Species Distribution Modelling Techniques Research Articles

The future of plant diversity within a Mediterranean endemism centre: Modelling the synergistic effects of climate and land-use change in Peloponnese, Greece

Climate- and land-use change stand as primary threats to terrestrial biodiversity. Yet, their synergistic impacts on species distributions remain poorly understood. To address this knowledge gap, we conducted the first-ever comprehensive species distribution analysis on an entire regional endemism centre within an eastern Mediterranean country, incorporating dynamic land-use/land-cover change data together with climate change scenarios. Specifically, we apply species distribution modelling and spatial data analysis techniques to compare the individual and synergistic effects of these environmental drivers on the endemic vascular flora of Peloponnese, focusing on potential range contractions, altitudinal shifts, and habitat fragmentation levels. Moreover, we identify fine-scale present and potential future endemism hotspots within our study area, incorporating taxonomic and phylogenetic information. Overall, we aim to enhance our current understanding of endemism patterns and contribute to the development of future-proof conservation strategies for safeguarding Greece's endangered endemic flora. The integration of land-use change projections with climate change yielded less severe impacts compared to the effects anticipated when considering climatic variables alone. Most taxa are expected to undergo significant range declines and nearly half might experience increased habitat fragmentation, due to the synergistic effects of climate- and land-use change. We identified endemism hotspots, which are concentrated in or along the main Peloponnesian mountain massifs. However, our predictions indicate that areas presently recognized as endemism hotspots will undergo a concerning area decline, across all future scenarios considered in this study. Our findings highlight the importance of including dynamic land-use variables alongside climatic predictors when projecting species distributions under global change. Moreover, we showed that endemism hotspots are not static and considering their potential geographic shifts is paramount to delineate effective forward-looking conservation strategies.

Co-occurrence and abundance of pollinators and pests in horticultural systems in Africa using an integrated Earth observation-based approach

ABSTRACT Flower-visiting insects that are pollinators play a critical role in promoting biodiversity in agroecosystems and agricultural food production through their pollination ecosystem service. However, several factors affect the survival of these pollinators and flower visitors, including the heavy and indiscriminate application of agrochemicals to control crop insect pests, which is impacted by various cropping patterns in a landscape and by shifting environmental conditions. Thus, this study focused on investigating the influence of cropping patterns on the spatial distribution of pollinators (Apis mellifera, Hymenoptera other than A. mellifera, and Syrphidae), flower visitors (Calliphoridae), and pests, i.e. fruit fly (Bactrocera dorsalis) and false codling moth (Thaumatotibia leucotreta) of the avocado, a pollinator-dependent crop. Cropping patterns, earth observation data and relevant environmental variables were used as the predictor variables for modeling the potential distribution and abundance of avocado pollinators, flower visitors and pests in one of the leading regions in avocado production in Kandara, Maragua, and Gatanga sub-Counties in Murang’a County, Kenya. In specific, species distribution modeling (SDM) and species abundance modeling (SAM) techniques, i.e. the maximum entropy (MaxEnt) model (presence-only data) and negative binomial (NB) distribution in a generalized linear model (GLM) (abundance data) were used, respectively. Additionally, the spatial distribution probability of the co-occurrence of the pollinators, flower visitors and pests was also analyzed. This study revealed that cropping patterns was the most consistent influential predictor variables for the distribution of avocado pollinators, flower visitors and pests. A large area of Kandara and some parts of Maragua and Gatanga sub-Counties showed a high spatial distribution probability of the studied avocado pollinators, flower visitors and pests. However, only the majority of Kandara sub-County had a high spatial distribution probability score of the potential co-occurrence of the avocado pollinators, flower visitors and pests. Further, A. mellifera was the most abundant flower-visiting pollinator compared with the other studied pollinators, while B. dorsalis was the most abundant avocado pest compared with T. leucotreta. In addition, GLM analysis indicated that no environmental variable was significant in explaining the abundance of the studied avocado pollinators, whereas precipitation and elevation derivatives of aspect and hillshade were statistically significant (p ≤ 0.05) in explaining the abundance of B. dorsalis. Solar radiation was significant in explaining only the abundance of T. leucotreta. Our study revealed that SDM and SAM modeling outputs can be used to inform decision-making for the implementation of sustainable management efforts regarding pollinators, flower visitors, and insect pests.

