Predictive Species Distribution Models Research Articles

Presence-only fisheries bycatch data produce biased species distribution predictions for Alcyonacean corals on British Columbia’s continental shelf and slope

Sedentary benthic species such as Alcyonacea corals form critical habitat for fishes and invertebrates. Assessing anthropogenic risks to these organisms requires unbiased, species distribution models (SDMs) that attempt to map probabilities of coral presence in relation to bio-physical ocean characteristics; however, in deep-water settings, the accuracy of SDMs is highly variable and dependent on spatial and taxonomic resolution. Here we investigated how data and model types affect SDM predictions of Alcyonacea probability of presence. We compared predictions from generalized additive models (GAMs) fitted to presence-absence observations over a stratified-random survey design with predictions from maximum entropy models (Maxent) fitted to presence-only bycatch records from commercial fisheries. We use a simulation analysis to show that both model types (i.e., GAM or Maxent) using presence-only bycatch data produced biased estimates of species distribution. Additionally, the maxent model fit using presence-only bycatch data produced biased estimates of performance metrics (AUC, TSS) that were overly optimistic. This study demonstrates a need for presence-absence data collected using a robust survey sampling design to fit SDMs that will better inform marine protected area placement while minimizing unnecessary economic losses, such as forgone fishing yield, due to sub-optimal placement.

Predictions of species distributions based only on models estimating future climate change are not reliable

Changes in climate and land use are the most often mentioned factors responsible for the current decline in species diversity. To reduce the effect of these factors, we need reliable predictions of future species distributions. This is usually done by utilizing species distribution models (SDMs) based on expected climate. Here we explore the accuracy of such projections: we use orchid (Orchidaceae) recordings and environmental (mainly climatic) data from the years 1901–1950 in SDMs to predict maps of potential species distributions in 1980–2014. This should enable us to compare the predictions of species distributions in 1980–2014, based on records of species distribution in the years 1901–1950, with real data in the 1980–2014 period. We found that the predictions of the SDMs often differ from reality in this experiment. The results clearly indicate that SDM predictions of future species distributions as a reaction to climate change must be treated with caution.

Evaluating species distribution model predictions through time against paleozoological records.

Species distribution models (SDMs) are widely used to project how species distributions may vary over time, particularly in response climate change. Although the fit of such models to current distributions is regularly enumerated, SDMs are rarely tested across longer time spans to gauge their actual performance under environmental change. Here, we utilise paleozoological presence/absence records to independently assess the predictive accuracy of SDMs through time. To illustrate the approach, we focused on modelling the Holocene distribution of the hartebeest, Alcelaphus buselaphus, a widespread savannah-adapted African antelope. We applied various modelling algorithms to three occurrence datasets, including a point dataset from online repositories and two range maps representing current and 'natural' (i.e. hypothetical assuming no human impact) distributions. We compared conventional model evaluation metrics which assess fit to current distributions (i.e. True Skill Statistic, TSSc, and Area Under the Curve, AUCc) to analogous 'paleometrics' for past distributions (i.e. TSSp, AUCp, and in addition Boycep, F2-scorep and Sorensenp). Our findings reveal only a weak correlation between the ranking of conventional metrics and paleometrics, suggesting that the models most effectively capturing present-day distributions may not be the most reliable to hindcast historical distributions, and that the choice of input data and modelling algorithm both significantly influences environmental suitability predictions and SDM performance. We thus advocate assessment of model performance using paleometrics, particularly those capturing the correct prediction of presences, such as F2-scorep or Sorensenp, due to the potential unreliability of absence data in paleozoological records. By integrating archaeological and paleontological records into the assessment of alternative models' ability to project shifts in species distributions over time, we are likely to enhance our understanding of environmental constraints on species distributions.

Shrinking suitable habitat of a sub-Arctic foundation kelp under future climate scenarios.

