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While much attention has been paid to the climatic controls of species' range limits, other factors such as dispersal limitation are also important. Temperature is an important control of the distribution of coastal mangrove forests, and mangrove expansion at multiple poleward range limits has been linked to increasing temperatures. However, mangrove abundances at other poleward range limits have been surprisingly insensitive to climate change, indicating other drivers of range limitation. For example, along the west coast of North America, the poleward mangrove range limits are found on the Baja California and mainland coasts of Mexico, between 26°48ʹ and 30°18ʹN. Non‐climatic factors may play an important role in setting these range limits as 1) the abundance of range limit populations has been relatively insensitive to climate variability and 2) an introduced population of mangroves has persisted hundreds of kilometers north of the natural range limits. We combined a species distribution model with a high‐resolution oceanographic transport model to identify the roles of climate and dispersal limitation in controlling mangrove distributions. We identified estuarine habitat that is likely climatically suitable for mangroves north of the current range limits. However, propagules from current mangrove populations are unlikely to reach these suitable locations due to prevailing ocean currents and geomorphic factors that create a patchy distribution of estuarine habitat with large between‐patch distances. Thus, although climate change is driving range shifts of mangroves in multiple regions around the world, dispersal is currently limiting poleward mangrove expansion at several range limits, including the west coast of North America. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Wildlife & Biodiversity (JWB) is the property of Arak University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The global decline of biodiversity threatens ecosystem stability and human well-being. This study modeled the future suitable habitats of twelve key multipurpose tree species (MPTS) in Benin under two climate scenarios, Shared Socioeconomic Pathways 245 (SSP245) and 585 (SSP585), based on a 2070 horizon. The research focused on peri‑urban areas (Cotonou, Abomey, Savalou, Parakou, Natitingou, and Kandi) and the protected areas network in Benin. We evaluated environmental variables influencing MPTS distribution, projected habitat changes, identified hotspots, and compared impacts on native versus non-native species. Four modelling algorithms—Generalized Additive Models, Generalized Linear Models, Maximum Entropy, and Random Forest—were used. Climate factors, particularly isothermality (Bio3) and annual precipitation (Bio12), predominantly influenced the distribution of the studied species. The models performed well, with a mean Area Under the Curve (AUC) of 0.88 and a mean True Skill Statistic (TSS) of 0.64. Projections indicated a decline in suitable habitats for 67 % of species, minor changes for 8 %, and increases for 25 %. The effectiveness of protected areas was mixed, with species showing varied responses. Savalou and Abomey peri‑urban areas emerged as key conservation hotspots, underscoring the need to shift conservation focus to these areas. Native species showed greater resilience to future climate conditions, emphasizing the importance of native species and species-specific conservation strategies under changing climates. These findings are relevant for promoting MPTS in Sub-Saharan Africa for sustainable ecological and socio-economic development.

In response to the pressing challenges of the ongoing biodiversity crisis, the protection of endangered species and their habitats, as well as the monitoring of invasive species are crucial. Habitat suitability modeling (HSM) is often treated as the silver bullet to address these challenges, commonly relying on generic variables sourced from widely available datasets. However, for species with high habitat requirements, or for modeling the suitability of habitats within the geographic range of a species, variables at a coarse level of detail may fall short. Consequently, there is potential value in considering the incorporation of more targeted data, which may extend beyond readily available land cover and climate datasets. In this study, we investigate the impact of incorporating targeted land cover variables (specifically tree species composition) and vertical structure information (derived from LiDAR data) on HSM outcomes for three forest specialist bat species (Barbastella barbastellus, Myotis bechsteinii, and Plecotus auritus) in Rhineland‐Palatinate, Germany, compared to commonly utilized environmental variables, such as generic land‐cover classifications (e.g., Corine Land Cover) and climate variables (e.g., Bioclim). The integration of targeted variables enhanced the performance of habitat suitability models for all three bat species. Furthermore, our results showed a high difference in the distribution maps that resulted from using different levels of detail in environmental variables. This underscores the importance of making the effort to generate the appropriate variables, rather than simply relying on commonly used ones, and the necessity of exercising caution when using habitat models as a tool to inform conservation strategies and spatial planning efforts. [ABSTRACT FROM AUTHOR]

Copyright of Avian Conservation & Ecology is the property of Resilience Alliance and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The Humboldt squid (Dosidicus gigas), is a significant economic species off Peru. The abundance and distribution of the species are highly susceptible to fluctuations in marine environmental conditions. The evolution of mesoscale eddies represents one of the dynamic processes in the ocean, exerting varying degrees of influence on regional biogeochemical processes from generation to dissipation. However, the mechanisms governing the regulation of abundance and spatial distribution of D. gigas off Peru during this evolution remain unclear. Therefore, this study employed normalization techniques and the habitat suitability index (HSI) model, utilizing data from the D. gigas fishery, mesoscale eddies, and marine environmental factors (including sea surface temperature, temperature at 50 m depth, and chlorophyll-a concentration) to analyze the environmental changes and the abundance, spatial distribution, and habitat changes of D. gigas during the evolution of mesoscale eddies. The results indicate that eddies undergo four stages: formation, intensification, maturity, and decay. During eddy evolution, the abundance of D. gigas exhibited an initial increase and a subsequent decrease within cyclonic eddies (CEs), whereas, within anticyclonic eddies (AEs), abundance showed a gradual decline. The outcome of the HSI model revealed that, the habitat suitability and the proportion of suitable habitat areas within eddies were highly consistent with the abundance of D. gigas during all stages of the eddies other than the intensification stage of AEs. The study speculated that both CEs and AEs can create favorable environmental conditions during the maturity stage, thereby leading to an increase in suitable habitat and abundance of D. gigas. These findings emphasize the significant impact of mesoscale eddy evolution on the abundance and habitat distribution of D. gigas. [ABSTRACT FROM AUTHOR]

Habitat suitability models have become a valuable tool for wildlife conservation and management, and are frequently used to better understand the range and habitat requirements of rare and endangered species. In this study, we employed two habitat suitability modeling techniques, namely Boosted Regression Tree (BRT) and Maximum Entropy (Maxent) models, to identify potential suitable habitats for the endangered mountain nyala (Tragelaphus buxtoni) and environmental factors affecting its distribution in the Arsi and Ahmar Mountains of Ethiopia. Presence points, used to develop our habitat suitability models, were recorded from fecal pellet counts (n = 130) encountered along 196 randomly established transects in 2015 and 2016. Predictor variables used in our models included major landcover types, Normalized Difference Vegetation Index (NDVI), greenness and wetness tasseled cap vegetation indices, elevation, and slope. Area Under the Curve model evaluations for BRT and Maxent were 0.96 and 0.95, respectively, demonstrating high performance. Both models were then ensembled into a single binary output highlighting an area of agreement. Our results suggest that 1864 km2 (9.1%) of the 20,567 km2 study area is suitable habitat for the mountain nyala with land cover types, elevation, NDVI, and slope of the terrain being the most important variables for both models. Our results highlight the extent to which habitat loss and fragmentation have disconnected mountain nyala subpopulations. Our models demonstrate the importance of further protecting suitable habitats for mountain nyala to ensure the species' conservation. [ABSTRACT FROM AUTHOR]

