Species distribution models in conservation biogeography: developments and challenges

Potential distribution of three invasive agricultural pests in China under climate change

Invasive pests reduce biodiversity and ecosystem service functions, thereby leading to economic and also agricultural losses. Banana skipper (Erionota torus Evans), red palm weevil (Rhynchophorus ferrugineus), and coconut caterpillar (Opisina arenosella Walker) are invasive insect pests in the palm-growing regions and they have had serious consequences for the planting of bananas (Musa nana), palms (Trachycarpus fortune) and coconut (Cocos nucifera). Based on screened occurrence data, the present research utilized Maximum Entropy model (Maxent) to simulate the distribution dynamics of these three invasive insects in China, under current and future climate (2050s, 2070s, 2090s) in two shared socio-economic pathways (SSPs: 126 and 585) of the newly released coupled model intercomparison project phase6 (CMIP6). The results show that: (1) Under current and future climate conditions, all model groups exhibited an AUC value exceeding 0.92, which shows that the model prediction results are very good;(2) The suitable habitat area of E. torus Evans remains relatively stable with some expansion in the SSP126 of 2090s and some contraction in the SSP585 of 2090s. The suitable habitat area of R. ferrugineus showed an overall contraction, with substantial contraction in the SSP585 of 2090s.The suitable habitat area of O. arenosella has an overall expansion, with the most pronounced expansion in the SSP585 of 2070s; (3) The current centroid of suitable habitats for R. ferrugineus and E. torus Evans is located in Guangxi Province and wholely shift toward the south direction under future climate. The centroid of suitable habitats for O. arenosella is currently located in the northeastern maritime area of Hainan Province and will shift toward the north direction under future climate; (4) Temperature, precipitation and Human disturbance factors (Population density and Human influence index) were crucial variables for describing the distribution of the three species. For E. torus Evans in particular, percentage contributions of Population density was up to 31.4, which is only 0.1 different from ranked first Bio19 (Precipitation of the coldest quarter). The dynamics of habitats of these three species and the correlating driver factors proposed in this work provide essential insights into future spatial management of the three invasive insects in China. Our work is necessary and timely in identifying newly areas at high risk of expansion of the three invasive insects in the future, then suggesting strategic control measures to prevent their spread, and finally providing scientific evidence for the early prevention and rapid response to the three invasive insects.

Distribution models of baleen whale species in the Irish Exclusive Economic Zone to inform management and conservation

Irish waters are under increasing pressure from anthropogenic sources including the development of offshore renewable energy, vessel traffic and fishing activity. Spatial planning requires robust datasets on species distribution and the identification of important habitats to inform the planning process. Despite limited survey effort, long-term citizen science data on whale presence are available and provide an opportunity to fill information gaps. Using presence-only data as well as a variety of environmental variables, we constructed seasonal ensemble species distribution models based on five different algorithms for minke whales, fin whales, humpback whales, sei whales, and blue whales. The models predicted that the coastal waters off the south and west of Ireland are particularly suitable for minke, fin and humpback whales. Offshore waters in the Porcupine Seabight area were identified as a relevant habitat for fin whales, sei whales and blue whales. We combined model outputs with data on maritime traffic, fishing activity and offshore wind farms to measure the exposure of all the species to these pressures, identifying areas of concern. This study serves as a baseline for the species presence in Irish waters over the last two decades to help develop appropriate marine spatial plans in the future.

Fitting individual-based models of spatial population dynamics to long-term monitoring data

Generating spatial predictions of species distribution is a central task for research and policy. Currently, correlative species distribution models (cSDMs) are among the most widely used tools for this purpose. However, a fundamental assumption of cSDMs, that species distributions are in equilibrium with their environment, is rarely fulfilled in real data and limits the applicability of cSDMs for dynamic projections. Process-based, dynamic SDMs (dSDMs) promise to overcome these limitations as they explicitly represent transient dynamics and enhance spatiotemporal transferability. Software tools for implementing dSDMs are becoming increasingly available, but their parameter estimation can be complex. Here, we test the feasibility of calibrating and validating a dSDM using long-term monitoring data of Swiss red kites (Milvus milvus). This population has shown strong increases in abundance and a progressive range expansion over the last decades, indicating a nonequilibrium situation. We construct an individual-based model using the RangeShiftR modeling platform and use Bayesian inference for model calibration. This allows the integration of heterogeneous data sources, such as parameter estimates from published literature and observational data from monitoring schemes, with a coherent assessment of parameter uncertainty. Our monitoring data encompass counts of breeding pairs at 267 sites across Switzerland over 22 years. We validate our model using a spatial-block cross-validation scheme and assess predictive performance with a rank-correlation coefficient. Our model showed very good predictive accuracy of spatial projections and represented well the observed population dynamics over the last two decades. Results suggest that reproductive success was a key factor driving the observed range expansion. According to our model, the Swiss red kite population fills large parts of its current range but has potential for further increases in density. We demonstrate the practicality of data integration and validation for dSDMs using RangeShiftR. This approach can improve predictive performance compared to cSDMs. The workflow presented here can be adopted for any population for which some prior knowledge on demographic and dispersal parameters as well as spatiotemporal observations of abundance or presence/absence are available. The fitted model provides improved quantitative insights into the ecology of a species, which can greatly aid conservation and management efforts.

Predicting suitable habitat for the endangered tree Ormosia microphylla in China

Climate change has significantly influenced the growth and distribution of plant species, particularly those with a narrow ecological niche. Understanding climate change impacts on the distribution and spatial pattern of endangered species can improve conservation strategies. The MaxEnt model is widely applied to predict species distribution and environmental tolerance based on occurrence data. This study investigated the suitable habitats of the endangered Ormosia microphylla in China and evaluated the importance of bioclimatic factors in shaping its distribution. Occurrence data and environmental variables were gleaned to construct the MaxEnt model, and the resulting suitable habitat maps were evaluated for accuracy. The results showed that the MaxEnt model had an excellent simulation quality (AUC = 0.962). The major environmental factors predicting the current distribution of O. microphylla were the mean diurnal range (bio2) and precipitation of the driest month (bio14). The current core potential distribution areas were concentrated in Guangxi, Fujian, Guizhou, Guangdong, and Hunan provinces in south China, demonstrating significant differences in their distribution areas. Our findings contribute to developing effective conservation and management measures for O. microphylla, addressing the critical need for reliable prediction of unfavorable impacts on the potential suitable habitats of the endangered species.

