Species distribution models can be highly sensitive to algorithm configuration

Milk yield prediction in Friesian cows using linear and flexible discriminant analysis under assumptions violations

BACKGROUND

The application of novel technologies is now widely used to assist in making optimal decisions. This study aimed to evaluate the performance of linear discriminant analysis (LDA) and flexible discriminant analysis (FDA) in classifying and predicting Friesian cattle's milk production into low ([Formula: see text]4500 kg), medium (4500-7500 kg), and high ([Formula: see text]7500 kg) categories. A total of 3793 lactation records from cows calved between 2009 and 2020 were collected to examine some predictors such as age at first calving (AFC), lactation order (LO), days open (DO), days in milk (DIM), dry period (DP), calving season (CFS), 305-day milk yield (305-MY), calving interval (CI), and total breeding per conception (TBRD).

RESULTS

The comparison between LDA and FDA models was based on the significance of coefficients, total accuracy, sensitivity, precision, and F1-score. The LDA results revealed that DIM and 305-MY were the significant (P < 0.001) contributors for data classification, while the FDA was a lactation order. Classification accuracy results showed that the FDA model performed better than the LDA model in expressing accuracies of correctly classified cases as well as overall classification accuracy of milk yield. The FDA model outperformed LDA in both accuracy and F1-score. It achieved an accuracy of 82% compared to LDA's 71%. Similarly, the F1-score improved from a range of 0.667 to 0.79 for LDA to a higher range of 0.81 to 0.83 for FDA.

CONCLUSION

The findings of this study demonstrated that FDA was more resistant than LDA in case of assumption violations. Furthermore, the current study showed the feasibility and efficacy of LDA and FDA in interpreting and predicting livestock datasets.

Revisiting current distribution and future habitat suitability models for the endemic Malabar Tree Toad (Pedostibes tuberculosus) using citizen science data

Climate change is one of the major drivers of biodiversity loss. Among vertebrates, amphibians are one of the more sensitive groups to climate change due to their unique ecology, habitat requirements, narrow thermal tolerance and relatively limited dispersal abilities. We projected the influence of climate change on an endemic toad, Malabar Tree Toad (Pedostibes tuberculosus; hereafter MTT) from the Western Ghats biodiversity hotspot, India, for two different shared socio-economic pathways (SSP) using multiple modeling approaches for current and future (2061-2080) scenarios. The data used predominantly comes from a citizen science program, 'Mapping Malabar Tree Toad' which is a part of the Frog Watch citizen science program, India Biodiversity Portal. We also evaluated the availability of suitable habitats for the MTT in Protected Areas (PAs) under the current and future scenarios. Our results show that annual precipitation was the most important bioclimatic variable influencing the distribution of MTT. We used MaxEnt (MEM) and Ensemble (ESM) modeling algorithms. The predicted distribution of MTT with selected environmental layers using MEM was 4556.95 km2 while using ESM was 18,563.76 km2. Overlaying PA boundaries on predicted distribution showed 37 PAs with 32.7% (1491.37 km2) and 44 PAs with 21.9% (4066.25 km2) coverage for MEM and ESM respectively. Among eight future climate scenarios, scenarios with high emissions showed a decreased distribution range from 33.5 to 68.7% of predicted distribution in PAs, while scenarios with low emissions showed an increased distribution range from 1.9 to 111.3% in PAs. PAs from the Central Western Ghats lose most suitable areas with a shift of suitable habitats towards the Southern Western Ghats. This suggests that MTT distribution may be restricted in the future and existing PAs may not be sufficient to conserve their habitats. Restricted and discontinuous distribution along with climate change can limit the dispersal and persistence of MTT populations, thus enhanced surveys of MTT habitats within and outside the PAs of the Western Ghats are an important step in safeguarding the persistence of MTT populations. Overall, our results demonstrate the use of citizen science data and its potential in modeling and understanding the geographic distribution and the calling phenology of an elusive, arboreal, and endemic amphibian species.

