) were identifed as governing factors.
Excessive salt, rooting conditions, land use type (grassland), characteristics of the plant community, and slope were
also noticed to have an impact on this species. Through distribution modelling of this species in both its native (western India) and exotic (North-east, Central Part of India, as well as northern and eastern Ghat) habitats, we were also
able to simulate both its fundamental niche and its realized niche. Our EOO and AOO analysis refects the possibility
of many new areas in India where this species can be planted and grown.
Conclusion According to the calculated area under the various suitability classes, we can conclude that C. wightii’s potentially suitable bioclimatic distribution under the optimum and moderate classes would increase under all
future bioclimatic scenarios (2090>2050 ≈ current), with the exception of 2070, demonstrating that there are more
suitable habitats available for C. wightii artifcial cultivation and will be available for future bioclimatic projections of
2050 and 2090. Predictive sites indicated that this species also favours various types of landforms outside rocky environments, such as sand dunes, sandy plains, young alluvial plains, saline areas, and so on. Our research also revealed
crucial information regarding the community dispersion variable, notably the coefcient of variation that, when bioclimatic+non-bioclimatic variables were coupled, disguised the efects of bioclimatic factors across all time frames
Tecomella undulata is a valuable tree that is threatened owing to unlawful harvesting and habitat fragmentation. The current study looked at this species’ ecological niches in hot, arid locations around the world. Ensemble modeling was used in this study to assess the species’ global distribution based on current and future bio-climatic (2050 and 2070) and four green house (RCPs 2.6, 4.5, 6.0, and 8.5) scenarios, as well as soil attributes. Our findings suggest that bioclimatic factors, rather than soil, are the primary constraint on this species’ spread. Isothermality and precipitation seasonality influenced the spread of this species. In 2050 and 2070, the largest region covered by the optimal and moderate classes dropped from RCP 2.6 to RCP 8.5. When current climatic circumstances are taken into account, optimal habitat suitability falls from − 13.09% in 2050 RCP2.6 to − 50.1% in 2050 and 2070 RCP8.5. Habitat loss in 2050 was greater than in RCP4.5 and 6.0 for 2070. When analyzing RCP combinations for this species, we came upon an unusual circumstance. Combining RCP6.0 and RCP8.5 with 2050 yielded the best results, whereas combining RCP 4.5 and 6.0 produced the worst. The findings may be useful to government and non-profit forest management organizations
Human activities and climate change are the main factors causing habitat loss, jeopardising the survival of many species, especially those with limited range, such as endemic species. Recently, species distribution models (SDMs) have been used in conservation biology to assess their extinction risk, environmental dynamics, and potential distribution. This study analyses the potential, current and future distribution range of Salvia ceratophylloides Ard., an endemic perennial species of the Lamiaceae family that occurs exclusively in a limited suburban area of the city of Reggio Calabria (southern Italy). The MaxEnt model was employed to configure the current potential range of the species using bioclimatic and edaphic variables, and to predict the potential suitability of the habitat in relation to two future scenarios (SSP245 and SSP585) for the periods 2021-2040 and 2041-2060. The field survey, which spanned 5 years (2017-2021), involved 17 occurrence points. According to the results of the MaxEnt model, the current potential distribution is 237.321 km 2 , which considering the preferred substrates of the species and land-use constraints is re-estimated to 41.392 km 2. The model obtained from the SSP245 future scenario shows a decrease in the area suitable for the species of 35% in the 2021-2040 period and 28% in the 2041-2060 period. The SSP585 scenario shows an increase in the range suitable for hosting the species of 167% in the 2021-2040 period and 171% in the 2041-2060 period. Assessing variation in the species distribution related to the impacts of climate change makes it possible to define priority areas for reintroduction and in situ conservation. Identifying areas presumably at risk or, on the contrary, suitable for hosting the species is of paramount importance for management and conservation plans for Salvia ceratophylloides.