The influence of scale-dependent geodiversity on species distribution models in a biodiversity hotspot

Improving models of species' distributions is essential for conservation, especially in light of global change. Species distribution models (SDMs) often rely on mean environmental conditions, yet species distributions are also a function of environmental heterogeneity and filtering acting at multiple spatial scales. Geodiversity, which we define as the variation of abiotic features and processes of Earth's entire geosphere (inclusive of climate), has potential to improve SDMs and conservation assessments, as they capture multiple abiotic dimensions of species niches, however they have not been sufficiently tested in SDMs. We tested a range of geodiversity variables computed at varying scales using climate and elevation data. We compared predictive performance of MaxEnt SDMs generated using CHELSA bioclimatic variables to those also including geodiversity variables for 31 mammalian species in Colombia. Results show the spatial grain of geodiversity variables affects SDM performance. Some variables consistently exhibited an increasing or decreasing trend in variable importance with spatial grain, showing slight scale-dependence and indicating that some geodiversity variables are more relevant at particular scales for some species. Incorporating geodiversity variables into SDMs, and doing so at the appropriate spatial scales, enhances the ability to model species-environment relationships, thereby contributing to the conservation and management of biodiversity. This article is part of the Theo Murphy meeting issue 'Geodiversity for science and society'.

A new threshold selection method for species distribution models with presence-only data: Extracting the mutation point of the P/E curve by threshold regression

Selecting thresholds to convert continuous predictions of species distribution models proves critical for many real-world applications and model assessments. Prevalent threshold selection methods for presence-only data require unproven pseudo-absence data or subjective researchers' decisions. This study proposes a new method, Boyce-Threshold Quantile Regression (BTQR), to determine thresholds objectively without pseudo-absence data. We summarize that the mutation point is a typical shape feature of the predicted-to-expected (P/E) curve after reviewing relevant articles. Analysis based on source-sink theory suggests that this mutation point may represent a transition in habitat types and serve as an appropriate threshold. Threshold regression is introduced to accurately locate the mutation point. To validate the effectiveness of BTQR, we used four virtual species of varying prevalence and a real species with reliable distribution data. Six different species distribution models were employed to generate continuous suitability predictions. BTQR and nine other traditional methods transformed these continuous outputs into binary results. Comparative experiments show that BTQR has advantages in terms of accuracy, applicability, and consistency over the existing methods.

Environmental predictors for the restoration of a critically endangered coral, Acropora palmata, along the Florida reef tract

The population decline and lack of natural recovery of multiple coral species along the Florida reef tract have instigated the expanding application of coral restoration and conservation efforts. Few studies, however, have determined the optimal locations for the survival of outplanted coral colonies from restoration nurseries. This study predicts the optimal locations for Acropora palmata colonies along the Florida reef tract using a boosted-regression-tree model to examine the relationships between the occurrence of wild A. palmata and ten environmental variables. Our model results predicted A. palmata was most likely to occur in shallow reef habitats with (i) generally low mean chlorophyll-a concentrations (< 1 mg m-3), (ii) moderate fetch (3 kJ m-2), (iii) salinities between 20 and 37.5 ppt, (iv) temperatures between 20 and 32°C, (vi) low mean concentrations of total nitrogen (0.16 ppm), and (iv) irradiance between 26.5 and 53.5 mol m-2 s-1. The most suitable habitats for A. palmata were disproportionately allocated to reefs in Biscayne Bay, the Upper Keys, the western-lower Florida Keys, the Marquesas, and the Dry Tortugas. The middle Florida Keys had unfavorable environmental conditions for A. palmata habitat. Results from this study inform where A. palmata, outplanted as part of restoration and conservation efforts, would have suitable environmental conditions to persist over time. This study also provides decision-making support for management focused on the conservation and restoration of the endangered species A. palmata along the Florida reef tract.

A species' response to spatial climatic variation does not predict its response to climate change

The dominant paradigm for assessing ecological responses to climate change assumes that future states of individuals and populations can be predicted by current, species-wide performance variation across spatial climatic gradients. However, if the fates of ecological systems are better predicted by past responses to in situ climatic variation through time, this current analytical paradigm may be severely misleading. Empirically testing whether spatial or temporal climate responses better predict how species respond to climate change has been elusive, largely due to restrictive data requirements. Here, we leverage a newly collected network of ponderosa pine tree-ring time series to test whether statistically inferred responses to spatial versus temporal climatic variation better predict how trees have responded to recent climate change. When compared to observed tree growth responses to climate change since 1980, predictions derived from spatial climatic variation were wrong in both magnitude and direction. This was not the case for predictions derived from climatic variation through time, which were able to replicate observed responses well. Future climate scenarios through the end of the 21st century exacerbated these disparities. These results suggest that the currently dominant paradigm of forecasting the ecological impacts of climate change based on spatial climatic variation may be severely misleading over decadal to centennial timescales.