Exploring the Climatic Niche Evolution of the Genus Falco (Aves: Falconidae) in Europe.

By integrating species distribution modeling techniques, phylogenetic comparative methods, and climatic data, we analyzed how European falcon climatic niches have changed over evolutionary time in order to understand their tempo and mode of evolution and gain phylogenetic insights related to the ecological context of falcon evolution. For this purpose, we tested the relative contributions of niche conservatism, convergent evolution, and divergent evolution in the evolutionary history of this group of species in Europe. The occupation of climatic niche spaces by falcon species in Europe was not similar, considering that their climatic niche evolution was characterized by heterotachy, especially after ca. 4 Mya. Our results indicate that convergent evolution and niche divergence played an important role in the evolutionary history of these species, with no significant evidence of closely related species retaining their fundamental niche over time (phylogenetic niche conservatism). In most analyses, less closely related falcon species occupied similar climatic environments. We found that speciation in the European genus Falco was influenced by climatic niche differentiation, more prevalent in the last 4 million years, with the main climatic niche shifts occurring between closely related falcon species.

Where Will Threatened Aegle marmelos L., a Tree of the Semi-Arid Region, Go under Climate Change? Implications for the Reintroduction of the Species

The conservation of threatened species and the restoration of ecosystems have emerged as crucial ecological prerequisites in the context of a changing global environment. One such species of significant commercial value is the Bael tree, scientifically known as Aegle marmelos, which is native to semi-arid regions in Pakistan. However, the species faces threats in Pakistan due to overexploitation and changing land use. To support sustainable production practices and agricultural planning, it is important to investigate how climate change has affected the geographic distribution of Aegle marmelos. Additionally, the impact of climate change on its frequency and distribution remains uncertain. To address these concerns, we employed species distribution modeling techniques using MaxEnt and GIS to predict the present and future distribution of favorable habitats for Aegle marmelos. Based on our findings, several key bioclimatic variables were identified as significant influencers of Aegle marmelos distribution. These variables include soil bulk density (bdod), isothermality (bio03), precipitation during the warmest quarter (bio18), and mean temperature during the wettest quarter (bio08). Currently, the potential suitable habitat for Aegle marmelos spans an area of approximately 396,869 square kilometers, primarily concentrated in the regions of Punjab, Khyber Pakhtunkhwa, and Balochistan in Pakistan. The habitats deemed highly suitable for Aegle marmelos are predominantly found in upper and central Punjab. However, if climate change persists, the suitable habitats in Pakistan are likely to become more fragmented, resulting in a significant shift in the overall suitable area. Moreover, the distribution center of the species is expected to relocate towards the southeast, leading to increased spatial separation over time. The results of this research significantly contribute to our understanding of the geo-ecological aspects related to Aegle marmelos. Furthermore, they provide valuable recommendations for the protection, management, monitoring, and sustainable production of this species.

Predictive modeling of cave entrance locations: relationships between surface and subsurface morphology