Climate change has profound effects on the distribution of kelp forests in the Arctic and sub-Arctic. However, studies on the responses of kelps to climate change, particularly along the sub-Arctic regions of the Alaska coast, are limited. Eualaria fistulosa is a foundational kelp species in the Aleutian Islands, with an east-west distribution that extends from Japan to southern southwest Alaska. In this study, we utilized a species distribution model (SDM) to explore changes in the future habitat suitability of E. fistulosa under contrasting Shared Socioeconomic Pathway (SSP) scenarios. Our model exhibited relatively high predictive performance, validating our SDM predictions. Notably, the SDM results indicate that minimum sea surface temperature, annual range in sea surface temperatures, and annual mean current velocities are the three most important predictor variables determining E. fistulosa's distribution. Furthermore, the projected geographic distribution of Eualaria is generally consistent with its observed occurrence records. However, under high emission scenarios (SSP5-8.5), E. fistulosa is predicted to contract its distribution range by 9.0% by 2100, with widespread disappearance along the southeast Alaskan coast and limited northward migration to Kamchatka Krai in Russia and Bristol Bay in Alaska. These findings contribute valuable insights for conservation strategies via addressing climate-induced alterations in sub-Arctic kelp distribution.

Cross‐validation matters in species distribution models: a case study with goatfish species

In an era of ongoing biodiversity, it is critical to map biodiversity patterns in space and time for better‐informing conservation and management. Species distribution models (SDMs) are widely applied in various types of such biodiversity assessments. Cross‐validation represents a prevalent approach to assess the discrimination capacity of a target SDM algorithm and determine its optimal parameters. Several alternative cross‐validation methods exist; however, the influence of choosing a specific cross‐validation method on SDM performance and predictions remains unresolved. Here, we tested the performance of random versus spatial cross‐validation methods for SDM using goatfishes (Actinopteri: Syngnathiformes: Mullidae) as a case study, which are recognized as indicator species for coastal waters. Our results showed that the random versus spatial cross‐validation methods resulted in different optimal model parameterizations in 57 out of 60 modeled species. Significant difference existed in predictive performance between the random and spatial cross‐validation methods, and the two cross‐validation methods yielded different projected present‐day spatial distribution and future projection patterns of goatfishes under climate change exposure. Despite the disparity in species distributions, both approaches consistently suggested the Indo‐Australian Archipelago as the hotspot of goatfish species richness and also as the most vulnerable area to climate change. Our findings highlight that the choice of cross‐validation method is an overlooked source of uncertainty in SDM studies. Meanwhile, the consistency in richness predictions highlights the usefulness of SDMs in marine conservation. These findings emphasize that we should pay special attention to the selection of cross‐validation methods in SDM studies.

Managing multiple uncertainties in species distribution modelling

AbstractAimWe present a methodology to address multifaceted uncertainties in species distribution models (SDMs), enhancing their robustness and providing vital insights to inform management and conservation decisions. Data uncertainties, including positional inaccuracies in historical data and absences in survey data that could be attributed to anthropogenic disturbances rather than habitat unsuitability, can compromise SDM predictions, risking the efficacy of resultant conservation strategies.LocationWhile the concepts and methodologies presented hold global applicability, our case study is situated in and around the Negev Desert of southern Israel and the Palestinian West Bank, focusing on the critically endangered Be'er Sheva fringe‐fingered lizard (Acanthodactylus beershebensis) that is endemic to this area.MethodsUtilizing calculated combinations of reliable and uncertain datasets, we created diverse dataset scenarios. Pre‐development distribution and habitat requirements were estimated for each scenario using a blend of statistical and machine‐learning algorithms in R. Additionally, a combined scenario was modelled using hierarchical model ensembles to effectively weight data by reliability.ResultsOur innovative approach produces more robust models and reveals the impact of uncertain datasets on model predictions. Incorporating potential anthropogenic absences led to erroneous model conclusions, particularly when historical data exclusion occurred—a practice often implemented in the pursuit of model robustness.Main ConclusionsUncertainties in SDMs can yield incorrect conclusions, imperilling conservation efforts. Initiated by land managers, our work actively informs conservation practices. The study's global relevance provides an approach for addressing real‐world challenges in estimating species distributions, advancing the application of conservation science.

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.

