Assess Habitat Suitability Research Articles

Study on habitat suitability and environmental variable thresholds of rare waterbirds

The conservation level of rare waterbirds reflects the quality of the regional ecological environment and wetlands, and suitable habitat patches and good environmental conditions are bases to support the activities of rare species in habitats. Establishing these conditions is also an important goal of habitat landscape and functional restoration. However, lack of these conditions limits population protection and habitat restoration of rare species. Based on the random forest (RF) algorithm and threshold indicator taxa analysis (TITAN), this paper performed habitat suitability assessment and environmental variable threshold analysis of rare waterbird species in Yancheng coastal wetlands. The results showed that the suitable area proportion of three waterbird species at different habitat sites was less than 20%. The unsuitable area proportions of red-crowned cranes and oriental storks at the CA habitat site were the highest, reaching 86.73% and 85.17%, respectively. In addition, analysis of the importance of environmental variables showed that the main influencing variables affecting the suitable habitat distribution of the three rare waterbirds were habitat type (T_hab), habitat area (A_hab), vegetation coverage (P_fvc), distance to farmland (D_far), distance to reeds (D_ree), ponds density (Ponds), distance to water surface (D_wat) and distance to main roads or seawalls (D_swa). These variables covered the type, area, coverage and distance indicators. With the exception of D_far, Ponds and D_swa, rare waterbirds had response thresholds to each environmental indicator, and these results supported the restoration of landscape structure and function of each habitat site. This study emphasized the importance of foods, water resources and hidden conditions for habitat selection in rare waterbirds. Finally, we proposed the maintenance and restoration patterns of the landscape structure and function of rare waterbird habitats, which are available for other coastal tidal wetlands.

The relationships between spawning horseshoe crabs and egg densities: Recommendations for the assessment of populations and habitat suitability

Abstract Horseshoe crab population sizes and trends have been previously studied using surveys of spawning adults and counts of eggs from surface (top 5 cm) and deep (20 cm) sediment samples. The correlations between surface and deep eggs were studied at two locations, Delaware Bay and Jamaica Bay, USA, and the correlations between egg densities and spawning counts were examined in Jamaica Bay. There were significantly higher densities of eggs in deep sediments than in surface sediments. Only about 10% of the variability in surface egg density was explained by deep egg density. The numerical patterns between surface and deep eggs were similar between Delaware Bay and Jamaica Bay and across sampling dates. Nearly 20% of the deep samples in the combined data from Delaware Bay and Jamaica Bay with an egg density of ≥100,000 m−2 had zero surface eggs. Therefore, the use of surface eggs as an indicator of habitat suitability and spawning intensity may seriously underestimate the importance of a beach for spawning horseshoe crabs. When paired with nearest survey date, Jamaica Bay spawning indices did not predict deep or surface egg densities. This may be related to a temporal mismatch between survey methods, the extreme overdispersion (patchiness) of the eggs, and/or the dynamics of egg distribution after exhumation. Both egg density and spawning surveys can provide useful data on habitat suitability for horseshoe crabs and can offer excellent opportunities for student and citizen scientist engagement. More labour is required for egg surveys than spawning surveys because of the time required to sample, sort, and enumerate the eggs.

Habitat Suitability Assessment of Wintering Herbivorous Anseriformes in Poyang Lake, China

To design a good conservation strategy for herbivorous Anseriformes wintering in Poyang Lake, knowledge of habitat suitability is essential. Therefore, this study aimed to assess the habitat suitability of herbivorous Anseriformes of China’s Poyang Lake. Landsat images with a resolution of 30 m downloaded from the United States Geological Survey, and other ancillary data were used. The ENVI 5.3 software and ArcGIS 10.2 software were used for preprocessing, classifying the satellite image, and mapping habitat suitability. The study reveals that land cover types were divided into vegetation, mudflats, water, and sand. Similarly, the study area’s habitats were also divided into unsuitable, fair, good, and best grades. However, the distribution of the habitat suitability for each grade reveals significant spatial variations. For instance, vegetation indicated the areas with the best habitat grade, followed by mudflats, and these areas cover (47.93%, 2015 and 55.78%, 2019) the majority of the study area. The unsuitable grades cover the smallest areas (0.48%) of the lake. Similarly, this study results showed a slight change in habitat suitability areas. Therefore, this study highlighted that Poyang Lake has valuable importance for the conservation of herbivorous Anseriformes. Extending the years of study and including some ecological variables from different stopovers could improve the results.