MaxEnt model strategies to studying current and future potential land suitability dynamics of wheat, soybean and rice cultivation under climatic change scenarios in East Asia

Climate change and variability are projected to alter the geographic suitability of lands for crops cultivation. Accurately predicting changes in the potential current and future land suitability distribution dynamics of wheat (Triticum aestivum), soybean (Glycine max) and rice (Oryza sativa) crops due to climate change scenarios is critical to adapting and mitigating the impacts of bioclimatic changes, and plays a significant role in securing food security in East Asia region. This study compiled large datasets of wheat, soybean and rice occurrence locations from GBIF and 19 bioclimatic variables obtained from the WorldClim database that affect crops growth. We recognized potential future suitable distribution regions for crops under the one socioeconomic pathway, (SSP585) for 2021-2040 and 2041-2060, using the MaxEnt model. The accuracy of the MaxEnt was highly significant with mean AUC values ranging from 0.833 to 0.882 for all models evaluated. The jackknife test revealed that for wheat, Bio4 and Bio12 contributed 17.6% and 12.6%, for soybean Bio10 and Bio12 contributed 15.6% and 49.5%, while for rice Bio12 and Bio14 contributed 12.9% and 36.0% to the MaxEnt model. In addition, cultivation aptitude for wheat, soybean, and rice increased in southeast China, North Korea, South Korea, and Japan, while decreasing in Mongolia and northwest China. Climate change is expected to increase the high land suitability for wheat, soybean, and rice in East Asia. Simulation results indicate an average decrease of unsuitable areas of -98.5%, -41.2% and -36.3% for wheat, soybean and rice from 2060 than that of current land suitability. In contrast, the high land suitable for wheat, soybean and rice cultivation is projected to increase by 75.1%, 68.5% and 81.9% from 2060 as compared with current. The findings of this study are of utmost importance in the East Asia region as they present an opportunity for policy makers to develop appropriate adaptation and mitigation strategies required to sustain crops distribution under future climates. Although the risks of wheat, soybean and rice cultivation may be significantly higher in the future because of high temperatures, heat waves, and droughts caused by climate change.

Impacts of physiological characteristics and human activities on the species distribution models of orchids taking the Hengduan Mountains as a case

The biogeography research of orchids through species distribution models (SDMs), a vital tool in the biogeography field, is critical to understanding the fundamental geographic distribution patterns and identifying conservation priorities. The correspondence between species occurrence and environmental information is crucial to the model's performance. However, ecological preferences unique to different orchid species, such as their life forms, are often overlooked during the modeling process. This oversight can introduce bias and increase model uncertainty. Additionally, human activities, as an important potential predictor, have not been quantified in any orchid SDMs. Taking the Hengduan Mountains as an example, we preprocessed all orchid species' occurrences based on physiological characteristics. Choosing five spatial factors related to human activities to quantify the interference and enter into models as HI factor. Using different modeling methods (GLM, MaxEnt, and RF) and evaluation indices (AUC, TSS, and Kappa), diverse modeling strategies have been constructed in the study. A double-ranking method has been adopted to select the critical orchid distribution regions. The results showed that classification models based on physiological characteristics significantly improved the model's accuracy while adding the HI factor had the same effect but the absence of enough significance. Suitability maps indicated that highly heterogeneous mountainous areas were vital for the distribution of orchids in the Hengduan Mountains. Different distribution patterns and critical regions existed between various orchid life forms geographically - terrestrial orchids were dominant in the mountain, and mycoherterophical orchids were primarily located in the north, more influenced by vegetation and temperature. Critical regions of epiphytic orchids were in the south due to a greater dependence on precipitation and temperature. These studies are informative for understanding the orchids' geographic distribution patterns in the Hengduan Mountains, promoting conservation and providing references for similar research beyond orchids.

Predicting of the current and future geographical distribution of Laurus nobilis L. under the effects of climate change

Today, climate change affects all living things on earth. It also leads to serious losses in terms of biodiversity, ecosystem services, and human welfare. In this context, Laurus nobilis L. is a very important species for Turkey, and the Mediterranean countries. This research aimed to simulate the current distribution of the suitable habitat for L. nobilis in Turkey and to predict its possible range shifts in future climate scenarios. To predict the geographical distribution of L. nobilis, the study used the maximum-entropy algorithm-based MaxEnt 3.4.1 with seven bioclimatic variables created using the Community Climate System Model 4.0 (CCSM4) and the prediction models RCP4.5-8.5 for the years 2050-2070. The results indicated that the most important bioclimatic variables that shape the distribution of L. nobilis are BIO11-mean temperature of coldest quarter, and BIO7-annual temperature range. Two climate change scenarios predicted that the geographical distribution of L. nobilis would increase slightly and then decrease in the future. However, the spatial change analysis showed that the general geographical distribution area of L. nobilis did not change significantly, but the "moderate," "high," and "very high" suitable habitats changed towards "low" suitable habitats. These changes were particularly effective in Turkey's Mediterranean region, which shows that climate change is instrumental in determining the future of the Mediterranean ecosystem. Therefore, suitability mapping and change analysis of potential future bioclimatic habitats can help in planning for land use, conservation, and ecological restoration of L. nobilis.

Modeling for Predicting the Potential Geographical Distribution of Three Ephedra Herbs in China

Ephedra species are beneficial for environmental protection in desert and grassland ecosystems. They have high ecological, medicinal, and economic value. To strengthen the protection of the sustainable development of Ephedra, we used occurrence records of Ephedra sinica Stapf., Ephedra intermedia Schrenk et C.A. Mey., and Ephedra equisetina Bge., combined with climate, soil, and topographic factors to simulate the suitable habitat of three Ephedra based on ensemble models on the Biomod2 platform. The results of the models were tested using AUC, TSS, and kappa coefficients. The results demonstrated that the ensemble model was able to accurately predict the potential distributions of E. sinica, E. intermedia, and E. equisetina. Eastern and central Inner Mongolia, middle and eastern Gansu, and northeastern Xinjiang were the optimum regions for the growth of E. sinica, E. intermedia, and E. equisetina, respectively. Additionally, several key environmental factors had a significant influence on the suitable habitats of the three Ephedra. The key factors affecting the distribution of E. sinica, E. intermedia, and E. equisetina were annual average precipitation, altitude, and vapor pressure, respectively. In conclusion, the results showed that the suitable ranges of the three Ephedra were mainly in Northwest China and that topography and climate were the primary influencing factors.

