Caveats for correlative species distribution modeling

Insufficient and biased representation of species geographic responses to climate change

The geographic redistributions of species due to a rapidly changing climate are poised to perturb ecological communities and significantly impact ecosystems and human livelihoods. Effectively managing these biological impacts requires a thorough understanding of the patterns and processes of species geographic range shifts. While substantial recent redistributions have been identified and recognized to vary by taxon, region, and range geometry, there are large gaps and biases in the available evidence. Here, we use the largest compilation of geographic range change observations to date, comprised of 33,016 potential redistributions across 12,009 species, to formally assess within- and cross-species coverage and biases and to motivate future data collection. We find that species coverage varies strongly by taxon and underrepresents species at high and low latitudes. Within species, assessments of potential redistributions came from parts of their geographic range that were highly uneven and non-representative. For most species and taxa, studies were strongly biased toward the colder parts of species' distributions and thus significantly underrepresented populations that might get pushed beyond their maximum temperature limits. Coverage of potential leading and trailing geographic range edges under a changing climate was similarly uneven. Only 8% of studied species were assessed at both high and low latitude and elevation range edges, with most only covered at one edge. This suggests that substantial within-species biases exacerbate the considerable geographic and taxonomic among-species unevenness in evidence. Our results open the door for a more quantitative accounting for existing knowledge biases in climate change ecology and a more informed management and conservation. Our findings offer guidance for future data collection that better addresses information gaps and provides a more effective foundation for managing the biological impacts of climate change.

Forecasting the Expansion of Bactrocera tsuneonis (Miyake) (Diptera: Tephritidae) in China Using the MaxEnt Model

The invasive pest, Bactrocera tsuneonis (Miyake), has become a significant threat to China's citrus industry. Predicting the area of potentially suitable habitats for B. tsuneonis is essential for optimizing pest control strategies that mitigate its impact on the citrus industry. Here, existing distribution data for B. tsuneonis, as well as current climate data and projections for four future periods (2021-2040, 2041-2060, 2061-2080, and 2081-2100) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) were obtained. The distribution of B. tsuneonis under current and different climate change scenarios in China was predicted using the optimized MaxEnt model, ArcGIS, and the ENMeval data package. Model accuracy was assessed using ROC curves, and the primary environmental factors influencing the distribution of the pest were identified based on the percent contribution. When the regularization multiplier (RM) was set to 1.5 and the feature combination (FC) was set to LQH, a model with lower complexity was obtained. Under these parameter settings, the mean training AUC was 0.9916, and the mean testing AUC was 0.9854, indicating high predictive performance. The most influential environmental variables limiting the distribution of B. tsuneonis were the Precipitation of Warmest Quarter (Bio18) and Temperature Seasonality (standard deviation ×100) (Bio4). Under current climatic conditions, potentially suitable habitat for B. tsuneonis in China covered an area of 215.9 × 104 km2, accounting for 22.49% of the country's land area. Potentially suitable habitat was primarily concentrated in Central China, South China, and East China. However, under future climatic projections, the area of suitable habitat for B. tsuneonis exhibited varying degrees of expansion. Furthermore, the centroid of the total suitable habitat for this pest gradually shifted westward and northward. These findings suggest that B. tsuneonis will spread to northern and western regions of China under future climate changes. The results of our study indicate that climate change will have a major effect on the invasion of B. tsuneonis and have implications for the development of strategies to control the spread of B. tsuneonis in China.

The devil is in the detail: Environmental variables frequently used for habitat suitability modeling lack information for forest-dwelling bats in Germany

In response to the pressing challenges of the ongoing biodiversity crisis, the protection of endangered species and their habitats, as well as the monitoring of invasive species are crucial. Habitat suitability modeling (HSM) is often treated as the silver bullet to address these challenges, commonly relying on generic variables sourced from widely available datasets. However, for species with high habitat requirements, or for modeling the suitability of habitats within the geographic range of a species, variables at a coarse level of detail may fall short. Consequently, there is potential value in considering the incorporation of more targeted data, which may extend beyond readily available land cover and climate datasets. In this study, we investigate the impact of incorporating targeted land cover variables (specifically tree species composition) and vertical structure information (derived from LiDAR data) on HSM outcomes for three forest specialist bat species (Barbastella barbastellus, Myotis bechsteinii, and Plecotus auritus) in Rhineland-Palatinate, Germany, compared to commonly utilized environmental variables, such as generic land-cover classifications (e.g., Corine Land Cover) and climate variables (e.g., Bioclim). The integration of targeted variables enhanced the performance of habitat suitability models for all three bat species. Furthermore, our results showed a high difference in the distribution maps that resulted from using different levels of detail in environmental variables. This underscores the importance of making the effort to generate the appropriate variables, rather than simply relying on commonly used ones, and the necessity of exercising caution when using habitat models as a tool to inform conservation strategies and spatial planning efforts.

Risk of spread of the Asian citrus psyllid Diaphorina citri Kuwayama (Hemiptera: Liviidae) in Ghana

The impact of invasive species on biodiversity, food security and economy is increasingly noticeable in various regions of the globe as a consequence of climate change. Yet, there is limited research on how climate change affects the distribution of the invasive Asian citrus psyllid Diaphorina citri Kuwayama (Hemiptera:Liviidae) in Ghana. Using maxnet package to fit the Maxent model in R software, we answered the following questions; (i) what are the main drivers for D. citri distribution, (ii) what are the D. citri-specific habitat requirements and (iii) how well do the risk maps fit with what we know to be correctly based on the available evidence?. We found that temperature seasonality (Bio04), mean temperature of warmest quarter (Bio10), precipitation of driest quarter (Bio17), moderate resolution imaging spectroradiometer land cover and precipitation seasonality (Bio15), were the most important drivers of D. citri distribution. The results follow the known distribution records of the pest with potential expansion of habitat suitability in the future. Because many invasive species, including D. citri, can adapt to the changing climates, our findings can serve as a guide for surveillance, tracking and prevention of D. citri spread in Ghana.