The Spatial Shifts and Vulnerability Assessment of Ecological Niches under Climate Change Scenarios for Betula luminifera, a Fast-Growing Precious Tree in China

The spatial shifts and vulnerability assessments of ecological niches for trees will offer fresh perspectives for sustainable development and preservation of forests, particularly within the framework of rapid climate change. Betula luminifera is a fast-growing native timber plantation species in China, but the natural resources have been severely damaged. Here, a comprehensive habitat suitability model (including ten niche-based GIS modeling algorithms) was developed that integrates three types of environmental factors, namely, climatic, soil, and ultraviolet variables, to assess the species contemporary and future distribution of suitable habitats across China. Our results suggest that the habitats of B. luminifera generally occur in subtropical areas (about 1.52 × 106 km2). However, the growth of B. luminifera is profoundly shaped by the nuances of its local environment, the most reasonable niche spaces are only 1.15 × 106 km2 when limiting ecological factors (soil and ultraviolet) are considered, generally considered as the core production region. Furthermore, it is anticipated that species-suitable habitats will decrease by 10 and 8% with climate change in the 2050s and 2070s, respectively. Our study provided a clear understanding of species-suitable habitat distribution and identified the reasons why other niche spaces are unsuitable in the future, which can warn against artificial cultivation and conservation planning.

Extralimital terrestrials: A reassessment of range limits in Alaska's land mammals

Understanding and mitigating the effects of anthropogenic climate change on species distributions requires the ability to track range shifts over time. This is particularly true for species occupying high-latitude regions, which are experiencing more extreme climate change than the rest of the world. In North America, the geographic ranges of many mammals reach their northernmost extent in Alaska, positioning this region at the leading edge of climate-induced distribution change. Over a decade has elapsed since the publication of the last spatial assessments of terrestrial mammals in the state. We compared public occurrence records against commonly referenced range maps to evaluate potential extralimital records and develop repeatable baseline range maps. We compared occurrence records from the Global Biodiversity Information Facility for 61 terrestrial mammal species native to mainland Alaska against a variety of range estimates (International Union for Conservation of Nature, Alaska Gap Analysis Project, and the published literature). We mapped extralimital records and calculated proportions of occurrences encompassed by range extents, measured mean direction and distance to prior range margins, evaluated predictive accuracy of published species models, and highlighted observations on federal lands in Alaska. Range comparisons identified 6,848 extralimital records for 39 of 61 (63.9%) terrestrial mainland Alaskan species. On average, 95.5% of Alaska Gap Analysis Project occurrence records and ranges were deemed accurate (i.e., > 90.0% correct) for 31 of 37 species, but overestimated extents for 13 species. The International Union for Conservation of Nature range maps encompassed 68.1% of occurrence records and were > 90% accurate for 17 of 39 species. Extralimital records represent either improved sampling and digitization or actual geographic range expansions. Here we provide new data-driven range maps, update standards for the archiving of museum-quality locational records and offer recommendations for mapping range changes for monitoring and conservation.

Unravelling the impact of climate change on honey bees: An ensemble modelling approach to predict shifts in habitat suitability in Queensland, Australia

Honey bees play a vital role in providing essential ecosystem services and contributing to global agriculture. However, the potential effect of climate change on honey bee distribution is still not well understood. This study aims to identify the most influential bioclimatic and environmental variables, assess their impact on honey bee distribution, and predict future distribution. An ensemble modelling approach using the biomod2 package in R was employed to develop three models: a climate-only model, an environment-only model, and a combined climate and environment model. By utilising bioclimatic data (radiation of the wettest and driest quarters and temperature seasonality) from 1990 to 2009, combined with observed honey bee presence and pseudo absence data, this model predicted suitable locations for honey bee apiaries for two future time spans: 2020-2039 and 2060-2079. The climate-only model exhibited a true skill statistic (TSS) value of 0.85, underscoring the pivotal role of radiation and temperature seasonality in shaping honey bee distribution. The environment-only model, incorporating proximity to floral resources, foliage projective cover, and elevation, demonstrated strong predictive performance, with a TSS of 0.88, emphasising the significance of environmental variables in determining habitat suitability for honey bees. The combined model had a higher TSS of 0.96, indicating that the combination of climate and environmental variables enhances the model's performance. By the 2020-2039 period, approximately 88% of highly suitable habitats for honey bees are projected to transition from their current state to become moderate (14.84%) to marginally suitable (13.46%) areas. Predictions for the 2060-2079 period reveal a concerning trend: 100% of highly suitable land transitions into moderately (0.54%), marginally (17.56%), or not suitable areas (81.9%) for honey bees. These results emphasise the critical need for targeted conservation efforts and the implementation of policies aimed at safeguarding honey bees and the vital apiary industry.