Assessing Habitat Suitability Models for the Deep Sea: Is Our Ability to Predict the Distributions of Seafloor Fauna Improving?

Methods that predict the distributions of species and habitats by developing statistical relationships between observed occurrences and environmental gradients have become common tools in environmental research, resource management, and conservation. The uptake of model predictions in practical applications remains limited, however, because validation against independent sample data is rarely practical, especially at larger spatial scales and in poorly sampled environments. Here, we use a quantitative dataset of benthic invertebrate faunal distributions from seabed photographic surveys of an important fisheries area in New Zealand as independent data against which to assess the usefulness of 47 habitat suitability models from eight published studies in the region. When assessed against the independent data, model performance was lower than in published cross-validation values, a trend of increasing performance over time seen in published metrics was not supported, and while 74% of the models were potentially useful for predicting presence or absence, correlations with prevalence and density were weak. We investigate the reasons underlying these results, using recently proposed standards to identify areas in which improvements can best be made. We conclude that commonly used cross-validation methods can yield inflated values of prediction success even when spatial structure in the input data is allowed for, and that the main impediments to prediction success are likely to include unquantified uncertainty in available predictor variables, lack of some ecologically important variables, lack of confirmed absence data for most taxa, and modeling at coarse taxonomic resolution.

Assessing habitat suitability and habitat fragmentation for endangered Siberian cranes in Poyang Lake region, China

Poyang Lake is located in the middle and lower reaches of the Yangtze River in China. It is the most important wintering area for the endangered Siberian cranes. Identifying the scale characteristics of habitat selection for Siberian cranes is essential for habitat conservation. In this study, a habitat suitability index (HSI) model based on multiscale habitat features was developed to assess the distribution and fragmentation degree of the suitable habitat for Siberian cranes before and after the construction of the proposed Poyang Lake Hydraulic Project (PLHP). The habitat of the entire lake area was divided into four ranks on the basis of HSI values. 2D shallow-water equations (SWEs), combined with remote sensing and geographic information system technology, were utilized to determine the key habitat features. Results indicated that the established HSI model could effectively recognize the potentially suitable habitats for Siberian cranes in Poyang Lake. The highly suitable habitat in the wet year was larger than that in the dry year, and the complexity of patch shapes and patch fragmentation increased. The influence of the PLHP on the suitable habitat for cranes mainly depended on its regulated water level, and this project could promote the formation and reduce the fragmentation degree of the suitable habitat under a properly regulated water level in the dry year. However, a higher regulated water level may cause a considerable negative impact on the distribution of suitable habitats in both years. This study provided a quantitative method to evaluate the potentially suitable habitats for Siberian cranes, and it could serve as a reference for the water level management of the proposed PLHP.

Using Holocene fossils to model the future: Distribution of climate suitability for tuatara, the last rhynchocephalian

AbstractAimCorrelative species distribution models (SDMs) are typically trained using only the contemporary distribution of species; however, recent records might reflect an incomplete description of a species' niche, limiting the reliability of predictions. SDMs linking fossil records have the potential to improve conservation decisions under human‐induced climate change. Here, we built SDMs using presence records from contemporary and Holocene records to enable estimations of climatically suitable area under current and future climate scenarios.LocationAotearoa New ZealandTaxonTuatara, Sphenodon punctatusMethodsFor an evolutionary relict found in Aotearoa New Zealand, the tuatara (Sphenodon punctatus), we built SDMs using presence records from contemporary and Holocene records to estimate climatically suitable area under current and future climate scenarios. We also use our detailed knowledge of the Holocene distribution and remnant populations to examine niche shifts following the arrival of humans and associated introduction of mammalian predators. To build SDMs, we use presence records from four sources: (a) remnant populations, (b) radiocarbon‐dated fossil deposits from the Holocene, (c) other fossil deposits containing tuatara bones of Holocene age and iv) islands from which tuatara are known or highly likely to have become extinct.ResultsWe found shifts in the niche of tuatara due to niche unfilling. Incorporating locations of Holocene deposits and/or all past locations in SDMs led to larger areas of climatically suitable area being identified compared to SDMs derived from remnant populations only. Using all presence records, under climate change projections for 2090, climatically suitable area increased slightly. However, many areas retain potential as translocation sites (e.g. northern South Island), some areas become unsuitable (e.g. inland Canterbury) and/or involve extrapolation into novel climates (e.g. Northland).Main ConclusionSDMs incorporating locations of Holocene deposits and/or all past locations identified areas of critical habitat for tuatara under current and future climate scenarios, that would not have been identified using contemporary occurrences only. Our results highlight the need to consider past locations when assessing habitat suitability for conservation translocations, both for tuatara and other relict species.