Cave entrances directly connect the surface and subsurface geomorphology in karst landscapes. Understanding the spatial distribution of these features can help identify areas on the landscape that are critical to flow in the karst groundwater system. Sinkholes and springs are major locations of inflow and outflow from the groundwater system, respectively, however not all sinkholes and springs are equally connected to the main conduit system. Predicting where on the landscape zones of high connectivity exist is a challenge because cave entrances are difficult to detect and imperfectly documented. Wildlife research has a similar issue of understanding the complexities of where a given species is likely to exist on a landscape given incomplete information and presence-only data. Species distribution models can address some of these issues to create accurate predictions of species or event occurrence across the landscape. Here we apply a species distribution model, MaxEnt, to predict cave entrance locations in three geomorphic regions of Kentucky. We built the models with cave locations from the Kentucky Speleological Survey database and landscape predictor variables, including distance from sinkholes, distance from springs, distance from faults, elevation, lithology, slope, and aspect. All three regional models predict cave locations well with the most important variables for predicting cave entrance locations consistent between models. Throughout all three models, sinkholes and springs had the largest influence on the likelihood of cave entrance presence. This unique use of species distribution modeling techniques shows that they are potentially valuable tools to understand spatial patterns of other landscape features that are either ephemeral or difficult to identify using standard techniques.

N‐SDM: a high‐performance computing pipeline for Nested Species Distribution Modelling

Predicting contemporary and future species distributions is relevant for science and decision making, yet the development of high‐resolution spatial predictions for numerous taxonomic groups and regions is limited by the scalability of available modelling tools. Uniting species distribution modelling (SDM) techniques into one high‐performance computing (HPC) pipeline, we developedN‐SDM, an SDM platform aimed at delivering reproducible outputs for standard biodiversity assessments.N‐SDMwas built around a spatially‐nested framework, intended at facilitating the combined use of species occurrence data retrieved from multiple sources and at various spatial scales.N‐SDMallows combining two models fitted with species and covariate data retrieved from global to regional scales, which is useful for addressing the issue of spatial niche truncation. The set of state‐of‐the‐art SDM features embodied inN‐SDMincludes a newly devised covariate selection procedure, five modelling algorithms, an algorithm‐specific hyperparameter grid search, and the ensemble of small‐models approach.N‐SDMis designed to be run on HPC environments, allowing the parallel processing of thousands of species at the same time. All the information required for installing and runningN‐SDMis openly available on the GitHub repositoryhttps://github.com/N‐SDM/N‐SDM.

Persisting while changing over time: modelling the historical biogeographic of cave crickets (Orthoptera, Grylloidea) in Neotropics

Abstract Using species distribution modelling (SDMs) techniques, we predicted the biogeographic history of crickets commonly found in Neotropical caves as a way to detect potential long-term environmental refuges in South America. Our models were built based on a thorough investigation of existing database regarding the genus Endecous Saussure, 1878 (Ensifera: Phalangopsidae) occurrences. The predictions of their distribution were obtained for two paleoclimate scenarios (LGM — 21 ka and Mid-Holocene — 6 ka), the current climate scenario (0 ka) and one future global warming climate scenario (RCP8.5, 2080–2100). Our findings suggest that in the past, the potential distribution of the crickets was wider, with potential forest corridors connecting different karst areas with caves within their occupancy area. The future prediction indicates a drastic reduction in their spatial distribution with an increased potential for isolation in subterranean ecosystems. Atlantic humid forest patches and caves represent the main environmental refuges for these crickets. Considering the ongoing impacts on surface environments and future climate change, the conservation of caves and karst landscapes has become one of the main strategies for the maintenance of these crickets and all the correlated subterranean communities.

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.

The effectiveness of a large protected area to conserve a global endemism hotspot may vanish in the face of climate and land-use changes