spatialMaxent: Adapting species distribution modeling to spatial data

Conventional practices in species distribution modeling lack predictive power when the spatial structure of data is not taken into account. However, choosing a modeling approach that accounts for overfitting during model training can improve predictive performance on spatially separated test data, leading to more reliable models. This study introduces spatialMaxent (https://github.com/envima/spatialMaxent), a software that combines state-of-the-art spatial modeling techniques with the popular species distribution modeling software Maxent. It includes forward-variable-selection, forward-feature-selection, and regularization-multiplier tuning based on spatial cross-validation, which enables addressing overfitting during model training by considering the impact of spatial dependency in the training data. We assessed the performance of spatialMaxent using the National Center for Ecological Analysis and Synthesis dataset, which contains over 200 anonymized species across six regions worldwide. Our results show that spatialMaxent outperforms both conventional Maxent and models optimized according to literature recommendations without using a spatial tuning strategy in 80 percent of the cases. spatialMaxent is user-friendly and easily accessible to researchers, government authorities, and conservation practitioners. Therefore, it has the potential to play an important role in addressing pressing challenges of biodiversity conservation.

Impact of climate change on the distribution and predicted habitat suitability of two fruit bats (Rousettus aegyptiacus and Epomophorus labiatus) in Ethiopia: Implications for conservation

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.

Prediction of the potentially suitable areas of Ligularia virgaurea and Ligularia sagitta on the Qinghai-Tibet Plateau based on future climate change using the MaxEnt model

Ligularia virgaurea and Ligularia sagitta are two species of poisonous plants with strong invasiveness in natural grasslands in China that have caused considerable harm to animal husbandry and the ecological environment. However, little is known about their suitable habitats and the key environmental factors affecting their distribution. Although some studies have reported the distributions of poisonous plants on the Qinghai-Tibet Plateau (QTP) and predicted their potential distributions at local scales in some regions under climate change, there have been few studies on the widespread distributions of L. virgaurea and L. sagitta. In this study, we recorded 276 and 118 occurrence points of L. virgaurea and L. sagitta on the QTP using GPS, and then used the MaxEnt model to predict the distribution of suitable habitats. Results showed that (1) under current climate conditions, L. virgaurea and L. sagitta are mainly distributed in southern Gansu, eastern Qinghai, northwestern Sichuan, eastern Tibet, and southwestern Yunnan, accounting for approximately 34.9% and 39.8% of the total area of the QTP, respectively; (2) the main environmental variables affecting the distribution of suitable habitats for L. virgaurea and L. sagitta are the Human Footprint Index (52.8%, 42.2%), elevation (11%, 4.4%), soil total nitrogen (18.9%, 4.2%), and precipitation seasonality (5.1%, 7.3%); and (3) in the future, in the 2050s and 2070s, the area of habitat of intermediate suitability for L. virgaurea will spread considerably in northwest Sichuan, while that of high suitability for L. sagitta will spread to eastern Tibet and western Sichuan.

Potential global geographical distribution of Lolium temulentum L. under climate change

