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Simple Summary: Based on the "Hutchinson niche hypothesis" and the "Eltonian Noise Hypothesis" about ecological niches, as well as the "resource dependent hypothesis" about biotic interactions, this study integrated climate data, host plants, and empirical expert maps to predict potential distributions based on the Maxent and random forest (RF) models. Utilizing the species richness of host plants as a surrogate for biotic interactions was a simple and effective way to significantly improve the predictive performance of Species Distribution Models (SDMs). Given climate change, their distribution is significantly shrinking, increasing their threatened level in the future. Abiotic factors could not only directly affect the distribution of T. aureus but also indirectly impact it through host plants. This was evident in the delayed response of T. aureus to climate change compared to its host plants, which is called the "hysteresis effect" caused by biotic interactions. Considering its narrow altitude range, vulnerability to climate change, host plant specialization, and the survival status of its highly dependent host plants, we recommended designating T. aureus as currently vulnerable. The accurate prediction of the niche and the potential distribution of a species is a fundamental and key content for biodiversity related research in ecology and biogeography, especially for protected species. Biotic interactions have a significant impact on species distribution but are often overlooked by SDMs. Therefore, it is crucial to incorporate biotic interaction factors into SDMs to improve their predictive performance. The Teinopalpus aureus Mell, 1923 is endemic to high altitudes in southern East Asia, renowned for its exceptional beauty and rarity. Despite the significant conservation value, its spatial distribution remains unclear. This study integrated climate data, host plants, and empirical expert maps to predict its potential distribution. The results indicated that utilizing the species richness of host plants as a surrogate for biotic interactions was a simple and effective way to significantly improve the predictive performance of the SDMs. The current suitable distribution of T. aureus and its host plants is highly fragmented, primarily concentrated in the Nanling and Wuyi Mountains, and consisting of numerous isolated small populations. Given climate change, their distribution is significantly shrinking, increasing the threatened level in the future. Especially for the population of T. aureus hainani Lee, the likelihood of extinction is extremely high. Abiotic factors not only directly affect the distribution of T. aureus but also indirectly impact it through the host plants. This was evident in the delayed response of T. aureus to climate change compared to its host plants, which is called the "hysteresis effect" caused by biotic interactions. Overall, we tentatively suggest regarding T. aureus as a vulnerable species. In the future, multiple measures could be taken to indirectly protect the feeding and habitat resources of T. aureus by conserving host plants, thereby enhancing its survival prospects. [ABSTRACT FROM AUTHOR]

Abstract: Aim: Understanding the impacts of past and future climate change on genetic diversity and structure is a current major research gap. We ask whether past range shifts explain the observed genetic diversity of surfgrass species and if future climate change projections anticipate genetic diversity losses. Our study aims to identify regions of long‐term climate suitability with higher and unique seagrass genetic diversity and predict future impacts of climate change on them. Location: Northeast Pacific. Time Period: Analyses considered a timeframe from the Last Glacial Maximum (LGM; 20 kybp) until one Representative Concentration Pathway (RCP) scenario of future climate changes (RCP 8.5; 2100). Major Taxa Studied: Two seagrass species belonging to the genus Phyllospadix. Methods: We estimated population genetic diversity and structure using 11 polymorphic microsatellite markers. We predicted the distribution of the species for the present, LGM, and near future (RCP 8.5, no climate mitigation) using Species Distribution Models (SDMs). Results: SDMs revealed southward range shifts during the LGM and potential poleward expansions in the future. Genetic diversity of Phyllospadix torreyi decreases from north to south, but in Phyllospadix scouleri the trend is variable. Phyllospadix scouleri displays signals of genome admixture at the southernmost and northernmost edges of its distribution. Main Conclusions: The genetic patterns observed in the present reveal the influence of climate‐driven range shifts in the past and suggest further consequences of climate change in the future, with potential loss of unique gene pools. This study also shows that investigating climate links to present genetic information at multiple timescales can establish a historical context for analyses of the future evolutionary history of populations.

Since its establishment in North America around 2013, the east-Asian spider Trichonephila clavata has expanded its range to 120,000 km2. We used ‘MaxEnt’ species distribution models (SDMs) to assess the potential range and invasive process of T. clavata based on its climatic niche preferences. Results reveal T. clavata exhibits a preference for climatic niches between subtropical and temperate latitudes, with high suitability primarily forecasted in montane forests and coastal regions spanning latitudes of 30–50 degrees. Geographic projections indicate high risk of expansion beyond the current invasive range. Areas with over 50% suitability are predicted as far north as 45–50° N, reaching into southern Canada, while the southern extent approaches the Gulf Coast. Response curves show peak suitability occurs at below-freezing temperatures for the minimum temperature of the coldest month. Similar drivers of distribution between native and invasive models, and an invasive niche occurring within the native range, each suggest niche conservatism for invasive populations. Overall, the vast regions of climatically suitable habitat predicted in North America indicates a highly permissive climate, highlighting the need for further research on biotic factors and management strategies to mitigate spread and impacts.

Abstract Background Climate change significantly influences the geographical distribution of plant species worldwide, especially endemics. Endemic species are plants that live in limited distribution ranges of unique ecology and, thus, are the most vulnerable species to climate change. Therefore, understanding the impacts of climate change on the distribution of these species can assist in developing appropriate plans for their conservation. In this study, we aimed to apply various species distribution models (SDMs) to predict the current potential distributions of two endangered plant species, Silene leucophylla (S. leucophylla, endemic) and Silene schimperiana (S. schimperiana, near-endemic), in Saint Catherine protected area (St. Catherine PA), Egypt. Then, using the best-fit model to project their future distribution under the maximum climate emission scenario (Representative Concentration Pathway 8.5 (RCP8.5)). Six different SDMs were constructed using different geospatial raster imagery sets of environmental factors. For each model, five machine learning (ML) algorithms were used. The results of these ML algorithms were then ensembled by calculating the weighted average of their predictions. Results Based on the analysis of digital geospatial imageries produced by the best-fitting model, the predicted suitable areas of S. leucophylla and S. schimperiana were 23.1 km2 and 125 km2, respectively. These sites are located mainly in the high-elevation middle northern part of the study area. Annual precipitation, mean temperature of the driest quarter, altitude, and precipitation seasonality were the essential predictors of the distributions of both species. Future predictions of both species indicated opposing results between the studied species. Predictions in the 2050 and 2070 future conditions revealed significant range contraction for the distribution of S. leucophylla. For S. schimperiana, a range shift is predicted, with both range contraction and range expansion of its current suitable habitats, for the same future projections. Unfortunately, in 2080 predictions, both species could be projected to a complete loss from the entire area. Conclusion This study highlights the importance of including diverse types of environmental variables in SDMs to produce more accurate predictions, rather than relying only on one variable type. It also revealed the potential negative impacts of future climate change on the distributions of two endangered plant species, S. leucophylla and S. schimperiana, inhabiting St. Catherine PA. Consequently, we urgently recommend the initiation of different plans and strategies seeking their conservation.