Ground Validation Reveals Limited Applicability of Species Distribution Models for Dakota Skipper (Hesperia dacotae, Lepidoptera: Hesperiidae) Recovery Efforts in Canada

Species distribution models (SDMs) use spatial relationships between species occurrence and habitat (predictor) variables to generate maps of habitat suitability across a region of interest. These maps are frequently used in recovery planning efforts for endangered species, but they are influenced by data availability, selection of predictor variables, and choice of model type. Ground validation is necessary to robustly evaluate map accuracy, but it is rarely done, making it difficult to determine which modeling approach is best-suited for a given species or region. To address this uncertainty, we used two SDM types (Maxent and GLM) and two methods of selecting predictor variables to build four SDMs for an endangered prairie butterfly (Dakota skipper, Hesperia dacotae) in two regions of Manitoba, Canada. We then conducted field-based habitat suitability assessments at 120 locations in each region to enable direct comparisons of model output and accuracy. We found that soil type and surrounding landcover (grassland versus cropland) were important predictors of species occurrence regardless of region, predictor selection method, or model type. Cross-validation statistics indicated that most SDMs performed well (AUC > 0.7), but ground validation revealed that the habitat suitability maps they generated were inaccurate (Cohen's kappa < 0.4). Maxent models produced more accurate maps than GLMs, likely because false species absences adversely affected the latter, but only one Maxent-based map was accurate enough to help locate sites for future field investigations (Cohen's kappa > 0.3). Our results emphasize the importance of ground-validating SDM-based habitat suitability maps before incorporating them into species recovery plans.

Assessing future shifts in habitat suitability and connectivity to old-growth forests to support the conservation of the endangered giant noctule

Background

Suitable climate and availability of habitats for roosting, foraging, and dispersing are critical for the long-term persistence of bat species. The giant noctule (Nyctalus lasiopterus) represents one of the lesser-known European bats, especially regarding the environmental factors which shape its distribution.

Methodology

We integrated climate-based ecological niche models with information about topography and rivers' network to model weighted suitability for N. lasiopterus in the western Palearctic. The weighted suitability map was then used to estimate connectivity among the distinct occurrence localities of N. lasiopterus, as well as from these latter towards European old-growth forests, under current conditions and different combinations of future timeframes (2030, 2050, 2070) and shared socioeconomic pathways (SSPs 3.70 and 5.85).

Results

Current weighted suitability is highest in Andalusia, northern Iberia, southwestern France, peninsular Italy, coastal Balkans and Anatolia, with dispersed suitable patches elsewhere. A north-eastward shift of weighted suitability emerges in the considered future scenarios, especially under SSP 5.85. The major current ecological corridors for N. lasiopterus are predicted within a 'belt' connecting northern Spain and southwestern France, as well as in the Italian Alps. However, following changes in weighted suitability, connectivity would increase in central-eastern Europe in the future. The bioclimatic niche of the western N. lasiopterus populations does not overlap with those of the central and eastern ones, and it only overlaps with climatic conditions characterizing old-growth forests in western Europe.

Conclusions

The outcomes of our analyses would help in designing specific conservation measures for the distinct groups of giant noctule populations, favoring the possibility of range expansion and movement towards forested habitats.

Genomic and ecological evidence shed light on the recent demographic history of two related invasive insects

Hypogeococcus pungens is a species complex native to southern South America that is composed of at least five putative species, each one specialized in the use of different host plants. Two of these undescribed species were registered as invasive in Central and North America: Hyp-C is a cactophagous mealybug that became an important pest that threatens endemic cactus species in Puerto Rico, and Hyp-AP feeds on Amaranthaceae and Portulacaceae hosts, but does not produce severe damage to the host plants. We quantified genomic variation and investigated the demographic history of both invasive species by means of coalescent-based simulations using high throughput sequencing data. We also evaluated the incidence of host plant infestation produced by both species and used an ecological niche modeling approach to assess potential distribution under current and future climatic scenarios. Our genetic survey evinced the footprints of strong effective population size reduction and signals of genetic differentiation among populations within each species. Incidence of plant attacks varied between species and among populations within species, with some host plant species preferred over others. Ecological niche modeling suggested that under future climatic scenarios both species would expand their distribution ranges in Puerto Rico. These results provide valuable information for the design of efficient management and control strategies of the Puerto Rican cactus pest and shed light on the evolutionary pathways of biological invasions.

Prediction of the Current and Future Distributions of the Hessian Fly, Mayetiola destructor (Say), under Climatic Change in China

The Hessian fly, Mayetiola destructor (Say) (Diptera: Cecidomyiidae), is a destructive wheat pest worldwide and an important alien species in China. Based on 258 distribution records and nine environmental factors of the Hessian fly, we predicted the potential distribution area in China under three current and future (2050s and 2070s) climate change scenarios (RCP2.6, RCP4.5, and RCP8.5) via the optimized MaxEnt model. Under the current climate conditions, the suitable distribution areas of the Hessian fly in China were 25-48° N, 81-123° E, and the total highly suitable distribution area is approximately 9.63 × 10⁵ km², accounting for 9.99% of the total national area. The highly suitable areas are mainly located in northern Xinjiang and central and eastern China. With the rising global temperatures, except for the high-suitable areas under the RCP8.5 scenario, most potential geographic distribution areas would expand in the future. The minimum temperature in February (tmin-2), precipitation in March (prec-3), maximum temperature in November (tmax-11), and precipitation seasonality (bio-15) are important factors that affect the potential geographic distribution of the Hessian fly. This study provides an important reference and empirical basis for management of the Hessian fly in the future.

The potential distribution of Cyclopes didactylus, a silky anteater, reveals a likely unknown population and urgent need for forest conservation in Northeast Brazil

Cyclopes didactylus, the smallest of all anteaters, inhabits Amazonian and Atlantic forests with an apparently disjunct distribution. Yet, phylogeography reveals historical connections through the forests of the Northeast Region of Brazil. Its populations in this region are classified by the Red List of Threatened Species as Data Deficient and with a trend towards decline. However, Northeast Brazil has a large sampling gap, and the potential distribution of this species has yet to be evaluated. We investigated the potential distribution of C. didactylus to evaluate the hypothesis of a disjunct distribution between Amazonian and Atlantic forests and estimate the amount of protected area in its predicted distribution. We generated a Maxent distribution model using occurrence records, according to the new taxonomic revision of Cyclopes, and selected current bioclimatic variables to evaluate the continuity of the predicted distribution of the species in Northeast Brazil. We also performed past projections to assess historical connections and overlapped maps of protected areas onto their current distribution. Although its distribution is probably disjunct, at least one as-yet-unknown population may be present in the forests of Northeast Brazil, an area poorly protected. The results are useful for targeting field efforts in this under-sampled region.