How citizen science could improve species distribution models and their independent assessment.

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.

Minimizing physical habitat impacts at downstream of diversion dams by a multiobjective optimization of environmental flow regime

Present study links physical habitat simulation with the irrigation loss function to assess optimal environmental flow regime. The main motivation is Lack of optimization framework in the structure of reliable environmental flow methods such as physical habitat simulation. Data-driven model was used to simulate physical habitats. Moreover, a multi objective particle swarm optimization was utilized to optimize environmental flow regime by considering two objective functions including weighted useable area function and normalized benefit function for irrigation demand. Based on results, physical habitat data-driven model was robust to assess habitat suitability. Moreover, proposed multi objective optimization model is able to assess environmental flow properly. In the case study, physical habitat impact was minimized to 30% that means 70% of useable habitats would be protected. In contrast, 60% of maximum requested water demand would be supplied which implies a fair balance between demand and environmental flow.

Quantifying suitable dynamic water levels in marsh wetlands based on hydrodynamic modelling

AbstractThe water level of marsh wetlands is a dominant force controlling the wetland ecosystem function, especially for aquatic habitat. For different species, water level requirements vary in time and space, and therefore ensuring suitable water levels in different periods is crucial for the maintenance of biodiversity in marsh wetlands. Based on hydrodynamic modelling and habitat suitability assessment, we determined suitable dynamic water levels considering aquatic habitat service at different periods in marsh wetlands. The two‐dimensional hydrodynamic model was used to simulate the temporal and spatial variation of water level. The habitat suitability for target species at various water levels was evaluated to obtain the fitting curves between Weighted Usable Area (WUA) and water levels. And then suitable water levels throughout the year were proposed according to the fitting curves. Using the Zhalong Wetland (located in northeastern China) as a case study, we confirmed that the proposed MIKE 21 model can successfully be used to simulate the water level process in the wetland. Suitable water levels were identified as being from 143.9–144.2 m for April to May, 144.1–144.3 m for June to September, and 144.3–144.4 m for October to November (before the freezing season). Furthermore, proposed water diversion schemes have been identified which can effectively sustain the proposed dynamic water levels. This study is expected to provide appropriate guidance for the determination of environmental flows and water management strategies in marsh wetlands.

Strengthening the Network of High Conservation Value Forests in Boreal Landscapes

The natural and old-growth forests and their associated biodiversity continues to fade worldwide due to anthropogenic impact in various forms. The boreal forests in Fennoscandia have been subject to intensive clearfelling forestry since the middle of twentieth century. As a result, only a fraction of forests with long temporal continuity remains at the landscape level. In Sweden, some of these primary forests have been formally protected, whereas other forests with known high conservation values are not. Collectively, both protected and not protected known valuable primary forests are included in a nationally delineated network of high conservation value forests (HCVF). In addition to HCVF, older forests that have not been clearfelled since the mid-1900s, i.e., “proxy continuity forests,” have recently been mapped across the entire boreal biome in Sweden. In this paper, we analyze how these proxy continuity forests may strengthen the HCVF network from a green infrastructure perspective. First, we evaluate the spatial overlap between proxy continuity forests and HCVF. Second, we perform a large-scale connectivity analysis, in which we show that adding proxy continuity forests located outside HCVF strongly increases the structural connectivity of the network of protected forests. Finally, by assessing habitat suitability for virtual species specialized in pine, spruce, and broadleaf forests, we find large regional differences in the ability to secure habitat and thereby functional green infrastructure by considering currently unprotected primary forest. We show that, by adding those forests to the network, the area of habitat for low-demanding species dependent on spruce or pine forests can be largely increased. For high-demanding species, additional habitat restoration in the landscape matrix is needed. By contrast, even counting all valuable broadleaf forests available is not enough to provide a suitable habitat for their associated species, which indicates a large need for landscape-scale habitat restoration initiatives, in particular, for broadleaf forests.