Aim: Understanding the impacts of past and future climate change on genetic diversity and structure is a current major research gap. We ask whether past range shifts explain the observed genetic diversity of surfgrass species and if future climate change projections anticipate genetic diversity losses. Our study aims to identify regions of long‐term climate suitability with higher and unique seagrass genetic diversity and predict future impacts of climate change on them. Location: Northeast Pacific. Time Period: Analyses considered a timeframe from the Last Glacial Maximum (LGM; 20 kybp) until one Representative Concentration Pathway (RCP) scenario of future climate changes (RCP 8.5; 2100). Major Taxa Studied: Two seagrass species belonging to the genus Phyllospadix. Methods: We estimated population genetic diversity and structure using 11 polymorphic microsatellite markers. We predicted the distribution of the species for the present, LGM, and near future (RCP 8.5, no climate mitigation) using Species Distribution Models (SDMs). Results: SDMs revealed southward range shifts during the LGM and potential poleward expansions in the future. Genetic diversity of Phyllospadix torreyi decreases from north to south, but in Phyllospadix scouleri the trend is variable. Phyllospadix scouleri displays signals of genome admixture at the southernmost and northernmost edges of its distribution. Main Conclusions: The genetic patterns observed in the present reveal the influence of climate‐driven range shifts in the past and suggest further consequences of climate change in the future, with potential loss of unique gene pools. This study also shows that investigating climate links to present genetic information at multiple timescales can establish a historical context for analyses of the future evolutionary history of populations. [ABSTRACT FROM AUTHOR]

Background: Climate change significantly influences the geographical distribution of plant species worldwide, especially endemics. Endemic species are plants that live in limited distribution ranges of unique ecology and, thus, are the most vulnerable species to climate change. Therefore, understanding the impacts of climate change on the distribution of these species can assist in developing appropriate plans for their conservation. In this study, we aimed to apply various species distribution models (SDMs) to predict the current potential distributions of two endangered plant species, Silene leucophylla (S. leucophylla, endemic) and Silene schimperiana (S. schimperiana, near-endemic), in Saint Catherine protected area (St. Catherine PA), Egypt. Then, using the best-fit model to project their future distribution under the maximum climate emission scenario (Representative Concentration Pathway 8.5 (RCP8.5)). Six different SDMs were constructed using different geospatial raster imagery sets of environmental factors. For each model, five machine learning (ML) algorithms were used. The results of these ML algorithms were then ensembled by calculating the weighted average of their predictions. Results: Based on the analysis of digital geospatial imageries produced by the best-fitting model, the predicted suitable areas of S. leucophylla and S. schimperiana were 23.1 km2 and 125 km2, respectively. These sites are located mainly in the high-elevation middle northern part of the study area. Annual precipitation, mean temperature of the driest quarter, altitude, and precipitation seasonality were the essential predictors of the distributions of both species. Future predictions of both species indicated opposing results between the studied species. Predictions in the 2050 and 2070 future conditions revealed significant range contraction for the distribution of S. leucophylla. For S. schimperiana, a range shift is predicted, with both range contraction and range expansion of its current suitable habitats, for the same future projections. Unfortunately, in 2080 predictions, both species could be projected to a complete loss from the entire area. Conclusion: This study highlights the importance of including diverse types of environmental variables in SDMs to produce more accurate predictions, rather than relying only on one variable type. It also revealed the potential negative impacts of future climate change on the distributions of two endangered plant species, S. leucophylla and S. schimperiana, inhabiting St. Catherine PA. Consequently, we urgently recommend the initiation of different plans and strategies seeking their conservation. [ABSTRACT FROM AUTHOR]

Climate change is altering the geographical distribution and abundance of species. Abundant genetic variation generally indicates a stronger adaptability and evolutionary potentiality, especially in case of sharply changing climates or environments. With the past global climate fluctuations, especially the climate oscillation since the Quaternary, the global temperature changes related to glaciation, many relict plant species have formed possible refugia in humid subtropical/warm temperate forests, thus retaining a high level of genetic diversity patterns. Based on the contraction and expansion of the geographical distribution of Taxus wallichiana Zucc. in the past driven by climate change, combined with the contemporary genetic diversity modeling, the distribution performance of Taxus wallichiana Zucc. in future climate change was predicted. The areas of highly suitable habitat will increase with climate change in the future. There were continuous and stable high suitable areas of T. wallichiana in the southeastern Tibet and northwestern Yunnan as long-term stable climate refugia. We made the genetic landscape surface of T. wallichiana complex and discovered geographical barriers against gene flow. Genetic barriers spatially isolated the center of genetic diversity into three regions: west (east Himalaya), middle (Yunnan plateau, Sichuan basin, Shennongjia, and the junction of Guizhou and Guangxi provinces), and east (Mt. Huangshan and Fujian). Southern Tibet was isolated from other populations. The central and western Yunnan, the Sichuan basin, and surrounding mountains were isolated from the southern China populations. We found that the positive correlationships between the present species genetic diversity and suitability index during LGM, MH, and 2070. This infers that T. wallichiana has provisioned certain genetic diversity and has strong evolutionary potential under conditions of climate change. [ABSTRACT FROM AUTHOR]

Understanding how past and current environmental conditions shape the demographic and genetic distributions of organisms facilitates our predictions of how future environmental patterns may affect populations. The Canyon Rubyspot damselfly (Odonata: Zygoptera: Hetaerina vulnerata) is an insect with a range distribution from Colombia to the arid southwestern United States, where it inhabits shaded mountain streams in the arid southwestern United States. Past spatial fragmentation of habitat and limited dispersal capacity of H. vulnerata may cause population isolation and genetic differentiation, and projected climate change may exacerbate isolation by further restricting the species' distribution. We constructed species distribution models (SDMs) based on occurrences of H. vulnerata and environmental variables characterizing the species' niche. We inferred seven current potential population clusters isolated by unsuitable habitat. Paleoclimate models indicated habitat contiguity in past conditions; projected models indicated some habitat fragmentation in future scenarios. Seventy‐eight H. vulnerata individuals from six of the current clusters were sequenced via ddRADseq and processed with Stacks. Principal components and phylogeographic analyses resolved three subpopulations; Structure resolved four subpopulations. FST values were low (<0.05) for nearby populations and >0.15 for populations separated by expanses of unsuitable habitat. Isolation by distance was an existing but weak factor in determining genomic structure; isolation by environment and the intervening landscape explained a significant proportion of genetic distance. Hetaerina vulnerata populations were shown to be isolated by a lack of tree canopy coverage, an important habitat predictor for oviposition and territoriality. Thus, H. vulnerata populations are likely separated and are genetically isolated. Integrating SDMs with landscape genetics allowed us to identify populations separated by distance and unsuitable habitat, explaining population genetic patterns and probable fates for populations under future climate scenarios. [ABSTRACT FROM AUTHOR]

Aim: To evaluate the performance of species distribution models in predicting observed colonisations, persistences and extirpations in response to changes in climate and land use over a multi‐decadal period. Location: Sweden. Methods: We use historical (early 20th century) land use and climate data to build species distribution models for 84 plant species across three provinces of Sweden. Model performance was then evaluated internally using a subset of the historical data for cross‐validation, as well as by using the models to project occurrences to the modern day and validating them with observed occurrences from 1990 to 2020. We then analysed predicted and observed occurrences in the modern period in terms of persistence, extirpation (local extinction) and colonisation in relation to species' habitat and climate associations. Results: We found overall high agreement between evaluation methods, although internal evaluation gave consistently higher values for model performance (using true skill statistic, TSS). Overall, extirpations were worst predicted, with on average fewer than one‐third of each species' extirpations being foreseen by the models. Colonisations were better predicted, while persistences were relatively well‐predicted. Predictive accuracy of colonisations was higher for species with relatively warmer temperature associations (climate‐driven expansion), while extirpations were better predicted in cool‐related species (retractions at cool edges). Colonisations of forest‐associated species were more common than predicted (underpredicted), despite widespread patterns of afforestation. Assessing grid‐cell level turnover, we found that in grid cells that experienced the largest changes in terms of climate and land use, predicted extirpations were less likely to have happened. Main Conclusions: We found that commonly applied modelling approaches have limited ability to predict observed changes in species occurrences, especially extirpations. This suggests that we should take predictions of future biodiversity loss very seriously. However, the ability for species to (at least temporarily) persist in unsuitable conditions could be an opportunity for biodiversity conservation. [ABSTRACT FROM AUTHOR]