During the last twenty years there has been recognition that a landscape scale approach is fundamental to the understanding of ecological processes. The landscape scale is considered to be the appropriate spatial framework for the analysis of sustainability. As a result landscape approaches have been adopted by international and national organisations to summarise pressures and threats and develop policies for sustainability. This shift towards these approaches has prompted the development of landscape typologies at the regional, national and European scales. Despite the widespread development and application of landscape typologies in a number of European countries, there has been no specific attempt to develop a methodology specifically for the Mediterranean region. Mediterranean coastal landscapes are ecologically and culturally diverse, characterised by a wider range of natural environments and historical influences. As a result, many of these landscapes are extremely sensitive and vulnerable to a range of pressures, especially the infrastructure development associated with modern tourism. The result is that the long-term sustainability of many coastal Mediterranean areas cannot be assured, requiring the development of techniques and policies that can provide the framework for conservation and sustainability efforts in these landscapes. The aim of the project is to establish a typology of Mediterranean coastal landscapes based upon the available spatial environmental data. It is intended that the resulting typology will provide the context for the derivation of sustainability indicators (SIs) in coastal landscapes. This report presents the overall approach, including a description of the available data sets and techniques to develop a typology of Mediterranean coastal landscapes. The results demonstrate that spatial environmental data can be used to group Mediterranean coastal landscapes into discrete, landscape types based on both natural and cultural attributes. The report evaluates the proposed methodology, assessing its value for providing the spatial context within which to derive sustainability indicators. The project was funded by the Priority Actions Programme/Regional Activity Centre UNEP.
A global strategy to conserve biodiversity must aim to protect representative examples of all of the world's ecosystems, as well as those areas that contain exceptional concentrations of species and endemics. Although lacking the richness of tropical forests, deserts, tropical lakes, and subpolar seas all contain distinct species, communities, and ecological phenomena. We analyzed global patterns of biodiversity to identify a set of the Earth's terrestrial, freshwater, and marine ecoregions that harbor exceptional biodiversity and are representative of its ecosystems. We placed each of the Earth's ecoregions within a system of 30 biomes and biogeographic realms to facilitate a representation analysis. Biodiversity features were compared among ecoregions to assess their irreplaceability or distinctiveness. These features included species richness, endemic species, unusual higher taxa, unusual ecological or evolutionary phenomena, and the global rarity of habitats. This process yielded 238 ecoregions—the Global 200—comprised of 142 terrestrial, 53 freshwater, and 43 marine priority ecoregions. Effective conservation in this set of ecoregions would help conserve the most outstanding and representative habitats for biodiversity on this planet.
Although spatial scale is important for understanding ecological processes and guiding conservation planning, studies combining a range of scales are rare. Habitat suitability modelling has been used traditionally to study broad-scale patterns of species distribution but can also be applied to address conservation needs at finer scales. We studied the ability of presence-only species distribution modelling to predict patterns of habitat selection at broad and fine spatial scales for one of the rarest mammals in the UK, the grey long-eared bat (Plecotus austriacus). Models were constructed with Maxent using broad-scale distribution data from across the UK (excluding Northern Ireland) and fine-scale radio-tracking data from bats at one colony. Fine-scale model predictions were evaluated with radio-tracking locations from bats from a distant colony, and compared with results of traditional radio-tracking data analysis methods (compositional analysis of habitat selection). Broad-scale models indicated that winter temperature, summer precipitation and land cover were the most important variables limiting the distribution of the grey long-eared bat in the UK. Fine-scale models predicted that proximity to unimproved grasslands and distance to suburban areas determine foraging habitat suitability around maternity colonies, while compositional analysis also identified unimproved grasslands as the most preferred foraging habitat type. This strong association with unimproved lowland grasslands highlights the potential importance of changes in agricultural practices in the past century for wildlife conservation. Hence, multi-scale models offer an important tool for identifying conservation requirements at the fine landscape level that can guide national-level conservation management practices.