Variable intraspecific response to climate change in a medicinally important African tree species, Vachellia sieberiana (DC.) (paperbark thorn)

Climate change is predicted to disproportionately impact sub-Saharan Africa, with potential devastating consequences on plant populations. Climate change may, however, impact intraspecific taxa differently. The aim of the study was to determine the current distribution and impact of climate change on three varieties of Vachellia sieberiana, that is, var. sieberiana, var. villosa and var. woodii. Ensemble species distribution models (SDMs) were built in "biomod2" using 66, 45, and 137 occurrence records for var. sieberiana, var. villosa, and var. woodii, respectively. The ensemble SDMs were projected to 2041-2060 and 2081-2100 under three general circulation models (GCMs) and two shared socioeconomic pathways (SSPs). The three GCMs were the Canadian Earth System Model version 5, the Institut Pierre-Simon Laplace Climate Model version 6A Low Resolution, and the Model for Interdisciplinary Research on Climate version 6. The suitable habitat of var. sieberiana predominantly occurs in the Sudanian and Zambezian phytochoria while that of var. villosa largely occurs in the Sudanian phytochorion. The suitable habitat of var. woodii mainly occurs in the Zambezian phyotochorion. There is coexistence of var. villosa and var. sieberiana in the Sudanian phytochorion while var. sieberiana and var. woodii coexist in the Zambezian phytochorion. Under SSP2-4.5 in 2041-2060 and averaged across the three GCMs, the suitable habitat expanded by 33.8% and 119.7% for var. sieberiana and var. villosa, respectively. In contrast, the suitable habitat of var. woodii contracted by -8.4%. Similar trends were observed in 2041-2060 under SSP5-8.5 [var. sieberiana (38.6%), var. villosa (139.0%), and var. woodii (-10.4%)], in 2081-2100 under SSP2-4.5 [var. sieberiana (4.6%), var. villosa (153.4%), and var. woodii (-14.4%)], and in 2081-2100 under SSP5-8.5 [var. sieberiana (49.3%), var. villosa (233.4%), and var. woodii (-30.7%)]. Different responses to climate change call for unique management and conservation decisions for the varieties.

Border fences reduce potential for transboundary migration of Marco Polo Sheep (Ovis ammon polii) in the Pamir Plateau

Border fences have severely impeded the transboundary migration of a number of large mammals worldwide, with central Asia being one of the most impacted. The Marco Polo sheep (Ovis ammon polii), an iconic species of Pamir, is threatened in its transboundary movement by increasing border fencing among their five distributed countries, including Tajikistan, Kyrgyzstan, China, Afghanistan, and Pakistan. In this study, by building ensemble species distribution models, we found that eastern Tajikistan had the largest suitable Macro Polo sheep habitat (about 42 % of the total suitable habitat), followed by China (about 32 %). We used least-cost paths to identify 51 ecological corridors including 5 transboundary ecological corridors, which may be important to maintain connectivity in both domestic and transboundary regions. To assess the potential barrier effect of border fences, we assessed four scenarios (30, 40, 50 and 60°) corresponding to the upper limit of the slope for the construction of fences. In areas too steep for fencing, these could be used by wild sheep to cross barriers or borders and may represent migration or movement routes, defined as natural passages. In the most pessimistic Scenario 60, only 25 migratory passages along the border fences were identified, compared to 997 in the most optimistic scenario (Scenario 30), indicating a strong negative effect of intensive border fencing on the transboundary movement of Marco Polo sheep. The establishment of transnational conservation parks, and ensuring permeability is maintained in key areas, could have a positive impact on the connectivity and persistence of Marco Polo sheep populations, and provide important lessons for other large migratory mammals in transboundary regions.

A MaxEnt Model of Citrus Black Fly Aleurocanthus woglumi Ashby (Hemiptera: Aleyrodidae) under Different Climate Change Scenarios

The citrus blackfly (CBF), Aleurocanthus woglumi Ashby, is an exotic pest native to Southeast Asia that has spread rapidly to the world's main centers of citrus production, having been recently introduced to Brazil. In this study, a maximum entropy niche model (MaxEnt) was used to predict the potential worldwide distribution of CBF under current and future climate change scenarios for 2030 and 2050. These future scenarios came from the Coupled Model Intercomparison Project Phase 6 (CMIP6), SSP1-2.6, and SSP5-8.5. The MaxEnt model predicted the potential distribution of CBF with area under receiver operator curve (AUC) values of 0.953 and 0.930 in the initial and final models, respectively. The average temperature of the coldest quarter months, precipitation of the rainiest month, isothermality, and precipitation of the driest month were the strongest predictors of CBF distribution, with contributions of 36.7%, 14.7%, 13.2%, and 10.2%, respectively. The model based on the current time conditions predicted that suitable areas for the potential occurrence of CBF, including countries such as Brazil, China, the European Union, the USA, Egypt, Turkey, and Morocco, are located in tropical and subtropical regions. Models from SSP1-2.6 (2030 and 2050) and SSP5-8.5 (2030) predicted that suitable habitats for CBF are increasing dramatically worldwide under future climate change scenarios, particularly in areas located in the southern US, southern Europe, North Africa, South China, and part of Australia. On the other hand, the SSP5-8.5 model of 2050 indicated a great retraction of the areas suitable for CBF located in the tropical region, with an emphasis on countries such as Brazil, Colombia, Venezuela, and India. In general, the CMIP6 models predicted greater risks of invasion and dissemination of CBF until 2030 and 2050 in the southern regions of the USA, European Union, and China, which are some of the world's largest orange producers. Knowledge of the current situation and future propagation paths of the pest serve as tools to improve the strategic government policies employed in CBF's regulation, commercialization, inspection, combat, and phytosanitary management.