Global biodiversity is declining at rates never seen before. At the same time, resources directed at conservation planning still fall orders of magnitude short. Thus, efficient allocation of resources is needed to prioritize regions that can shelter more biodiversity. A usual approach to identify priority areas for conservation is to map species distribution and use this information as input in spatial prioritization proceedings. Since data on common species are more readily available, final maps of priority areas for conservation are heavily biased towards this group, overlooking rare species. Thus, we created a ranking of priority maps for common and rare tree species, separately, aiming to comprehend if prioritizing areas for the conservation of common trees is enough to protect rare trees all at once. Additionally, we overlapped our priority maps with maps of current protected areas (PAs) to identify if the latter already cover areas designated in our models. Our results show that common tree species can act as surrogates for rare tree species since priority maps for both groups overlap with more than 70% of their area. Yet, most of the current PAs do not cover significant areas for protecting tree species, revealing an urgent need to expand or create new PAs to properly safeguard the local biodiversity. [ABSTRACT FROM AUTHOR]

Aims: The Middle East, located in the arid belt of the Earth, is home to a diverse range of biodiversity, with its mountain ecosystems being the most important centres of species diversity and endemism. In this study, the impact of climate change on alpine bird species in the Middle East was assessed across five mountain systems: Alborz–Kopet‐Dagh, Caucasus–Pontic, Levant–Taurus, Sarawat–Hijaz and Zagros–Central Iran. Location: Middle East. Methods: Using species distribution models (SDMs), 38 native alpine bird species were analysed under different climate change scenarios. We also identified future multispecies in situ and ex situ climate refugia and assessed the efficiency of the current protected areas (PAs) system in protecting them. Results: The results indicated that, on average, habitat suitability for these species is projected to decline by 36.83% (2050, SSP2‐4.5) to 60.10% (2070, SSP5‐8.5) with an upward range shift. Based on stacking range change of the species, Levant–Taurus, Zagros–Central Iran and Alborz–Kopet–Dagh mountain ranges will experience the highest amount of habitat loss, respectively, with Caucasus–Pontic being least affected. The gap analysis showed that the existing PAs system covers only 13% and 10% of the in situ and ex situ climatic refugia, respectively. Conclusions: Our findings underscore the significance of mountainous regions in the Middle East for the persistence of alpine bird species and the urgent need to prioritize climate refugia in transboundary and participatory conservation plans. It is crucial to prevent habitat degradation and alteration resulting from human activities in these areas to ensure the persistence of alpine species and their habitats. [ABSTRACT FROM AUTHOR]

Global climate change has profound impacts on the habitats of marine organisms, and predicting the habitat changes of species under climate change conditions is crucial for species sustainability. Boleophthalmus pectinirostris is an intertidal fish species that holds significant ecological and economic value. To better protect and manage its resources, this study aimed to predict its current potential distribution and habitat changes under different climate scenarios in the future. This study firstly quantified the hypervolume niches of the three lineages (AE1, AE2, and AES lineages) and compared the niche differentiation among them. Furthermore, this study constructed species-level and lineage-level species distribution models (SDMs) to assess the impact of climate change on the habitat suitability of B. pectinirostris. The result of the niche differentiation assessment showed that there was marked differentiation in niches among the three lineages. The responses of different lineages to environmental variables were different, suggesting that lineage-level models may provide more accurate prediction results. According to the model predictions, the AES may have greater resilience to climate change andmay experience habitat expansion in the future, while the AE1 and the AE2 may face habitat loss in some regions. Climate change-driven shifts in oceanic conditions were anticipated to affect the distribution and community structure of marine organisms. This study assessed the impact of climate change on the suitable habitat range of three lineages of B. pectinirostris using SDMs. Consistent with previous studies, the results of our study indicated that lineage-level SDMs may be more reliable than species-level SDMs for species with population differentiation in terms of the accuracy of predictions. In addition, considering the vulnerability of the AE1 and AE2 lineages to climate change, conserving these two lineages should be given a higher priority. The results of this study will provide important information for the future management and conservation of this species. [ABSTRACT FROM AUTHOR]

The use of species distribution models (SDMs) to predict local abundance has been often proposed and contested. We tested whether SDMs at different spatiotemporal resolutions may predict the local density of 14 bird species of open/semi‐open habitats. SDMs were built at 1 ha and 1 km, and with long‐term versus a mix of current and long‐term climatic variables. The estimated environmental suitability was used to predict local abundance obtained by means of 275 linear transects. We tested SDM ability to predict abundance for all sampled sites versus occurrence sites, using N‐mixture models to account for imperfect detection. Then, we related the R2 of N‐mixture models to SDM traits. Fine‐grain SDMs appeared generally more robust than large‐grain ones. Considering the all‐transects models, for all species environmental suitability displayed a positive and highly significant effect at all the four combinations of spatial and temporal grains. When focusing only on occurrence transects, at the 1 km grain only one species showed a significant and positive effect. At the 1 ha grain, 62% of species models showed (over both climatic sets) a significant or nearly significant positive effect of environmental suitability on abundance. Grain was the only factor significantly affecting the model's explanatory power: 1 km grain led to lower amounts of variation explained by models. Our work re‐opens the debate about predicting abundance using SDM‐derived suitability, emphasizing the importance of grains and of spatiotemporal resolution more in general. The incorporation of local variables into SDMs at fine grains is key to predict local abundance. SDMs worked out at really fine grains, approaching the average size of territory or home range of target species, are needed to predict local abundance effectively. This may result from the fact that each single cell may represent a potential territory/home range, and hence a higher suitability over a given area means that more potential territories occur there. [ABSTRACT FROM AUTHOR]

Cold-water corals (CWCs) are bioengineering species that can increase habitat heterogeneity and improve the deep sea’s biological diversity and ecosystem functioning. Knowledge of their distribution provides a critical baseline for assessing the effect of natural and anthropogenic impacts on these important deep-sea habitats. The aims of this study are: i) provide new data on the spatial distribution of six CWCs species in the Strait of Sicily, ii) describe the principal environmental and anthropogenic variables that play a role in shaping their distribution, iii) identify hotspots in which individuals belonging to the various species co-occur. Presence-only data of six CWCs species, ten environmental variables (depth, slope, rugosity, aspect, flowdir, temperature, salinity, north bottom current, east bottom current, chlorophyll-a), and one variable relating to bottom trawling effort (Automatic Information System – AIS) were used to predict the suitable habitats. We used Maximum Entropy modelling (MaxEnt) approach and used the AUC (area under the receiver operating characteristic curve) and TSS (true skill statistics) to evaluate the model performance. The results showed excellent AUC, TSS and AUC’s standard deviation mean values for all six species. The validation show high predictive performance. MaxEnt identified slope, depth, and rugosity as the most important predictors, showing the highest percentage contribution for all six species considered. Throughout the study area, highlyinterspecific persistent density hotspot of CWCs co-occurrence were discovered, with a total extension of 4.05 km2 where all species co-occur. Although studies on the effect of environmental and anthropogenic factors that impact the distribution of these species of conservation interest remain scarce, the results of this study offer useful guidance for decision-makers to develop necessary conservation measures. [ABSTRACT FROM AUTHOR]