Endemic vertebrates are a crucial component of biodiversity, yet face disproportionally high extinction risk as climate and land-use changes drive habitat loss. Large protected areas are therefore deemed necessary to mitigate biodiversity loss. In 2021, China’s Giant Panda National Park (GPNP, 27,134 km2) was established in one of the global endemism hotspots. In this study we ask the question whether this large national park is able to conserve the many threatened endemic vertebrates occurring in the region in the face of climate and land-use changes, in order to assess the long-term effectiveness of the GPNP. We used species distribution modeling techniques to project the distributions of 40 threatened terrestrial (and freshwater) endemic vertebrates under land-use and climate change scenarios SSP2–4.5, SSP3–7.0 and SSP5–8.5 in 2081–2100, and assessed the extent to which their distributions are covered by the GPNP, now and in the future. We found that by 2081–2100, two thirds of the threatened endemic vertebrates are predicted to lose part (15–79%, N = 4) of or (nearly) their entire (80–100% loss, N = 23) range under all three climate and land-use change scenarios. Consequently, fewer species are predicted to occur in the GPNP than at present. Our findings confirm the high vulnerability of threatened endemic species to climate and land-use changes, despite protected areas. Habitat loss due to climate and land-use changes elevate extinction risk of species in endemism hotspots across the globe. Urgent, widespread and intensified mitigation measures and adaptation measures are required at a landscape scale for effective conservation efforts in the future.

Predicting habitat suitability for Ixodesricinus and Ixodespersulcatus ticks in Finland

BackgroundTicks are responsible for transmitting several notable pathogens worldwide. Finland lies in a zone where two human-biting tick species co-occur: Ixodesricinus and Ixodespersulcatus. Tick densities have increased in boreal regions worldwide during past decades, and tick-borne pathogens have been identified as one of the major threats to public health in the face of climate change.MethodsWe used species distribution modelling techniques to predict the distributions of I.ricinus and I.persulcatus, using aggregated historical data from 2014 to 2020 and new tick occurrence data from 2021. By aiming to fill the gaps in tick occurrence data, we created a new sampling strategy across Finland. We also screened for tick-borne encephalitis virus (TBEV) and Borrelia from the newly collected ticks. Climate, land use and vegetation data, and population densities of the tick hosts were used in various combinations on four data sets to estimate tick species’ distributions across mainland Finland with a 1-km resolution.ResultsIn the 2021 survey, 89 new locations were sampled of which 25 new presences and 63 absences were found for I.ricinus and one new presence and 88 absences for I.persulcatus. A total of 502 ticks were collected and analysed; no ticks were positive for TBEV, while 56 (47%) of the 120 pools, including adult, nymph, and larva pools, were positive for Borrelia (minimum infection rate 11.2%, respectively). Our prediction results demonstrate that two combined predictor data sets based on ensemble mean models yielded the highest predictive accuracy for both I.ricinus (AUC = 0.91, 0.94) and I.persulcatus (AUC = 0.93, 0.96). The suitable habitats for I.ricinus were determined by higher relative humidity, air temperature, precipitation sum, and middle-infrared reflectance levels and higher densities of white-tailed deer, European hare, and red fox. For I.persulcatus, locations with greater precipitation and air temperature and higher white-tailed deer, roe deer, and mountain hare densities were associated with higher occurrence probabilities. Suitable habitats for I.ricinus ranged from southern Finland up to Central Ostrobothnia and North Karelia, excluding areas in Ostrobothnia and Pirkanmaa. For I.persulcatus, suitable areas were located along the western coast from Ostrobothnia to southern Lapland, in North Karelia, North Savo, Kainuu, and areas in Pirkanmaa and Päijät-Häme.ConclusionsThis is the first study conducted in Finland that estimates potential tick species distributions using environmental and host data. Our results can be utilized in vector control strategies, as supporting material in recommendations issued by public health authorities, and as predictor data for modelling the risk for tick-borne diseases.

Ensemble modeling for American chestnut distribution: Locating potential restoration sites in Pennsylvania