Field monitoring can vary from simple volunteer opportunistic observations to professional standardised monitoring surveys, leading to a trade-off between data quality and data collection costs. Such variability in data quality may result in biased predictions obtained from species distribution models (SDMs). We aimed to identify the limitations of different monitoring data sources for developing species distribution maps and to evaluate their potential for spatial data integration in a conservation context. Using Maxent, SDMs were generated from three different bird data sources in Catalonia, which differ in the degree of standardisation and available sample size. In addition, an alternative approach for modelling species distributions was applied, which combined the three data sources at a large spatial scale, but then downscaling to the required resolution. Finally, SDM predictions were used to identify species richness and high quality areas (hotspots) from different treatments. Models were evaluated by using high quality Atlas information. We show that both sample size and survey methodology used to collect the data are important in delivering robust information on species distributions. Models based on standardized monitoring provided higher accuracy with a lower sample size, especially when modelling common species. Accuracy of models from opportunistic observations substantially increased when modelling uncommon species, giving similar accuracy to a more standardized survey. Although downscaling data through a SDM approach appears to be a useful tool in cases of data shortage or low data quality and heterogeneity, it will tend to overestimate species distributions. In order to identify distributions of species, data with different quality may be appropriate. However, to identify biodiversity hotspots high quality information is needed.
Transforming the results of species distribution modelling from probabilities of or suitabilities for species occurrence to presences/absences needs a specific threshold. Even though there are many approaches to determining thresholds, there is no comparative study. In this paper, twelve approaches were compared using two species in Europe and artificial neural networks, and the modelling results were assessed using four indices: sensitivity, specificity, overall prediction success and Cohen's kappa statistic. The results show that prevalence approach, average predicted probability/suitability approach, and three sensitivity-specificity-combined approaches, including sensitivity-specificity sum maximization approach, sensitivity-specificity equality approach and the approach based on the shortest distance to the top-left corner (0,1) in ROC plot, are the good ones. The commonly used kappa maximization approach is not as good as the afore-mentioned ones, and the fixed threshold approach is the worst one. We also recommend using datasets with prevalence of 50% to build models if possible since most optimization criteria might be satisfied or nearly satisfied at the same time, and therefore it's easier to find optimal thresholds in this situation.
Aim Using predictive species distribution and ecological niche modelling our objectives are: (1) to identify important climatic drivers of distribution at regional scales of a locally complex and dynamic system – California sage scrub; (2) to map suitable sage scrub habitat in California; and (3) to distinguish between bioclimatic niches of floristic groups within sage scrub to assess the conservation significance of analysing such species groups.
Location Coastal mediterranean-type shrublands of southern and central California.
Methods Using point localities from georeferenced herbarium records, we modelled the potential distribution and bioclimatic envelopes of 14 characteristic sage scrub species and three floristic groups (south-coastal, coastal–interior disjunct and broadly distributed species) based upon current climate conditions. Maxent was used to map climatically suitable habitat, while principal components analysis followed by canonical discriminant analysis were used to distinguish between floristic groups and visualize species and group distributions in multivariate ecological space.
Results Geographical distribution patterns of individual species were mirrored in the habitat suitability maps of floristic groups, notably the disjunct distribution of the coastal–interior species. Overlap in the distributions of floristic groups was evident in both geographical and multivariate niche space; however, discriminant analysis confirmed the separability of floristic groups based on bioclimatic variables. Higher performance of floristic group models compared with sage scrub as a whole suggests that groups have differing climate requirements for habitat suitability at regional scales and that breaking sage scrub into floristic groups improves the discrimination between climatically suitable and unsuitable habitat.
Main conclusions The finding that presence-only data and climatic variables can produce useful information on habitat suitability of California sage scrub species and floristic groups at a regional scale has important implications for ongoing efforts of habitat restoration for sage scrub. In addition, modelling at a group level provides important information about the differences in climatic niches within California sage scrub. Finally, the high performance of our floristic group models highlights the potential a community-level modelling approach holds for investigating plant distribution patterns.