Enhancing pest control interventions by linking species distribution model prediction and population density assessment of pine wilt disease vectors in South Korea

Pine wilt disease caused by pinewood nematode is one of the most destructive forest diseases, and still spreading in South Korea despite the various control efforts. Japanese pine sawyer (JPS) and Sakhalin pine sawyer (SPS) are the main vectors of the disease. Understanding the distribution and density of the vectors is crucial since the control period is determined by the different emergence periods of the two vectors and the control method by its density and the expected damage severity. In this study, we predicted the distribution of JPS and SPS using Maxent and investigated the relationship between the resulting suitability value and the density. The population densities of JPS and SPS were obtained through a national survey using pheromone traps between 2020-2022. We converted the density data into presence/absence points to externally validate each species distribution model, then we used quantile regression to check the correlation between the suitability and population density, and finally we used three widely used thresholds to convert the model results into binary maps, and tested if they could distinguish the density by comparing the Rb value of biserial correlation. The quantile regression revealed a positive relationship between the habitat suitability and population density sampled in the field. Moreover, the binary map with threshold criteria that maximizes the sum of the sensitivity and specificity had the best density discrimination capacity with the highest Rb. A quantitative relationship between suitability and vector density measured in the field from our study provides reliability to species distribution model as practical tools for forest pest management.

Modelling complex spatial–temporal drivers of habitat suitability for an imperilled stream fish

Fish populations rely on complex environmental conditions involving physical, chemical, and biological factors. Understanding the factors that control population persistence and productivity is essential for species management. We assessed the distribution and associated habitat features of a species at risk in Canada, Silver Shiner (Notropis photogenis), within Sixteen Mile Creek, a tributary of Lake Ontario. Using random forest models, we quantified a range of ecological factors (n = 25) to estimate habitat associations for sampled populations and life stages (juvenile, adult). A complex set of ecological factors were informative predictors of Silver Shiner distribution, including physical (stream morphology, water velocity, substrate type), and biological (aquatic and riparian vegetation) conditions. Juveniles were less responsive to habitat conditions but exhibited high seasonal variability in occurrence. Adults were most common in stream sections with greater than 0.5 m depth and stream velocity less than 0.6 m/s, and areas without silt substrate. Broadly, the models predicted Silver Shiner distribution with 68–92% accuracy in non-training data. Our findings describe the habitat conditions that Silver Shiner currently occupies in an urban drainage, which may serve as a point of reference for habitat protection and restoration. Further, predictive species distribution models can serve to identify habitat for further monitoring and restoration.

Considering biotic interactions exacerbates the predicted impacts of climate change on coral‐dwelling species

AbstractAimClimate change is affecting the geographic distributions of many species and researchers are increasingly relying on species distribution models (SDMs) to forecast species' redistributions under climate change. Such modelling studies, however, often ignore biotic interactions that shape species' geographic ranges. This is especially problematic for coral reefs, which host a high diversity of species and interactions. We tested how biotic interactions affect the distribution patterns of obligate coral‐dwelling Trapezia crabs.LocationGlobal coastal ocean.Time Period2000–2014, 2040–2050, 2090–2100.Major Taxa StudiedCorals and coral‐dwelling Trapezia crabs.MethodsWe determined the symbiotic relationships between 22 crab species in the genus Trapezia and corals via field survey and extensive literature review. We first developed SDMs for coral and crab species using exclusively abiotic variables (abiotic‐only models for corals and crabs). Then we constructed a second set of models where we accounted for coral distributions into crab predictions by combining model predictions for the two taxa a posteriori (abiotic‐plus‐biotic models for crabs only).ResultsWe obtained 30 commonly accepted coral‐crab symbiotic relationships from nine Trapezia crab and six stony coral species. The abiotic‐only model predictions showed that six corals may lose approximately one‐sixth of suitable ranges under RCP 8.5 in 2040–2050. The two types of models for crabs yielded largely different habitat suitability predictions and accounting for biotic interactions into SDM predictions exacerbates the predicted impacts of climate change on coral‐dwelling crabs.Main ConclusionsOur results show large discrepancies in crab spatial distribution patterns with and without accounting for symbiotic interactions. Our findings highlight the important role of modeller's decision on accounting for biotic interactions when predicting the geographical ranges of coral‐dwelling species, with important implications for designing future conservation and management strategies for marine species.