The American chestnut (Castanea dentata Borkh.) was an economically, ecologically, and culturally important tree in eastern American hardwood forests. However, the American chestnut is currently functionally absent from these forests due to the introduction of an invasive fungus (Cryphonectria parasitica (Murr.) Barr) and causal agent of chestnut blight in the early 1900s. Field experiments are being carried out to develop a blight-resistant American chestnut tree, but range-wide restoration will require localized understanding of its current distribution and what factors contribute to suitable American chestnut habitat. While previous studies have researched species distribution of the American chestnut, it is important to understand how species distribution modeling (SDM) technique impacts model results. In this paper we create an ensemble model that combines multiple different SDM techniques to predict areas of suitable American chestnut habitat in Pennsylvania. Results indicate that model accuracy varied considerably by SDM technique – with artificial neural networks performing the worst (Area-Under-the-Curve, AUC = 0.705) and gradient boosting models performing the best (AUC = 0.877). Even though SDM technique accuracy varied, most models identified the same environmental variables as the most important: ratio of sand to clay in the soil, canopy cover, topographic convergence index, and topographic position index. This study offers insight into the best SDM techniques to use, as well as a method of combining SDMs for higher prediction confidence.

Karuk ecological fire management practices promote elk habitat in northern California

Abstract After a century of fire suppression and accumulating fuel loads in North American forests, prescribed burns are increasingly used to prevent conditions leading to catastrophic megafire. There is widespread evidence that prescribed fire was used by Indigenous communities to manage natural and cultural resources for thousands of years. Wildlife habitat is an example of an ecological response that was actively managed with prescribed burns by Indigenous American peoples and is an important factor in western US forest management planning, restoration and climate resilience efforts. We analysed the effects of modern prescribed burns informed by traditional ecological knowledge (TEK) on the predicted change in elk winter habitat in Karuk aboriginal territory in Northern California between 2013 and 2018 using species distribution and simultaneous autoregressive modelling techniques. Burn types most closely resembling Karuk traditional practices, specifically those incorporating multiple‐year broadcast burns, had significant positive effects on elk winter habitat suitability. Conversely, concentrated burns focused solely on reducing fuel loads had significant negative effects on elk winter habitat suitability. However, areas where these fuel‐reduction burns were combined with multiple years of broadcast burns featured the highest increases in habitat. Synthesis and applications. Our results suggest that transitioning to prescribed burns that more closely follow Karuk traditional ecological knowledge will promote elk habitat in the region. This would be best achieved through continuing to work closely with Indigenous representatives to plan and implement cultural fire prescriptions on a landscape scale, a trend we posit would benefit environmental management efforts across the globe.

Northward expansion of fire-adaptative vegetation in future warming

Fire frequency and intensity are increasing due to higher temperatures and more droughts. The distributions of fuels (vegetation in natural conditions) are also changing in response to climate change. The vegetation in cold environments such as high latitudes and high altitudes is found to move upward or northward due to global warming. However, few studies have investigated the distribution changes of fire adaptive species in warm environments. This study estimated and compared the distributions of a typical fuelwood, the Eucalyptus globulus, under different climate scenarios. The species distribution modeling techniques were adopted to estimate the current distributions of the Eucalyptus globulus and the future distributions under scenarios of both SSP245 and SSP585 in 2060. Results show that the probability of the existence of the Eucalyptus globulus in the Northern Hemisphere increases significantly (p < 0.001) under both SSP245 and SSP585, especially in North America and Europe. However, the probability in the Southern Hemisphere decreases. The distribution of the Eucalyptus globulus expands in the mid-latitude (40 N–60 N) of the Northern Hemisphere. High carbon emissions contribute to the boost of the establishment of the Eucalyptus globulus in the Northern Hemisphere. These findings demonstrate that the fire adaptive species shows the tendency of shifting northward in response to climate change, highlighting the challenge of northward expansion of fires in future warming.