Raptor nests on human-built structures represent a significant source of conflict, as they can result in bird mortality, fires, or power outages due to falling nest materials or animals connecting with energized conductors. Power companies typically try to mitigate these conflicts to avoid service disruptions. Performing mitigation measures across all potentially problematic power infrastructure is generally not a practical solution given logistical, time, or budgetary constraints. Therefore, there is a need for quantitative, landscapescale tools to identify conflict risk and prioritize mitigation. We examined the influence of habitat and transmission infrastructure distribution on the potential risk of osprey (Pandion haliaetus) nesting-infrastructure conflict in the Tennessee Valley Authority (TVA) power service area within Tennessee, Alabama, Georgia, Kentucky, Mississippi, North Carolina, and Virginia, USA, using osprey nesting and conflict records from 1990-2020. We modeled risk using a 3-stage approach, which intersected a nesting habitat suitability model and density of infrastructure to evaluate the potential risk of conflict. Habitat suitability was greater on or near open water and closer to developed areas. Transmission line density was low (0.14 ± 0.29 lines/km²) and heterogenous across the service area with more lines within urban centers and near power facilities. Integrating habitat suitability and transmission infrastructure information revealed that very low and low-risk areas comprised 99% of the service area. Less than 1% comprised 1,113.7 km² of moderate and 82.2 km² of high or very high risk, mostly concentrated along major rivers and lakes, and around urban centers. Risk was more prevalent in the northeastern portion of the service area. This work presents a top-down approach to mitigating osprey--power infrastructure conflict, which allows for the prioritization of mitigation actions and can facilitate long-term coexistence with this protected species. [ABSTRACT FROM AUTHOR]

Himalayan Musk deer, Moschus chrysogaster is widely distributed but one of the least studied species in Nepal. In this study, we compiled a total of 429 current presence points of direct observation of the species, pellets droppings, and hoofmarks based on field‐based surveys during 2018–2021 and periodic data held by the Department of National Park and Wildlife Conservation. We developed the species distribution model using an ensemble modeling approach. We used a combination of bioclimatic, anthropogenic, topographic, and vegetation‐related variables to predict the current suitable habitat for Himalayan Musk deer in Nepal. A total of 16 predictor variables were used for habitat suitability modeling after the multicollinearity test. The study shows that the 6973.76 km2 (5%) area of Nepal is highly suitable and 8387.11 km2 (6%) is moderately suitable for HMD. The distribution of HMD shows mainly by precipitation seasonality, precipitation of the warmest quarter, temperature ranges, distance to water bodies, anthropogenic variables, and land use and land cover change (LULC). The probability of occurrence is less in habitats with low forest cover. The response curves indicate that the probability of occurrence of HMD decreases with an increase in precipitation seasonality and remains constant with an increase in precipitation of the warmest quarter. Thus, the fortune of the species distribution will be limited by anthropogenic factors like poaching, hunting, habitat fragmentation and habitat degradation, and long‐term forces of climate change. [ABSTRACT FROM AUTHOR]

Abstract In response to the pressing challenges of the ongoing biodiversity crisis, the protection of endangered species and their habitats, as well as the monitoring of invasive species are crucial. Habitat suitability modeling (HSM) is often treated as the silver bullet to address these challenges, commonly relying on generic variables sourced from widely available datasets. However, for species with high habitat requirements, or for modeling the suitability of habitats within the geographic range of a species, variables at a coarse level of detail may fall short. Consequently, there is potential value in considering the incorporation of more targeted data, which may extend beyond readily available land cover and climate datasets. In this study, we investigate the impact of incorporating targeted land cover variables (specifically tree species composition) and vertical structure information (derived from LiDAR data) on HSM outcomes for three forest specialist bat species (Barbastella barbastellus, Myotis bechsteinii, and Plecotus auritus) in Rhineland‐Palatinate, Germany, compared to commonly utilized environmental variables, such as generic land‐cover classifications (e.g., Corine Land Cover) and climate variables (e.g., Bioclim). The integration of targeted variables enhanced the performance of habitat suitability models for all three bat species. Furthermore, our results showed a high difference in the distribution maps that resulted from using different levels of detail in environmental variables. This underscores the importance of making the effort to generate the appropriate variables, rather than simply relying on commonly used ones, and the necessity of exercising caution when using habitat models as a tool to inform conservation strategies and spatial planning efforts.

Abstract Habitat suitability models have become a valuable tool for wildlife conservation and management, and are frequently used to better understand the range and habitat requirements of rare and endangered species. In this study, we employed two habitat suitability modeling techniques, namely Boosted Regression Tree (BRT) and Maximum Entropy (Maxent) models, to identify potential suitable habitats for the endangered mountain nyala (Tragelaphus buxtoni) and environmental factors affecting its distribution in the Arsi and Ahmar Mountains of Ethiopia. Presence points, used to develop our habitat suitability models, were recorded from fecal pellet counts (n = 130) encountered along 196 randomly established transects in 2015 and 2016. Predictor variables used in our models included major landcover types, Normalized Difference Vegetation Index (NDVI), greenness and wetness tasseled cap vegetation indices, elevation, and slope. Area Under the Curve model evaluations for BRT and Maxent were 0.96 and 0.95, respectively, demonstrating high performance. Both models were then ensembled into a single binary output highlighting an area of agreement. Our results suggest that 1864 km2 (9.1%) of the 20,567 km2 study area is suitable habitat for the mountain nyala with land cover types, elevation, NDVI, and slope of the terrain being the most important variables for both models. Our results highlight the extent to which habitat loss and fragmentation have disconnected mountain nyala subpopulations. Our models demonstrate the importance of further protecting suitable habitats for mountain nyala to ensure the species' conservation.

Abstract Himalayan Musk deer, Moschus chrysogaster is widely distributed but one of the least studied species in Nepal. In this study, we compiled a total of 429 current presence points of direct observation of the species, pellets droppings, and hoofmarks based on field‐based surveys during 2018–2021 and periodic data held by the Department of National Park and Wildlife Conservation. We developed the species distribution model using an ensemble modeling approach. We used a combination of bioclimatic, anthropogenic, topographic, and vegetation‐related variables to predict the current suitable habitat for Himalayan Musk deer in Nepal. A total of 16 predictor variables were used for habitat suitability modeling after the multicollinearity test. The study shows that the 6973.76 km2 (5%) area of Nepal is highly suitable and 8387.11 km2 (6%) is moderately suitable for HMD. The distribution of HMD shows mainly by precipitation seasonality, precipitation of the warmest quarter, temperature ranges, distance to water bodies, anthropogenic variables, and land use and land cover change (LULC). The probability of occurrence is less in habitats with low forest cover. The response curves indicate that the probability of occurrence of HMD decreases with an increase in precipitation seasonality and remains constant with an increase in precipitation of the warmest quarter. Thus, the fortune of the species distribution will be limited by anthropogenic factors like poaching, hunting, habitat fragmentation and habitat degradation, and long‐term forces of climate change.