Climate change and more disturbed land-use types will further the invasion of a non-native annual grass, Ventenata dubia

Identification of suitable habitat for invasive weeds and their projected infestation extent across different land use cover types under a changing climate is crucial for the broad management goals of prevention, detection, and rapid response. In this study, we adopted an ensemble approach of species distribution models to project potential habitat of the invasive annual grass, Ventenata dubia, within the Gallatin County and along its road corridors, in Montana, USA, under current and future climates. The model prediction of V. dubia habitat was excellent with an AUC value of > 0.90. The climate predictors with most influence on V. dubia occurrence were precipitation, potential evapo-transpiration, relative humidity, vapor pressure deficit, and solar radiation for growing season months. Under current climate, the model projected 243 and 1,371 km2 coverage of V. dubia along road corridors and the entire County, respectively. The projected coverage of V. dubia was greatest for road corridors (239% under RCP4.5 and 302% under RCP8.5) compared to that of Gallatin County (127% under RCP4.5 and 241% under RCP8.5). Among the land use cover types, the model projected greatest expansion of V. dubia across agriculture land with 425% and 484%, and grasslands with 278% and 442% under RCP4.5 and RCP8.5 respectively. Our modelling approach suggests that the changing climate will facilitate spread and establishment of non-native species in disturbed habitats. We conclude that V. dubia with a short history of invasion is expanding at an alarming rate and requires greater investment in detection and monitoring to prevent further expansion.

Predicting the potential distribution of Campsis grandiflora in China under climate change

Because the research on the geographical distribution of species significantly influences people's understanding of species protection and utilization, it is important to study the influence of climate change on plants' geographical distribution patterns. Based on 166 distribution records and 11 climate and terrain variables, we used MaxEnt (Maximum Entropy) model and ArcGIS software to predict the potential distribution of Campsis grandiflora under climate change and then determined the dominant climate variables that significantly affected its geographical distribution. In our study, the area under the curve (AUC) value of the training data was 0.939, proving the accuracy of our prediction. Under current climate conditions, the area of potentially suitable habitat is 238.29 × 10⁴ km², mainly distributed in northern, central, southern, and eastern China. The dominant variables that affect the geographical distribution of C. grandiflora are temperature, precipitation and altitude. In the future climate change scenario, the total area of suitable habitat and highly suitable habitat will increase, whereas the area of moderately suitable habitat and poorly suitable habitat will decrease. In addition, the centroid of the potentially suitable area of C. grandiflora will migrate to higher latitude and higher altitudes areas. The results could give strategic guidance for development, protection, and utilization of C. grandiflora in China.

Predicting Climate Change Impacts on the Rare and Endangered Horsfieldia tetratepala in China

Global climate change has become a major threat to biodiversity, posing severe challenges to species conservation. This is particularly true for species such as Horsfieldia tetratepala that have extremely small populations in the wild. Little is known about the species distribution of H. tetratepala in the current climate, as well as how that will change with potential future climates. The key environmental factors that influence its expansion, especially its habitat sustainability and its potential to adapt to climate change, are also unknown, though such information is vital for the protection of this endangered species. Based on six climate factors and 25 species distribution points, this study used the maximum entropy model (MaxEnt) to simulate the potential distribution for H. tetratepala in three periods (current, 2050s, and 2070s), and to investigate the changes in distribution patterns and the main environmental factors affecting species distribution. The modeling results show that the most important bioclimatic variables affecting H. tetratepala were precipitation of the warmest quarter (Bio_18) and temperature seasonality (Bio_4). The suitable areas for H. tetratepala will gradually be lost in Yunnan but will be generally offset in the northeastward direction, expanding in Hainan, Guangzhou, and Taiwan provinces under the future climate conditions. Therefore, we recommend protecting the habitats of H. tetratepala in Yunnan and strengthening the in-depth species investigation and monitoring in areas (Hainan, Guangzhou, and Taiwan) where no related reports of H. tetratepala have been reported. The results improve our understanding of this species’ response under the changing climate and benefit strategies for its conservation.

Predicting the Suitable Current and Future Potential Distribution of the Native Endangered Tree Tecomella undulata (Sm.) Seem. in Pakistan

The burgeoning human population exhibited a rapid amplification in demand for timber and fuelwood and as a result, the natural population of the native tree Tecomella undulata reduced rapidly due to its high economic and medicinal significance. The recognition of appropriate regions for threatened plants in the climate change scenario is a fundamental step for the restoration and conservation of biodiversity. The current study predicts the potentially suitable areas in Pakistan for T. undulata restoration. This research identifies the highly appropriate regions for vulnerable T. undulata through the maximum entropy model from MaxEnt software. The model’s Area Under Curve 0.968 suggested its accuracy. The mean temperature of the wettest quarter, precipitation of the warmest quarter, and mean temperature in the driest quarter significantly shaped the T. undulata distribution. Future suitable areas for T. undulata were made by using RCP (4.5 and 8.5) for the years 2050 and 2070 through 19 bioclimatic variables and 66 occurrence points. The current highly suitable area for T. undulata is approximately 135,749 km2 (15.4%) while the unsuitable area identified is approximately 404,917 km2 (45.91%). The highly suitable area for T. undulata increases by 3.6–7% under climate change regimes (RCP 4.5 and RCP 8.5). The Central Punjab (District Faisalabad, Nankana sahib, Jhang, Kasur, and Okara), Salt Range, Western Khayber Pakhtunkhwa (KPK), FATA area, Eastern Balochistan, and Thar and Tharparker in Sindh are the current appropriate habitats for T. undulata. Under all future climatic circumstances, the extremely appropriate area for T. undulata was anticipated to expand, whereas the unsuitable zones would all shrink. The research would be significant for the further development of T. undulata management and conservation techniques.