A comparison of presence-only analytical techniques and their application in forest pest modeling

Insect pests are natural disturbance agents that can significantly alter the structure and composition of forested landscapes, and thus impact their ability to provide critical ecosystem services. Predicting population levels of pest species has become crucial for the management of healthy forests, and species distribution modeling techniques may assist with predictions. Due to the nature of sampling in pest assessments there is often a lack of absence data which requires practitioners to rely on presence-only information. Modeling approaches have been developed for presence-only data but have not been tested for pest species that have major impacts on forest ecosystems. Our research objectives were to compare species distribution models for traditional techniques (i.e., generalized linear and additive models) and contemporary machine learning algorithms (i.e., maximum entropy, random forest, gradient boosted decision trees, and extreme gradient boosting), as well as assess how varying background points influence model performance. True presence-absence data and presences combined with background point data at one, two, three, and ten times the number of presences were compared. Comparisons were done using a comprehensive dataset from 2405 survey plots that assessed the presence and absence of non-native Sirex woodwasp (Sirex noctilio Fabricius) collected in pine plantations in Chile. Contemporary machine learning techniques (>84% average accuracy) outperformed traditional modeling techniques (<82% average accuracy) when utilizing true presence-absence data. For presence-background point models, accuracy tended to increase as the number of background points increased, except for generalized additive models and MaxEnt which had relatively similar performances. Generalized linear models, MaxEnt, and random forest substantially underperformed as compared to other modeling frameworks when using background point data. Gradient boosting and extreme gradient boosting had the highest prediction accuracies when combined with background points (74–81% depending on the number of background points) and may provide valuable alternative analyses to traditional techniques for presence-only data that contain complex correlations and interactions. Increasing the precision of these models, while reducing the inherent biases due to data structure, will allow for more informed forest pest management. This is becoming increasingly important, as changes in population and outbreak dynamics and the introduction of invasive species are projected to increase in the coming decades, partially due to global climate change and increased international trade and travel.

Invasion risk assessment using trait-environment and species distribution modelling techniques in an arid protected area: Towards conservation prioritization

Invasive species are considered as one of the key threats to biodiversity and human livelihoods globally. The most effective strategy for handling invasion would be based on profiling invasive species and identifying areas at risk of invasion before they occur. The current study used a trait-environment modelling approach to identify alien species with high probability of invasiveness and combined this with the species distribution models (SDMs) to predict areas in the arid Saint Katherine Protectorate (SKP) in Egypt that were at highest risk of invasion by these species. The specific leaf area, number of leaves, soil nitrogen, and prevalence of disturbances were the most important biotic and abiotic indicators for predicting invasiveness in SKP. Of the investigated 33 alien plant species, three species were identified to have the highest probability of invasiveness, including Salvia rosmarinus, Eucalyptus globulus, and Acacia saligna. The outcome of the SDMs revealed that precipitation seasonality and temperature-related variables were the most important bioclimatic predictors determining the potential invasion risk of the studied alien species. Potential invasion is more likely in the eastern and northern parts of SKP, where biodiversity-rich common microhabitats are found and where there is a prevalence of disturbances such as tourism activities. The Etalaa, Shaq Mosa, and El-Mesirdi wadis microhabitats, in particular, were identified in the current study to be potentially highly suitable microhabitats for alien invasiveness and should be prioritized for monitoring and conservation actions. Moreover, the three identified species could be used as early indicators for microhabitats at risk of invasion along the environmental gradients of temperature and precipitation in arid ecosystems. This approach can be applied to other taxa and other ecosystems and can provide opportunities for formulating proactive management strategies against biological invasions.

Effects of non-representative sampling design on multi-scale habitat models: flammulated owls in the Rocky Mountains.