Niche shifts undermine the prediction performance of species distribution models: estimating potentially suitable areas for Myriophyllum aquaticum at the global scale

Invasive alien species (IAS) can undergo niche shifts upon establishment in novel environments that differ from their native ranges. The validity of species distribution models (SDMs) depends on the underlying assumption of niche conservatism that IAS can only successfully establish populations in environments that closely resemble their native regions. This has led us to examine how ecologists can use SDMs to predict potentially suitable areas for invasive alien species in the context of niche shifts. Here, we compared the niches of Myriophyllum aquaticum in its native and invasive ranges to quantify the shifts that occurred during its global invasion using the centroid shift, overlap, unfilling, and expansion (COUE) framework. We also aimed to investigate the effects of niche shift on the prediction performance of SDMs. The SDMs were calibrated using data from three different sources: the native range, the invasive range, and a combination of both. The study examined how these different data sources influenced the accuracy of the SDM predictions. The findings indicated the niche expansion between the invasive and native ranges. The invasive (based on the occurrence data of invasive ranges) and global (based on the occurrence data of global ranges) models underestimated the potentially suitable areas (PSAs) of M. aquaticum in the Southern Hemisphere. The native model (based on the occurrence data of native ranges) underestimated the PSAs of M. aquaticum in the Northern Hemisphere. We combined the results of the invasive and native range models to estimate the global PSAs of M. aquaticum, addressing the predictive limitations of a single model in a complementary form. The PSAs of M. aquaticum were predominantly distributed in eastern Asia, western Europe, southern North America, southern South America, and southern Oceania and expanded into higher latitudes under future climate scenarios. Our study has applied a simple yet effective method for predicting the global PSAs of M. aquaticum given niche shifts, which confirmed the observations of the previous studies and provided a theoretical basis for the early warning, prevention and control of M. aquaticum.

Veni, vidi, vici? Future spread and ecological impacts of a rapidly expanding invasive predator population.

Economic and ecological consequences of invasive species make biological invasions an influential driver of global change. Monitoring the spread and impacts of non-native species is essential, but often difficult, especially during the initial stages of invasion. The Jorō spider, Trichonephila clavata (L. Koch, 1878, Araneae: Nephilidae), is a large-bodied orb weaver native to Asia, likely introduced to northern Georgia, U.S. around 2010. We investigated the nascent invasion of T. clavata by constructing species distribution models (SDMs) from crowd-sourced data to compare the climate T. clavata experiences in its native range to its introduced range. We found evidence that the climate of T. clavata's native range differs significantly from its introduced range. Species distribution models trained with observations from its native range predict that the most suitable habitats in North America occur north of its current introduced range. Consistent with SDM predictions, T. clavata appears to be spreading faster to the north than to the south. Lastly, we conducted surveys to investigate potential ecological impacts of T. clavata on the diversity of native orb weaving spiders. Importantly, Trichonephila clavata was the most common and abundant species observed in the survey, and was numerically dominant at half of the sites it was present in. Our models also suggest that there is lower native orb weaver species richness and diversity closer to where T. clavata was initially found and where it has been established the longest, though human population density complicates this finding. This early study is the first to forecast how widely this spider may spread in its introduced range and explore its potential ecological impacts. Our results add evidence that T. clavata is an invasive species and deserves much more ecological scrutiny.

Cold comfort: metabolic rate and tolerance to low temperatures predict latitudinal distribution in ants.

Metabolic compensation has been proposed as a mean for ectotherms to cope with colder climates. For example, under the metabolic cold adaptation and the metabolic homeostasis hypotheses (MCA and MHH), it has been formulated that cold-adapted ectotherms should display both higher (MCA) and more thermally sensitive (MHH) metabolic rates (MRs) at lower temperatures. However, whether such compensation can truly be associated with distribution, and whether it interplays with cold tolerance to predict species' climatic niches, remains largely unclear despite broad ecological implications thereof. Here, we teased apart the relationship between MRs, cold tolerance and distribution, to test the MCA/MHH among 13 European ant species. We report clear metabolic compensation effects, consistent with the MCA and MHH, where MR parameters strongly correlated with latitude and climatic factors across species' distributions. The combination of both cold tolerance and MRs further upheld the best predictions of species' environmental temperatures and limits of northernmost distribution. To our knowledge, this is the first study showing that the association of metabolic data with cold tolerance supports better predictive models of species' climate and distribution in social insects than models including cold tolerance alone. These results also highlight that adaptation to higher latitudes in ants involved adjustments of both cold tolerance and MRs, to allow this extremely successful group of insects to thrive under colder climates.

