Distribution Modelling Methods Research Articles

First records distribution models to guide biosurveillance for non‐native species

Quickly locating new populations of non‐native species can reduce the ecological and economic costs of species invasions. However, the difficulty of predicting which new non‐native species will establish, and where, has limited active post‐border biosurveillance efforts. Because pathways of introduction underlie spatial patterns of establishment risk, an intuitive approach is to search for new non‐native species in areas where many non‐native species have first been detected in the past. We formalize this intuition via first records distribution models (FRDMs), which apply species distribution modeling methods to the collection of first occurrence records across species (i.e. one record per species). We define FRDMs as statistical models that quantify environmental conditions associated with species' first naturalized records to predict spatial patterns of establishment risk. We model the first records of non‐native plants in the conterminous USA as a proof‐of‐concept. The novelty of FRDMs is that their inferences apply not just to the species that contributed data; they provide a rigorous framework for predicting hotspots of invasion for new non‐native taxa that share a pathway of introduction with the modeled species. FRDMs can guide survey efforts for new non‐native taxa at multiple scales and across ecosystems.

Niche based distribution modelling and mapping of Brutian pine in the Gölhisar district

The purpose of this work was to elucidate the fundamental characteristics and application areas of various modelling techniques that are widely employed in current ecological modelling research. Using five distinct distribution modelling techniques, possible species distribution modelling and mapping of the Brutian pine species in the Gölhisar district were conducted. The data was collected from Brutian pine species in 400 sampling plots in the area. The variables used in the models were elevation, slope, aspect, radiation index, heat index, topographic position index and bedrock types. Logistic regression, classification tree, random forest, generalized additive model and maximum entropy were used as the species distribution modelling methods. Receiver Operating Characteristics (ROC) curves were created and the performance of the species distribution models was evaluated with the Area Under the ROC curve (AUC). The statistical analyses revealed that the best models were generalized additive model, random forest, classification tree, maximum entropy and logistic regression, respectively. Elevation and bedrock types had the highest contribution to the Brutian pine distribution models. The outputs of the generalized additive model technique that had the highest AUC value were mapped. Some ecological and statistical differences were found between the models and their reasons were presented. Compared to the methods commonly used in species distribution modelling studies, generalized additive model technique has a specific smoothing function which ensures both fittings between the envirenvironmental changes and explanatory curves and more accurate ecological interpretation of the models obtained.

Brazil nut journey under future climate change in Amazon.

Climate change is among the principal threats to global terrestrial biodiversity, especially to megadiverse ecosystems such as the Amazon rainforest. In this study, we investigate how it could affect an iconic forest species-Bertholletia excelsa-(the Brazil nut) which has values in multiple dimensions in an Amazonian context. We used an ensemble from various distribution modeling methods designed for four different climate scenarios from CMIP6 by the end of the century. Then, we simulate how spatial dynamics under climate change, including explicitly dispersal events, can affect the persistence, colonization, and potential extinction of Bertholletia excelsa in the future. Our results show that by the end of the century there would be a generalized loss of suitability on the Amazon biome, regardless of the climate scenario evaluated, which could promote a significant loss (up to 94%) of the area available for the species via extinction. Our results also show that, in the future, the species would colonize higher altitudes in search of favorable conditions for its survival. Finally, we detected that areas that had previously become unsuitable because of climate change would have favorable conditions by the end of the century. Such an outcome could be useful in fostering an active restoration agenda that can mitigate the negative effects of climate change on species in this study.

A revision of Anadolua (Orthoptera: Tettigoniidae: Tettigoniinae) based on morphological and bioacoustic characters: Newly confirmed data suggesting a new synonym and two new species.

In the present paper, a number of twelve populations of genus Anadolua have been studied by focusing on their morphological and bioacoustic characters. Both bioacoustics and morphological CDF analyses (especially in males) indicate the existence of two new species (A. bergeri sp. nov. and A. moli sp. nov.) and of a new synonym (A. burri syn. nov. synonymized with A. schwarzi). The description of the two new species and the redescription of A. schwarzi are provided with IUCN categories, suggestions and figures, which would comprehensively illustrate the species representing the Anatolian members of genus Anadolua. Two bioacoustic song types (schwarzi and bergeri) are found for Anadolua according to temporal and spectral parameters data of the male calling song. Also presented are the morphological and bioacoustic keys to species of Anadolua. The distribution modelling methods of the species have been carried out for Anadolua species. It has been concluded that the dramatic decrease occurring in potentially suitable habitats necessitates the development in the near future of some conservation action plans for all species of Anadolua.