Globally, amphibians are one of the most threatened vertebrate groups and are hypersensitive to human-imposed habitat changes. Here we explore ways to conserve amphibians in two of the least-known biodiversity hotspots on earth, the Caucasus and Irano–Anatolian regions. We used two techniques: (ⅰ) combining species richness, endemism, and endangerness indices and (ⅱ) a species distribution model (SDM) to identify high-priority areas for Iran's seven endemic and/or threatened amphibian species. The identified amphibian high-priority areas were then targeted to assess the levels of protection granted by the network of conservation areas (CAs) in Iran. We also computed the species-specific extent of occurrence (EOO) and the area of occupancy (AOO) to detect conservation gaps for the targeted species. Our results indicate that amphibian high-priority areas in Iran are mostly distributed across the Hyrcanian forest in the north and Zagros Mountains in the west. The gap analysis revealed that based on the most optimistic metric, 40% of amphibian hotspots are covered by CAs in Iran. However, the species-specific gap analysis showed that Iran's CAs perform poorly at representing the EOO of all of the endemic and/or threatened amphibian species. These results suggest that expansion of CAs in Iran is essential for amphibian conservation. [ABSTRACT FROM AUTHOR]

Species distribution models (SDMs) are numerical tools that combine species occurrence (or abundance) data with environmental variables, to predict the species’ distribution spatially. SDMs are increasingly used for purposes of conservation planning and management of ecosystems. The model performance can be measured as the goodness-of-fit (GOF), which describes how well it fits (e.g., the discrepancy between the statistical model and the data observed). However, there is still a need for a deeper understanding of the ecological characteristics of the modelled species which can affect the accuracy of those models. Here, we compared the goodness-of-fit of SDMs, considering several ecological characteristics of 56 bird species: Most frequently used environment, body mass, home-range, species specialization index (SSI), diet specialization and detectability. All SDMs were performed on the same dataset, and the relative frequency of each species was also incorporated to account for occurrence heterogeneity. GOF of SDMs was not significantly correlated with species’ frequency, home-range, body mass, degree of detectability or level of diet specialization. Overall, the birds with more accurate SDMs (GOF) were species of grasslands and the GOF was positively associated with SSI, indicating that more habitat-specialized species are better predictable. Our findings suggest that is important to focus not only on statistical issues potentially related to model performance but also on ecological characteristics of single species because can improve the performance of modellistic procedures, increasing their predictive power.

Cold-water corals (CWCs) are bioengineering species that can increase habitat heterogeneity and improve the deep sea’s biological diversity and ecosystem functioning. Knowledge of their distribution provides a critical baseline for assessing the effect of natural and anthropogenic impacts on these important deep-sea habitats. The aims of this study are: i) provide new data on the spatial distribution of six CWCs species in the Strait of Sicily, ii) describe the principal environmental and anthropogenic variables that play a role in shaping their distribution, iii) identify hotspots in which individuals belonging to the various species co-occur. Presence-only data of six CWCs species, ten environmental variables (depth, slope, rugosity, aspect, flowdir, temperature, salinity, north bottom current, east bottom current, chlorophyll-a), and one variable relating to bottom trawling effort (Automatic Information System – AIS) were used to predict the suitable habitats. We used Maximum Entropy modelling (MaxEnt) approach and used the AUC (area under the receiver operating characteristic curve) and TSS (true skill statistics) to evaluate the model performance. The results showed excellent AUC, TSS and AUC’s standard deviation mean values for all six species. The validation show high predictive performance. MaxEnt identified slope, depth, and rugosity as the most important predictors, showing the highest percentage contribution for all six species considered. Throughout the study area, highlyinterspecific persistent density hotspot of CWCs co-occurrence were discovered, with a total extension of 4.05 km2 where all species co-occur. Although studies on the effect of environmental and anthropogenic factors that impact the distribution of these species of conservation interest remain scarce, the results of this study offer useful guidance for decision-makers to develop necessary conservation measures.

Background: To determine species distributions and the factors underlying them, reliable occurrence data are crucial. Assembling such data can be challenging for species with cryptic life histories or that occur at low densities. Methods: We developed species-specific eDNA protocols, from sampling through data interpretation, to detect the common musk turtle (Sternotherus odoratus) and tested whether eDNA occurrences change our understanding of the species distribution and the factors that shape its northern range limit. We used Species Distribution Models (SDMs) with full parameter optimization on citizen science observations of S. odoratus in Southern Ontario alone and together with eDNA occurrences. Results: Our eDNA protocol was robust and sensitive. SDMs built from traditional observations and those supplemented with eDNA detections were comparable in prediction accuracy. However, models with eDNA detections suggested that the distribution of S. odoratus in Southern Ontario is underestimated, especially near its northern range limit, and that it is shaped by thermal conditions, hydrology, and elevation. Our study underscores the promise of eDNA for surveying cryptic aquatic organisms in undocumented areas, and how such insights can help us to improve our understanding of species distributions. [ABSTRACT FROM AUTHOR]

Habitat suitability calculated from species distribution models (SDMs) has been used to assess population performance, but empirical studies have provided weak or inconclusive support to this approach. Novel approaches measuring population distances to niche centroid and margin in environmental space have been recently proposed to explain population performance, particularly when populations experience exceptional environmental conditions that may place them outside of the species niche. Here, we use data of co‐occurring species' decay, gathered after an extreme drought event occurring in the southeast of the Iberian Peninsula which highly affected rich semiarid shrubland communities, to compare the relationship between population decay (mortality and remaining green canopy) and 1) distances between populations' location and species niche margin and centroid in the environmental space, and 2) climatic suitability estimated from frequently used SDMs (here MaxEnt) considering both the extreme climatic episode and the average reference climatic period before this. We found that both SDMs‐derived suitability and distances to species niche properly predict populations performance when considering the reference climatic period; but climatic suitability failed to predict performance considering the extreme climate period. In addition, while distance to niche margins accurately predict both mortality and remaining green canopy responses, centroid distances failed to explain mortality, suggesting that indexes containing information about the position to niche margin (inside or outside) are better to predict binary responses. We conclude that the location of populations in the environmental space is consistent with performance responses to extreme drought. Niche distances appear to be a more efficient approach than the use of climate suitability indices derived from more frequently used SDMs to explain population performance when dealing with environmental conditions that are located outside the species environmental niche. The use of this alternative metrics may be particularly useful when designing conservation measures to mitigate impacts of shifting environmental conditions. [ABSTRACT FROM AUTHOR]

Aim: Climate Change Vulnerability Assessment (CCVA) prescribes the quantification of species vulnerability based on three components: sensitivity, adaptive capacity and exposure. Such assessments should be performed through combined approaches that integrate trait‐based elements (e.g., measures of species sensitivity such as niche width) with correlative tools quantifying exposure (magnitude of changes in climate within species habitat). Furthermore, as land use alterations may increase climate impacts on biodiversity, CCVAs should focus on both climate and land use change effects. Unfortunately, most of such assessments have so far focused exclusively on exposure to climate change. Location: Himalaya. Methods: We evaluated the vulnerability of three otter species occurring in the Himalayan region, that is, Aonyx cinereus, Lutra lutra and Lutrogale perspicillata, to 2050 climate and land use change through the recently proposed Climate Niche Factor Analysis (CNFA) framework combined with Species Distribution Models. Results: Future climate and land use change will reduce (6%–15%) and shift (10%–18%) the geographical range of the three species in the Himalaya, with land use alterations exerting far more severe effects than climate change. Among vulnerability components, sensitivity played a greater role than exposure in determining the vulnerability of the otters. Specifically, the most specialist species, L. perspicillata, showed the highest vulnerability in comparison with the most generalist, L. lutra. Main conclusions: Our results underline how coupling climate and land use change components in CCVAs can generate diverging predictions of species vulnerability compared to approaches relying on climate change only. Moreover, intrinsic components, such as species sensitivity, proved significantly more important in determining vulnerability than extrinsic metrics such as habitat exposure. [ABSTRACT FROM AUTHOR]