Invasive alien plants posed a significant threat to natural ecosystems, biodiversity, agricultural production, as well as human and livestock health. Lolium temulentum, an annual invasive alien weed with fibrous roots, can reduce wheat production and cause economic losses. Moreover, the consumption of grains or cereal products mixed with darnel can cause dizziness, vomiting, and even death. Therefore, darnel is regarded as one of ″the worst weeds around the world″. In the present study, we predicted the potential global geographical distribution of L. temulentum using an optimal MaxEnt model, based on occurrence records and related environmental variables. The mean AUC, TSS, and KAPPA were 0.95, 0.778, and 0.75, indicating the MaxEnt model accuracy was excellent. The significant environmental variables, including the mean temperature of coldest quarter (bio 11), precipitation of coldest quarter (bio 19), temperature annual range (bio 7), and annual precipitation (bio 12), produced a great impact on the potential global geographical distribution of L. temulentum. Under the current climate, L. temulentum was primarily distributed in south-eastern Asia, Europe, and south-eastern North America. The widest total suitable habitat was distributed in Asia, covering nearly 796 × 104 km2. By the 2050s, the potential geographical distribution of L. temulentum was expected to decrease in the Northern Hemisphere, and shrink gradually in southern America, Africa, and Oceania. Moreover, the distribution center of L. temulentum was expected to shift from Asia to Europe. Based on these predictions, changes in the suitable habitats for L. temulentum between Europe and Asia warrant close attention to prevent further spread.

Scale-dependent environmental effects on phenotypic distributions in Heliconius butterflies

Identifying the relative importance of different mechanisms responsible for the emergence and maintenance of phenotypic diversity can be challenging, as multiple selective pressures and stochastic events are involved in these processes. Therefore, testing how environmental conditions shape the distribution of phenotypes can offer important insights on local adaptation, divergence, and speciation. The red-yellow Müllerian mimicry ring of Heliconius butterflies exhibits a wide diversity of color patterns across the Neotropics and is involved in multiple hybrid zones, making it a powerful system to investigate environmental drivers of phenotypic distributions. Using the distantly related Heliconius erato and Heliconius melpomene co-mimics and a multiscale distribution approach, we investigated whether distinct phenotypes of these species are associated with different environmental conditions. We show that Heliconius red-yellow phenotypic distribution is strongly driven by environmental gradients (especially thermal and precipitation variables), but that phenotype and environment associations vary with spatial scale. While co-mimics are usually predicted to occur in similar environments at large spatial scales, patterns at local scales are not always consistent (i.e., different variables are best predictors of phenotypic occurrence in different locations) or congruent (i.e., co-mimics show distinct associations with environment). We suggest that large-scale analyses are important for identifying how environmental factors shape broad mimetic phenotypic distributions, but that local studies are essential to understand the context-dependent biotic, abiotic, and historical mechanisms driving finer-scale phenotypic transitions.

Assessing the Effectiveness of Correlative Ecological Niche Model Temporal Projection through Floristic Data

Simple Summary

Climate change is the main threat for conservation in the 21st century. Reliable methodologies and tools for the evaluation of its impact are urgently needed. Correlative ecological niche models (ENMs) are effective tools for predicting the future distribution of species under climate change scenarios. Despite this, many alternative different methods have been proposed, and objective reasons for a proper selection are unclear. Therefore, a comparative study to evaluate the consistency of predictions of the main ENM algorithms was performed. To test the effectiveness of correlative ENM temporal projection, we compared predictions generated using historical data and projected to the modern climate with predictions generated using modern distribution and climate data. In total, 600 case studies were generated, by using 25 Italian endemic plant species, 12 algorithms and 2 alternative sets of environmental variables. As a result, we highlighted the similarity of eight algorithms and the poor performance of four.

Abstract

Correlative ecological niche modelling (ENM) is a method widely used to study the geographic distribution of species. In recent decades, it has become a leading approach for evaluating the most likely impacts of changing climate. When used to predict future distributions, ENM applications involve transferring models calibrated with modern environmental data to future conditions, usually derived from Global Climate Models (GCMs). The number of algorithms and software packages available to estimate distributions is quite high. To experimentally assess the effectiveness of correlative ENM temporal projection, we evaluated the transferability of models produced using 12 different algorithms on historical and modern data. In particular, we compared predictions generated using historical data and projected to the modern climate (simulating a “future” condition) with predictions generated using modern distribution and climate data. The models produced with the 12 ENM algorithms were evaluated in geographic (range size and coherence of predictions) and environmental space (Schoener’s D index). None of the algorithms shows an overall superior capability to correctly predict future distributions. On the contrary, a few algorithms revealed an inadequate predictive ability. Finally, we provide hints that can be used as guideline to plan further studies based on the adopted general workflow, useful for all studies involving future projections.