Unveiling mid-century conservation priorities: Co-occurrence of biodiversity, climate change exposure, and carbon storage in the Middle and Lower Yangtze River Basin, China

The global challenges of biodiversity loss and climate change necessitate the implementation of integrated conservation strategies. A forward-looking framework is necessary to reconcile climate change adaptation and mitigation efforts with biodiversity goals, supporting effective long-term planning and management of protected areas (PAs) network. This study develops a comprehensive approach to identify priority areas for biodiversity conservation until 2050, integrating assessments of species distributions, climate change exposure and carbon storage under distinct shared socioeconomic pathways. The Middle and Lower Yangtze River Basin (MLYRB) in China serves as a case study. Using an ensemble species distribution modeling (ESDM) method, we predicted the distributions of 435 threatened terrestrial vertebrate species and plants. We then mapped their richness in the MLYRB. Distance-based climate velocity analysis was performed to identify climate change coldspots and hotspots. The patch-generating land use simulation (PLUS) model and InVEST model were coupled to simulate carbon storage changes. Systematic conservation planning tool, Zonation, was used to prioritize species conservation hotspots. By examining the co-occurrence of these hotspots with climate change coldspots/hotspots and areas of high carbon storage, conservation priorities for the MLYRB were revealed. Our findings indicate that 18.27–24.94 % of the MLYRB risks over 20 % species richness declines by 2050 under various scenarios, while increases over 10 % are projected for only 0.81–1.75 % of the area. Co-occurrence analysis highlights significant associations, such as a 32.26 % overlap between species conservation hotspots and climate change hotspots in SSP1–2.6. Particularly noteworthy is the substantial co-occurrence (57.93–59.50 % across scenarios) between areas maximizing species conservation and carbon storage. The identified conservation priority areas, covering 41.95 % of the MLYRB (441554 km2), hold potential for long-term species conservation, climate resilience, and nature-based climate solutions by 2050. However, only 6.08 % of these priorities currently benefit from protection. These results offer valuable guidance for region-specific landscape management and conservation policy aligned with international goals. The presented methodology provides a broader application, serving as a valuable resource for prioritizing conservation efforts in other regions integrating biodiversity, climate change adaptation, and mitigation goals.

Geographical and genetic clines in Dracocephalum kotschyi X Dracocephalum oligadenium hybrids: landscape genetics and genocline analyses

Conservation and management of medicinally important plants are among the necessary tasks all over the world. The genus Dracocephalum (Lamiaceae) contains about 186 perennials, or annual herb species that have been used for their medicinal values in different parts of the world as an antihyperlipidemic, analgesic, antimicrobial, antioxidant, as well as anticancer medicine. Producing detailed data on the genetic structure of these species and their response against climate change and human landscape manipulation can be very important for conservation purposes. Therefore, the present study was performed on six geographical populations of two species in the Dracocephalum genus, namely, Dracocephalum kotschyi, and Dracocephalum oligadenium, as well as their inter-specific hybrid population. We carried out, population genetic study, landscape genetics, species modeling, and genetic cline analyses on these plants. We present here, new findings on the genetic structure of these populations, and provide data on both geographical and genetic clines, as well as morphological clines. We also identified genetic loci that are potentially adaptive to the geographical spatial features and genocide conditions. Different species distribution modeling (SDM) methods, used in this work revealed that bioclimatic variables related to the temperature and moisture, play an important role in Dracocephalum population’s geographical distribution within IRAN and that due to the presence of some potentially adaptive genetic loci in the studied plants, they can survive well enough by the year 2050 and under climate change. The findings can be used for the protection of these medicinally important plant.