Species distribution modeling of two poorly known populations of Spalerosophis diadema (Reptilia: Colubridae): are the Zagros Mountains a true barrier for fragmentation in this species?

ABSTRACT Predictive models of species distribution can be beneficial for identifying the most important and influential environmental factors in species distribution and understanding the current and future habitat suitability of species. In this study, we used species distribution modeling to find out the potential distribution and habitat suitability of two spatially separated populations of Spalerosophis diadema and show the most important environmental factors that affect their habitat separation. The findings of SDM analyses demonstrated that the most important factors for the two populations are annual mean temperature, precipitation of coldest quarter, NDVI in May, and elevation, as these factors contributing about 80–85% of the environmental factors to the whole model. Two populations react similarly to these environmental predictors and there is no significant difference between the important, and influential environmental factors of their habitats. Although our results show some overlap of the potential distributions of populations among Mesopotamian plain and Afghanistan, there is no confirmed report or voucher specimen proving the overlap distribution in these regions. Our results support the hypothesis that the Zagros Mountains acted as a barrier to the east–west fragmentation of these two populations.

Filling the data gaps: Transferring models from data-rich to data-poor deep-sea areas to support spatial management

Spatial management of the deep sea is challenging due to limited available data on the distribution of species and habitats to support decision making. In the well-studied North Atlantic, predictive models of species distribution and habitat suitability have been used to fill data gaps and support sustainable management. In the South Atlantic and other poorly studied regions, this is not possible due to a massive lack of data. In this study, we investigated whether models constructed in data-rich areas can be used to inform data-poor regions (with otherwise similar environmental conditions). We used a novel model transfer approach to identify to what extent a habitat suitability model for Desmophyllum pertusum reef, built in a data-rich basin (North Atlantic), could be transferred usefully to a data-poor basin (South Atlantic). The transferred model was built using the Maximum Entropy algorithm and constructed with 227 presence and 3064 pseudo-absence points, and 200 m resolution environmental grids. Performance in the transferred region was validated using an independent dataset of D. pertusum presences and absences, with assessments made using both threshold-dependent and -independent metrics. We found that a model for D. pertusum reef fitted to North Atlantic data transferred reasonably well to the South Atlantic basin, with an area under the curve of 0.70. Suitable habitat for D. pertusum reef was predicted on 20 of the assessed 27 features including seamounts. Nationally managed Marine Protected Areas provide significant protection for D. pertusum reef habitat in the region, affording full protection from bottom trawling to 14 of the 20 suitable features. In areas beyond national jurisdiction (ABNJ), we found four seamounts that provided suitable habitat for D. pertusum reef to be at least partially protected from bottom trawling, whilst two did not fall within fisheries closures. There are factors to consider when developing models for transfer including data resolution and predictor type. Nevertheless, the promising results of this application demonstrate that model transfer approaches stand to provide significant contributions to spatial planning processes through provision of new, best available data. This is particularly true for ABNJ and areas that have previously undergone little scientific exploration such as the global south.

Influence of Parameters in SDM Application on Citrus Presence in Mediterranean Area