MaxEnt Modeling to Estimate the Impact of Climate Factors on Distribution of Pinus densiflora

Pinus densiflora is an important evergreen coniferous species with both economic and ecological value. It is an endemic species in East Asia. Global climate warming greatly interferes with species survival. This study explored the impact of climate change on the distribution of this species and the relationship between its geographical distribution and climate demand, so as to provide a theoretical basis for the protection of P. densiflora under the background of global warming. This research used 565 valid data points and 19 typical climatic environmental factors distributed in China, Japan, and South Korea. The potential distribution area of P. densiflora in East Asia under the last glacial maximum (LGM), mid-Holocene, the current situation and two scenarios (RCP 2.6 and RCP 8.5) in the future (2050s and 2070s) was simulated by the MaxEnt model. The species distribution model toolbox in ArcGIS software was used to analyze the potential distribution range and change of P. densiflora. The contribution rates, jackknife test and environmental variable response curves were used to assess the importance of key climate factors. The area under the receiver-operating characteristic curve (AUC) was used to evaluate model accuracy. The MaxEnt model had an excellent simulation effect (AUC = 0.982). The forecast showed that the Korean Peninsula and Japan were highly suitable areas for P. densiflora, and the area had little change. Moreover, during the LGM, there was no large-scale retreat to the south, and it was likely to survive in situ in mountain shelters. The results suggested that Japan may be the origin of P. densiflora rather than the Shandong Peninsula of China. The distribution area of P. densiflora in the mid-Holocene and future scenarios was reduced compared with the current distribution, and the reduction of future distribution was greater, indicating that climate warming will have certain negative impacts on the distribution of P. densiflora in the future. The precipitation of the warmest quarter (Bio18), temperature seasonality (Bio4), mean annual temperature (Bio1) and mean temperature of the wettest quarter (Bio8) had the greatest impact on the distribution area of P. densiflora.

Selection of predictor variables for species distribution models: a case study with an invasive marine bryozoan

Species distribution models (SDMs) are important tools for predicting the occurrence and abundance of organisms in space and time, with numerous applications in ecology. However, the accuracy and utility of SDMs can be compromised when predictor variables are selected without careful consideration of their ecophysiological relevance to the focal organism. We conducted an in-depth examination of the variable selection process by evaluating predictors to be used in SDMs for Membranipora membranacea, an ecologically significant marine invasive species with a complex lifecycle, as a case study. Using an information-theoretic and multi-model inference approach based on generalized linear mixed models, we assessed multiple environmental variables (depth, kelp density, kelp substrate, temperature, and wave exposure) as predictors of the abundance of multiple life stages of M. membranacea, investigating species-environment relationships and relative and absolute variable importance. We found that the relative importance of a predictor, the metric calculated to represent a predictor, and whether a predictor was proximal or distal were important considerations in the variable selection process. Data constraints (e.g. sample size, characteristics of available predictor data) may inhibit accurate assessment of predictor variables during variable selection. Importantly, our results suggest that species-environment relationships derived from small-scale studies can inform variable selection for SDMs at larger spatiotemporal scales. We developed a conceptual framework for variable selection for SDMs which can be applied to most contexts of species distribution modelling, but particularly those with several candidate predictors and a large dataset.

Automatic variable selection in ecological niche modeling: A case study using Cassin’s Sparrow (Peucaea cassinii)

MERRA/Max provides a feature selection approach to dimensionality reduction that enables direct use of global climate model outputs in ecological niche modeling. The system accomplishes this reduction through a Monte Carlo optimization in which many independent MaxEnt runs, operating on a species occurrence file and a small set of randomly selected variables in a large collection of variables, converge on an estimate of the top contributing predictors in the larger collection. These top predictors can be viewed as potential candidates in the variable selection step of the ecological niche modeling process. MERRA/Max’s Monte Carlo algorithm operates on files stored in the underlying filesystem, making it scalable to large data sets. Its software components can run as parallel processes in a high-performance cloud computing environment to yield near real-time performance. In tests using Cassin’s Sparrow (Peucaea cassinii) as the target species, MERRA/Max selected a set of predictors from Worldclim’s Bioclim collection of 19 environmental variables that have been shown to be important determinants of the species’ bioclimatic niche. It also selected biologically and ecologically plausible predictors from a more diverse set of 86 environmental variables derived from NASA’s Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2) reanalysis, an output product of the Goddard Earth Observing System Version 5 (GEOS-5) modeling system. We believe these results point to a technological approach that could expand the use global climate model outputs in ecological niche modeling, foster exploratory experimentation with otherwise difficult-to-use climate data sets, streamline the modeling process, and, eventually, enable automated bioclimatic modeling as a practical, readily accessible, low-cost, commercial cloud service.

Combining Citizen Science Data and Satellite Descriptors of Ecosystem Functioning to Monitor the Abundance of a Migratory Bird during the Non-Breeding Season

Migratory birds are particularly exposed to habitat changes in their breeding and non-breeding grounds. Remote sensing technologies offer an excellent opportunity to monitor species’ habitats from space at unprecedented spatiotemporal scales. We analyzed if remotely sensed ecosystem functioning attributes (EFAs) adequately predict the spatiotemporal variation of the Woodcock’s (Scolopax rusticola) relative abundance in southwest Europe, during autumn migration and wintering periods. We used data gathered from Woodcock monitoring through citizen science (N = 355,654 hunting trips) between 2009 and 2018. We computed a comprehensive set of EFAs on a weekly basis from three MODIS satellite products: enhanced vegetation index (EVI), tasseled cap transformation (TCT), and land surface temperature (LST). We developed generalized linear mixed models to explore the predictive power of EFAs on Woodcock’s abundance during the non-breeding season. Results showed that Woodcock abundance is correlated with spatiotemporal dynamics in primary productivity (measured through the EVI), water cycle dynamics (wetness component of TCT), and surface energy balance (LST) in both periods. Our findings underline the potential of combining citizen science and remote sensing data to monitor migratory birds throughout their life cycles—an issue of critical importance to ensure adequate habitat management in the non-breeding areas.