Do aposematic species have larger range sizes? A case study with neotropical poison frogs

AbstractAimAposematic animals, i.e., those that are defended and warn potential predators through signals, are suggested to have resource‐gathering advantages against non‐aposematic ones. We here explore this in a biogeographic framework expecting that aposematic species are better dispersers, which translates into larger geographic range size.LocationSouth America.TaxonPoison frogs (Amphibia; Aromobatidae and Dendrobatidae).MethodsWe use 43 toxic and 26 non‐toxic poison frog species from the lowlands only as representatives of aposematic and non‐aposematic study organisms, respectively. Realised and potential geographic ranges are calculated using minimum convex polygon and species distribution modelling methods, respectively. Accounting for species body size and phylogeny, we test if both range and aposematism are correlated using linear mixed‐effects models.ResultsAposematic and non‐aposematic species neither differ in realised nor in potential geographic range size. There was no effect on body size.Main ConclusionsThe role of aposematism is not yet as clear as suggested and determinants of poison frog range sizes are multifaceted. A more integrative approach is needed using the information on behaviour, predation risk, and reproductive biology to assess the role of aposematism on observed species distributions. Such data are not yet available for most species, neither poison frogs nor other aposematic animals.

Temporal variables improve a spatiotemporal species distribution model for the non-native freshwater fish Candidia temminckii

Ecosystem conservation requires a deeper understanding of species-habitat relationships and population dynamics at a fine spatiotemporal resolution. We propose a new distribution modeling method based on a 5-year monthly survey that considers the temporal continuity of species distributions and physical habitat datasets by inputting continuous time-related variables. We employed random forests to relate the presence/absence of the non-native freshwater fish Candidia temminckii to physical habitat data at 15 sampling sites along a 1.4km spring-fed river in Japan. The proposed method outperforms all conventional methods using datasets split into a specific time period to incorporate temporality into the model. The order of variable importance and shape of the partial dependence plots of the proposed method reflect species ecology and show a gradual shift over time compared to the conventional methods. These results demonstrate the applicability of the proposed method to species distribution modeling using fine-scale spatiotemporal data.

PopSED: Population-level Inference for Galaxy Properties from Broadband Photometry with Neural Density Estimation

We present PopSED , a framework for the population-level inference of galaxy properties from photometric data. Unlike the traditional approach of first analyzing individual galaxies and then combining the results to determine the physical properties of the entire galaxy population, we directly make the population distribution the inference objective. We train normalizing flows to approximate the population distribution by minimizing the Wasserstein distance between the synthetic photometry of the galaxy population and the observed data. We validate our method using mock observations and apply it to galaxies from the GAMA survey. PopSED reliably recovers the redshift and stellar mass distribution of 105 galaxies using broadband photometry within <1 GPU hr, being 105–6 times faster than the traditional spectral energy distribution modeling method. From the population posterior, we also recover the star-forming main sequence for GAMA galaxies at z < 0.1. With the unprecedented number of galaxies in upcoming surveys, our method offers an efficient tool for studying galaxy evolution and deriving redshift distributions for cosmological analyses.

Dynamic species distribution models of Antarctic blue whales in the Weddell Sea using visual sighting and passive acoustic monitoring data

AbstractAimSpecies distribution models (SDMs) are essential tools in ecology and conservation. However, the scarcity of visual sightings of marine mammals in remote polar areas hinders the effective application of SDMs there. Passive acoustic monitoring (PAM) data provide year‐round information and overcome foul weather limitations faced by visual surveys. However, the use of PAM data in SDMs has been sparse so far. Here, we use PAM‐based SDMs to investigate the spatiotemporal distribution of the critically endangered Antarctic blue whale in the Weddell Sea.LocationThe Weddell Sea.MethodsWe used presence‐only dynamic SDMs employing visual sightings and PAM detections in independent models. We compared the two independent models with a third combined model that integrated both visual and PAM data, aiming at leveraging the advantages of each data type: the extensive spatial extent of visual data and the broader temporal/environmental range of PAM data.ResultsVisual and PAM data prove complementary, as indicated by a low spatial overlap between daily predictions and the low predictability of each model at detections of other data types. Combined data models reproduced suitable habitats as given by both independent models. Visual data models indicate areas close to the sea ice edge (SIE) and with low‐to‐moderate sea ice concentrations (SIC) as suitable, while PAM data models identified suitable habitats at a broader range of distances to SIE and relatively higher SIC.Main ConclusionsThe results demonstrate the potential of PAM data to predict year‐round marine mammal habitat suitability at large spatial scales. We provide reasons for discrepancies between SDMs based on either data type and give methodological recommendations on using PAM data in SDMs. Combining visual and PAM data in future SDMs is promising for studying vocalized animals, particularly when using recent advances in integrated distribution modelling methods.