The Marbled Murrelet ( Brachyramphus marmoratus ) is a tree-nesting seabird found along the Pacific coast of North America whose forest nesting habitat has dramatically declined over the last century. Mapping the remaining nesting habitat is a core step in the conservation of this species at risk. Fine-scale mapping efforts are expected to be enhanced by airborne laser scanning (ALS) data (an application of lidar technology), which provides quantitative measures of forest structure. We present an ALS-informed model for Marbled Murrelet nesting habitat in British Columbia (BC), Canada, using ecologically relevant predictors. Two rare species modeling approaches, i.e., ensembles of small models (ESMs) and maximum entropy modeling (MaxEnt), were evaluated to link 58 nest locations with ALS and non-ALS predictors at a 100-m resolution in Desolation Sound (DS). An independent model transfer was conducted in Clayoquot Sound (CS) with 21 nests. The top model that balanced parsimony with strong performance in both areas was built with MaxEnt using only two predictors, forest vertical complexity and internal forest gaps. This model achieved good performance in DS (AUC = 0.77, Boyce index = 0.99) and reasonable performance in CS (AUC = 0.63, Boyce index = 0.67). On average, the most suitable nesting habitat was found in small clusters (< 2 ha) within older forest and less suitable habitat within younger forest. Our results broadly align with existing maps of nesting habitat produced from low-level aerial surveys. However, we predicted additional suitable nesting areas not detected by aerial habitat mapping and found large variations in habitat quality within mapped areas of suitable forest. This study offers a quantitative method to predict the nesting habitat of an elusive species with a small sample of occurrence records. With successful field verification, our model could be a valuable tool for Marbled Murrelet management in BC.

The Humboldt squid (Dosidicus gigas), is a significant economic species off Peru. The abundance and distribution of the species are highly susceptible to fluctuations in marine environmental conditions. The evolution of mesoscale eddies represents one of the dynamic processes in the ocean, exerting varying degrees of influence on regional biogeochemical processes from generation to dissipation. However, the mechanisms governing the regulation of abundance and spatial distribution of D. gigas off Peru during this evolution remain unclear. Therefore, this study employed normalization techniques and the habitat suitability index (HSI) model, utilizing data from the D. gigas fishery, mesoscale eddies, and marine environmental factors (including sea surface temperature, temperature at 50 m depth, and chlorophyll-a concentration) to analyze the environmental changes and the abundance, spatial distribution, and habitat changes of D. gigas during the evolution of mesoscale eddies. The results indicate that eddies undergo four stages: formation, intensification, maturity, and decay. During eddy evolution, the abundance of D. gigas exhibited an initial increase and a subsequent decrease within cyclonic eddies (CEs), whereas, within anticyclonic eddies (AEs), abundance showed a gradual decline. The outcome of the HSI model revealed that, the habitat suitability and the proportion of suitable habitat areas within eddies were highly consistent with the abundance of D. gigas during all stages of the eddies other than the intensification stage of AEs. The study speculated that both CEs and AEs can create favorable environmental conditions during the maturity stage, thereby leading to an increase in suitable habitat and abundance of D. gigas. These findings emphasize the significant impact of mesoscale eddy evolution on the abundance and habitat distribution of D. gigas.

Desert regions are becoming both warmer and more arid, potentially challenging the ability of even arid-adapted species to exist within their current ranges. Here we analyzed the sensitivity of the common chuckwalla, Sauromalus ater, a vegetarian lizard restricted to western North America's warm deserts, to predicted increases in temperature and aridity. We also assessed the response by their primary food plants to these changing conditions. Our study area included both east-west and elevational aridity gradients. At the eastern, most arid end of this gradient the highest population densities of chuckwallas were restricted to the top elevation category, 600-699 m. In the middle of the east-west gradient, higher chuckwalla densities occurred at elevation categories of 400-599 m and above. At the western, least arid end of the gradient, high chuckwalla densities occurred from elevation categories beginning at 200 m. Their food plants mirrored that distribution trend. We also constructed independent habitat models to predict current and future suitable ranges for both the lizards and their food plants. Potential climate refugia exist where modeled current and predicted future ranges overlap. Our empirical elevation data for where chuckwallas and their food plants exist at higher densities mirrored the climate refugia predicted by our models; current higher density populations largely already reside in putative climate refugia. Chuckwallas residing below these refugia will find conditions increasingly challenging, and all populations will need to shift to higher elevations if future levels of aridity exceed the values used in our models.

Fruit bats serve as crucial bioindicators, seed dispersers, pollinators, and contributors to food security within ecosystems. However, their population and distribution were threatened by climate change and anthropogenic pressures. Understanding the impacts of these pressures through mapping distribution and habitat suitability is crucial for identifying high‐priority areas and implementing effective conservation and management plans. We predicted the distribution and extent of habitat suitability for Rousettus aegyptiacus and Epomophorus labiatus under climate change scenarios using average predictions from four different algorithms to produce an ensemble model. Seasonal precipitation, population index, land‐use land cover, vegetation, and the mean temperature of the driest quarter majorly contributed to the predicted habitat suitability for both species. The current predicted sizes of suitable habitats for R. aegyptiacus and E. labiatus were varied, on average 60,271.4 and 85,176.1 km2, respectively. The change in species range size for R. aegyptiacus showed gains in suitable areas of 24.4% and 22.8% in 2050 and 2070, respectively. However, for E. labiatus, suitable areas decreased by 0.95% and 2% in 2050 and 2070, respectively. The range size change of suitable areas between 2050 and 2070 for R. aegyptiacus and E. labiatus shows losses of 1.5% and 1.2%, respectively. The predicted maps indicate that the midlands and highlands of southern and eastern Ethiopia harbor highly suitable areas for both species. In contrast, the areas in the northern and central highlands are fragmented. The current model findings show that climate change and anthropogenic pressures have notable impacts on the geographic ranges of two species. Moreover, the predicted suitable habitats for both species are found both within and outside of their historical ranges, which has important implications for conservation efforts. Our ensemble predictions are vital for identifying high‐priority areas for fruit bat species conservation efforts and management to mitigate climate change and anthropogenic pressures. [ABSTRACT FROM AUTHOR]

Louisiana is facing a land loss crisis with 5,000 km2 lost since the 1930s, and another 10,000 km2 may disappear over the next 50 years. In response, the state developed a Comprehensive Master Plan for a Sustainable Coast, which includes predictions for how wildlife species respond to futures with or without restoration action. However, the 2017 Master Plan did not include a bird species representative of brackish or freshwater wetland habitats. Our objective therefore was to evaluate the potential for the Bald Eagle (Haliaeetus leucocephalus) to serve as an indicator of freshwater wetland health, and to develop a model to predict Bald Eagle responses to land cover change 20 and 50 years into the future, with or without restoration action. Using nest location data collected by the Louisiana Department of Wildlife and Fisheries from the Barataria Basin, we modeled the relationship between Bald Eagle nest occurrence and land cover using boosted regression trees. Model performance was strong, explaining 54.4 ± 0.1% of the deviance in nest occurrence, and showed that Bald Eagle nests were more common in areas with greater fresh forested wetland, freshwater marsh, and flotant marsh land cover. Future suitability for nesting Bald Eagles was higher in 20 and 50 years with restoration action compared to without, although suitability in both years was lower than contemporary suitability, regardless of restoration action. Our model suggests that comprehensive restoration action is needed, including the implementation of Mississippi River diversions, to sustain high densities of nesting Bald Eagles in coastal Louisiana. [ABSTRACT FROM AUTHOR]

Climate change manifestation in the ocean, through warming, oxygen loss, increasing acidification, and changing particulate organic carbon flux (one metric of altered food supply), is projected to affect most deep-ocean ecosystems concomitantly with increasing direct human disturbance. Climate drivers will alter deep-sea biodiversity and associated ecosystem services, and may interact with disturbance from resource extraction activities or even climate geoengineering. We suggest that to ensure the effective management of increasing use of the deep ocean (e.g., for bottom fishing, oil and gas extraction, and deep-seabed mining), environmental management and developing regulations must consider climate change. Strategic planning, impact assessment and monitoring, spatial management, application of the precautionary approach, and full-cost accounting of extraction activities should embrace climate consciousness. Coupled climate and biological modeling approaches applied in the water and on the seafloor can help accomplish this goal. For example, Earth-System Model projections of climate-change parameters at the seafloor reveal heterogeneity in projected climate hazard and time of emergence (beyond natural variability) in regions targeted for deep-seabed mining. Models that combine climate-induced changes in ocean circulation with particle tracking predict altered transport of early life stages (larvae) under climate change. Habitat suitability models can help assess the consequences of altered larval dispersal, predict climate refugia, and identify vulnerable regions for multiple species under climate change. Engaging the deep observing community can support the necessary data provisioning to mainstream climate into the development of environmental management plans. To illustrate this approach, we focus on deep-seabed mining and the International Seabed Authority, whose mandates include regulation of all mineral-related activities in international waters and protecting the marine environment from the harmful effects of mining. However, achieving deep-ocean sustainability under the UN Sustainable Development Goals will require integration of climate consideration across all policy sectors.