Human impacts on the natural environment have reached such proportions that in addition to an ‘extinction crisis’, we now also face a broader ‘biome crisis’. Here we identify the world's terrestrial biomes and, at a finer spatial scale, ecoregions in which biodiversity and ecological function are at greatest risk because of extensive habitat conversion and limited habitat protection. Habitat conversion exceeds habitat protection by a ratio of 8 : 1 in temperate grasslands and Mediterranean biomes, and 10 : 1 in more than 140 ecoregions. These regions include some of the most biologically distinctive, species rich ecosystems on Earth, as well as the last home of many threatened and endangered species. Confronting the biome crisis requires a concerted and comprehensive response aimed at protecting not only species, but the variety of landscapes, ecological interactions, and evolutionary pressures that sustain biodiversity, generate ecosystem services, and evolve new species in the future.
Aim To evaluate a suite of species distribution models for their utility as predictors of suitable habitat and as tools for new population discovery of six rare plant species that have both narrow geographical ranges and specialized habitat requirements.
Location The Rattlesnake Creek Terrane (RCT) of the Shasta‐Trinity National Forest in the northern California Coast Range of the United States.
Methods We used occurrence records from 25 years of US Forest Service botanical surveys, environmental and remotely sensed climate data to model the distributions of the target species across the RCT. The models included generalized linear models (GLM), artificial neural networks (ANN), random forests (RF) and maximum entropy (ME). From the results we generated predictive maps that were used to identify areas of high probability occurrence. We made field visits to the top‐ranked sites to search for new populations of the target species.
Results Random forests gave the best results according to area under the curve and Kappa statistics, although ME was in close agreement. While GLM and ANN also gave good results, they were less restrictive and more varied than RF and ME. Cross‐model correlations were the highest for species with the most records and declined with record numbers. Model assessment using a separate dataset confirmed that RF provided the best predictions of appropriate habitat. Use of RF output to prioritize search areas resulted in the discovery of 16 new populations of the target species.
Main conclusions Species distribution models, such as RF and ME, which use presence data and information about the background matrix where species do not occur, may be an effective tool for new population discovery of rare plant species, but there does appear to be a lower threshold in the number of occurrences required to build a good model.
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.
Aim Explaining why some invasions fail while others succeed is a prevailing question in invasion biology. Different factors have been proposed to explain the success or failure of exotics. Evidence suggests that climate similarities may be crucial. We tested this using 12 species of the genus Pinus that have been widely planted and shown to be highly invasive. Pinus is among the best-studied group of exotic species and one that has been widely introduced world-wide, so we were able to obtain data on invasive and non-invasive introductions (i.e. unsuccessful invasions; areas where after many decades of self-sowing seeds there is no invasion).
Location World-wide.
Methods We developed species distribution models for native ranges using a maximum entropy algorithm and projected them across the globe. We tested whether climate-based models were able to predict both invasive and non-invasive introductions.
Results Appropriate climatic conditions seem to be required for these long-lived species to invade because climates accurately predicted invasions. However, climate matching is necessary, but not sufficient to predict the fate of an introduction because most non-invasive introductions were predicted to have triggered an invasion.
Main conclusions Other factors, possibly including biotic components, may be the key to explaining why some introductions do not become invasions, because many areas where Pinus is not invading were predicted to be suitable for invasion based solely on climate.
Aim Data on geographical ranges are essential when defining the conservation status of a species, and in evaluating levels of human disturbance. Where locality data are deficient, presence‐only ecological niche modelling (ENM) can provide insights into a species’ potential distribution, and can aid in conservation planning. Presence‐only ENM is especially important for rare, cryptic and nocturnal species, where absence is difficult to define. Here we applied ENM to carry out an anthropogenic risk assessment and set conservation priorities for three threatened species of Asian slow loris (Primates: Nycticebus ).
Location Borneo, Java and Sumatra, Southeast Asia.