ABSTRACT Aquatic herbaceous plants are especially suitable for mapping environmental variability in wetlands, as they respond quickly to environmental gradients and are good indicators of habitat preference. We describe the composition of herbaceous species in two oligotrophic wetland ecosystems, floodplains along black-water rivers (igapó) and wetlands upon hydromorphic sand soils (campinarana) in the Parque Nacional do Jaú and the Reserva de Desenvolvimento Sustentável Uatumã in Central Amazonia, both protected areas. We tested for the potential distribution range (PDR) of the most frequent species of these ecosystems, which are the ones that occurred in at least two of the sampled wetlands, using species distribution models (SDMs). In total, 98 aquatic herbaceous species were recorded, of which 63 occurred in igapós and 44 in campinaranas. Most igapó species had ample PDRs across the Neotropics, while most campinarana species were restricted to the Amazon Basin. These results are congruent with studies that described similar distribution patterns for tree and bird species, which emphasizes a high degree of endemism in Amazonian campinaranas. However, we also found differences in the potential distribution of species between the two protected areas, indicating high environmental variability of oligotrophic ecosystems that deserve further investigation to develop effective measures for their conservation and protection.

The spatial distribution modeling can simulate the suitability of species habitats on different spatial scales, based on species records and site characteristics to gain insight into ecological, and evolutionary drivers or help predict habitat suitability across large scales. Species distribution models (SDMs) based on presence-absence or presence-only data are widely used in biogeography to indicate the ecological niche and predict the geographical distribution of species habitats. Although presence-absence data is generally of higher quality, it is also less common than presence-only data because it requires more rigorous planning to visit a set of pre-determined sites. Among the algorithms available, the MaxEnt approach is one of the most widely used methods of developing habitat modeling. The MaxEnt uses maximum entropy to generalize specific observations of presence-only data and does not require data where the species is absent within the theoretical framework. This study aims to predict the suitable habitat for Goitered gazelle (Gazella subgutturosa) in the Samelghan plain in northeastern Iran. The results showed that the variables of the Mediterranean climate classes, slope 0-5% class, and semi-dense pastures with type Acantholimon-Astragalus are more important than other environmental variables used in modeling. The area under the curve (AUC), Receiver Operating Characteristic (ROC), and the classification threshold illustrate the model performance. Based on the ROC (AUC=0.99) results in this study, it was found that Maxent's performance was very good. [ABSTRACT FROM AUTHOR]

Accurately predicting the future distribution of species is crucial for understanding how species will response to global environmental change and for evaluating the effectiveness of current protected areas (PAs). Here, we assessed the effect of climate and land use change on the projected suitable habitats of Davidia involucrata Baill under different future scenarios using the following two types of models: (a) only climate covariates (climate SDMs) and (b) climate and land use covariates (full SDMs). We found that full SDMs perform significantly better than climate SDMs in terms of both AUC (p <.001) and TSS (p <.001) and also projected more suitable habitat than climate SDMs both in the whole study area and in its current suitable range, although D. involucrate is predicted to loss at least 26.96% of its suitable area under all future scenarios. Similarly, we found that these range contractions projected by climate SDMs would negate the effectiveness of current PAs to a greater extent relative to full SDMs. These results suggest that although D. involucrate is extremely vulnerability to future climate change, conservation intervention to manage habitat may be an effective option to offset some of the negative effects of a changing climate on D. involucrate and can improve the effectiveness of current PAs. Overall, this study highlights the necessity of integrating climate and land use change to project the future distribution of species. [ABSTRACT FROM AUTHOR]

The dramatic rise of human population growth and urban development, especially in developing countries, has led to a decline in the quality, fragmentation and isolation of habitats which support rare or endangered fauna species. New areas designed to protect such species and form corridors and networks to support populations are needed. Two rare species of Chinkara and Blanford's fox habitats in Isfahan Province are used to evaluate the optimal method to determine suitable habitat. Eight statistical methods, including four widely-used methods (SVM, Random Forest, GBM and MARS) and five new methods (XGBtree, XGBLinear, Treebag and SVMRadial) are evaluated in this study; to do so two Modis products and 19 worldclim parameters are applied. In addition, the ensemble of these methods was applied using gap analysis and areas with potential for protection were prioritized. Finally, minimum cost analysis was utilized to identify possible corridors between new areas and current protected areas. Results demonstrate that the eastern parts of Isfahan province have the highest potential for these two species, and because of the diverse habitats demands for these two species, the regions are able to support a large number of fauna and flora species. [ABSTRACT FROM AUTHOR]

• Background points are widely used in species distribution models. • The standard background point sampling method is inferior. • Sampling background points in environmental space yields more stable and more accurate models. • Stratified random in environmental space sampling yields the most accurate models. • Fully random in environmental space sampling yields the most stable models. Species distribution models (SDMs) have become central tools in ecology and biogeography. Although they can be fitted with different types of species data (e.g. presence-absence, abundance), the most common approach, based on data from large species repositories, is to use simple occurrences (i.e. presence-only) combined with background points (BP; also called pseudo-absences). But how should we sample these background points, and how does this choice affect SDMs? In most studies so far, BP were sampled randomly in geographic space, yet theory rather suggests, if a species is at equilibrium, that it is better to sample them in a stratified way in environmental space. However, this potential improvement of SDM predictions has never been tested. Furthermore, a typical assumption behind SDMs is that the modelled species are at equilibrium with their environment. But how do these models perform when species are in disequilibrium, as is the case for most invasive species? To answer these questions, we selected 30 different species (10 insects, 10 mammals and 10 plants; for each group 5 were invasive and 5 were considered at equilibrium) and for each we calibrated SDMs with different types of background selections: random in environmental space, random-stratified in environmental space, random in geographic space, and random-stratified in geographic space. For each SDM we assessed both predictive performance using standard metrics and their stability using a new approach that compares the model's habitat suitability projection with those of a SDM calibrated with virtual occurrence data generated from the most suitable areas. Finally, we compared the predictive performance of species distribution models of invasive alien (disequilibrium) species versus native (equilibrium) species by comparing model stability and performance metrics of the two groups. We found that sampling BP in a stratified-random way in environmental space yields the highest performance metrics, and that sampling fully randomly in environmental space yields the most stable models. This has implications for the use of SDMs in conservation, as the classical and frequently used fully random in geographic space BP are found to produce both less accurate and less stable models. Our results indicate that the best approach is to use stratified random in environmental space BP sampling if accuracy is essential, and fully random in environmental space BP sampling if model stability is essential. [ABSTRACT FROM AUTHOR]