Climate and land cover change are critical drivers of avian species range shift. Thus, predicting avian species' response to the land and climate changes and identifying their future suitable habitats can help their conservation planning. The common pheasant (Phasianus colchicus) is a species of conservation concern in Iran and is included in the list of Iran's protected avian species. The species faces multiple threats such as habitat destruction, land cover change, and overhunting in the country. In this study, we model the potential impacts of future climate and land cover change on the habitat suitability of common pheasant (Phasianus colchicus) along elevational gradients in Mazandaran province in Iran. We used shared socioeconomic pathways (SSP) scenarios and the 2015–2020 trend to generate possible future land cover projections for 2050. As for climate change projections, we used representative concentration pathway (RCP) scenarios. Next, we applied current and future climate and land cover projections to investigate how habitat suitability of common pheasant will change between 2020 and 2050 using species distribution modeling (SDM). Our results show that the species has 6000 km2 suitable habitat; however, between 900 and 1965 km2 of its habitat may be reduced by 2050. Furthermore, we found that the severity of the effects of climate and land cover change varies at different altitudes. At low altitudes, the impact of changing land structure is superior. Instead, climate change has a critical role in habitat loss at higher altitudes and imposes a limiting role on the potential range shifts. Overall, this study demonstrates the vital role of land cover and climate change in better understanding the potential alterations in avian distribution. [ABSTRACT FROM AUTHOR]

In Big Cypress National Preserve, the federally threatened Everglades bully (Sideroxylon reclinatum subsp. austrofloridense) is sympatric with its conspecific, more widespread relative, the Florida bully (Sideroxylon reclinatum subsp. reclinatum). In this area of overlap, the only reliable characters to distinguish the two are cryptic, micromorphological traits of the abaxial laminar surface. In order to better understand the distribution of the federally threatened taxon, we used a combination of habitat suitability modeling (HSM), field surveys, and microscopy. Using models to inform initial surveys, we collected leaf material of 96 individuals in the field, 86 of which we were able to identify to subspecies. Of these, 73 (85%) were identified as the threatened taxon, expanding both the known range and population size within Big Cypress. We used these 73 new occurrences to rerun HSMs to create a more accurate picture of where the taxon is likely to occur. A total of 15,015 hectares were predicted to be suitable habitat within Big Cypress, with 34,069 hectares across the entire study area. These model results could be used to inform the critical habitat designation for this taxon. For at-risk, cryptic taxa, such as the Everglades bully, multiple approaches are needed to inform management and conservation priorities, including the consideration of a hybridization zone. [ABSTRACT FROM AUTHOR]

Mountain vipers of the genus Montivipera, generally, and the species of the Raddei clade, specifically, are interesting examples of species neo-endemism in Iran, Anatolia, and the Caucasus. Given the critical conservation status of these species, it is necessary to identify their suitable habitats for prioritizing conservation measures. We modeled ecological niche of each species based on four species distribution models, including generalized linear models, generalized boosting models, random forest, and maximum entropy and combined them in an ensemble model. Also, using a new principal component analysis (PCA-env), the ecological niche divergence of the species was investigated. Results indicated that all models with AUC and TSS > 0.9 had excellent predictive performance. The species of the Raddei clade showed different patterns of ecological niche occupation and the greatest differentiation was seen in the gradient of temperature seasonality, annual precipitation, and topographical ruggedness. M. latifii and M. kuhrangica, despite the great geographical distance, revealed the highest degree of niche overlap and niche similarity. Due to the restricted distribution and limited evolutionary adaptability of mountain vipers in the mountainous regions of Alborz and Zagros, it is imperative to plan conservation measures to reduce the factors that threaten the long-term survival of these species.

• Large increase in suitable area for the winter breeding populations by 2100. • Increased overlap between the distribution of migratory and resident monarchs. • Increased risk of disease spread from resident monarchs to the migratory monarchs. The charismatic migratory populations of monarch butterflies have declined precipitously in North America. A contributing threat might be the expansion of winter breeding populations in the southern portions of their historical eastern and western summer breeding ranges. Recent research suggests individuals from winter breeding populations are prone to high parasite burdens, resulting in lower fitness compared to migratory counterparts. Temporal and spatial overlap between these individuals and migratory monarchs in both fall and spring mean that interbreeding and use of the same host plants could result in transfer of parasites, especially the debilitating neogregarine Ophryocystis elektroscirrha , increasing the parasite load in migrating populations. We aimed to predict how climate change could affect the distribution of winter breeding monarchs in North America. We used ecological niche modeling of monarch larval observations for winter and current climate data to predict the current and future distributions of winter breeding monarchs across North America. Our analyses predict up to a 38% and 160% increase and a 574 and 340 km northward shift in suitable area for winter breeding monarchs in response to climate change by 2100 for eastern and western migratory populations, respectively. Our results support concerns over potential risk of disease spread from resident monarchs to the migratory monarch populations. In both eastern and western migratory populations this is due to an increase in overlap between the resident population and the areas through which the migratory populations travel during fall and spring migrations. Our results support calls for controlling the spread of non-native tropical milkweed, as winter breeding monarchs depend on this plant for reproduction. [ABSTRACT FROM AUTHOR]

Global change is impacting the oceans in an unprecedented way, and multiple lines of evidence suggest that species distributions are changing in space and time. There is increasing evidence that multiple environmental stressors act together to constrain species habitat more than expected from warming alone. Here, we conducted a comprehensive study of how temperature and aragonite saturation state act together to limit Limacina helicina, globally distributed pteropods that are ecologically important pelagic calcifiers and an indicator species for ocean change. We co‐validated three different approaches to evaluate the impact of ocean warming and acidification (OWA) on the survival and distribution of this species in the California Current Ecosystem. First, we used colocated physical, chemical, and biological data from three large‐scale west coast cruises and regional time series; second, we conducted multifactorial experimental incubations to evaluate how OWA impacts pteropod survival; and third, we validated the relationships we found against global distributions of pteropods and carbonate chemistry. OWA experimental work revealed mortality increases under OWA, while regional habitat suitability indices and global distributions of L. helicina suggest that a multi‐stressor framework is essential for understanding pteropod distributions. In California Current Ecosystem habitats, where pteropods are living close to their thermal maximum already, additional warming and acidification through unabated fossil fuel emissions (RCP 8.5) are expected to dramatically reduce habitat suitability. [ABSTRACT FROM AUTHOR]