Methods Distribution models were built using maximum entropy (MaxEnt) ENM. We input 20 environmental variables comprising temperature, precipitation and altitude, along with species locality data. We clipped predicted distributions to forest cover and altitudinal data to generate remnant distributions. These were then applied to protected area (PA) and human land‐use data, using specific criteria to define low‐, medium‐ or high‐risk areas. These data were analysed to pinpoint priority study sites, suitable reintroduction zones and protected area extensions.
Results A jackknife validation method indicated highly significant models for all three species with small sample sizes ( n = 10 to 23 occurrences). The distribution models represented high habitat suitability within each species’ geographical range. High‐risk areas were most prevalent for the Javan slow loris ( Nycticebus javanicus ) on Java, with the highest proportion of low‐risk areas for the Bornean slow loris ( N. menagensis ) on Borneo. Eighteen PA extensions and 23 priority survey sites were identified across the study region.
Main conclusions Discriminating areas of high habitat suitability lays the foundations for planning field studies and conservation initiatives. This study highlights potential reintroduction zones that will minimize anthropogenic threats to animals that are released. These data reiterate the conclusion of previous research, showing MaxEnt is a viable technique for modelling species distributions with small sample sizes.
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.
European Bison (Bison bonasus) barely escaped extinction in the early 20th century and now only occur in small isolated herds scattered across Central and Eastern Europe. The species’ survival in the wild depends on identifying suitable habitat for establishing bison metapopulations via reintroductions of new herds. We assessed European Bison habitat across the Carpathian Mountains, a stronghold of European Bison and one of the only places where a viable bison metapopulation may be possible. We used maximum entropy models to analyze herd range maps and habitat use data from radio-collared bison to identify key habitat variables and map European Bison habitat across the entire Carpathian ecoregion (210,000 km2). Forest cover (primarily core and perforated forests) and variables linked to human disturbance best predict bison habitat suitability. Bison show no clear preference for particular forest types but prefer managed grasslands over fallow and abandoned fields. Several large, suitable, but currently unoccupied habitat patches exist, particularly in the eastern Carpathians. This available suitable habitat suggests that European Bison have an opportunity to establish a viable Carpathian metapopulation, especially if recent trends of declining human pressure and reforestation of abandoned farmland continue. Our results also confirm the suitability of a proposed Romanian reintroduction site. Establishing the first European Bison metapopulation would be a milestone in efforts to conserve this species in the wild and demonstrate a significant and hopeful step towards conserving large grazers and their ecological roles in human-dominated landscapes across the globe.
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.
Red List Category & Criteria: Least Concern ver 3.1
Year Published: 2008
Date Assessed: 2008-06-30
Assessor(s): Aplin, K., Molur, S. & Nameer, P.O.
Reviewer(s): Amori, G. (Small Nonvolant Mammal Red List Authority) & Cox, N. (Global Mammal Assessment Team)
The International Union for Conservation of Nature (IUCN) considers that a decrease in the area of occupancy (AOO) is a critical criterion for listing a species as rare, endemic or threatened. However, methods for the accurate measurement of changes in the area of occupancy are relatively limited, and existing methods are influenced by spatial scale and sampling methods. To overcome such cartographic problems, different cartographic methods were proposed in this study and implemented in a free and open source GIS library. A single species (Helianthemum caput-felis Boiss.), which is distributed in many countries of the western Mediterranean, was used as a case study. This plant is threatened and protected because its preferred habitat is near the sea where urban growth is high in the Mediterranean regions. Intensive field work was initially conducted to create a geodatabase with more than 13,000 GPS points. Cartographic methods were then applied to the geodatabase to obtain AOO measurements at different scales to support sustainable urban planning. Based on positive experiences with the use of the programming library, it is believed that these open source tools can be customised and extended to other similar biogeographic studies that require data analysis at different scales.
To conserve biodiversity, it is necessary to understand how species are distributed and which aspects of the environmen