Cytotype and genotype predict mortality and recruitment in Colorado quaking aspen (Populus tremuloides)

Species responses to climate change depend on environment, genetics, and interactions among these factors. Intraspecific cytotype (ploidy level) variation is a common type of genetic variation in many species. However, the importance of intraspecific cytotype variation in determining demography across environments is poorly known. We studied quaking aspen (Populus tremuloides), which occurs in diploid and triploid cytotypes. This widespread tree species is experiencing contractions in its western range, which could potentially be linked to cytotype-dependent drought tolerance. We found that interactions between cytotype and environment drive mortality and recruitment across 503 plots in Colorado. Triploids were more vulnerable to mortality relative to diploids and had reduced recruitment on more drought-prone and disturbed plots relative to diploids. Furthermore, there was substantial genotype-dependent variation in demography. Thus, cytotype and genotype variation are associated with decline in this foundation species. Future assessment of demographic responses to climate change will benefit from knowledge of how genetic and environmental mosaics interact to determine species' ecophysiology and demography.

Temporal changes in the potential geographic distribution of Histiotus velatus (Chiroptera, Vespertilionidae), the "decade effect"

We investigated how the potential distribution of Histiotus velatus is affected by the addition of new records over decades (decade effect). Assuming that (1: hypothesis of the effect of the decade) the addition of new occurrence records over time increases the potential size of the species distribution; and (2: Wallacean distance hypothesis) over the years, the new points added are increasingly distant from the research centers. Considering the geographic knowledge gap of this species, our objective is to report a new record of this species and estimate its potential distribution in South America through environment niche models (ENMs). For this, we compiled records of occurrence of species, selected from 1900 to 2015. We used 19 bioclimatic variables available in the WorldClim database to estimate the potential distribution of the species, and we used three modeling algorithms: Maximum Entropy (MXT), Random Forest (RDF), and Support Vector Machine. To test the Wallacean distance hypothesis, we calculated the Euclidian distance from occurrences to bat research centers in Brazil, located using a national researchers' information dataset ("Plataforma Lattes"). To test the hypothesis of the decade effect, we used the beta regression analysis, taking conservative and non-conservative approaches. The results showed that the predicted area expanded and retracted with the addition of new occurrences over the decades, with an improvement in the accuracy of models. Most records are located in the southeastern region of Brazil, but algorithms predicted areas in regions where there are no records. Only the conservative approach has had a positive relationship over the decades. The distance from new points does not increase over the years of research centers.

Modelling Potential Distribution of Snow Leopards in Pamir, Northern Pakistan: Implications for Human–Snow Leopard Conflicts

The snow leopard (Panthera uncia) is a cryptic and rare big cat inhabiting Asia’s remote and harsh elevated areas. Its population has decreased across the globe for various reasons, including human–snow leopard conflicts (HSCs). Understanding the snow leopard’s distribution range and habitat interactions with human/livestock is essential for understanding the ecological context in which HSCs occur and thus gives insights into how to mitigate HSCs. In this study, a MaxEnt model predicted the snow leopard’s potential distribution and analyzed the land use/cover to determine the habitat interactions of snow leopards with human/livestock in Karakoram–Pamir, northern Pakistan. The results indicated an excellent model performance for predicting the species’ potential distribution. The variables with higher contributions to the model were the mean diurnal temperature range (51.7%), annual temperature range (18.5%), aspect (14.2%), and land cover (6.9%). The model predicted approximately 10% of the study area as a highly suitable habitat for snow leopards. Appropriate areas included those at an altitude ranging from 2721 to 4825 m, with a mean elevation of 3796.9 ± 432 m, overlapping between suitable snow leopard habitats and human presence. The human encroachment (human settlements and agriculture) in suitable snow leopard habitat increased by 115% between 2008 and 2018. Increasing encroachment and a clear overlap between snow leopard suitable habitat and human activities, signs of growing competition between wildlife and human/livestock for limited rangeland resources, may have contributed to increasing HSCs. A sound land use plan is needed to minimize overlaps between suitable snow leopard habitat and human presence to mitigate HSCs in the long run.

Evaluating the Fitness for Use of Citizen Science Data for Wildlife Monitoring

Contributory citizen science programs focused on ecological monitoring can produce fine-grained and expansive data sets across spatial and temporal scales. With this data collection potential, citizen scientists can significantly impact the ability to monitor ecological patterns. However, scientists still harbor skepticism about using citizen science data in their work, generally due to doubts about data quality. Numerous peer-reviewed articles have addressed data quality in citizen science. Yet, many of these methods are not useable by third-party scientists (scientists who are not directly involved in the citizen science program). In addition, these methods generally capture internal data quality rather than a dataset’s potential to be used for a specific purpose. Assessing data fitness for use represents a promising approach to evaluating data accuracy and quality for different applications and contexts. In this article, we employ a Spatial, Temporal, Aptness, and Application (STAAq) assessment approach to assess data fitness for use of citizen science datasets. We tested the STAAq assessment approach through a case study examining the distribution of caribou in Denali National Park and Preserve. Three different datasets were used in the test, Map of Life data (a global scale citizen science mobile application for recording species observations), Ride Observe and Record data (a program sponsored by the park staff where incentivized volunteers observe species in the park), and conventionally collected radio collar data. The STAAq assessment showed that the Map of Life and Ride Observe and Record program data are fit for monitoring caribou distribution in the park. This data fitness for use approach is a promising way to assess the external quality of a dataset and its fitness to address particular research or monitoring questions. This type of assessment may help citizen science skeptics see the value and potential of citizen science collected data and encourage the use of citizen science data by more scientists.