Predicting the Presence of Groundwater-Influenced Ecosystems in the Northeastern United States with Ensembled Models

Globally, groundwater-influenced ecosystems (GIEs) are increasingly vulnerable to groundwater extraction and land use practices. Groundwater supports these ecosystems by providing inflow, which can maintain water levels, water temperature, and the chemistry necessary to sustain the biodiversity that they support. Many aquatic systems receive groundwater as a portion of baseflow, and in some systems, the connection with groundwater is significant and important to the system’s integrity and persistence. There is a lack of information about where these systems are found and their relationships with environmental conditions in the surrounding landscape. Additionally, groundwater management for human use often does not address maintaining the ecological functions of GIEs. We used correlative distribution modeling methods (GLM, GAM, MaxEnt, Random Forest) to predict landscape-scale habitat suitability for GIEs in two ecologically distinct ecoregions (EPA Level II ecoregions: Atlantic Highlands and Mixed Wood Plains) in the northeastern United States. We evaluated and combined the predictions to create ensemble models for each ecoregion. The accuracy of the ensemble models was 75% in the Atlantic Highlands and 86% in the Mixed Wood Plains. In the Mixed Wood Plains, hydric soil, surface materials, and soil permeability were the best predictors of GIE presence, whereas hydric soil, topographic wetness index, and elevation were the best predictors of GIE presence in the Atlantic Highlands. Approximately 1% of the total land area in each ecoregion was predicted to be suitable for GIEs, highlighting that there likely is a small proportion of the landscape occupied by these systems.

Environmental drivers and the distribution of cold-water corals in the global ocean

Species distribution models (SDMs) are useful tools for describing and predicting the distribution of marine species in data-limited environments. Outputs from SDMs have been used to identify areas for spatial management, analyzing trawl closures, quantitatively measuring the risk of bottom trawling, and evaluating protected areas for improving conservation and management. Cold-water corals are globally distributed habitat-forming organisms that are vulnerable to anthropogenic impacts and climate change, but data deficiency remains an ongoing issue for the effective spatial management of these important ecosystem engineers. In this study, we constructed 11 environmental seabed variables at 500 m resolution based on the latest multi-depth global datasets and high-resolution bathymetry. An ensemble species distribution modeling method was used to predict the global habitat suitability for 10 widespread cold-water coral species, namely, 6 Scleractinian framework-forming species and 4 large gorgonian species. Temperature, depth, salinity, terrain ruggedness index, carbonate saturation state, and chlorophyll were the most important factors in determining the global distributions of these species. The Scleractinian Madrepora oculata showed the widest niche breadth, while most other species demonstrated somewhat limited niche breadth. The shallowest study species, Oculina varicosa, had the most distinctive niche of the group. The model outputs from this study represent the highest-resolution global predictions for these species to date and are valuable in aiding the management, conservation, and continued research into cold-water coral species.

Mapping current and future risk of scorpion sting from a species with low medical concern, Mesobuthus phillipsii (Scorpiones: Buthidae) in Iran.

Scorpion stings are one of the most important health challenges and high priority research topic in public health. In this study, we aimed to model habitat suitability of the Mesobuthus phillipsii (Pocock 1889), a species with low medical concern, under current and future climatic conditions in Iran. We also identified vulnerable populations to scorpion stings in the country. Scorpion sting risk modeling was done using an ensemble approach by considering two species distribution modeling methods: MaxEnt and Random Forest methods. Distribution modeling was performed using the sdm R package. The results showed that due to climate change in 2070, the high-risk areas will increase from 20,839 to 79,212 km2. Habitats with a moderate risk of scorpion stings will also increase from 139,347 to 222,833 km2. Consequently, the number of villages in high-risk areas of scorpion stings will increase from 2,870 to 7,017, while this number will increase from 12,759 to 20,104 in the case of medium-risk villages. The results of this study can be used for scorpion stings management in Iran. This study can be used as an example for similar studies on scorpions with high medical emergency.