A comparison in species distribution model performance of succulents using key species and subsets of environmental predictors

Identifying the environmental drivers of the global distribution of succulent plants using the Crassulacean acid metabolism pathway of photosynthesis has previously been investigated through ensemble-modeling of species delimiting the realized niche of the natural succulent biome. An alternative approach, which may provide further insight into the fundamental niche of succulent plants in the absence of dispersal limitation, is to model the distribution of selected species that are globally widespread and have become naturalized far beyond their native habitats. This could be of interest, for example, in defining areas that may be suitable for cultivation of alternative crops resilient to future climate change. We therefore explored the performance of climate-only species distribution models (SDMs) in predicting the drivers and distribution of two widespread CAM plants, Opuntia ficus-indica and Euphorbia tirucalli. Using two different algorithms and five predictor sets, we created distribution models for these exemplar species and produced an updated map of global inter-annual rainfall predictability. No single predictor set produced markedly more accurate models, with the basic bioclim-only predictor set marginally out-performing combinations with additional predictors. Minimum temperature of the coldest month was the single most important variable in determining spatial distribution, but additional predictors such as precipitation and inter-annual precipitation variability were also important in explaining the differences in spatial predictions between SDMs. When compared against previous projections, an a posteriori approach correctly does not predict distributions in areas of ecophysiological tolerance yet known absence (e.g., due to biotic competition). An updated map of inter-annual rainfall predictability has successfully identified regions known to be depauperate in succulent plants. High model performance metrics suggest that the majority of potentially suitable regions for these species are predicted by these models with a limited number of climate predictors, and there is no benefit in expanding model complexity and increasing the potential for overfitting.

Biogeographic consequences of shifting climate for the western massasauga (Sistrurus tergeminus)

The western massasauga (Sistrurus tergeminus) is a small pit viper with an extensive geographic range, yet observations of this species are relatively rare. They persist in patchy and isolated populations, threatened by habitat destruction and fragmentation, mortality from vehicle collisions, and deliberate extermination. Changing climates may pose an additional stressor on the survival of isolated populations. Here, we evaluate historic, modern, and future geographic projections of suitable climate for S. tergeminus to outline shifts in their potential geographic distribution and inform current and future management. We used maximum entropy modeling to build multiple models of the potential geographic distribution of S. tergeminus. We evaluated the influence of five key decisions made during the modeling process on the resulting geographic projections of the potential distribution, allowing us to identify areas of model robustness and uncertainty. We evaluated models with the area under the receiver operating curve and true skill statistic. We retained 16 models to project both in the past and future multiple general circulation models. At the last glacial maximum, the potential geographic distribution associated with S. tergeminus occurrences had a stronghold in the southern part of its current range and extended further south into Mexico, but by the mid-Holocene, its modeled potential distribution was similar to its present-day potential distribution. Under future model projections, the potential distribution of S. tergeminus moves north, with the strongest northward trends predicted under a climate scenario increase of 8.5 W/m2. Some southern populations of S. tergeminus have likely already been extirpated and will continue to be threatened by shifting availability of suitable climate, as they are already under threat from desertification of grasslands. Land use and habitat loss at the northern edge of the species range are likely to make it challenging for this species to track suitable climates northward over time.

Modeling the effects of climate change on the potential distribution of the rangeland species Gymnocarpus decander Forssk (case study: Arid region of southeastern Iran)