Potentially suitable habitats of Daodi goji berry in China under climate change

Introduction

Goji berry (Lycium barbarum L.) is a famous edible and medicinal herb worldwide with considerable consumption. The recent cultivation of goji berries in the Daodi region was seriously reduced due to increased production costs and the influence of policy on preventing nongrain use of arable land in China. Consequently, production of Daodi goji berry was insufficient to meet market demands for high-quality medicinal materials. Searching for regions similar to the Daodi region was necessary.

Methods

The MaxEnt model was used to predicted the current and future potential regions suitable for goji berry in China based on the environmental characteristics of the Daodi region (including Zhongning County of Zhongwei prefecture-level city, and its surroundings), and the ArcGIS software was used to analyze the changes in its suitable region.

Results

The results showed that when the parameters were FC = LQHP and RM = 2.1, the MaxEnt model was optimal, and the AUC and TSS values were greater than 0.90. The mean temperature and precipitation of the coldest quarter were the most critical variables shaping the distribution of Daodi goji berries. Under current climate conditions, the suitable habitats of the Daodi goji berry were 45,973.88 km2, accounting for 0.48% of China's land area, which were concentrated in the central and western Ningxia Province (22,589.42 km2), and the central region of Gansu Province (18,787.07 km2) bordering western Ningxia. Under future climate scenarios, the suitable area was higher than that under current climate conditions and reached the maximum under RCP 6.0 (91,256.42 km2) in the 2050s and RCP 8.5 (82,459.17 km2) in the 2070s. The expansion regions were mainly distributed in the northeast of the current suitable ranges, and the distributional centroids were mainly shifted to the northeast. The moderately and highly suitable overlapping habitats were mainly distributed in Baiyin (7,241.75 km2), Zhongwei (6,757.81 km2), and Wuzhong (5, 236.87 km2) prefecture-level cities.

Discussion

In this stduy, MaxEnt and ArcGIS were applied to predict and analyze the suitable habitats of Daodi goji berry in China under climate change. Our results indicate that climate warming is conducive to cultivating Daodi goji berry and will not cause a shift in the Daodi region. The goji berry produced in Baiyin could be used to satisfy the demand for high-quality medicinal materials. This study addresses the insufficient supply and guides the cultivation of Daodi goji berry.

Predicting the current and future distribution of Monochamus carolinensis (Coleoptera: Cerambycidae) based on the maximum entropy model

BACKGROUND

Monochamus carolinensis is an important vector of pinewood nematodes in North America that is under quarantine in several countries worldwide. The distribution of M. carolinensis was previously thought to be limited to North America; however, we discovered it during trapping in China in 2022. Using this discovery and information regarding the area of origin, we applied a machine-learning algorithm based on the maximum entropy principle to predict the current and future (2050s, 2070s) potential distribution areas of M. carolinensis using bioclimatic variables.

RESULTS

The biological suitability of M. carolinensis was mainly driven by precipitation factors (BIO18, BIO15, BIO19), with 87.18% of the potential distribution areas located in South America, Asia, North America and Africa. Future potential distribution areas of M. carolinensis are predicted to expand to high latitudes, with an average increase of 10 245 874.88 km2 , and only 6.89% of the current suitable areas will become unsuitable. The potential distribution areas in 2070 are largest under the SSP585 scenario, with a 41.40% predicted increase (52 309 803.61 km2 ) above the current distribution, mainly reflecting an increase of the marginally and highly suitable areas.

CONCLUSION

The determination of dominant climatic factors and potential distribution areas will help provide an early warning for an M. carolinensis invasion, as well as provide a scientific basis for the spread and outbreak, facilitating development of effective governmental prevention and control measures. © 2023 Society of Chemical Industry.

Space-for-time substitutions in climate change ecology and evolution

In an epoch of rapid environmental change, understanding and predicting how biodiversity will respond to a changing climate is an urgent challenge. Since we seldom have sufficient long-term biological data to use the past to anticipate the future, spatial climate-biotic relationships are often used as a proxy for predicting biotic responses to climate change over time. These 'space-for-time substitutions' (SFTS) have become near ubiquitous in global change biology, but with different subfields largely developing methods in isolation. We review how climate-focussed SFTS are used in four subfields of ecology and evolution, each focussed on a different type of biotic variable - population phenotypes, population genotypes, species' distributions, and ecological communities. We then examine the similarities and differences between subfields in terms of methods, limitations and opportunities. While SFTS are used for a wide range of applications, two main approaches are applied across the four subfields: spatial in situ gradient methods and transplant experiments. We find that SFTS methods share common limitations relating to (i) the causality of identified spatial climate-biotic relationships and (ii) the transferability of these relationships, i.e. whether climate-biotic relationships observed over space are equivalent to those occurring over time. Moreover, despite widespread application of SFTS in climate change research, key assumptions remain largely untested. We highlight opportunities to enhance the robustness of SFTS by addressing key assumptions and limitations, with a particular emphasis on where approaches could be shared between the four subfields.

Spatial and seasonal variation in thermal sensitivity within North American bird species

Responses of wildlife to climate change are typically quantified at the species level, but physiological evidence suggests significant intraspecific variation in thermal sensitivity given adaptation to local environments and plasticity required to adjust to seasonal environments. Spatial and temporal variation in thermal responses may carry important implications for climate change vulnerability; for instance, sensitivity to extreme weather may increase in specific regions or seasons. Here, we leverage high-resolution observational data from eBird to understand regional and seasonal variation in thermal sensitivity for 21 bird species. Across their ranges, most birds demonstrated regional and seasonal variation in both thermal peak and range, or the temperature and range of temperatures when observations peaked. Some birds demonstrated constant thermal peaks or ranges across their geographical distributions, while others varied according to local and current environmental conditions. Across species, birds typically demonstrated either geographical or seasonal adaptation to climate. Local adaptation and phenotypic plasticity are likely important but neglected aspects of organismal responses to climate change.