Habitat suitability models are useful tools for a variety of wildlife management objectives. Distributions of wildlife species can be predicted for geographical areas that have not been extensively surveyed. The basis of these models' work is to minimize the relationship between species distribution and biotic and abiotic environments. For some species, there is information about presence and absence that allows the use of a variety of standard statistical methods. However, absence data is not available for most species. Nowadays, the methods that need presence-only data have been expanded. One of these methods is the Maximum Entropy (MaxEnt) model. The purpose of this study is to model the habitat of Urial (Ovis orientalis arkal) in the Samelghan plain in the North East of Iran with the MaxEnt method. This algorithm uses the Jackknife plot and percent contribution values to determine the significance of the variables. The results showed that variables such as southern aspects, Juniperus-Acer, Artemisia-Perennial plants, slope 0-5%, and asphalt road were the most important factors affecting the species’ habitat selection. The area under the curve (AUC) Receiver Operating Characteristic (ROC) showed excellent model performance. Suitable habitat was classified based on the threshold value (0.0513) and the ROC, which, based on the results, 28% of the area was a suitable habitat for Urial. Doi: 10.28991/HEF-2021-02-04-05 Full Text: PDF

Successful species reintroduction requires restoration of receiving habitats to support growth, survival, and reproduction that reverse the initial causes of decline. Little is known about whether present habitat conditions can support all life stages of reintroduced southern Lake Sturgeon populations that were possibly extirpated by the mid‐1900s due to overharvest and habitat degradation. Therefore, we conducted a telemetry study to assess annual adult and subadult and overwinter age‐0 Lake Sturgeon habitat selection and suitability in two Missouri River U.S. tributaries near the southern edge of the species range. Spring habitat selection models were unable to define spawning habitat criteria, but criteria from other studies suggest that substrate and depths for spawning are suitable in both rivers. In the summer and winter, adult and subadult Lake Sturgeon exhibited strong selection for pools greater than 8 m deep, which comprised less than 5% of our study streams. Habitat selection in the fall and winter by age‐0 Lake Sturgeon differed from adults with age‐0s selecting shallower habitats both rivers and swifter current velocities in the Gasconade River. General habitat patterns persisted for both life stages in each river regardless of habitat availability, suggesting specialized habitat requirements in southern Lake Sturgeon that differ from previously studied populations further north. These results may be used to direct sampling for validation of reproduction and restoration of not only spawning habitats, but age‐0 and summer and winter refugia that may be potential restoration bottlenecks for southern Lake Sturgeon populations. [ABSTRACT FROM AUTHOR]

Mountain vipers of the genus Montivipera, generally, and the species of the Raddei clade, specifically, are interesting examples of species neo-endemism in Iran, Anatolia, and the Caucasus. Given the critical conservation status of these species, it is necessary to identify their suitable habitats for prioritizing conservation measures. We modeled ecological niche of each species based on four species distribution models, including generalized linear models, generalized boosting models, random forest, and maximum entropy and combined them in an ensemble model. Also, using a new principal component analysis (PCA-env), the ecological niche divergence of the species was investigated. Results indicated that all models with AUC and TSS > 0.9 had excellent predictive performance. The species of the Raddei clade showed different patterns of ecological niche occupation and the greatest differentiation was seen in the gradient of temperature seasonality, annual precipitation, and topographical ruggedness. M. latifii and M. kuhrangica, despite the great geographical distance, revealed the highest degree of niche overlap and niche similarity. Due to the restricted distribution and limited evolutionary adaptability of mountain vipers in the mountainous regions of Alborz and Zagros, it is imperative to plan conservation measures to reduce the factors that threaten the long-term survival of these species. [ABSTRACT FROM AUTHOR]

Abstract Species distribution models (SDM) have been increasingly developed in recent years, but their validity is questioned. Their assessment can be improved by the use of independent data, but this can be difficult to obtain and prohibitive to collect. Standardized data from citizen science may be used to establish external evaluation datasets and to improve SDM validation and applicability. We used opportunistic presence‐only data along with presence–absence data from a standardized citizen science program to establish and assess habitat suitability maps for 9 species of amphibian in western France. We assessed Generalized Additive and Random Forest Models’ performance by (1) cross‐validation using 30% of the opportunistic dataset used to calibrate the model or (2) external validation using different independent datasets derived from citizen science monitoring. We tested the effects of applying different combinations of filters to the citizen data and of complementing it with additional standardized fieldwork. Cross‐validation with an internal evaluation dataset resulted in higher AUC (Area Under the receiver operating Curve) than external evaluation causing overestimation of model accuracy and did not select the same models; models integrating sampling effort performed better with external validation. AUC, specificity, and sensitivity of models calculated with different filtered external datasets differed for some species. However, for most species, complementary fieldwork was not necessary to obtain coherent results, as long as the citizen science data were strongly filtered. Since external validation methods using independent data are considered more robust, filtering data from citizen sciences may make a valuable contribution to the assessment of SDM. Limited complementary fieldwork with volunteer's participation to complete ecological gradients may also possibly enhance citizen involvement and lead to better use of SDM in decision processes for nature conservation.

Understanding the impacts of climate change, landscape composition, topographic attributes, and anthropogenic disturbance on wildlife distribution is critical to selecting prior conservation areas and to executing successful management plans. Habitat suitability mapping aims to predict species' ecological niches and ranges using these factors as predictors. There has been no study of the potentially suitable habitat for the data-deficient Djaffa Mountains Guereza and the factors affecting its distribution in the Ahmar Mountains. The objective of this study was to predict the habitat suitability and distribution models of Djaffa Mountains Guereza in eastern Ethiopian highland under different climate change scenarios using maximum entropy (MaxEnt) model. We used 47 occurrence datasets assembled between 2020 and 2021 and 10 ecological predictor variables processed in geographical information systems and R software. We found that 133,604.52 ha (1.77%) of the 753, 0749.39 ha study area is suitable habitat for the Djaffa Mountains Guereza, of which 18,326.12 (13.33%) ha were highly suitable, whereas the remaining area was moderately suitable. Our models also showed that precipitation of the wettest quarter had the highest contribution to predicting the habitat suitability (65.90%), followed by land cover (17.40%). Habitat suitability was directly related to precipitation seasonality and the precipitation of the wettest quarter, while it is indirectly related to temperature seasonality and the temperature mean diurnal range. The models showed the overall gain in the suitable habitat of Djaffa Mountains Guereza under all future climate scenarios, however, the projected habitat distribution show fragmentation. Our habitat suitability and distribution models provide critical information for the conservation and management of Djaffa Mountains Guereza by recommending stocking of the fragmented forests, assessment of conservation challenges, and mitigations of climate change. A comprehensive population assessment throughout their restricted distribution is also crucial to understand conservation status and population size.

In this study we aim at refining our understanding of the floristic connectivity of the loma- and precordillera floras of southern Peru and northern Chile and the parameters determining vegetation cover in this region. We used multivariate analyses to test for floristic- and environmental similarity across 53 precordillera and loma locations in Peru and Chile. We propose the use of predictive modeling in estimating the extent of desert vegetation as a complementary method to remote sensing. We created habitat suitability models for the vegetation on the coast and in the precordillera based on a combination of latent bioclimatic variables and additional environmental predictors using Maxent. We found Peruvian and Chilean lomas to be strongly floristically differentiated, as are the Chilean precordillera and lomas. Conversely, there is clear connectivity between both the Peruvian loma- and precordillera floras on the one hand and the Peruvian and Chilean precordillera floras on the other. Divergent environmental conditions were retrieved as separating the precordillera and lomas, while environmental conditions are not differentiated between Peruvian and Chilean lomas. Peruvian and Chilean precordilleras show a gradual change in environmental conditions. Habitat suitability models of vegetation cover retrieve a gap for the loma vegetation along the coast between Peru and Chile, while a continuous belt of suitable habitats is retrieved along the Andean precordillera. Unsuitable habitat for loma vegetation north and south of the Chilean and Peruvian border likely represents an ecogeographic barrier responsible for the floristic divergence of Chilean and Peruvian lomas. Conversely, environmental parameters change continuously along the precordilleras, explaining the moderate differentiation of the corresponding floras. Our results underscore the idea of the desert core acting as an ecogeographic barrier separating the coast from the precordillera in Chile, while it has a more limited isolating function in Peru. We also find extensive potentially suitable habitats for both loma- and precordillera vegetation so far undetected by methods of remote sensing.