Aim: Detection of rare species is limited by their intrinsic nature and by the constraints associated with traditional field surveys. Remote sensing (RS) provides a powerful alternative to traditional detection methods through the increasing availability of RS products. Here, we assess the capacity of RS at high and medium resolution to detect rare plants with direct and indirect approaches, and how the performance of RS can be influenced by the characteristics of species. Methods: An extensive literature review was conducted to synthesize the use of RS to detect or predict rare plant occurrence at high and medium resolution (<30 m and 30–300 m, respectively). The concept of "rarity" was based on Rabinowitz's rare species classification. The literature review was performed in Scopus for the period 1990–2020. Results: While direct detection is often limited, it is possible with high and very high spatial resolution data for rare plants with distinctive traits. RS is also able to capture biophysical conditions driving rare plant distributions, which can indirectly provide accurate predictions for them. Both approaches have the potential to discover new populations of rare plants. RS can also feed SAMs of rare plants, which combined with SDMs can provide a valuable approach for rare plant detection. While direct detection is limited by the space occupied by a species within its habitat and its morphological, phenological and physiological characteristics, the predictive performance of RS‐based SDMs (indirect detection) can be influenced by habitat size, habitat specificity and phenological features of rare plants. Similarly, model predictive performance can be influenced by the rarity form of the target species according to the rarity classification criteria. Main conclusions. With this synthesis, the strong potential of RS for the purposes of detection and prediction of rare plant has been highlighted, with practical applications for conservation and management. [ABSTRACT FROM AUTHOR]

The striped dolphin (Stenella coeruleoalba) is one of the most abundant cetaceans of the Mediterranean Sea. Nevertheless, it is ranked as 'Vulnerable' by IUCN Red List experts.To implement conservation strategies within the Mediterranean Sea, it is crucial to understand how striped dolphins are distributed at different geographical scales. The present study investigates its distribution within the EU Adriatic and Ionian Sea Region.To model striped dolphin distribution, topographic (distance from shore, depth and slope) and oceanographic variables (sea surface temperature and phytoplankton concentration) were employed, and species distribution models (SDMs) were developed using Maxent software. Generalized additive models (GAMs) were used to improve the information supplied by the SDMs, allowing the description of response curves to striped dolphin occurrences.Statistical analysis shows that striped dolphins are not evenly distributed within the investigated area. The higher encounter rates observed in the Gulf of Taranto and the Gulf of Corinth confirm that both sub‐regions are crucial for its conservation. Furthermore, SDMs also indicate suitable areas in the offshore waters of the Adriatic Sea, along the continental slope of the north‐eastern Ionian Sea and over its continental shelf. Within the EU Adriatic and Ionian Sea Region, striped dolphins prefer areas at a greater distance from shore, with steeper slopes, deeper waters, and higher sea surface temperature. The topographic variables are the most explicative factors for both modelling approaches.The analysis of striped dolphin SDMs allows the identification of important areas for conservation and highlights zones where appropriate mitigation strategies could reduce the impact of human activities on this Vulnerable species. Further analysis of SDMs for different periods of the year, as well as a genetic study, could highlight seasonal movements across and within sub‐regions. [ABSTRACT FROM AUTHOR]

Aim: As a prominent geographical distribution centre for the dark coniferous forests, mountains of Southwest China (MSWC) is experiencing an unprecedented warming trend, posing severe challenges to the survival of dominant fir (Abies) species. Although plant's migration ability is a prerequisite for its survival in changing environments, it has often been ignored in species distribution models (SDMs). This study aimed to quantify the magnitude and direction of range changes by the year 2080 for six dominant fir species, that is Abies recurvata, Abies faxoniana, Abies squamata, Abies ernestii, Abies forrestii and Abies georgei, with an emphasis on exploring the relationship between migration ability and projected distributions. Location: The mountains of Southwest China. Methods: We applied the Maximum Entropy (Maxent) algorithm to calibrate ecological niche models and to project the climatically suitable areas (CSAs) of each species under two emission scenarios (RCP 4.5 and RCP 8.5). Additionally, we delimited future species ranges by three migration scenarios (full‐, no‐ and partial‐migration scenarios). Results: The simulations showed the distinctive responses of the six fir species to anthropogenic climate change (ACC). By 2080, the distribution areas of Abies recurvata were projected to decline only in the no‐migration scenario but increase under the full‐ and partial‐migration scenarios, while the other five species were projected to decline in the majority of emission × migration scenarios. Fir species in the southern region were predicted to be more vulnerable to ACC due to the larger losses in CSAs and a stronger effect of the partial‐migration scenario on the newly colonized areas of this group. The studied species showed a simulated migration trend (northward and westward) to the interior Qinghai‐Tibet Plateau under ACC. Main conclusions: Benefits or losses for species under ACC depended on the geographical location, their ecological niches and migration abilities, which provide essential insights for a spatial conservation assessment of biodiversity hotspots in the future. [ABSTRACT FROM AUTHOR]

• During the period 2009–2022 R. raciborskii was registered in 27 % of the lakes. • R. raciborskii has colonised 14 new localities during the same period. • Its contribution to the total phytoplankton biomass has increased significantly. • The main factor for the successful expansion is high summer temperatures. • Models predict future risk of colonisation even at altitudes above 500 m asl. The cyanobacterial species Raphidiopsis raciborskii (Wołoszyńska) Aguilera et al. has a high invasiveness potential, which in less than a century leads to its cosmopolitan spread. In the temperate climate of Europe , R. raciborskii has been reported in many countries, but there is still a lack of detailed information about the current status of its distribution in lakes of Bulgaria, as a part of the southeastern range of its spread in Europe. We investigated the distribution of the species using data on the phytoplankton of 122 lakes surveyed during 13-years period (2009–2022). The species was found in 33 lakes (up to 324 m asl), and 14 new localities were registered during the studied period. The results reveal that the number of lakes with the presence of R. raciborskii (27 % of all research lakes) and its contribution to the total phytoplankton biomass, has increased significantly over the last decade. The species has successfully adapted and dominated the phytoplankton in 9 lakes, forming a bloom in 8 of them. The dominant position of R. raciborskii causes loss of species and functional diversity of phytoplankton and displaces the native bloom-forming cyanobacteria. Lakes with and without the species were compared based on the available data on bioclimatic and local environmental variables. Statistically significant differences were established with respect to water transparency, conductivity, maximum depth and maximum air temperature in the warmest month. Species distribution models (SDMs) were used to identify lakes in high risk of future invasion by R. raciborskii. The results of the SDMs implementation confirmed the high maximum air temperature and low water transparency to be important predictors of the occurrence of R. raciborskii in freshwater lakes in Bulgaria. In the areas with high summer temperatures the most suitable for R. raciborskii development were found to be shallow polymictic or medium deep lakes with small surface area and low water transparency. In areas with a suitable climate, the large, deep reservoirs with high transparency as well as macrophyte dominated lakes have a low probability of occurrence of R. raciborskii. Future colonization of lakes above 500 m asl (but most likely below 700 m asl) is also possible, especially in the conditions of global warming. SDMs account for climatic and biogeographic differences of lakes and could help in elucidating the underlying factors that control the occurrence and adaptation of R. raciborskii in a given area. [ABSTRACT FROM AUTHOR]

Oxytropis ochrocephala Bunge is an herbaceous perennial poisonous weed. It severely affects the production of local animal husbandry and ecosystem stability in the source region of Yellow River (SRYR), China. To date, however, the spatiotemporal distribution of O. ochrocephala is still unclear, mainly due to lack of high-precision observation data and effective methods at a regional scale. In this study, an efficient sampling method, based on unmanned aerial vehicle (UAV), was proposed to supply basic sampling data for species distribution models (SDMs, BIOMOD in this study). A total of 3232 aerial photographs were obtained, from 2018 to 2020, in SRYR, and the potential and future distribution of O. ochrocephala were predicted by an ensemble model, consisting of six basic models of BIOMOD. The results showed that: (1) O. ochrocephala mainly distributed in the southwest, middle, and northeast of the SRYR, and the high suitable habitat of O. ochrocephala accounted for 3.19%; (2) annual precipitation and annual mean temperature were the two most important factors that affect the distribution of O. ochrocephala, with a cumulative importance of 60.45%; and (3) the distribution probability of O. ochrocephala tends to increase from now to the 2070s, while spatial distribution ranges will remain in the southwest, middle, and northeast of the SRYR. This study shows that UAVs can potentially be used to obtain the basic data for species distribution modeling; the results are both beneficial to establishing reasonable management practices and animal husbandry in alpine grassland systems.