Within the context of Agriculture 4.0, the importance of predicting species distribution is increasing due to climatic change. The use of predictive species distribution models represents an essential tool for land planning and resource conservation. However, studies in the literature on Suitability Distribution Models (SDMs) under specific conditions are required to optimize the model accuracy in a specific context through map inspection and sensitivity analyses. The aim of this study was to optimize the simulation of the citrus distribution probability in a Mediterranean area based on presence data and a random background sample, in relation to several predictors. It was hypothesized that different parameter settings affected the SDM. The objectives were to compare different parameter settings and assess the effect of the number of input points related to species presence. Simulation of citrus occurrence was based on five algorithms: Boosted Regression Tree (BRT), Generalized Linear Model (GLM), Multivariate Adaptive Regression Splines (MARS), Maximum Entropy (MaxEnt), and Random Forest (RF). The predictors were categorized based on 19 bioclimatic variables, terrain elevation (represented by a Digital Terrain Model), soil physical properties, and irrigation. Sensitivity analysis was carried out by (a) modifying the values of the main models’ parameters; and (b) reducing the input presence points. Fine-tuning the parameters for each model according to the literature in the field produced variations in the selection of predictors. Consequently, probability changed in the maps and values of the accuracy measures modified. Results obtained by using refined parameters showed a reduced overfitting for BRT, yet associated with a decrease in the AUC value from 0.91 to 0.81; minor variations in AUC for GLM (equal to about 0.85) and MARS (about 0.83); a slight AUC reduction for MaxEnt (from 0.86 to 0.85); a slight AUC increase for RF (from 0.88 to 0.89). The reduction in presence points produced a decrease in the surface area for citrus probability of presence in all the models. Therefore, for the case study analyzed, it is suggested to keep input presence points above 250. In these simulations, we also analyzed which covariates and related ranges contributed most to the predicted value of citrus presence, for this case study, for different amounts of input presence points. In RF simulations, for 250 points, isothermality was one of the major predictors of citrus probability of presence (up to 0.8), while at increasing of the input points the contribution of the covariates was more uniform (0.4–0.6) in their range of variation.

Expanded distribution and predicted suitable habitat for the critically endangered yellow-tailed woolly monkey (Lagothrix flavicauda) in Perú.

The Tropical Andes Biodiversity Hotspot holds a remarkable number of species at risk of extinction due to anthropogenic habitat loss, hunting, and climate change. One of these species, the critically endangered yellow-tailed woolly monkey (Lagothrix flavicauda), was recently observed in the region Junín, 206 kmsouth of its previously known distribution. This range extension, combined with continued habitat loss, calls for a reevaluation of the species distribution, and available suitable habitat. Here, we present novel data from surveys at 53 sites in the regions of Junín, Cerro de Pasco, Ayacucho, and Cusco. We encountered L. flavicauda at 9 sites, all in Junín, and the congeneric Lagothrix lagotricha tschudii at 20 sites, but never in sympatry. Using these new localities along with all previous geographic localities for the species, we made predictive species distribution models based on ecological niche modeling using a generalized linear model and maximum entropy. Each model incorporated bioclimatic variables, forest cover, vegetation measurements, and elevation as predictor variables. The model evaluation showed >80% accuracy for all measures. Precipitation was the strongest predictor of species presence. Habitat suitability maps illustrate potential corridors for gene flow between the southern and northern populations, although much of this area is inhabited by L. l. tschudii whereas L. flavicauda has yet to be officially confirmed in these areas, by these or any other scientific surveys. An analysis of the current protected area (PA) network showed that ~75% of remaining suitable habitat is unprotected. With this, we suggest priority areas for new PAs or expansions to existing reserves that would conserve potential corridors between L. flavicauda populations. Further surveys and characterization of the distribution in intermediate areas, combined with studies on gene flow through these areas, are still needed to protect this species.

Selecting tree species to restore forest under climate change conditions: Complementing species distribution models with field experimentation

The long-term success of forest restoration programs can be improved using climate-based species distribution models (SDMs) to predict which tree species will tolerate climate change. However, as SDMs cannot estimate if species will recruit at these habitats, determining whether their predictions apply to early life-cycle stages of trees is critical to support such a usage. For this, we propose sowing seeds of the focal tree species under the current climate and simulated climate change conditions in target restoration sites. Thus, using of SDMs to design climate-adaptive forest restoration programs would be supported if the differences in habitat occupancy probabilities of species they predict between the current and future climate concurs with the observed differences in recruitment rates of species when sowed under the current climate and simulated climate change conditions. To test this hypothesis, we calibrated SDMs for Vachellia pennatula and Prosopis laevigata, two pioneer tree species widely recommended to restore human-degraded drylands in Mexico, and transferred them to climate change scenarios. After that, we applied the experimental approach proposed above to validate the predictions of SDMs. These models predicted that V. pennatula will decrease its habitat occupancy probabilities across Mexico, while P. laevigata was predicted to keep out their current habitat occupancy probabilities, or even increase them, in climate change scenarios. The results of the field experiment supported these predictions, as recruitment rates of V. pennatula were lower under simulated climate change than under the current climate, while no differences were found for the recruitment rates of P. laevigata between these environmental conditions. These findings demonstrate that SDMs provide meaningful insights for designing climate-adaptive forest restoration programs but, before applying this methodology, predictions of these models must be validated with field experiments to determine whether the focal tree species will recruit under climate change conditions. Moreover, as the pioneer trees used to test our proposal seem to be differentially sensitive to climate change, this approach also allows establishing what species must be prescribed to restore forests with a view to the future and what species must be avoided in these practices.