Predicting Impacts of Climate Change on Northward Range Expansion of Invasive Weeds in South Korea

Predicting the distribution of invasive weeds under climate change is important for the early identification of areas that are susceptible to invasion and for the adoption of the best preventive measures. Here, we predicted the habitat suitability of 16 invasive weeds in response to climate change and land cover changes in South Korea using a maximum entropy modeling approach. Based on the predictions of the model, climate change is likely to increase habitat suitability. Currently, the area of moderately suitable and highly suitable habitats is estimated to be 8877.46 km², and 990.29 km², respectively, and these areas are expected to increase up to 496.52% by 2050 and 1439.65% by 2070 under the representative concentration pathways 4.5 scenario across the country. Although habitat suitability was estimated to be highest in the southern regions (<36° latitude), the central and northern regions are also predicted to have substantial increases in suitable habitat areas. Our study revealed that climate change would exacerbate the threat of northward weed invasions by shifting the climatic barriers of invasive weeds from the southern region. Thus, it is essential to initiate control and management strategies in the southern region to prevent further invasions into new areas.

Landscape-level habitat management plan through geometric reserve design for critically endangered Hangul (Cervus hanglu hanglu)

Hangul (Cervus hanglu hanglu), the only red deer subspecies surviving in the Indian subcontinent, is of top conservation priority with global importance. Unfortunately, it has lost much of its historical distribution range, and it is now confined to Dachigam landscape within the Kashmir valley of India. The Government of India initiated a recovery plan in 2008 to augment their numbers through ex-situ conservation programs. However, it was necessary to identify potential hangul habitats in Kashmir valley for adopting landscape-level conservation planning for the species. Based on geometric aspects of reserve design, we modeled hangul habitat using an ensemble approach to identify hangul habitats. The present model indicates that the conifer and broadleaf mixed forests were the most suitable habitats. Only 9% of the total study landscape was found suitable for the species. We identified corridors among the suitable habitat blocks, which may be vital for the species' long-term genetic viability. We suggest reorganizing the existing management of Dachigam National Park (NP) following the landscape level and habitat block-level management planning based on the core principles of geometric reserve design. We recommend that the identified patch (PID-6) in the southern region of the landscape to be converted into a Conservation Reserve or merged with the Overa-Aru Wildlife Sanctuary. This habitat patch PID-6 may be a stepping stone habitat and vital for maintaining the species landscape connectivity and metapopulation dynamics.

Assessing the impact of climate change on the distribution of Osmanthus fragrans using Maxent

Models that evaluate the potential geographic distribution of species can be used with a variety of important applications in conservation biology. Osmanthus fragrans has high ornamental, culinary, and medicinal value, and is widely used in landscaping. However, its preferred habitat and the environmental factors that determine its distribution remain largely unknown; the environmental factors that shape its suitability also require analysis. Based on 89 occurrence records and 30 environmental variables, this study constructed Maxent models for current as well as future appropriate habitats for O. fragrans. The results indicate that UV-B seasonality (19.1%), precipitation seasonality (18.8%), annual temperature range (13.1%), and mean diurnal temperature range (12.5%) were the most important factors used for interpreting the environmental demands for this species. Highly appropriate habitats for O. fragrans were mainly distributed in southwestern Jiangsu, southern Anhui, Shanghai, Zhejiang, Fujian, northern Guangdong, Guangxi, southern Hunan, southern Hubei, Sichuan, and Taiwan. Under climate change scenarios, the spatial extent of the area of suitable distribution will decrease, and the distribution center of O. fragrans will shift to the southwest. The results of this study will help land managers to avoid blindly introducing this species into inappropriate habitat while improving O. fragrans yield and quality.

Species distribution and conservation assessment of the black-headed night monkey (Aotus nigriceps): a species of Least Concern that faces widespread anthropogenic threats

Deforestation rates in the Brazilian Amazon have been steadily increasing since 2007. Recent government policy, the projected growth of agriculture, and the expansion of the cattle industry are expected to further pressure primates within the Amazon basin. In this study, we examined the anthropogenic impact on the widely distributed black-headed night monkey, Aotus nigriceps, whose distribution and population status have yet to be assessed. We (1) modeled potential species distribution in A. nigriceps, (2) estimated the impact of habitat loss on population trends, and (3) highlight landscape-based conservation actions that maximize the potential for their long-term sustainability. We found the black-headed night monkey to be restricted by several biotic and environmental factors including forest cover, isothermality, precipitation, temperature, and elevation. Over the last two decades, over 132,908 km² of tree cover (18%) has been lost within their currently recognized range based on satellite imagery. Based on a balance training omission, predicted area, and threshold values (BPTP), suitable habitat was only 67% (1,069,948 km²) of their hypothesized range, a loss of 16.5% from 2000, with just nearly a third of suitable habitat currently within protected areas. Over the last two decades, an estimated minimum 1.6 million individuals have been lost due to loss of suitable habitat. Projected deforestation rates equate to an additional loss of 94,458 km² of suitable habitat over the next decade. Although classified as a species of Least Concern, we suggest that A. nigriceps may likely be more at risk than previously described. The future impact of the continued expansion of monoculture crops, cattle ranching, and wildfires is still unknown. However, we outline several steps to ensure the long-term viability of this nocturnal primate and other sympatric species throughout the Amazon Basin.

How to make use of unlabeled observations in species distribution modeling using point process models

Species distribution modeling, which allows users to predict the spatial distribution of species with the use of environmental covariates, has become increasingly popular, with many software platforms providing tools to fit such models. However, the species observations used can have varying levels of quality and can have incomplete information, such as uncertain or unknown species identity.In this paper, we develop two algorithms to classify observations with unknown species identities which simultaneously predict several species distributions using spatial point processes. Through simulations, we compare the performance of these algorithms using 7 different initializations to the performance of models fitted using only the observations with known species identity.We show that performance varies with differences in correlation among species distributions, species abundance, and the proportion of observations with unknown species identities. Additionally, some of the methods developed here outperformed the models that did not use the misspecified data. We applied the best-performing methods to a dataset of three frog species (Mixophyes).These models represent a helpful and promising tool for opportunistic surveys where misidentification is possible or for the distribution of species newly separated in their taxonomy.