Commonly collected thermal performance data can inform species distributions in a data-limited invader

Predicting potential distributions of species in new areas is challenging. Physiological data can improve interpretation of predicted distributions and can be used in directed distribution models. Nonnative species provide useful case studies. Panther chameleons (Furcifer pardalis) are native to Madagascar and have established populations in Florida, USA, but standard correlative distribution modeling predicts no suitable habitat for F. pardalis there. We evaluated commonly collected thermal traits- thermal performance, tolerance, and preference-of F. pardalis and the acclimatization potential of these traits during exposure to naturally-occurring environmental conditions in North Central Florida. Though we observed temperature-dependent thermal performance, chameleons maintained similar thermal limits, performance, and preferences across seasons, despite long-term exposure to cool temperatures. Using the physiological data collected, we developed distribution models that varied in restriction: time-dependent exposure near and below critical thermal minima, predicted activity windows, and predicted performance thresholds. Our application of commonly collected physiological data improved interpretations on potential distributions of F. pardalis, compared with correlative distribution modeling approaches that predicted no suitable area in Florida. These straightforward approaches can be applied to other species with existing physiological data or after brief experiments on a limited number of individuals, as demonstrated here.

Predicting current and future high-risk areas for vectors and reservoirs of cutaneous leishmaniasis in Iran

Climate change will affect the distribution of species in the future. To determine the vulnerable areas relating to CL in Iran, we applied two models, MaxEnt and RF, for the projection of the future distribution of the main vectors and reservoirs of CL. The results of the models were compared in terms of performance, species distribution maps, and the gain, loss, and stable areas. The models provided a reasonable estimate of species distribution. The results showed that the Northern and Southern counties of Iran, which currently do not have a high incidence of CL may witness new foci in the future. The Western, and Southwestern regions of the Country, which currently have high habitat suitability for the presence of some vectors and reservoirs, will probably significantly decrease in the future. Furthermore, the most stable areas are for T. indica and M. hurrianae in the future. So that, this species may remain a major reservoir in areas that are present under current conditions. With more local studies in the field of identifying vulnerable areas to CL, it can be suggested that the national CL control guidelines should be revised to include a section as a climate change adaptation plan.

Spatial distribution of Glossina morsitans (Diptera: Glossinidae) in Zambia: A vehicle-mounted sticky trap survey and Maxent species distribution model

BACKGROUND

Tsetse-transmitted African trypanosomiasis is a debilitating and fatal disease of humans and livestock if left untreated. While knowledge of the spatial distribution patterns of tsetse is essential for the development of risk-based vector control strategies, existing distribution maps in Zambia are more than 40 years old and were based on coarse spatial resolution data. The recently developed vehicle-mounted sticky trap (VST) provides an alternative sampling device to aid in updating existing distribution maps but has not been applied outside an experimental setting and is limited to motorable tracks. Therefore, the objective of the present study was to demonstrate the effectiveness of utilizing the VST for area-wide surveys of Glossina morsitans and to use the occurrence records to predict its spatial distribution in Zambia under current environmental conditions using Maxent.

METHODOLOGY/PRINCIPAL FINDINGS

Two-sided all-blue VST baited with butanone and 1-octen-3-ol was used to survey 692 and 1020 km of transect routes in G. m. centralis Machado and G. m. morsitans Westwood previously published distribution in Zambia. Maxent species distribution technique was used to predict the potential distribution of the two subspecies using current climatic and environmental data which was then compared to the historical distribution. A total of 15,602 tsetse were captured with G. m. morsitans (58%) being the most abundant. G. m. centralis and G. pallidipes Austin represented 39 and 2% of the catch respectively, and G. brevipalpis Newstead was also detected. The predicted potential distribution for G. m. centralis was 80,863 km2 while that of G. m. morsitans was 70,490 km2 representing a 47 and 29% reduction compared to their historical distributions, respectively.

CONCLUSION/SIGNIFICANCE

The VST is effective for sampling G. morsitans outside experimental settings and is recommended for use as an additional tsetse survey tool. The spatial distribution of G. morsitans in Zambia has reduced by 101,051 km2 due to temperature and land cover changes.

Contrasting adaptive genetic consequences of stream insects under changing climate

Freshwater biodiversity undergoes degradation due to climate change. Researchers have inferred the effects of climate change on neutral genetic diversity, assuming the fixed spatial distributions of alleles. However, the adaptive genetic evolution of populations that may change the spatial distribution of allele frequencies along environmental gradients (i.e., evolutionary rescue) have largely been overlooked. We developed a modeling approach that projects the comparatively adaptive and neutral genetic diversities of four stream insects, using empirical neutral/ putative adaptive loci, ecological niche models (ENMs), and a distributed hydrological-thermal simulation at a temperate catchment under climate change. The hydrothermal model was used to generate hydraulic and thermal variables (e.g., annual current velocity and water temperature) at the present and the climatic change conditions, projected based on the eight general circulation models and the three representative concentration pathways scenarios for the two future periods (2031-2050, near future; 2081-2100, far future). The hydraulic and thermal variables were used for predictor variables of the ENMs and adaptive genetic modeling based on machine learning approaches. The increases in annual water temperature in the near- (+0.3-0.7 °C) and far-future (+0.4-3.2 °C) were projected. Of the studied species, with different ecologies and habitat ranges, Ephemera japonica (Ephemeroptera) was projected to lose rear-edge habitats (i.e., downstream) but retain the adaptive genetic diversity owing to evolutionary rescue. In contrast, the habitat range of the upstream-dwelling Hydropsyche albicephala (Trichoptera) was found to remarkably decline, resulting in decreases in the watershed genetic diversity. While the other two Trichoptera species expanded their habitat ranges, the genetic structures were homogenized over the watershed and experienced moderate decreases in gamma diversity. The findings emphasize the evolutionary rescue potential, depending on the extent of species-specific local adaptation.