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Abstract Global biodiversity declines, largely driven by climate and land‐use changes, urge the development of transparent guidelines for effective conservation strategies. Species distribution modeling (SDM) is a widely used approach for predicting potential shifts in species distributions, which can in turn support ecological conservation where environmental change is expected to impact population and community dynamics. Improvements in SDM accuracy through incorporating intra‐ and interspecific processes have boosted the SDM field forward, but simultaneously urge harmonizing the vast array of SDM approaches into an overarching, widely adoptable, and scientifically justified SDM framework. In this review, we first discuss how climate warming and land‐use change interact to govern population dynamics and species’ distributions, depending on species’ dispersal and evolutionary abilities. We particularly emphasize that both land‐use and climate change can reduce the accessibility to suitable habitat for many species, rendering the ability of species to colonize new habitat and to exchange genetic variation a crucial yet poorly implemented component of SDM. We then unite existing methodological SDM practices that aim to increase model accuracy through accounting for multiple global change stressors, dispersal, or evolution, while shifting our focus to model feasibility. We finally propose a roadmap harmonizing model accuracy and feasibility, applicable to both common and rare species, particularly those with poor dispersal abilities. This roadmap (a) paves the way for an overarching SDM framework allowing comparison and synthesis of different SDM studies and (b) could advance SDM to a level that allows systematic integration of SDM outcomes into effective conservation plans.

In Big Cypress National Preserve, the federally threatened Everglades bully (Sideroxylon reclinatum subsp. austrofloridense) is sympatric with its conspecific, more widespread relative, the Florida bully (Sideroxylon reclinatum subsp. reclinatum). In this area of overlap, the only reliable characters to distinguish the two are cryptic, micromorphological traits of the abaxial laminar surface. In order to better understand the distribution of the federally threatened taxon, we used a combination of habitat suitability modeling (HSM), field surveys, and microscopy. Using models to inform initial surveys, we collected leaf material of 96 individuals in the field, 86 of which we were able to identify to subspecies. Of these, 73 (85%) were identified as the threatened taxon, expanding both the known range and population size within Big Cypress. We used these 73 new occurrences to rerun HSMs to create a more accurate picture of where the taxon is likely to occur. A total of 15,015 hectares were predicted to be suitable habitat within Big Cypress, with 34,069 hectares across the entire study area. These model results could be used to inform the critical habitat designation for this taxon. For at-risk, cryptic taxa, such as the Everglades bully, multiple approaches are needed to inform management and conservation priorities, including the consideration of a hybridization zone.

Habitat suitability modeling and mapping are important aspects of long-term strategies for sustaining plant ecosystems. In this study, seven state-of-the-art machine learning models including boosted regression tree (BRT), functional discriminant analysis (FDA), generalized linear model (GLM), multivariate adaptive regression splines (MARS), mixture discriminant analysis (MDA), random forest (RF), and support vector machine (SVM) were applied to model habitat suitability for Ferula gummosa medicinal plant in the Firozkuh County of Tehran. Different factors that affect the habitat of this plant were prepared for modeling, including slope angle, silt percentage, sand percentage, aspect, annual mean rainfall, clay percentage, topographic wetness index, elevation, distance from rivers, drainage density, annual mean temperature, plan curvature, profile curvature, land use, lithological units, and organic carbon. After running the models in R software, their evaluation using various measures (area under the curve, accuracy, precision, F-measure, fallout, true skill statistics, and corrected classify instances) indicated that the RF model was the best one for assessing Ferula gummosa habitat suitability. The SVM, MARS, MDA, GLM, FDA, and BRT models also displayed acceptable performances. The results of our study contribute to the understanding of the stability of the medicinal plant Ferula gummosa and to help avoid its extinction in the future. [ABSTRACT FROM AUTHOR]

Globally, there is growing interest in restoring previously widespread oyster reefs to reinstate key ecosystem services such as shoreline protection, fisheries productivity and water filtration. Yet, since peak expiration of oysters in the 1800s, significant and ongoing environmental change has occurred. Estuaries and coasts are undergoing some of the highest rates of urbanization, warming and ocean acidification on the planet, necessitating novel approaches to restoration. Here, we review key design considerations for oyster reef restoration projects that maximize the probability that they will meet biological and socio-economic goals not only under present-day conditions, but into the future. This includes selection of sites, and where required, substrates and oyster species and genotypes for seeding, not only on the basis of their present and future suitability in supporting oyster survival, growth and reproduction, but also based on their match to specific goals of ecosystem service delivery. Based on this review, we provide a road map of design considerations to maximize the success of future restoration projects.

Populations of large mammals have declined at alarming rates, especially in areas with intensified land use where species can only persist in small habitat fragments. To support conservation planning, we developed habitat suitability models for the Walia ibex (Capra walie), an endangered wild goat endemic to the Simen Mountains, Ethiopia. We calibrated several models that differ in statistical properties to estimate the spatial extent of suitable habitats of the Walia ibex in the Simen Mountains, as well as in other parts of the Ethiopian highlands to assess potentially suitable areas outside the current distribution range of the species. We further addressed the potential consequences of future climate change using a climate model with four emission scenarios. Model projections estimated the potential suitable habitat under current climate to 501–672 km2 in Simen and 6,251–7,732 km2 in other Ethiopian mountains. Under projected climate change by 2,080, the suitable habitat became larger in Simen but smaller in other parts of Ethiopia. The projected expansion in Simen is contrary to the general expectation of shrinking suitable habitats for high-elevation species under climate warming and may partly be due to the ruggedness of these particular mountains. The Walia ibex has a wide altitudinal range and is able to exploit very steep slopes, allowing it to track the expected vegetation shift to higher altitudes. However, this potential positive impact may not last long under continued climate warming, as the species will not have much more new space left to colonize. Our study indicates that the current distribution range can be substantially increased by reintroducing and/or translocating the species to other areas with suitable habitat. Indeed, to increase the viability and prospects for survival of this flagship species, we strongly recommend human-assisted reintroduction to other Ethiopian mountains. Emulating the successful reintroduction of the Alpine ibex that has spread from a single mountain in Italy to its historical ranges of the Alps in Europe might contribute to saving the Walia ibex from extinction.