Sampling bias and autocorrelation can lead to erroneous estimates of habitat selection, model overfitting and elevated omission rates. We developed a multi-scale habitat suitability model of the flammulated owl (Psiloscops flammeolus) in the Northern Rocky Mountains based on extensive but spatially clustered survey data, and then used simulations to evaluate the effects of spatially non-representative and spatially representative sampling strategies on model performance and predictions. Our hypothesis was that models trained with spatially non-representative simulated datasets would suffer from bias in parameter estimates, and would show lower predictive performance. The models trained with the spatially representative simulated datasets greatly outperformed the models trained with the spatially non-representative simulated datasets judged on standard metrics of model performance. However, the spatially non-representative models produced superior predictions based on their ability to identify the correct spatial scales, covariates, signs and magnitudes of the species-environment relationships, when compared to the spatially representative models. Thus, it is likely that representative spatial sampling across a broad range of environmental gradients also resulted in over-dispersion of sampling data, with a higher proportion of samples falling in areas of low probability of presence, leading to lower ability to resolve the relationships between species presence-absence and environmental covariates. In contrast, the spatially non-representative sampling, by concentrating sampling along environmental gradients that are characterized by higher probability of presence of the modelled species, produced predictions that, while seeming to be weaker based on standard measures of model performance (e.g., AUC, Kappa, PCC), greatly outperformed the spatially representative models based on measures of true model prediction (e.g., correctly describing the actual spatial scales, direction and strength of species-environment relationships). Further work using simulation approaches is warranted to more fully evaluate the ability of species distribution modelling techniques to correctly identify scales, driving covariates, signs and magnitudes of relationships between species presence-absence patterns, and environmental covariates.

Modeling the geographic spread and proliferation of invasive alien plants (IAPs) into new ecosystems using multi-source data and multiple predictive models in the Heuningnes catchment, South Africa

ABSTRACT The geographic spread and proliferation of Invasive Alien Plants (IAPs) into new ecosystems requires accurate, constant, and frequent monitoring particularly under the changing climate to ensure the integrity and resilience of affected as well as vulnerable ecosystems. This study thus aimed to understand the distribution and shifts of IAPs and the factors influencing such distribution at the catchment scale to minimize their risks and impacts through effective management. Three machine learning Species Distribution Modeling (SDM) techniques, namely, Random Forest (RF), Maximum Entropy (MaxEnt), Boosted Regression Trees (BRT) and their respective ensemble model were used to predict the potential distribution of IAPs within the catchment. The current and future bioclimatic variables, environmental and Sentinel-2 Multispectral Instrument satellite data were used to fit the models to predict areas at risk of IAPs invasions in the Heuningnes catchment, South Africa. The present and two future climatic scenarios from the Community Climate System Model (CCSM4) were considered in modeling the potential distribution of these species. The two future scenarios represented the minimum and maximum atmospheric carbon Representative Concentration Pathways (RCP) 2.6 and 8.5 for 2050 (average for 2041–2060). The results show that IAPs are predicted to expand under the influence of climate change in the catchment. Concurrently, riparian zones, bare areas, and the native vegetation which is rich in biodiversity will greatly be affected. The mean diurnal range (Bio2), warmest quarter maximum temperature (Bio5), and the warmest quarter precipitation (Bio18) were the most important bioclimatic variables in modeling the spatial distribution of IAPs in the catchment. Comparatively, all the models were successful in predicting the potential distribution of IAPs for all the scenarios. The BRT, MaxEnt, and RF predicted the spatial distribution of IAPs with an Area Under Curve (AUC) of 0.89, 0.92, and 0.94, respectively. The study highlighted the importance of multi-source data and multiple predictive models in predicting the current and potential future IAP distribution. The results from this study provide baseline information for effective land management, planning, and continuous monitoring of the further spread of IAPs within the Heuningnes catchment.

Phenotypic plasticity and the colonization of new habitats: a study of a colonial spider in the Chaco region and the Cerrado