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.

Landscape-level heterogeneity of agri-environment measures improves habitat suitability for farmland birds

Agri-environment schemes (AESs), ecological focus areas (EFAs), and organic farming are the main tools of the common agricultural policy (CAP) to counteract the dramatic decline of farmland biodiversity in Europe. However, their effectiveness is repeatedly doubted because it seems to vary when measured at the field-versus-landscape level and to depend on the regional environmental and land-use context. Understanding the heterogeneity of their effectiveness is thus crucial to developing management recommendations that maximize their efficacy. Using ensemble species distribution models and spatially explicit field-level information on crops grown, farming practice (organic/conventional), and applied AES/EFA from the Integrated Administration and Control System, we investigated the contributions of five groups of measures (buffer areas, cover crops, extensive grassland management, fallow land, and organic farming) to habitat suitability for 15 farmland bird species in the Mulde River Basin, Germany. We used a multiscale approach to identify the scale of effect of the selected measures. Using simulated land-use scenarios, we further examined how breeding habitat suitability would change if the measures were completely removed and if their adoption by farmers increased to meet conservation-informed targets. Buffer areas, fallow land, and extensive grassland were beneficial measures for most species, but cover crops and organic farming had contrasting effects across species. While different measures acted at different spatial scales, our results highlight the importance of land-use management at the landscape level-at which most measures had the strongest effect. We found that the current level of adoption of the measures delivers only modest gains in breeding habitat suitability. However, habitat suitability improved for the majority of species when the implementation of the measures was increased, suggesting that they could be effective conservation tools if higher adoption levels were reached. The heterogeneity of responses across species and spatial scales indicated that a mix of different measures, applied widely across the agricultural landscape, would likely maximize the benefits for biodiversity. This can only be achieved if the measures in the future CAP will be cooperatively designed in a regionally targeted way to improve their attractiveness for farmers and widen their uptake.

Shashank P, Kereyagalahalli Mallaiah K, Bedar J. Diagnosis and potential invasion risk of Thrips parvispinus under current and future climate change scenarios

Background and Objective

Invasive thrips, Thrips parvispinus Karny recently reported in India, causing a widespread severe infestation in more than 0.4 million ha of chilli (Capsicum annum L.) growing areas. This species is native to Thailand and most prevalent in other South East Asian countries. Large scale cultivation of the major host plants (chilli and papaya), and favourable climatic conditions in India and other countries similar to native range of Thrips parvispinus expected to favour its further spread and establishment to new areas.

Materials and Methods

The present study was undertaken to confirm invasive thrips species identity through both morphological and molecular approaches and predict its potential invasion using the maximum entropy (MaxEnt) algorithm.

Results

The model predicted species range in respect of discrimination of suitable and unsuitable areas for its occurrence both in current and future climatic scenarios. The model provided a good fit for species distribution with a high value of area under the curve (0.957). The jackknife test indicated annual mean temperature and precipitation were found to be the most important bioclimatic variable in determining the distribution of T. parvispinus. High suitability areas were predicted in the countries wherever its occurrence was reported with high discrimination ability of suitable and unsuitable areas. Key distinguishing morphological characters of T. parvispinus were illustrated through high-resolution scanning electron microscopic images.

Conclusion

The identity of the thrips causing wide spread damage in chilli confirmed through morphological and molecular approaches. Key identifying characters were described through high resolution scanning electron microscopic images for accurate identification of the species. MaxEnt model identified high suitability regions for the potential establishment of T. parvispinus in India and other parts of the world. This study facilitates forecasting of further spread and also suggests imposing strict domestic quarantine measures to curtail its establishment in the new areas.

Climate-induced range shifts of invasive species (Diaphorina citri Kuwayama)

BACKGROUND

The Asian citrus psyllid (ACP) Diaphorina citri Kuwayama (Hemiptera: Liviidae) is a destructive, invasive species that poses a serious threat to the citrus industry wherever it occurs. The psyllid vectors the phloem-limited bacteria 'Candidatus Liberibacter americanus' and 'Ca. L. asiaticus', causal agents of the incurable citrus greening disease or huanglongbing (HLB). It is essential to understand which regions and areas are suitable for colonization by ACP to formulate appropriate policy and preventive measures. Considering its biology and ecology, we used a machine learning algorithm based on the MaxEnt (Maximum Entropy) principle, to predict the potential global distribution of ACP using bioclimatic variables and elevation.

RESULTS

The model predictions are consistent with the known distribution of ACP and also highlight the potential occurrence outside its current ecological range, that is, primarily in Africa, Asia and the Americas. The most important abiotic variables driving the global distribution of ACP were annual mean temperature, seasonality of temperature and annual precipitation.

CONCLUSION

Our findings highlight the need for international collaboration in slowing the spread of invasive pests like D. citri. © 2022 Society of Chemical Industry.

East palearctic treefrog past and present habitat suitability using ecological niche models

Ecological niche modeling is a tool used to determine current potential species' distribution or habitat suitability models which can then be used to project suitable areas in time. Projections of suitability into past climates can identify locations of climate refugia, or areas with high climatic stability likely to contain the highest levels of genetic diversity and stable populations when climatic conditions are less suitable in other parts of the range. Modeling habitat suitability for closely related species in recent past can also reveal potential periods and regions of contact and possible admixture. In the east palearctic, there are five Dryophytes (Hylid treefrog) clades belonging to two groups: Dryophytes japonicus group: Clades A and B; and Dryophytes immaculatus group: Dryophytes immaculatus, Dryophytes flaviventris, and Dryophytes suweonensis. We used maximum entropy modeling to determine the suitable ranges of these five clades during the present and projected to the Last Glacial Maximum (LGM) and Last Interglacial (LIG) periods. We also calculated climatic stability for each clade to identify possible areas of climate refugia. Our models indicated suitable range expansion during the LGM for four clades with the exclusion of D. immaculatus. High climatic stability in our models corresponded to areas with the highest numbers of recorded occurrences in the present. The models produced here can additionally serve as baselines for models of suitability under climate change scenarios and indicate species ecological requirements.

