A review of evidence about use and performance of species distribution modelling ensembles like BIOMOD

Open Data Practices among Users of Primary Biodiversity Data

Presence-only biodiversity data are increasingly relied on in biodiversity, ecology, and conservation research, driven by growing digital infrastructures that support open data sharing and reuse. Recent reviews of open biodiversity data have clearly documented the value of data sharing, but the extent to which the biodiversity research community has adopted open data practices remains unclear. We address this question by reviewing applications of presence-only primary biodiversity data, drawn from a variety of sources beyond open databases, in the indexed literature. We characterize how frequently researchers access open data relative to data from other sources, how often they share newly generated or collated data, and trends in metadata documentation and data citation. Our results indicate that biodiversity research commonly relies on presence-only data that are not openly available and neglects to make such data available. Improved data sharing and documentation will increase the value, reusability, and reproducibility of biodiversity research.

Effect of Climate Change on Introduced and Native Agricultural Invasive Insect Pests in Europe

Climate change and invasive species are major environmental issues facing the world today. They represent the major threats for various types of ecosystems worldwide, mainly managed ecosystems such as agriculture. This study aims to examine the link between climate change and the biological invasion of insect pest species. Increased international trade systems and human mobility have led to increasing introduction rates of invasive insects while climate change could decrease barriers for their establishment and distribution. To mitigate environmental and economic damage it is important to understand the biotic and abiotic factors affecting the process of invasion (transport, introduction, establishment, and dispersal) in terms of climate change. We highlight the major biotic factors affecting the biological invasion process: diet breadth, phenological plasticity, and lifecycle strategies. Finally, we present alien insect pest invasion management that includes prevention, eradication, and assessment of the biological invasion in the form of modelling prediction tools.

A novel methodology for Groundwater Flooding Susceptibility assessment through Machine Learning techniques in a mixed-land use aquifer

Many areas around the world are affected by Groundwater Level rising (GWLr). One of the most severe consequences of this phenomenon is Groundwater Flooding (GF), with serious impacts for the human and natural environment. In Europe, GF has recently received specific attention with Directive 2007/60/EC, which requires Member States to map GF hazard and propose measures for risk mitigation. In this paper a methodology has been developed for Groundwater Flooding Susceptibility (GFS) assessment, using for the first time Spatial Distribution Models. These Machine Learning techniques connect occurrence data to predisposing factors (PFs) to estimate their distributions. The implemented methodology employs aquifer type, depth of piezometric level, thickness and hydraulic conductivity of unsaturated zone, drainage density and land-use as PFs, and a GF observations inventory as occurrences. The algorithms adopted to perform the analysis are Generalized Boosting Model, Artificial Neural Network and Maximum Entropy. Ensemble Models are carried out to reduce the uncertainty associated with each algorithm and increase its reliability. GFS is mapped by choosing the ensemble model with the best predictivity performance and dividing occurrence probability values into five classes, from very low to very high susceptibility, using Natural Breaks classification. The methodology has been tested and statistically validated in an area of 14,3 km² located in the Metropolitan City of Naples (Italy), affected by GWLr since 1990 and GF in buildings and agricultural soils since 2007. The results of modeling show that about 93% of the inventoried points fall in the high and very high GFS classes, and piezometric level depth, thickness of unsaturated zone and drainage density are the most influencing PFs, in accordance with field observations and the triggering mechanism of GF. The outcomes provide a first step in the assessment of GF hazard and a decision support tool to local authorities for GF risk management.

Integrating demography and distribution modeling for the iconic Leontopodium alpinum Colm. in the Romanian Carpathians

Both climate change and human exploitation are major threats to plant life in mountain environments. One species that may be particularly sensitive to both of these stressors is the iconic alpine flower edelweiss (Leontopodium alpinum Colm.). Its populations have declined across Europe due to over-collection for its highly prized flowers. Edelweiss is still subject to harvesting across the Romanian Carpathians, but no study has measured to what extent populations are vulnerable to anthropogenic change.Here, we estimated the effects of climate and human disturbance on the fitness of edelweiss. We combined demographic measurements with predictions of future range distribution under climate change to assess the viability of populations across Romania.We found that per capita and per-area seed number and seed mass were similarly promoted by both favorable environmental conditions, represented by rugged landscapes with relatively cold winters and wet summers, and reduced exposure to harvesting, represented by the distance of plants from hiking trails. Modeling these responses under future climate scenarios suggested a slight increase in per-area fitness. However, we found plant ranges contracted by between 14% and 35% by 2050, with plants pushed into high elevation sites.Synthesis. Both total seed number and seed mass are expected to decline across Romania despite individual edelweiss fitness benefiting from a warmer and wetter climate. More generally, our approach of coupling species distribution models with demographic measurements may better inform conservation strategies of ways to protect alpine life in a changing world.