Models combining multiple scales of inference capture hydrologic and climatic drivers of riparian tree distributions

AbstractPredicting species geographic distributions is key to managing invasive species, conserving biodiversity, and understanding species' environmental requirements. Species distribution models (SDMs) commonly focus on climatic predictors, but other environmental factors can also be essential, particularly for species with specialized habitats defined by hydrologic, topographic, or edaphic conditions (e.g., riparian, wetland, alpine, coastal, serpentine). Here, we demonstrate a novel approach for capturing strong effects of both hydrologic and climatic predictors in SDMs for riparian plants, by merging analyses targeted at environmental drivers within riparian ecosystems and across the western USA (3.8 × 106 km2). We developed presence‐background SDMs from five algorithms for three invasive riparian trees (Tamarix ramossisima/chinensis [saltcedar], Elaeagnus angustifolia [Russian olive], and Ulmus pumila [Siberian elm]) and three native Populus spp. (cottonwoods). We used separate background datasets to develop models with different spatial scales of inference: (1) spatially filtered random points to represent available habitat across the study area and (2) target‐group points from Salix (willow) occurrences to represent available riparian habitat. Random‐background models captured hydrologic drivers of riparian tree distributions relative to the largely upland western USA, whereas Salix‐background models captured climatic drivers within the context of riparian ecosystems. Combining predictions from the two backgrounds identified hydrologically suitable habitats within climatically suitable regions, resulting in fewer false “absences” than either background alone, improving predictions over previous SDMs, and providing more complete information to guide management decisions. Surprisingly, the predicted habitat for U. pumila, a newly recognized riparian invader, was as or more extensive than Populus deltoides/fremontii, T. ramossisima/chinensis, and E. angustifolia, the most common riparian tree complexes in the western USA. Watersheds constituting 20% of U. pumila predicted habitat contained no occurrence records, indicating high risk of future and unrecognized invasions. Combining models from random and ecosystem‐specific target‐group backgrounds may improve SDMs for species from many specialized habitats, providing a method to link predicted distributions to localized geographic features while capturing broad‐scale climatic requirements.

Disentangling the contributions of biotic and abiotic predictors in the niche and the species distribution model of Trypanosoma cruzi, etiological agent of Chagas disease

The potential benefits of incorporating biotic, as well as abiotic, predictors in niche and species distribution models (SDMs), as well as how to achieve this, is still debated, with their interpretability and explanatory potential being particularly questioned. It is therefore important to stress test modelling methodologies that include biotic factors against use cases where there is ample knowledge of the potential biotic component of the niche. Relatively well studied and important vector-borne diseases offer just such an opportunity, where knowledge of the agents involved in the transmission cycle –vectors and hosts– can serve to calibrate and test the niche model and corresponding SDM. Here, we study the contributions of biotic –14 vectors, 459 potential hosts– and abiotic –258 climatic categories– predictors to the explanatory and predictive features of the niche and corresponding SDM for the etiological agent of Chagas disease, Trypanosoma cruzi, in Mexico. Using an established spatial data mining technique, we generate biotic, abiotic and biotic+abiotic niche and SDM models. We test our models by comparing predictions of the most important probable hosts of Chagas disease with a previously published list of confirmed hosts. We quantify, compare, and contrast the individual and total contributions of predictors to the niche and distribution of Chagas disease in Mexico. We assess the relative predictive potential of these variables to model performance, showing that models that include relevant biotic niche variables lead to more predictive, more ecologically realistic SDMs. Our research illustrates a useful general procedure for identifying and ranking potential biotic interactions and for assessing the relative importance of biotic and abiotic predictors. We conclude that the inclusion of both abiotic and biotic predictors in SDMs not only provides more predictive and accurate models but also models that are more understandable and explainable from an ecological niche perspective.