Current and future potential distributions of Helicoverpa punctigera (Lepidoptera: Noctuidae): is this the next FAW?

Helicoverpa punctigera (Wallengren), the native budworm, is an important highly polyphagous pest that has caused serious damage on a wide variety of crops in Australia. In Australia, its range overlaps that of its congener, Helicoverpa armigera (Hübner), a notorious invasive pest globally. We used CLIMEX, a bioclimatic niche modelling software package, to estimate the potential geographical distribution of H. punctigera under current and future climates (A1B scenario). Under both current and future climate conditions, the model indicates that H. punctigera could establish throughout the tropics and subtropics. Comparing the potential distributions under each climate scenario revealed that in the future its potential distribution is likely to shift poleward and into higher altitudes, into areas that are currently too cold as observed in the South of Brazil, Europe, North America, South East Asia, and South Pacific Islands including New Zealand. The projected potential distribution can inform pre- and post-border biosecurity strategies for the management of this pest in each country.

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

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

Modelling the biogeographic boundary shift of Calanus finmarchicus reveals drivers of Arctic Atlantification by subarctic zooplankton

Biological communities in the Arctic are changing through the climate-driven encroachment of subarctic species. This "Atlantification" extends to keystone Calanoid copepods, as the small-bodied Calanus finmarchicus increases in abundance in areas where it overlaps with larger Arctic congeners. The environmental factors that are facilitating this shift, whether related to optimal conditions in temperature or seasonality, remain unclear. Assessing these drivers at an Arctic-wide scale is necessary to predict future ecosystem change and impacts. Here we have compiled range-wide occurrences of C. finmarchicus and a suite of seasonal biophysical climatologies to build a boreo-Arctic ecological niche model. The data set was divided into two eras, 1955-1984 and 1985-2017, and an optimized MaxEnt model was used to predict the seasonal distribution of the abiotic niche of C. finmarchicus in both eras. Comparing outputs between eras reveals an increase in habitat suitability at the Arctic range edge. Large and significant increases in suitability are predicted in the regions of the Greenland, Labrador, and Southern Barents Seas that have experienced reduced sea-ice cover. With the exception of the Barents Sea, these areas also show a seasonal shift in the timing of peak habitat suitability toward an earlier season. Our findings suggest that the Atlantification of Arctic zooplankton communities is accompanied by climate-driven phenology changes. Although seasonality is a critical constraint to the establishment of C. finmarchicus at Arctic latitudes, earlier sea-ice retreat and associated productivity is making these environments increasingly favorable for this subarctic species.

Adult cold tolerance and potential North American distribution of Myllocerus undecimpustulatus undatus (Coleoptera: Curculionidae)

Cold tolerance and potential distribution of Myllocerus undecimpustulatus undatus Marshall, a polyphagous pest in the United States, were investigated. Adult survivorship after 2 days at 0 °C and − 5 °C averaged 60% and 18%, respectively. Four days of exposure resulted in survivorship of 11% at 0 °C and 4% at − 5 °C, respectively. Summer-collected weevils at − 5 °C through repeated cold exposure of 2 h survived 3 times longer than those subjected to sustained cold period of 10 h. Leaf consumption did not differ among summer-collected weevils at constant 20 °C and repeated cold exposure treatments; weevils under sustained cold exposure consumed less than weevils in repeated cold exposure treatments. Leaf area consumed after cold exposure was 2–4 times greater in winter-collected weevils compared to summer-collected weevils. Leaf consumption by winter-collected weevils decreased as the number of repeated cold exposure periods increased. Locality data from collections in Florida during 2000–2012 were used to produce a correlative model complemented by a mechanistic model from the cold tolerance data to project the potential distribution of M. undecimpustulatus undatus in North America. The models support the hypothesis that M. undecimpustulatus undatus could spread to areas of the southeastern and western United States. The predicted northern distribution followed an isothermal line about 33° North. The niche model defined an area along the western Gulf Coast as unsuitable for the weevil, possibly because the area receives greater annual rainfall than other areas of the southeastern United States and has aquic or udic soil unlike the well-drained sandy soil of peninsular Florida.

The use of singlebeam echo-sounder depth data to produce demersal fish distribution models that are comparable to models produced using multibeam echo-sounder depth

Seafloor characteristics can help in the prediction of fish distribution, which is required for fisheries and conservation management. Despite this, only 5%-10% of the world's seafloor has been mapped at high resolution, as it is a time-consuming and expensive process. Multibeam echo-sounders (MBES) can produce high-resolution bathymetry and a broad swath coverage of the seafloor, but require greater financial and technical resources for operation and data analysis than singlebeam echo-sounders (SBES). In contrast, SBES provide comparatively limited spatial coverage, as only a single measurement is made from directly under the vessel. Thus, producing a continuous map requires interpolation to fill gaps between transects. This study assesses the performance of demersal fish species distribution models by comparing those derived from interpolated SBES data with full-coverage MBES distribution models. A Random Forest classifier was used to model the distribution of Abalistes stellatus, Gymnocranius grandoculis, Lagocephalus sceleratus, Loxodon macrorhinus, Pristipomoides multidens, and Pristipomoides typus, with depth and depth derivatives (slope, aspect, standard deviation of depth, terrain ruggedness index, mean curvature, and topographic position index) as explanatory variables. The results indicated that distribution models for A. stellatus, G. grandoculis, L. sceleratus, and L. macrorhinus performed poorly for MBES and SBES data with area under the receiver operator curves (AUC) below 0.7. Consequently, the distribution of these species could not be predicted by seafloor characteristics produced from either echo-sounder type. Distribution models for P. multidens and P. typus performed well for MBES and the SBES data with an AUC above 0.8. Depth was the most important variable explaining the distribution of P. multidens and P. typus in both MBES and SBES models. While further research is needed, this study shows that in resource-limited scenarios, SBES can produce comparable results to MBES for use in demersal fish management and conservation.