A Modeling Framework to Frame a Biological Invasion: Impatiens glandulifera in North America.

Biological invasions are a major component of global environmental change with severe ecological and economic consequences. Since eradicating biological invaders is costly and even futile in many cases, predicting the areas under risk to take preventive measures is crucial. Impatiens glandulifera is a very aggressive and prolific invasive species and has been expanding its invasive range all across the Northern hemisphere, primarily in Europe. Although it is currently spread in the east and west of North America (in Canada and USA), studies on its fate under climate change are quite limited compared to the vast literature in Europe. Hybrid models, which integrate multiple modeling approaches, are promising tools for making projections to identify the areas under invasion risk. We developed a hybrid and spatially explicit framework by utilizing MaxEnt, one of the most preferred species distribution modeling (SDM) methods, and we developed an agent-based model (ABM) with the statistical language R. We projected the I. glandulifera invasion in North America, for the 2020-2050 period, under the RCP 4.5 scenario. Our results showed a predominant northward progression of the invasive range alongside an aggressive expansion in both currently invaded areas and interior regions. Our projections will provide valuable insights for risk assessment before the potentially irreversible outcomes emerge, considering the severity of the current state of the invasion in Europe.

VoluModel: Modelling species distributions in three‐dimensional space

Abstract Ecological niche modelling (ENM), species distribution modelling and related spatial analytical methods were first developed in two‐dimensional (2‐D) terrestrial systems; many common ENM workflows organize and analyse geographically structured occurrence and environmental data based on 2‐D latitude and longitude coordinates. This may be suitable for most terrestrial organisms, but pelagic marine species are distributed not only horizontally but also vertically. Extracting environmental data for marine species based only on latitude and longitude coordinates may result in poorly trained ENMs and inaccurate prediction of species' geographical distributions, as water conditions may vary strikingly with depth. We developed the voluModel R package to efficiently extract three‐dimensional (3‐D) environmental data for training ENMs (i.e. presences and absences/pseudoabsences/background). voluModel also provides tools for 3‐D ENM projection visualization and estimation of model extrapolation risk. We present the main features of the voluModel R package and provide a simple modelling workflow for Luminous Hake, Steindachneria argentea, as an example. We also compare results from 2‐D and 3‐D spatial models to demonstrate differences in how the modelling methods perform. The use of 3‐D environmental data generates more precise estimates of environmental conditions for training ENMs. This method also improves inference of species' suitable abiotic ecological niches and potential geographic ranges. 3‐D niche modelling is important step forward for marine macroecology and biogeography, as it will yield more accurate estimates of ocean species richness and potential past and future changes in the horizontal and vertical dimensions of species' geographic ranges. The latter is particularly relevant considering ongoing climate change that may cause redistribution of species in environmental space (both in latitude and depth) over time.

Flexible species distribution modelling methods perform well on spatially separated testing data

AbstractAimTo assess whether flexible species distribution models that perform well at nearby testing locations still perform strongly when evaluated on spatially separated testing data.LocationAustralian Wet Tropics (AWT), Ontario, Canada (CAN), north‐east New South Wales, Australia (NSW), New Zealand (NZ), five countries of South America (SA), and Switzerland (SWI).Time periodMost species data were collected between 1950 and 2000.Major taxa studiedBirds, mammals, plants and reptiles.MethodsWe compared 10 species distribution modelling methods with varying flexibility in terms of the allowed complexity of their fitted functions [boosted regression trees (BRT), generalized additive model (GAM), multivariate adaptive regression splines (MARS), maximum entropy (MaxEnt), support vector machine (SVM), variants of generalized linear model (GLM) and random forest (RF), and an Ensemble model]. We used established practices for model selection to avoid overfitting, including parameter tuning in learning methods. Models were trained on presence–background data for 171 species and tested on presence–absence data. Training and testing data were separated using both random and spatial partitioning, the latter based on 75‐km blocks. We calculated the average performance and mean rank of the methods (focussing on the area under the receiver operating characteristic and precision‐recall gain curves, and correlation) and assessed the statistical significance of the differences between them.ResultsThe ranking of methods did not change when evaluated on spatially separated testing data. Methods with the strongest predictive performance were nonparametric methods known to be flexible. An ensemble formed by averaging predictions of five pre‐selected modelling methods was the best model in both random and spatial partitioning, followed by MaxEnt and a variant of random forest.Main conclusionsWhilst some modellers expect methods limited to simple smooth functions to predict better spatially separated data, we found no evidence of that using blocks of 75 km. We conclude that flexible models that are tuned well enough to avoid overfitting are effective at predicting to spatially distinct areas.