According to climate projection models, the global temperature is expected to rise by at least 1.5°C by the end of this century. According to some studies the expected rise in Serbia is even higher. Global warming may result in creating new areas for forest growth. Although creating new forests would be a positive outcome in some areas, global warming can cause negative impacts in other areas, and this can lead to forest loss and the shift of geographical ranges, or even extinction, of plant species. The European beech is the dominant forest tree species in Serbia, featuring high ecological importance and economic value. In mixed or pure stands, beech forests cover approximately 660,400 ha, accounting for 29.3% of the total Serbian forest area. In the present study, the effects of climate change on the distribution of the European beech stands in Serbia, with an emphasis on their elevation shifts, were examined using species distribution models (SDMs). Data for the present tree cover in Serbia, climate projections, and environmental data were used for model building. The models were first tested against present inventory data. In these tests, the models were found to provide accurate projections, as shown by their true skills statistics (TSS) values ranging from 0.652 to 0.736 and area under the curve (AUC) values ranging from 0.868 to 0.937. The potential distribution patterns predicted by the models indicate that the European beech elevational distribution in Serbia would decrease, exhibiting a significant upward shift in elevation during the first part of this century. Current beech stand locations could be changed, and other areas at higher elevations may be more suitable for beech growth. After 2071, European beech stands at elevations below 500 m would be even smaller. This change is caused by temperature rise and occurrence of climate extremes. However, on the highest elevations, further upward shift of the species is not expected.

Climate change is altering species distributions worldwide. Particularly, global warming is driving range contractions and expansions of tropical species, such as corals. The use of climatic projections, via species distribution models to predict species distributional shifts, can identify threaten species and help to set priority areas of conservation. In this study, we assessed if distributional shifts of 45 Atlantic reef-forming corals (scleractinian), and the main environmental variables driving their distributions, correlated with their phylogeny and/or their functional traits; i.e. whether expected contractions and expansions affected specific clades, or specific coral traits. We also estimated the potential loss and/or gain of species richness, phylogenetic diversity (PD) and phylogenetic species variability (PSV), as well as the phylogenetic structure of Atlantic reef communities ('clustering', 'overdispersion' or 'randomness'), under a future climate scenario (A2-IPCC-2100). The potential loss of Atlantic corals in the future will be randomly distributed across their phylogeny, i.e. potential extinctions will not only affect one section of the phylogeny, therefore alleviating an inordinate loss of evolutionary history. Nearly all current and future communities presented a 'random' phylogenetic structure. No correlation was found between distributional shifts and coral traits. Environmental variables did not show a significant correlation with the phylogeny neither with coral traits. Predicted changes in species richness, PD and PSV vary across the Atlantic; certain areas display large evolutionary diversity losses. Species belonging to isolated clades (high evolutionary distinctiveness) contribute to quantitative increases, or decreases, of PD and PSV, becoming crucial species for conservation. These findings highlight the importance of combining SDMs with phylogenetic/functional metrics to develop conservation strategies to assess the future of corals. [ABSTRACT FROM AUTHOR]

With the advancement of spatial analysis and remote sensing technology, potentially devastating forest pathogens can be managed through spatial modeling. This study used Maxent, a presence-only species-distribution model, to map the potential probability distribution of the invasive forest pathogen oak wilt (Bretziella fagacearum) in eastern and southeastern Minnesota. The model related oak wilt occurrence data to environmental variables including climate, topography, land cover, soil, and population density. Results showed that areas with the highest probability of oak wilt occur within and surrounding the Minneapolis/St. Paul metropolitan area. The jackknife test of variable importance indicated land cover and soil type as important variables contributing to the prediction of the distribution. Multiple methods of analysis showed that the model performed better than random at predicting the occurrence of oak wilt. This study shows Maxent's potential as an accurate tool in the early detection and management of forest diseases. [ABSTRACT FROM AUTHOR]

In recent years, species distribution models have been used to gain a better understanding of past and future range dynamics of species. Here, we focus on a keystone species of the North African forest ecosystem (Cedrus atlantica) by calculating a consensus model of the species current geographic potential distribution in North Africa, based on a weighted average method aiming to decrease uncertainty. The consensus model is obtained using seven species distribution model algorithms taking into account 24 environmental variables. The model is then applied to several past and future time slices. Past projections refer to the Middle-Holocene and the Last Glacial Maximum, whereas those of future are related to expect conditions around 2050 and 2070. We found that the current potential distribution of Cedrus atlantica is larger than its actual geographical distribution. For some explanatory variables, the analysis revealed their importance for the species current distribution. Among all obtained models, that for the Middle-Holocene showed the maximum expansion of the species potential distribution. The Last Glacial Maximum model provided new putative glacial refugia of Cedrus atlantica, not shown by other mechanistic models and palaeorecord localities. Future projections revealed a significant and fast contraction with shifting in altitude of the species range, showing more fragmented areas and even species disappearance in many North African localities. These findings can help to restore cedar forests and conserve them by ex situ strategies according to the future defined refugia in North Africa. Attention should be paid to the resolution of related output maps, the current biotic interactions, and those that may arise under climate change. [ABSTRACT FROM AUTHOR]

According to climate projection models, the global temperature is expected to rise by at least 1.5°C by the end of this century. According to some studies the expected rise in Serbia is even higher. Global warming may result in creating new areas for forest growth. Although creating new forests would be a positive outcome in some areas, global warming can cause negative impacts in other areas, and this can lead to forest loss and the shift of geographical ranges, or even extinction, of plant species. The European beech is the dominant forest tree species in Serbia, featuring high ecological importance and economic value. In mixed or pure stands, beech forests cover approximately 660,400 ha, accounting for 29.3% of the total Serbian forest area. In the present study, the effects of climate change on the distribution of the European beech stands in Serbia, with an emphasis on their elevation shifts, were examined using species distribution models (SDMs). Data for the present tree cover in Serbia, climate projections, and environmental data were used for model building. The models were first tested against present inventory data. In these tests, the models were found to provide accurate projections, as shown by their true skills statistics (TSS) values ranging from 0.652 to 0.736 and area under the curve (AUC) values ranging from 0.868 to 0.937. The potential distribution patterns predicted by the models indicate that the European beech elevational distribution in Serbia would decrease, exhibiting a significant upward shift in elevation during the first part of this century. Current beech stand locations could be changed, and other areas at higher elevations may be more suitable for beech growth. After 2071, European beech stands at elevations below 500 m would be even smaller. This change is caused by temperature rise and occurrence of climate extremes. However, on the highest elevations, further upward shift of the species is not expected. [ABSTRACT FROM AUTHOR]