Summer weather conditions influence winter survival of honey bees (Apis mellifera) in the northeastern United States

Honey bees are crucial pollinators for agricultural and natural ecosystems, but are experiencing heavy mortality in North America and Europe due to a complex suite of factors. Understanding the relative importance of each factor would enable beekeepers to make more informed decisions and improve assessment of local and regional habitat suitability. We used 3 years of Pennsylvania beekeepers’ survey data to assess the importance of weather, topography, land use, and management factors on overwintering mortality at both apiary and colony levels, and to predict survival given current weather conditions and projected climate changes. Random Forest, a tree-based machine learning approach suited to describing complex nonlinear relationships among factors, was used. A Random Forest model predicted overwintering survival with 73.3% accuracy for colonies and 65.7% for apiaries where Varroa mite populations were managed. Growing degree days and precipitation of the warmest quarter of the preceding year were the most important predictors at both levels. A weather-only model was used to predict colony survival probability, and to create a composite map of survival for 1981–2019. Although 3 years data were likely not enough to adequately capture the range of possible climatic conditions, the model performed well within its constraints.

Spatial dynamics of Chinese Muntjacrelated to past and future climate fluctuations.

Climate fluctuations in the past and in the future are likely to result in population expansions, shifts, or the contraction of the ecological niche of many species, and potentially leading to the changes in their geographical distributions. Prediction of suitable habitats has been developed as a useful tool for the assessment of habitat suitability and resource conservation to protect wildlife. Here, we model the ancestral demographic history of the extant modern Chinese Muntjac Muntiacus reevesi populations using approximate Bayesian computation (ABC) and used the maximum entropy model to simulate the past and predict the future spatial dynamics of the species under climate oscillations. Our results indicated that the suitable habitats for the M. reevesi shifted to the Southeast and contracted during the Last Glacial Maximum, whereas they covered a broader and more northern position in the Middle Holocene. The ABC analyses revealed that the modern M. reevesi populations diverged in the Middle Holocene coinciding with the significant contraction of the highly suitable habitat areas. Furthermore, our predictions suggest that the potentially suitable environment distribution for the species will expand under all future climate scenarios. These results indicated that the M. reevesi diverged in the recent time after the glacial period and simultaneously as its habitat’s expanded in the Middle Holocene. Furthermore, the past and future climate fluctuation triggered the change of Chinese muntjac spatial distribution, which has great influence on the Chinese muntjac’s population demographic history.

松嫩平原苍鹭秋季栖息地选择及适宜性分布

松嫩平原苍鹭秋季栖息地选择及适宜性分布

Mapping Wetland Plant Communities Using Unmanned Aerial Vehicle Hyperspectral Imagery by Comparing Object/Pixel-Based Classifications Combining Multiple Machine-Learning Algorithms

Understanding the spatial patterns of plant communities is important for sustainable wetland ecosystem management and biodiversity conservation. With the rapid development of unmanned aerial vehicle (UAV) technology, UAV-borne hyperspectral data with high spatial resolution have become ideal for accurate classification of wetland plant communities. In this article, four dominant plant communities (Phragmites australis, Typha orientalis, Suaeda glauca, and Scirpus triqueter) and two unvegetated cover types (water and bare land) in the Momoge Ramsar wetland site were classified. This was achieved using UAV hyperspectral images and three object- and pixel-based machine-learning classification algorithms [random forest (RF), convolutional neural network (CNN), and support vector machine (SVM)]. First, spectral derivative analysis, logarithmic analysis, and continuum removal analysis identified the wavelength at which the greatest difference in reflectance occurs. Second, dimensionality reduction of hyperspectral images was conducted using principal component analysis. Subsequently, an optimal feature combination for community mapping was formed based on data transformation (spectral features, vegetation indices, and principal components). Image objects were obtained by segmenting the optimum object feature subsets. Finally, distribution maps of communities were produced by using three machine-learning classification algorithms. Our results reveal that object-based image analysis outperforms pixel-based methods, with overall accuracies (OAs) of 80.29-87.75%; RF has the highest OA of 87.75% (Kappa = 0.864), followed consecutively by CNN (OA = 83.31%, Kappa = 0.829) and SVM (OA = 80.29%, Kappa = 0.813). Phragmites australis dominates the plant community (55.9%) at the study area, followed by Typha orientalis (16.2%), Suaeda glauca (16.2%), and Scirpus triqueter (4.6%). The results highlight the importance of spectral transformation features in red-edge regions. The mapping results will help establish basic information for subsequent studies involving habitat suitability assessment at this study site.