It is predicted that climate change will strongly affect plant distributions in high elevation “sky islands” of tropical Andes. Polylepis forests are a dominant element of the treeline throughout the Andes Cordillera in South America. However, little is known about the climatic factors underlying the current distribution of Polylepis trees and the possible effect of global climate change. The species Polylepis quadrijuga is endemic to the Colombian Eastern Cordillera, where it plays a fundamental ecological role in high-altitude páramo-forest ecotones. We sought to evaluate the potential distribution of P. quadrijuga under future climate change scenarios using ensemble modeling approaches. We conducted a comprehensive assessment of future climatic projections deriving from 12 different general circulation models (GCMs), four Representative Concentration Pathways (R) emissions scenarios, and two different time frames (2041–2060 and 2061–2080). Additionally, based on the future projections, we evaluate the effectiveness of the National System of Protected Natural Areas of Colombia (SINAP) and Páramo Complexes of Colombia (PCC) in protecting P. quadrijuga woodlands. Here, we compiled a comprehensive set of observations of P. quadrijuga and study them in connection with climatic and topographic variables to identify environmental predictors of the species distribution, possible habitat differentiation throughout the geographic distribution of the species, and predict the effect of different climate change scenarios on the future distribution of P. quadrijuga. Our results predict a dramatic loss of suitable habitat due to climate change on this key tropical Andean treeline species. The ensemble Habitat Suitability Modeling (HSM) shows differences in suitable scores among north and south regions of the species distribution consistent with differences in topographic features throughout the available habitat of P. quadrijuga. Future projections of the HSM predicted the Páramo complex “Sumapaz-Cruz Verde” as a major area for the long-term conservation of P. quadrijuga because it provides a wide range of suitable habitats for the different evaluated climate change scenarios. We provide the first set of priority areas to perform both in situ and ex situ conservation efforts based on suitable habitat projections.

Abstract The Akikiki (Oreomystis bairdi) and Akekee (Loxops caeruleirostris) are two honeycreepers endemic to Kauai, Hawaii, that were listed as federally endangered in 2010. Both species are rare, little‐studied, and occur in a remote, roadless area. We analyzed high‐resolution airborne lidar data to identify forest structure and topography metrics associated with Akikiki and Akekee nest locations (88 for Akikiki and 22 for Akekee) and occurrences (3706 for Akikiki and 1581 for Akekee) from 2012 to 2017 on the Alakai Plateau to predict their distribution in unsurveyed areas. Akikiki and Akekee nested in areas with similar forest structure at 10 m resolution, but different maximum tree heights. Akikiki and Akekee foraged in areas with significantly different forest structure (maximum tree height, mean canopy height, relative heights) and topography (slope) based on occurrences. Elevation was consistently one of the most important metrics for predicting both species nest locations and occurrences across scales (10, 100, 250 m) and it appears that both species are at the upper limits of their elevational range. We estimate that the area of suitable nesting habitat for Akikiki is 17.59 km2 while the area of suitable nesting habitat for Akekee is 11.10 km2 at 10 m resolution. The Akikiki has a potential range of 38 km2 while the Akekee has a range of 58 km2 at 100 m resolution. We produce predictive nest and occurrence maps at 10 m and 100 m resolutions to spatially target conservation actions. Results suggest that if avian malaria cannot be controlled and both species populations do not stabilize over the coming years, translocation may be needed to insure their viability.

The Akikiki (Oreomystis bairdi) and Akekee (Loxops caeruleirostris) are two honeycreepers endemic to Kauai, Hawaii, that were listed as federally endangered in 2010. Both species are rare, little‐studied, and occur in a remote, roadless area. We analyzed high‐resolution airborne lidar data to identify forest structure and topography metrics associated with Akikiki and Akekee nest locations (88 for Akikiki and 22 for Akekee) and occurrences (3706 for Akikiki and 1581 for Akekee) from 2012 to 2017 on the Alakai Plateau to predict their distribution in unsurveyed areas. Akikiki and Akekee nested in areas with similar forest structure at 10 m resolution, but different maximum tree heights. Akikiki and Akekee foraged in areas with significantly different forest structure (maximum tree height, mean canopy height, relative heights) and topography (slope) based on occurrences. Elevation was consistently one of the most important metrics for predicting both species nest locations and occurrences across scales (10, 100, 250 m) and it appears that both species are at the upper limits of their elevational range. We estimate that the area of suitable nesting habitat for Akikiki is 17.59 km2 while the area of suitable nesting habitat for Akekee is 11.10 km2 at 10 m resolution. The Akikiki has a potential range of 38 km2 while the Akekee has a range of 58 km2 at 100 m resolution. We produce predictive nest and occurrence maps at 10 m and 100 m resolutions to spatially target conservation actions. Results suggest that if avian malaria cannot be controlled and both species populations do not stabilize over the coming years, translocation may be needed to insure their viability. [ABSTRACT FROM AUTHOR]

Species distribution models (SDM) have been increasingly developed in recent years, but their validity is questioned. Their assessment can be improved by the use of independent data, but this can be difficult to obtain and prohibitive to collect. Standardized data from citizen science may be used to establish external evaluation datasets and to improve SDM validation and applicability.We used opportunistic presence‐only data along with presence–absence data from a standardized citizen science program to establish and assess habitat suitability maps for 9 species of amphibian in western France. We assessed Generalized Additive and Random Forest Models' performance by (1) cross‐validation using 30% of the opportunistic dataset used to calibrate the model or (2) external validation using different independent datasets derived from citizen science monitoring. We tested the effects of applying different combinations of filters to the citizen data and of complementing it with additional standardized fieldwork.Cross‐validation with an internal evaluation dataset resulted in higher AUC (Area Under the receiver operating Curve) than external evaluation causing overestimation of model accuracy and did not select the same models; models integrating sampling effort performed better with external validation. AUC, specificity, and sensitivity of models calculated with different filtered external datasets differed for some species. However, for most species, complementary fieldwork was not necessary to obtain coherent results, as long as the citizen science data were strongly filtered.Since external validation methods using independent data are considered more robust, filtering data from citizen sciences may make a valuable contribution to the assessment of SDM. Limited complementary fieldwork with volunteer's participation to complete ecological gradients may also possibly enhance citizen involvement and lead to better use of SDM in decision processes for nature conservation. [ABSTRACT FROM AUTHOR]

Egyptian flower mantis Blepharopsis mendica (Order: Mantodea) is a widespread mantis species throughout the southwest Palearctic region. The ecological and geographical distribution of such interesting species is rarely known. So, through this work, habitat suitability models for its distribution through Egyptian territory were created using MaxEnt software from 90 occurrence records. One topographic (altitude) and eleven bioclimatic variables influencing the species distribution were selected to generate the models. The predicted distribution in Egypt was focused on the Delta, South Sinai, the north-eastern part of the country, and some areas in the west including Siwa Oasis. Temporal analysis between the two periods (1900–1961) and (1961–2017) show current reduction of this species distribution through Delta and its surrounding areas, may be due to urbanization. On the other hand, it increases in newly protected areas of South Sinai. Under the future climate change scenario, the MaxEnt model predicted the habitat gains for B. mendica in RCP 2.6 for 2070 and loss of habitat in RCP 8.5 for the same year. Our results can be used as a basis for conserving this species not only in Egypt, but also throughout the whole of its range, also, it show how the using of geo-information could help in studying animal ecology. [ABSTRACT FROM AUTHOR]

Previous studies of habitat suitability of sessile organisms on subtidal rocky substrata have been focused only one or two terrain attributes. In this study, we propose a new method to construct a centimeter resolution seafloor topographic model by using underwater photogrammetry to obtain multiple terrain variables and to investigate relationships between the distribution of sessile organisms and multiple terrain variables. Point cloud models of five square sections (11.3–25.5 m2) of the bedrock surface of Otsuchi Bay were reconstructed with a 0.05 m resolution. Using the 0.01 m resolution point cloud models, five terrain variables were calculated on each face of the mesh models: height above seafloor, topological position index, slope, aspect, and ruggedness. The presence/absence data of four species of sessile organisms (ascidian Halocynthia roretzi, barnacle Balanus trigonus, polychaete Paradexiospira nakamurai, and articulated coralline algae Pachyarthron cretaceum) were located on the mesh models. H. roretzi and B. trigonus were more abundant on vertical and high faces above the seafloor, and P. nakamurai were more abundant at high faces above the surroundings. In high position where the current velocity increases, the three sessile animals may have an advantage for their suspension feeding. In contrast, P. cretaceum, unlike the other three sessile animal species, occurred at various heights and on gentle slope faces suitable for photosynthesis.