To protect native biodiversity and habitats from the negative impacts of biological invasions, comprehensive studies and measures to anticipate invasions are required, especially across countries in a transfrontier context. Species distribution models (SDMs) can be particularly useful to integrate different types of data and predict the distribution of invasive species across borders, both for current conditions and under scenarios of future environmental changes. We used SDMs to test whether predicting invasions and potential spatial conflicts with protected areas in a transfrontier context, under current and future climatic conditions, would provide additional insights on the patterns and drivers of invasion when compared to models obtained from predictions for individual regions/countries (different modelling strategies). The framework was tested with the invasive alien plant Acacia dealbata in North of Portugal/NW Spain Euro-region, where the species is predicted to increase its distribution under future climatic conditions. While SDMs fitted in a transfrontier context and using "the national strategy (with Portugal calibration data) presented similar patterns, the distribution of the invasive species was higher in the former. The transfrontier strategy expectedly allowed to capture a more complete and accurate representation of the species' niche. Predictions obtained in a transfrontier context are therefore more suitable to support resource prioritisation for anticipation and monitoring impacts of biological invasions, while also providing additional support for international cooperation when tackling issues of global change. Our proposed framework provided useful information on the potential patterns of invasion by A. dealbata in a transfrontier context, with an emphasis on protected areas. This information is crucial for decision-makers focusing on the prevention of invasions by alien species inside protected areas in a transfrontier context, opening a new way for collaborative management of invasions. [ABSTRACT FROM AUTHOR]

• Using ecological niche models and niche analyses to predict distribution of Prymnesium parvum. • Populations in the USA, Europe and Australia occupies non-overlapping climatic conditions. • MaxEnt models accurately uncovered novel suitable areas, not yet occupied by P. parvum. • Lower minimum temperatures in the coldest month are associated with higher occurrence probabilities of the species, in agreement with ecophysiology of blooms. • Increasing precipitation reduced occurrence probabilities, suggesting spread under on-going salinization of freshwaters. Freshwater ecosystems are highly susceptible to harmful algal blooms (HABs), which are often caused by monospecific dense blooms. Effective preventive management strategies are urgently needed to avoid wide-ranging and severe impacts often resulting in costly damage to resources and unsustainable management options. In this study, we utilized SDM techniques focused on Prymnesium parvum , one of the most notorious HABs species worldwide. We first compare the climatic space occupied by P. parvum in North America, Europe and Australia. Additionally, we use MaxEnt algorithm to infer, for the first time, the potentially suitable freshwater environments in the aforementioned ranges. We also discuss the risks of invasion in reservoirs – prone habitats to persistent blooms of pests and invasive phytoplanktonic species. Our results show populations with distinctive niches suggesting ecophysiological tolerances, perhaps reflecting different strains. Our model projections revealed that the potential extent for P. parvum invasions is much broader than its current geographic distribution. The spatial configuration of reservoirs, if not sustaining dense blooms due to non-optimal conditions, favors colonization of multiple basins and ecoregions not yet occupied by P. parvum. Our models can provide valuable insights to decision-makers and monitoring programs while reducing the resources required to control the spread of P. parvum in disturbed habitats. Lastly, as impact magnitude is influenced by toxicity which in turn varies between different strains, we suggest future studies to incorporate intraspecific genetic information and fine-scale environmental variables to estimate potential distribution of P. parvum. [ABSTRACT FROM AUTHOR]

The differential responses of co-occurring species in rich communities to climate change—particularly to drought episodes—have been fairly unexplored. Species distribution models (SDMs) are used to assess changes in species suitability under environmental shifts, but whether they can portray population and community responses is largely undetermined, especially in relation to extreme events. Here we studied a shrubland community in SE Spain because this region constitutes an ecotone between the Mediterranean biome and subtropical arid areas, and it has recently suffered its driest hydrological year on record. We used four different modeling algorithms (Mahalanobis distance, GAM, BRT, and MAXENT) to estimate species' climatic suitability before (1950-2000) and during the extreme drought. For each SDM, we related species' climatic suitability with their remaining green canopy as a proxy for species resistance to drought. We consistently found a positive correlation between remaining green canopy and species' climatic suitability before the event. This relationship supports the hypothesis of a higher vulnerability of populations living closer to their species' limits of aridity tolerance. Contrastingly, climatic suitability during the drought did not correlate with remaining green canopy, likely because the exceptional episode led to almost zero suitability values. Overall, our approach highlights climatic niche modeling as a robust approach to standardizing and comparing the behavior of different co-occurring species facing strong climatic fluctuations. Although many processes contribute to resistance to climatic extremes, the results confirm the relevance of populations' position in the species' climatic niche for explaining sensitivity to climate change. [ABSTRACT FROM AUTHOR]

Aedes aegypti (Diptera: Culicidae) is an urban mosquito involved in the transmission of numerous viruses, including dengue, chikungunya and Zika. In Argentina, Ae. aegypti is the main vector of dengue virus and has been involved in several outbreaks in regions ranging from northern to central Argentina since 2009. In order to evaluate areas of potential vector‐borne disease transmission in the city of Córdoba, Argentina, the present study aimed to identify the environmental, socioeconomic and demographic factors driving the distribution of Ae. aegypti larvae through spatial analysis in the form of species distribution models (SDMs). These models elucidate relationships between known occurrences of a species and environmental data in order to identify areas with suitable habitats for that species and the consequent risk for disease transmission. The maximum entropy species distribution model was able to fit the training data well, with an average area under the receiver operating characteristic curve (AUC) of > 0.8, and produced models with fair extrapolation capacity (average test AUC: > 0.75). Human population density, distance to vegetation and water channels were the main variables predictive of the vector suitability of an area. The results of this work will be used to target surveillance and prevention measures, as well as in mosquito management. Factors driving the distribution of Aedes aegypti (Diptera: Culicidae) larvae in Córdoba, Argentina were identified using species distribution models.Local socioeconomic and demographic studies, as well as city cleaning campaigns, are important to avoid the availability of man‐made containers in public places.Intrinsic knowledge of the study area is key to producing empirical models of mosquito vectors. [ABSTRACT FROM AUTHOR]

While the relatively stable land use and land cover (LULC) patterns is an important feature of protected areas (PAs), the influence of this feature on future species distribution and the effectiveness of the PAs has rarely been explored. Here, we assessed the role of land use patterns within PAs on the projected range of the giant panda (Ailuropoda melanoleuca) by comparing projections inside and outside of PAs for four model configurations: (1) only climate covariates, (2) climate and dynamic land use covariates, (3) climate and static land use covariates and (4) climate and hybrid dynamic-static land use covariates. Our objectives were twofold: to understand the role of protected status on projected panda habitat suitability and evaluate the relative efficacy of different climate modeling approaches. The climate and land use change scenarios used in the models include two shared socio-economic pathways (SSPs) scenarios: SSP126 [an optimistic scenario] and SSP585 [a pessimistic scenario]. We found that models including land-use covariates performed significantly better than climate-only models and that these projected more suitable habitat than climate-only models. Static land-use models projected more suitable habitat than both the dynamic and hybrid models under SSP126, while these models did not differ under SSP585. China's panda reserve system was projected to effectively maintain suitable habitat inside PAs. Panda dispersal ability also significantly impacted outcomes, with most models assuming unlimited dispersal forecasting range expansion and models assuming zero dispersal consistently forecasting range contraction. Our findings highlight that policies targeting improved land-use practices should be an effective means for offsetting some of the negative effects of climate change on pandas. As the effectiveness of PAs is projected to be maintained, we recommend the judicious management and expansion of the PA system to ensure the resilience of panda populations into the future. • Models including land-use covariates performed significantly better than climate-only models. • Models including land-use covariates projected more suitable habitat than climate-only models. • Static land-use models projected more suitable habitat than both the dynamic and hybrid models. • China's panda reserve system was projected to effectively maintain suitable habitat inside PAs. • Panda dispersal ability significantly impacted outcomes. [ABSTRACT FROM AUTHOR]