Application of Integrated Modelling Framework Consisted of Delft3D and HABITAT for Habitat Suitability Assessment

Application of Integrated Modelling Framework Consisted of Delft3D and HABITAT for Habitat Suitability Assessment

Assessing habitat suitability for wintering geese by using Normalized Difference Water Index (NDWI) in a large floodplain wetland, China

Hydrological condition is one of the most important factors affecting the abundance and distribution of waterbirds in large freshwater floodplains, as it influences their habitat and food availability. The hydrological condition is commonly indicated with site-based hydrological data such as water level. Site-based data have high temporal resolution but are less effective to represent the spatial characteristics, a requirement for ecological/habitat models. In this study, we propose a method to identify suitable habitats for wintering geese based on the Normalized Difference Water Index (NDWI) derived from Sentinel-2 satellite images. The relationships between NDWI, water level and geese occurrence were analyzed. The results showed that the NDWI of Poyang Lake can explain 75% of the changes in water level and at least 42% of the geese occurrence during wintering periods (with the exception of February 2019). The monthly NDWI optimal ranges suitable for wintering geese were identified to be from −0.12 to 0 and suitable NDWI threshold range is from −0.27 to 0.14. Based on this threshold range, we identified the suitable habitat for geese and found that the suitable habitat area first increased and later decreased during the whole wintering periods, following an inverted U-shaped dynamic. We concluded that NDWI can be used to improve distribution models of waterbird species to support their protection and wetland management.

Caught in a bottleneck: Habitat loss for woolly mammoths in central North America and the ice‐free corridor during the last deglaciation

AbstractAimIdentifying how climate change, habitat loss, and corridors interact to influence species survival or extinction is critical to understanding macro‐scale biodiversity dynamics under changing environments. In North America, the ice‐free corridor was the only major pathway for northward migration by megafaunal species during the last deglaciation. However, the timing and interplay among the late Quaternary megafaunal extinctions, climate change, habitat structure, and the opening and reforestation of the ice‐free corridor have been unclear.LocationNorth America.Time period15–10 ka.Major taxa studiedWoolly mammoth (Mammuthus primigenius).MethodsFor central North America and the ice‐free corridor between 15 and 10 ka, we used a series of models and continental‐scale datasets to reconstruct habitat characteristics and assess habitat suitability. The models and datasets include biophysical and statistical niche models Niche Mapper and Maxent, downscaled climate simulations from CCSM3 SynTraCE, LPJ‐GUESS simulations of net primary productivity (NPP) and woody cover, and woody cover based upon fossil pollen from Neotoma.ResultsThe ice‐free corridor may have been of limited suitability for traversal by mammoths and other grazers due to persistently low productivity by herbaceous plants and quick reforestation after opening 14 ka. Simultaneously, rapid reforestation and decreased forage productivity may have led to declining habitat suitability in central North America. This was possibly amplified by a positive feedback loop driven by reduced herbivory pressures, as mammoth population decline led to the further loss of open habitat.Main conclusionsDeclining habitat availability south of the Laurentide Ice Sheet and limited habitat availability in the ice‐free corridor were contributing factors in North American extinctions of woolly mammoths and other large grazers that likely operated synergistically with anthropogenic pressures. The role of habitat loss and attenuated corridor suitability for the woolly mammoth extinction reinforce the critical importance of protected habitat connectivity during changing climates, particularly for large vertebrates.