Открытые источники данных для моделирования ареалов: информационные системы о биоразнообразии и наборы пространственных данных условий среды

Открытые источники данных для моделирования ареалов: информационные системы о биоразнообразии и наборы пространственных данных условий среды

A Consistency Evaluation Method of Pavement Performance Based on K-Means Clustering and Cumulative Distribution

This paper proposes a cumulative distribution modelling method for pavement performance indexes based on the sampling theorem and implements clustering analysis of similar road sections through the K-means algorithm. The results show that: (1) The modelling method proposed in this paper can convert discrete pavement performance data into a continuous function of pavement performance indexes and a continuous function of pavement performance cumulative distribution and achieve the acquisition of a large amount of pavement performance data. (2) Based on the cumulative distribution and K-means clustering, it is possible to understand the overall pavement performance status of the network and identify road sections with similar decay models and poor decay status for focused attention, which constructed the pavement performance evaluation system of the three-level system of road network–road section–unit road section.

Habitat suitability model and range shift analysis for American Chestnut (Castanea dentata) in the United States

American chestnut (Castanea dentata) was a multifunctional hardwood tree species that provide both economic and ecological benefits in the United States. It was a major source of timber before the chestnut blight (Cryphonectria parasitica) invasion wiped out the stands of this species from the forest between the early 1900s and 1950s. Our study focuses on the estimation of suitable habitat in the eastern US, range shift predictions, and identification of habitat drivers for American chestnut (AMC) in the eastern US to help corroborate the restoration efforts of C. dentata using an ensemble model from five species distribution modeling (SDMs) methods while also projecting the prediction until 2100 in response to the changing climate. 1806 occurrence records were obtained from four sources including the United States Department of Agriculture (USDA) Forest Service. The result from the SDM techniques shows a model accuracy of the true skill statistics (TSS) and Area-Under-the-Curve (AUC) of greater than 0.8 and 0.90 respectively, which were ensembled. The historic suitable habitat of AMC compared to the present habitat suitability prediction in the US shows a significant reduction. The mean temperature of the warmest quarter, precipitation seasonality, and temperature seasonality are the top three variables dictating the distribution of the American chestnut in the US. The future projection also shows up to 100% less suitable habitats by the end of the century under high emissions and high precipitation with a northeast range shift up to 476.9 km. Modeling the American chestnut's habitat suitability is imperative for a successful restoration program and these results highlight the potential need for greater conservation and use of southern genotypes.

Influence of species interaction on species distribution simulation and modeling methods.

The species distribution models (SDMs) simulate and predict the potential distribution of species in geographical space by quantifying the relationships between species distribution and environmental variables, and extrapolating these relationships to unknown landscape units, which makes them important tools in ecology, biogeo-graphy, and conservation biology. Current SDMs mainly take abiotic factors as prediction variables, whereas biotic factors, especially species interactions, are often ignored due to the difficulties in data quantification and modeling. Incorporating species interactions into SDMs is considered as the main challenge of SDMs. We reviewed the influence of species interactions on species distribution simulations, clarified the necessity of incorporating species interactions into SDMs, summarized four main ways to incorporate species interactions into SDMs, analyzed their strengths and limitations, and discussed the future development direction of incorporating species interactions into SDMs. The study showed that incorporating species interaction into SDMs was based on the premise that the spatial scale of species distribution simulation was consistent with that of species interactions, and that the training data should be collected from large environmental heterogeneous space to ensure the diversity of species interactions in heterogeneous habitats. In order to eliminate the influence of multicollinearity on the prediction of SDMs, all abiotic and biotic factors should be fully considered and accurately quantified. Modeling the complex population/community dynamics would be an important development direction of incorporating species interactions into SDMs.