Species distribution models (SDMs) are widely used in ecology, biogeography and conservation biology to estimate relationships between environmental variables and species occurrence data and make predictions of how their distributions vary in space and time. During the past two decades, the field has increasingly made use of machine learning approaches for constructing and validating SDMs. Model accuracy has steadily increased as a result, but the interpretability of the fitted models, for example the relative importance of predictor variables or their causal effects on focal species, has not always kept pace. Here we draw attention to an emerging subdiscipline of artificial intelligence, explainable AI (xAI), as a toolbox for better interpreting SDMs. xAI aims at deciphering the behavior of complex statistical or machine learning models (e.g. neural networks, random forests, boosted regression trees), and can produce more transparent and understandable SDM predictions. We describe the rationale behind xAI and provide a list of tools that can be used to help ecological modelers better understand complex model behavior at different scales. As an example, we perform a reproducible SDM analysis in R on the African elephant and showcase some xAI tools such as local interpretable model‐agnostic explanation (LIME) to help interpret local‐scale behavior of the model. We conclude with what we see as the benefits and caveats of these techniques and advocate for their use to improve the interpretability of machine learning SDMs. [ABSTRACT FROM AUTHOR]

Global biodiversity declines, largely driven by climate and land‐use changes, urge the development of transparent guidelines for effective conservation strategies. Species distribution modeling (SDM) is a widely used approach for predicting potential shifts in species distributions, which can in turn support ecological conservation where environmental change is expected to impact population and community dynamics. Improvements in SDM accuracy through incorporating intra‐ and interspecific processes have boosted the SDM field forward, but simultaneously urge harmonizing the vast array of SDM approaches into an overarching, widely adoptable, and scientifically justified SDM framework. In this review, we first discuss how climate warming and land‐use change interact to govern population dynamics and species' distributions, depending on species' dispersal and evolutionary abilities. We particularly emphasize that both land‐use and climate change can reduce the accessibility to suitable habitat for many species, rendering the ability of species to colonize new habitat and to exchange genetic variation a crucial yet poorly implemented component of SDM. We then unite existing methodological SDM practices that aim to increase model accuracy through accounting for multiple global change stressors, dispersal, or evolution, while shifting our focus to model feasibility. We finally propose a roadmap harmonizing model accuracy and feasibility, applicable to both common and rare species, particularly those with poor dispersal abilities. This roadmap (a) paves the way for an overarching SDM framework allowing comparison and synthesis of different SDM studies and (b) could advance SDM to a level that allows systematic integration of SDM outcomes into effective conservation plans. [ABSTRACT FROM AUTHOR]

Climate change and human activities influence species biodiversity by altering their habitats. This paper quantitatively analyzed the effects of climate change on a migratory bird. The Lesser White-fronted Goose (LWfG), a species which migrates via the middle and lower reaches of the Yangtze River region, is an herbivorous species of high ecological value. It is an endangered species threatened by climate change and human activities, so comprehensive information about its distribution is required. To assess the effectiveness of conservation of the LWfG under climate change, both climate variables and human activities are often used to predict the potential changes in the distribution and habitat suitability for LWfG. In this work, the current scenario and the Global Circulation Models (GCMs) climate scenarios were used to simulate the future distribution of the species. However, besides climate change and human activities, the spatial pattern of plants surrounding the wetland is also known to be closely related to the distribution of LWfG. Therefore, the distribution model results of six plant species related to LWfG's diet selection were used as environment variables to reflect the changes of suitable LWfG habitat. These environmental variables significantly improved the model's performance for LWfG, since the birds were clearly influenced by the plant distribution factors. Meanwhile, the suitable habitat area decreases by 2070 in GCM models under two representative concentration pathways scenarios (RCP2.6 and RCP8.5). More appropriate management and conservation policies should be taken to adapt to future climate change. These adjustments include modifications of the size, shape and use of the conservation area for this species. [ABSTRACT FROM AUTHOR]

Coastal habitats have experienced significant degradation and fragmentation in recent decades under the strain of interacting ecosystem stressors. To maintain biodiversity and ecosystem functioning, coastal managers and restoration practitioners face the urgent tasks of identifying priority areas for protection and developing innovative, scalable approaches to habitat restoration. Facilitating these efforts are models of seascape connectivity, which represent ecological linkages across heterogeneous marine environments by predicting species-specific dispersal between suitable habitat patches. However, defining the suitable habitat patches and migratory pathways required to construct ecologically realistic connectivity models remains challenging. Focusing on two reef-associated fish species of the Florida Keys, United States of America (USA), we compared two methods for constructing species- and life stage-specific spatial models of habitat suitability—penalized logistic regression and maximum entropy (MaxEnt). The goal of the model comparison was to identify the modeling algorithm that produced the most realistic and detailed products for use in subsequent connectivity assessments. Regardless of species, MaxEnt’s ability to distinguish between suitable and unsuitable locations exceeded that of the penalized regressions. Furthermore, MaxEnt’s habitat suitability predictions more closely aligned with the known ecology of the study species, revealing the environmental conditions and spatial patterns that best support each species across the seascape, with implications for predicting connectivity pathways and the distribution of key ecological processes. Our research demonstrates MaxEnt’s promise as a scalable, species-specific, and spatially explicit tool for informing models of seascape connectivity and guiding coastal conservation efforts.

We used 19 bioclimatic variables, five species distribution modeling (SDM) algorithms, four general circulation models, and two climate scenarios (2050 and 2070) to model nine bird species. Identified as Species of Concern (SOC), we highlighted these birds: Northern/Masked Bobwhite Quail (Colinus virginianus), Scaled Quail (Callipepla squamata), Pinyon Jay (Gymnorhinus cyanocephalus), Juniper Titmouse (Baeolophus ridgwayi), Mexican Spotted Owl (Strix occidentalis lucida), Cassin’s Sparrow (Peucaea cassinii), Lesser Prairie-Chicken (Tympanuchus pallidicinctus), Montezuma Quail (Cyrtonyx montezumae), and White-tailed Ptarmigan (Lagopus leucurus). The Generalized Linear Model, Random Forest, Boosted Regression Tree, Maxent, Multivariate Adaptive Regression Splines, and an ensemble model were used to identify present day core bioclimatic-envelopes for the species. We then projected future distributions of suitable climatic conditions for the species using data derived from four climate models run according to two greenhouse gas Representative Concentration Pathways (RCPs 2.6 and 8.5). Our models predicted changes in suitable bioclimatic-envelopes for all species for the years 2050 and 2070. Among the nine species of birds, the quails were found to be highly susceptible to climate change and appeared to be of most future conservation concern. The White-tailed Ptarmigan would lose about 62% of its suitable climatic habitat by 2050 and 67% by 2070. Among the species distribution models (SDMs), the Boosted Regression Tree model consistently performed fairly well based on Area Under the Curve (AUC range: 0.89 to 0.97) values. The ensemble models showed improved True Skill Statistics (all TSS values > 0.85) and Kappa Statistics (all K values > 0.80) for all species relative to the individual SDMs.