The phenomenon of climate change is the biggest environmental challenge in the world. Climate is a determinant factor in species distribution, and climate change will affect the species' abilities to occupy geographic regions. In this study which was conducted in May of 2019, spatio-temporal changes in potential habitats of Gymnocarpus decander were assessed using the MRI-CGCM3 climate change model for RCP2.6 and RCP8.5 scenarios for the near future (2041-2061) and far future (2061-2080) periods for this purpose, climatic variables of 24 synoptic stations across a case study, bio-climatic data and vegetation cover maps of G. decander were used. First, using the factor analysis process, the dimensions of the station-observed climatic variables were reduced to five factors with a total variance of 88.3%. Then, the region was divided into five homogeneous climatic regions using partitional clustering analysis. In this study by using the logistic regression modeling technique, the probability of the presence of the desired species for two groups of independent variables including climatic factors and bioclimatic variables in each of the groups was modeled. The results showed that the best models for determining the potential habitats of G. decander are logistic regression models in groups with independent bioclimatic variables. According to the results obtained from both scenarios, the habitats of G. decander species will decrease in the future. In the most optimistic case, about 8% of G. decander habitats will be lost by 2060 and about 12% by 2080. According to modeling results, currently, 48.2% total area of the region under study has a high potential for the presence of G. decander. Also, results indicate that region number 4 in this study with an altitude range of about 800-1250 m, 16 °C average temperature in the growing season and annual precipitation around 150-170 mm is the major habitat for G. decander. According to climate change under the RCP2.6 scenario, the area of potential habitats of G. decander will decrease to 40% in the near future and 36.4% in the far future; and according to climate change under the RCP8.5 scenario, the area of potential habitats of G. decander will decrease to 23.9% in the near future and 32.5% in the far future. In the far future, because of the increase in total precipitation, some of the lost potential habitats during the near future will be suitable again for G. decander. Due to its stability in harsh environmental conditions, G. decander appears as a type-forming species in a wide range of natural habitats in the study area and is therefore important in terms of soil protection and forage production.

Spatial patterns of phylogenetic diversity and endemism in the Western Ghats, India: A case study using ancient predatory arthropods

The Western Ghats (WG) mountain chain in peninsular India is a global biodiversity hotspot, one in which patterns of phylogenetic diversity and endemism remain to be documented across taxa. We used a well-characterized community of ancient soil predatory arthropods from the WG to understand diversity gradients, identify hotspots of endemism and conservation importance, and highlight poorly studied areas with unique biodiversity. We compiled an occurrence dataset for 19 species of scolopendrid centipedes, which was used to predict areas of habitat suitability using bioclimatic and geomorphological variables in Maxent. We used predicted distributions and a time-calibrated species phylogeny to calculate taxonomic and phylogenetic indices of diversity, endemism, and turnover. We observed a decreasing latitudinal gradient in taxonomic and phylogenetic diversity in the WG, which supports expectations from the latitudinal diversity gradient. The southern WG had the highest phylogenetic diversity and endemism, and was represented by lineages with long branch lengths as observed from relative phylogenetic diversity/endemism. These results indicate the persistence of lineages over evolutionary time in the southern WG and are consistent with predictions from the southern WG refuge hypothesis. The northern WG, despite having low phylogenetic diversity, had high values of phylogenetic endemism represented by distinct lineages as inferred from relative phylogenetic endemism. The distinct endemic lineages in this subregion might be adapted to life in lateritic plateaus characterized by poor soil conditions and high seasonality. Sites across an important biogeographic break, the Palghat Gap, broadly grouped separately in comparisons of species turnover along the WG. The southern WG and Nilgiris, adjoining the Palghat Gap, harbor unique centipede communities, where the causal role of climate or dispersal barriers in shaping diversity remains to be investigated. Our results highlight the need to use phylogeny and distribution data while assessing diversity and endemism patterns in the WG.

Estimation of Current and Future Suitable Areas for Tapirus pinchaque in Ecuador

At present, climate change is a direct threat to biodiversity and its effects are evidenced by an increasingly accelerated loss of biodiversity. This study identified the main threats presently facing the Tapirus pinchaque species in Ecuador, generated predictive models regarding its distribution, and analyzed the protected areas as a conservation tool. The methodology was based on a literature review and the application of binary predictive models to achieve these objectives. The main results indicate that the T. pinchaque is seriously threatened, mainly by changes in land use. In addition, three models were selected that show current and future suitable areas for the conservation of the species. Its current distribution amounts to 67,805 km2, 33% (22,872 km2) of which is located in 31 of the 61 protected areas. Finally, it is important to take timely actions focused on biodiversity conservation, considering the importance of balance in ecosystems to the humans dependent thereof, and the results regarding the changes in the current and future distribution areas of the mountain tapir are a great contribution to be used as a management tool for its conservation.