First assessment of habitat suitability and connectivity for the golden jackal in north-eastern Italy

Compared with the rapid expansion across Europe, the golden jackal colonization of Italy is still limited and slow. No study focused on the habitat selection or landscape connectivity for this species was performed in Italy; thus, the potential distribution and dispersal patterns in the country remain unknown. Our objectives were to evaluate the suitability of the Friuli-Venezia Giulia region (north-eastern Italy) for the golden jackal, as well as to identify the ecological corridors connecting the areas currently occupied by the species. Corridors modelling allowed us both to hypothesize the dispersal dynamics occurring in the study region and to identify possible obstacles to future range expansion. We surveyed golden jackal presence in two study areas, covering an area of 500 km2, from March 2017 to February 2018. Using collected data, we modelled the species home-range scale habitat suitability based on an ensemble modelling approach. Subsequently, a habitat suitability prediction at a finer scale was used to estimate landscape resistance, starting from which, we modelled dispersal corridors among areas currently occupied by the species using a factorial least cost path and a cumulative resistant kernel approach. Our results indicated a moderate potential for large parts of the study region to support the occurrence of golden jackal family groups, whose presence seems to be mainly driven by the presence of wide areas covered by broadleaved forests and shrublands and by the absence of wide intensive agricultural areas. The predicted connectivity networks showed that three main permeable corridors are likely to connect golden jackal occurrence areas within the study region, while all the other corridors are characterized by a very low path density. Both the habitat selection and connectivity analyses showed a strong negative impact of the intensive cultivated plain on species stable presence and movement providing critical information for the conservation of the golden jackal in Italy.

Habitat Suitability Analysis of Himalayan Musk Deer (Moschus leucogaster) in Part of Western Himalaya, India

Himalayan musk deer (Moschus leucogaster) is considered endangered by International Union for Conservation of Nature (IUCN) and is under Schedule-I of the Indian Wildlife (Protection) Act, 1972, because of its illicit hunting for musk as well as habitat loss. The present study aims to study the summer range habitat suitability of this important Himalayan ungulate in Kedarnath Wildlife Sanctuary (KWS) of Western Himalaya, India. The study integrated ground presence point locations of the species with satellite data-derived variables in a binomial multiple logistic regression (BMLR) model to analyse its suitability status in KWS. Moist temperate forest (323.38 km2) was found to be the dominant vegetation type, followed by sub-alpine forest (128.24 km2) and alpine pasture (119.51 km2) in KWS. It was found that 7.39% (72.04 km2) of the study area is highly suitable, followed by 14.73% (143.68 km2), 13.43% (131.02 km2) and 64.44% (628.46 km2) area as suitable, moderately suitable and least suitable, respectively, for the species. The results also revealed that 71.59% (51.57 km2) of the highly suitable habitat occurs in sub-alpine forest, 18.41% (13.26 km2) in alpine pasture and 5.63% (4.06 km2) in alpine scrub. It was found that the vegetation type/land use was the most significant variable for habitat suitability assessment of the Himalayan musk deer in KWS. The BMLR model could successfully predict the potential summer range habitat of the species (Relative Operating Characteristics curve = 0.85). The findings of this study could be of immense use in the management of this Wildlife Sanctuary for better protection and conservation of the Himalayan musk deer.

Climate change effects on turtles of the genus Kinosternon (Testudines: Kinosternidae): an assessment of habitat suitability and climate niche conservatism

The Chelonian lineage has been exposed to several climate change events along its evolutionary history, but the rapid contemporary change in climate patterns has the potential to erode turtle populations. This study focuses on (1) evaluating the climatically suitable area available for 15 species of mud turtles of the genus Kinosternon, and on (2) assessing whether or not these species retain their ancestral climate niche. Occurrence data was collected for these species and, using the Maxent algorithm and WorldClim bioclimatic variables, suitable present and future climatic niche areas were modeled. In addition, we also carried out climatic niche similarity analyses between pairs of species to evaluate whether these conserve their climatic niche. Our models suggest that most species of Kinosternon will lose a high proportion of their suitable habitat in the future. Most mud turtle species seem to conserve their climatic niche, suggesting the prevalence of niche conservatism in the group. Our results indicate that several mud turtle species could be at severe risk of disappearing over the next few decades due to the loss of climatically suitable areas and of the conservation of their climatic niches.