In social animals, group prey capture could facilitate colonization of new areas with low resource availability. Parawixia bistriata is a colonial spider inhabiting seasonal dry forests and mesic habitats in South America. Individuals capture prey as a group, which allows individuals to broaden their foraging niche by incorporating large prey that cannot be subdued in solitary captures. P. bistriata exhibits two behavioural ecotypes a “dry” (plastic) ecotype which modifies individual’s tendency to capture prey in a group depending on food resources and a “wet” (fixed) ecotype, whose tendency to group prey capture is only modulated by the size of the prey but not by prey availability. By reconstructing the range expansion of the species using phylogeographic and species distribution modelling techniques, we indirectly examined whether group prey capture could have helped P. bistriata in colonization of low resource habitats. Based on cytochrome c oxidase subunit I gene genealogy, we found older populations in northern Cerrado in Brazil with more recent populations located further south in Dry and Humid Chaco in Argentina, with the latter being the most derived. Species distribution modelling for each ecotype suggests that suitable habitat for each ecotype started to overlap at some point during the Last Glacial Maximum (21 ky BP). These results suggest that P. bistriata expanded from northern Cerrado south to the Gran Chaco, being able to colonize mesic habitats at a later stage when individuals reached southern territories in the Chaco. This evidence is opposite to the idea that GPC facilitated P. bistriata colonization from mesic to harsher environments. However, plasticity in group prey capture could have been important to allow individuals to establish in mesic habitats by reducing the cost of group capture when under high resource levels.

Ensemble Species Distribution Model Identifies Survey Opportunities for At-Risk Bearded Beaksedge (Rhynchospora crinipes) in the Southeastern United States

Locating additional occurrences of at-risk species can inform assessments of their status and conservation needs (including potential legal protections). The perennial bearded beaksedge (Rhynchospora crinipes) ranges from Mississippi to North Carolina, but known occurrences are limited. Because of the species' apparent rarity, a model to identify areas with suitable habitat conditions for the species will allow conservationists to effectively prioritize and allocate scarce surveying resources. We used known occurrence records, a suite of environmental datasets, and four species distribution modeling techniques (generalized additive, GAM; maximum entropy, MaxEnt; generalized boosted, GBM; and weighted ensemble) to generate maps to inform surveys for R. crinipes. The ensemble approach improved predictive performance (AUC-PR = 0.95) compared to other techniques (individual model AUC-PR ranged from 0.7 to 0.8). We also obtained quantitative insights on the species' habitat relationships, including the likelihood of R. crinipes's presence near Atlantic white cedar (Chamaecyparis thyoides) habitat and floodplains, which is consistent with prior field observations. The ensemble model indicated that 3.6% of the study area could be suitable habitat, but only 0.38% had high suitability. Small stream riparian habitats and Atlantic swamp forests in Alabama, Florida, and Georgia had the highest proportion of suitable areas. Prioritizing surveys in areas with model-indicated high habitat suitability is expected to reveal additional R. crinipes occurrences. We suggest surveying efforts for other at-risk species may benefit from using an ensemble modeling approach to identify and prioritize survey areas and improve ecological knowledge of these species.

Mapping of Freshwater Snails’ Habitat—A Source of Transmitting Bilharzia in Mwea Sub-County, Kenya

Bilharzia is vector-borne disease carried by a parasite that is hosted by fresh water snails. The distribution of the disease is concurrent with the existence of the freshwater snails and is dependent on certain suitable environmental conditions. It is difficult to identify the specific habitats of the snails as they are often inaccessible on the ground, the snails also migrate by means of flowing water, making it difficult to keep a track of the freshwater snails’ habitat. This paper aimed at using GIS, Remote Sensing and Species Distribution Modelling techniques to model the suitable habitats for the freshwater snails and to prove that the snails migrate when there are sudden changes in water levels whilst showing the population at risk of bilharzia. The SDM used is the Maximum Entropy (MAXENT) for its ability to make right predictions even with small presence sites. The AUC value of the model was 0.951. The research results showed that the environmental variables; brightness Index, elevation, temperatures were negatively correlated with the snails’ presence while the wetness index, MSAVI, greenness index and soil pH were positively correlated. The snails are observed to favor clay soils of the montmorillonite type and the crop-lands land cover. Areas consistently submerged by water especially after flooding are shown to be the most suitable areas where snails migrate by means of river or canal water. The research proves that Mwea is not the source habitat of the freshwater snails. The neighboring sub-counties within Kirinyaga County should be investigated using such models as a likely source-habitat of the freshwater snails. Destroying the source habitats will lead to complete eradication of the freshwater snails within Mwea.