Prediction of habitat suitability for Patrinia sibirica Juss. in the Southern Urals

The paper presents the results of predictions of the habitat persistence for rare relict of the Pleistocene floristic complex Patrinia sibirica (L.) Juss. in the Southern Urals under various forecasted climate change scenarios. Climate variables from CHELSA BIOCLIM, elevation data (GMTED2010) and coarse fragment content in the top level of soil were used as predictors for modeling in the MaxEnt software. The impact of climate change on P. sibirica habitats under the RCP4.5 and RCP8.5 scenarios calculated from an ensemble of four general circulation models has been analyzed. The modeling has shown that the changes in the habitat suitability depend on the altitude. Deterioration of the habitats could be attributed to a temperature increase in mountain forest locations, and to a precipitation of driest quarter increase in mountain forest-steppe locations. In both cases, this leads to the expansion of forest and shrub vegetation. Monitoring of the habitat persistence of P. sibirica and other relict species of the Pleistocene floristic complex can play a major role in predictions, as their massive decline would constitute that climatic changes exceed the ranges of their fluctuations in the Holocene.

Exploring hybrid consensus models to assess roadkill

Accurate information provided by reliable models is essential for identifying hotspots and mitigating roadkill. However, existing methods, such as kernel density estimation (KDE) and maximum entropy modeling (ME) may individually identify only a subset of the suitable locations for mitigation, because KDE cannot detect hotspots once local abundances are depressed, and ME may only partially identify current hotspots due to imperfect discrimination skill. Here, we propose a hybrid consensus modeling (HCM) approach that leverages the strengths of both KDE and ME by using their consensus to identify the core subset of hotspots. We collected herpetofauna (amphibians and reptiles) roadkill data (N = 839) along four roads in Taiwan (R.O.C.) to evaluate the statistical performance and theoretical mitigation efficiency of HCM, KDE and ME, and to compare the allocation among roads, spatial clustering, and environmental conditions in the identified hotspots. HCM was applied on the herpetofauna dataset as well as separately on amphibians and reptiles. Although the discrimination skill of KDE and ME models for both target clades together was good to excellent (AUC_KDE = 0.944, AUC_ME = 0.822), the highest theoretical mitigation efficiency, was displayed by HCM Consensus (2.89), followed by KDE (2.58), and ME (1.91). Furthermore, we show that theoretical mitigation efficiency increases with decreasing spatial clustering (Moran's I). Given pervasive budget constraints, we recommend to limit permanent mitigation measures such as fenced culverts to HCM Consensus hotspots, temporary measures to KDE hotspots, and to target additional monitoring at ME hotspots.

Invaders from islands: thermal matching, potential or flexibility?

Native-range thermal constraints may not reflect the geographical distributions of species introduced from native island ranges in part due to rapid physiological adaptation in species introduced to new environments. Correlative ecological niche models may thus underestimate potential invasive distributions of species from islands. The northern curly-tailed lizard (Leiocephalus carinatus) is established in Florida, including populations north of its native range. Competing hypotheses may explain this distribution: Thermal Matching (distribution reflects thermal conditions of the native range), Thermal Potential (species tolerates thermal extremes absent in the native range) and/or Thermal Flexibility (thermal tolerance reflects local thermal extremes). We rejected the Thermal Matching hypothesis by comparing ecological niche models developed from native vs. native plus invasive distributions; L. carinatus exists in areas of low suitability in Florida as predicted by the native-distribution model. We then compared critical thermal limits of L. carinatus from two non-native populations to evaluate the Thermal Potential and Flexibility hypotheses: one matching native range latitudes, and another 160 km north of the native range that experiences more frequent cold weather events. Critical thermal minima in the northern population were lower than in the south, supporting the Thermal Flexibility hypothesis, whereas critical thermal maxima did not differ.

Modelling and validation of the spatial distribution of suitable habitats for the recruitment of invasive plants on climate change scenarios: An approach from the regeneration niche

The regeneration niche concept states that plant species only occur in habitats where the environmental conditions allow their recruitment. This study focuses on this concept and proposes a novel approach for modelling and experimentally validating the distribution of suitable habitats for the recruitment of invasive plants under the current and future climate. The biological invasion of the Peruvian peppertree (Schinus molle) in Mexico is used as practical example. The values of eight bioclimatic variables associated to sites in which young, naturally established seedlings and saplings were detected were used to model the current distribution of recruitment habitats. A machine-learning algorithm of maximum entropy (MaxEnt) was used to calibrate the model and its output indicated the distribution of occurrence probabilities of young peppertrees in Mexico under the current climate. This model was projected on climate change scenarios predicted for the middle of this century, which indicated that the cover of suitable recruitment habitats for this invasive species will shrink. To validate these predictions, field experiments were performed at three sites where the model predicted reduced occurrence probabilities of young peppertrees. In these experiments, emergence and survival rates of peppertree seedlings were assessed under the current climate and under simulated climate change conditions. As seedling emergence and survival rates were lower under simulated climate change conditions, the experiments validated the model predictions. These results supported our proposal, which combines modelling and experimental approaches to make accurate and valid predictions about the distribution of suitable recruitment habitats for invasive plants in a warmer and drier world.