Nested Species Distribution Models of Chlamydiales in Ixodes ricinus (Tick) Hosts in Switzerland

The tick Ixodes ricinus is the vector of various pathogens, including Chlamydiales bacteria, which potentially cause respiratory infections. In this study, we modeled the spatial distribution of I. ricinus and associated Chlamydiales over Switzerland from 2009 to 2019. We used a total of 2,293 ticks and 186 Chlamydiales occurrences provided by a Swiss Army field campaign, a collaborative smartphone application, and a prospective campaign. For each tick location, we retrieved from Swiss federal data sets the environmental factors reflecting the topography, climate, and land cover. We then used the Maxent modeling technique to estimate the suitability of particular areas for I. ricinus and to subsequently build the nested niche of Chlamydiales bacteria. Results indicate that I. ricinus habitat suitability is determined by higher temperature and normalized difference vegetation index (NDVI) values, lower temperature during the driest months, and a higher percentage of artificial and forest areas. The performance of the model was improved when extracting the environmental variables for a 100-m radius buffer around the sampling points and when considering the climatic conditions of the 2 years previous to the sampling date. Chlamydiales bacteria were favored by a lower percentage of artificial surfaces, drier conditions, high precipitation during the coldest months, and short distances to wetlands. From 2009 to 2018, we observed an extension of areas suitable to ticks and Chlamydiales, associated with a shift toward higher altitude. The importance of considering spatiotemporal variations in the environmental conditions for obtaining better prediction was also demonstrated.IMPORTANCEIxodes ricinus is the vector of pathogens including the agent of Lyme disease, the tick-borne encephalitis virus, and the less well-known Chlamydiales bacteria, which are responsible for certain respiratory infections. In this study, we identified the environmental factors influencing the presence of I. ricinus and Chlamydiales in Switzerland and generated maps of their distribution from 2009 to 2018. We found an important expansion of suitable areas for both the tick and the bacteria during the last decade. Results also provided the environmental factors that determine the presence of Chlamydiales within ticks. Distribution maps as generated here are expected to bring valuable information for decision makers in controlling tick-borne diseases in Switzerland and establishing prevention campaigns. The methodological framework presented could be used to predict the distribution and spread of other host-pathogen pairs to identify environmental factors driving their distribution and to develop control or prevention strategies accordingly.

Predicting Habitat Suitability and Conserving Juniperus spp. Habitat Using SVM and Maximum Entropy Machine Learning Techniques

Support vector machine (SVM) and maximum entropy (MaxEnt) machine learning techniques are well suited to model the habitat suitability of species. In this study, SVM and MaxEnt models were developed to predict the habitat suitability of Juniperus spp. in the Southern Zagros Mountains of Iran. In recent decades, drought extension and climate alteration have led to extensive changes in the geographical occurrence of this species and its growth and regeneration are extremely limited in this area. This study evaluated the habitat suitability of Juniperus through spatial modeling and predicts appropriate regions for future cultivation and resource conservation. We modeled the natural habitat of Juniperus for an area of 700 ha in Sepidan Area in the Fars province using (1) data regarding the presence of the species (295 samples) collected through field surveys and GPS, (2) habitat soil information and indices derived from 60 soil samples collected in the study area, and (3) climatic and topographic datasets collected from various sources. In total, 15 conditioning factors were used for this spatial modeling approach. Receiver operator characteristic (ROC) curves were applied to estimate the accuracy of the habitat suitability models produced by the SVM and MaxEnt techniques. Results indicated logical and similar area under the curve (AUC)-ROC values for the SVM (0.735) and MaxEnt (0.728) models. Both the SVM and MaxEnt methods revealed a significant relationship between the Juniperus spp. distribution and conditioning factors. Environmental factors played a vital role in evaluating the presence of Juniperus sp. as Max and Min temperatures and annual mean rainfall were the three most important factors for habitat suitability in the study area. Finally, an area with high and very high suitability for the future cultivation of Juniperus sp. and for landscape conservation was suggested based on the SVM model.