Distribution and invasion risk assessments of Chrysodeixis includens (Walker, [1858]) (Lepidoptera: Noctuidae) using CLIMEX

Chrysodeixis includens is a polyphagous pest restricted to the American continent. The occurrence of C. includens is allied, among other factors, by favorable conditions such as temperature, humidity, presence of hosts, and migratory behavior. In this work, we built spatiotemporal species distribution models at continental and global levels for the distribution of C. includens using CLIMEX to determine times and regions favorable for year-round survival and migration of this species and in case of invasion on other continents to apply timely and right phytosanitary measures. Our models estimated high climate suitability for C. includens in Central and large proportions of South America throughout the year. Moreover, there is suitability for C. includens growth in all months of the year in Central and northern part of South America. In the northern hemisphere, these conditions range from April to October, while in mid-southern parts of South America, favorable periods comprise October through June. The countries with the highest suitability for C. includens outside the American continent are located on the African and Asian continents. Our results show variable climate suitability for C. includens during the year that help to understand likely migration pattern in North America. This information would direct efforts for appropriate C. includens management during warm and moist periods of the year. Furthermore, our models notify the need for the development of strategies for the inspection and interception of C. includens especially in central Africa, India, South and Southeast Asia, and Northeast Australia.

Modeling impacts of climate change on the potential distribution of six endemic baobab species in Madagascar

Madagascar, a globally renowned biodiversity hotspot characterized by high rates of endemism, is one of the few remaining refugia for many plants and animal species. However, global climate change has greatly affected the natural ecosystem and endemic species living in Madagascar, and will likely continue to influence species distribution in the future. Madagascar is home to six endemic baobab (Adansonia spp., Bombacoideae [Malvaceae]) species (Adansonia grandidieri, A. suarezensis, A. madagascariensis, A. perrieri, A. rubrostipa, A. za), which are remarkable and endangered plants. This study aimed to model the current distribution of suitable habitat for each baobab species endemic to Madagascar and determine the effect that climate change will have on suitable baobab habitat by the years 2050 and 2070. The distribution was modeled using MaxEnt based on locality information of 245 occurrence sites of six species from both online database and our own field work. A total of seven climatic variables were used for the modeling process. The present distribution of all six Madagascar's baobabs was largely influenced by temperature-related factors. Although both expansion and contraction of suitable habitat are predicted for all species, loss of original suitable habitat is predicted to be extensive. For the most widespread Madagascar baobab, A. za, more than 40% of its original habitat is predicted to be lost because of climate change. Based on these findings, we recommend that areas predicted to contract in response to climate change should be designated key protection regions for baobab conservation.

Comment on "A global-scale ecological niche model to predict SARS-CoV-2 coronavirus infection rate", author Coro

In this letter we present comments on the article “A global-scale ecological niche model to predict SARS-CoV-2 coronavirus” by Coro published in 2020.

Climate-based seasonal dynamics of the invasive red palm mite Raoiella indica

BACKGROUND

Raoiella indica Hirst (Acari: Tenuipalpidae) is the most critical coconut and banana pest recently introduced in Brazil. Once the mite pests are introduced, it is essential to understand their dynamics in important crops under open-field climatic conditions to implement strategies for their management and determine the periods when species populations may increase in the field. Modelling tools have been used to determine the potential distribution of species and implications for the management of invasive species. Thus, our aim in this study was to determine the seasonal variation in R. indica and the influence of the monthly climate using CLIMEX modelling. We adjusted the CLIMEX model for R. indica based on distribution data, additional biological characteristics, and fluctuations in the R. indica population in a commercial coconut plantation.

RESULTS

The model for the current climate shows a good match between the ecoclimatic index and the global distribution of R. indica. The model results demonstrate that most states of Brazil and several regions worldwide include areas with highly suitable climatic conditions for R. indica. We observed variations in the density of R. indica in commercial coconut crops, with the highest incidence occurring during the first months of the year.

CONCLUSION

Our results showed different alterations in seasonal suitability for R. indica that may provide information for the implementation of methods for time management, such as strategies for sampling and control during periods with a high degree of suitability for R. indica. © 2020 Society of Chemical Industry.

Simulating highly disturbed vegetation distribution: the case of China's Jing-Jin-Ji region

Background

Simulating vegetation distribution is an effective method for identifying vegetation distribution patterns and trends. The primary goal of this study was to determine the best simulation method for a vegetation in an area that is heavily affected by human disturbance.

Methods

We used climate, topographic, and spectral data as the input variables for four machine learning models (random forest (RF), decision tree (DT), support vector machine (SVM), and maximum likelihood classification (MLC)) on three vegetation classification units (vegetation group (I), vegetation type (II), and formation and subformation (III)) in Jing-Jin-Ji, one of China’s most developed regions. We used a total of 2,789 vegetation points for model training and 974 vegetation points for model assessment.

Results

Our results showed that the RF method was the best of the four models, as it could effectively simulate vegetation distribution in all three classification units. The DT method could only simulate vegetation distribution in units I and II, while the other two models could not simulate vegetation distribution in any of the units. Kappa coefficients indicated that the DT and RF methods had more accurate predictions for units I and II than for unit III. The three vegetation classification units were most affected by six variables: three climate variables (annual mean temperature, mean diurnal range, and annual precipitation), one geospatial variable (slope), and two spectral variables (Mid-infrared ratio of winter vegetation index and brightness index of summer vegetation index). Variables Combination 7, including annual mean temperature, annual precipitation, mean diurnal range and precipitation of driest month, produced the highest simulation accuracy.

Conclusions

We determined that the RF model was the most effective for simulating vegetation distribution in all classification units present in the Jing-Jin-Ji region. The RF model produced high accuracy vegetation distributions in classification units I and II, but relatively low accuracy in classification unit III. Four climate variables were sufficient for vegetation distribution simulation in such region.