Artificial illumination influences niche segregation in bats

Artificial light at night (ALAN) is a pervasive form of pollution largely affecting wildlife, from individual behaviour to community structure and dynamics. As nocturnal mammals, bats are often adversely affected by ALAN, yet some "light-opportunistic" species exploit it by hunting insects swarming near lights. Here we used two potentially competing pipistrelle species as models, Kuhl's (Pipistrellus kuhlii) and common (Pipistrellus pipistrellus) pipistrelles, both known to forage in artificially illuminated areas. We set our study in a mountainous area of central Italy, where only recently did the two species become syntopic. We applied spatial modelling and radiotracking to contrast potential vs. actual environmental preferences by the two pipistrelles. Species distribution models and niche analysis showed a large interspecific niche overlap, including a preference for illuminated areas, presenting a potential competition scenario. Pipistrellus pipistrellus association with ALAN, however, was weakened by adding P. kuhlii as a biotic variable to the model. Radiotracking showed that the two species segregated habitats at a small spatial scale and that P. kuhlii used artificially illuminated sites much more frequently than P. pipistrellus, despite both species potentially being streetlamp foragers. We demonstrate that ALAN influences niche segregation between two potentially competing species, confirming its pervasive effects on species and community dynamics, and provide an example of how light pollution and species' habitat preferences may weave a tapestry of complex ecological interactions.

Addressing alpine plant phylogeography using integrative distributional, demographic and coalescent modeling

Phylogeographic studies of alpine plants have evolved considerably in the last two decades from ad hoc interpretations of genetic data to statistical model-based approaches. In this review we outline the developments in alpine plant phylogeography focusing on the recent approach of integrative distributional, demographic and coalescent (iDDC) modeling. By integrating distributional data with spatially explicit demographic modeling and subsequent coalescent simulations, the history of alpine species can be inferred and long-standing hypotheses, such as species-specific responses to climate change or survival on nunataks during the last glacial maximum, can be efficiently tested as exemplified by available case studies. We also discuss future prospects and improvements of iDDC.

A Test of Species Distribution Model Transferability Across Environmental and Geographic Space for 108 Western North American Tree Species

Predictions from species distribution models (SDMs) are commonly used in support of environmental decision-making to explore potential impacts of climate change on biodiversity. However, because future climates are likely to differ from current climates, there has been ongoing interest in understanding the ability of SDMs to predict species responses under novel conditions (i.e., model transferability). Here, we explore the spatial and environmental limits to extrapolation in SDMs using forest inventory data from 11 model algorithms for 108 tree species across the western United States. Algorithms performed well in predicting occurrence for plots that occurred in the same geographic region in which they were fitted. However, a substantial portion of models performed worse than random when predicting for geographic regions in which algorithms were not fitted. Our results suggest that for transfers in geographic space, no specific algorithm was better than another as there were no significant differences in predictive performance across algorithms. There were significant differences in predictive performance for algorithms transferred in environmental space with GAM performing best. However, the predictive performance of GAM declined steeply with increasing extrapolation in environmental space relative to other algorithms. The results of this study suggest that SDMs may be limited in their ability to predict species ranges beyond the environmental data used for model fitting. When predicting climate-driven range shifts, extrapolation may also not reflect important biotic and abiotic drivers of species ranges, and thus further misrepresent the realized shift in range. Future studies investigating transferability of process based SDMs or relationships between geodiversity and biodiversity may hold promise.

Assessing the conservation of Miombo timber species through an integrated index of anthropogenic and climatic threats

AIM

Angolan Miombo woodlands, rich in timber species of the Leguminosae family, go through one of the highest rates of deforestation in sub-Saharan Africa. This study presents, on the basis of updated information of the distribution of Leguminosae timber species native to Angola, an integrated index framing the main threats for trees, which aims to support new conservation measures.

LOCATION

Sub-Saharan Africa, Republic of Angola.

METHODS

The current distribution areas of six Leguminosae timber species (i.e., Afzelia quanzensis, Brachystegia spiciformis, Guibourtia coleosperma, Isoberlinia angolensis, Julbernardia paniculata, and Pterocarpus angolensis) were predicted through ensemble modeling techniques. The level of threat to each species was analyzed, comparing the species potential distribution with a threat index map and with the protected areas. The threat index of anthropogenic and climatic factors encompasses the effects of population density, agriculture, proximity to roads, loss of tree cover, overexploitation, trends in wildfires, and predicted changes in temperature and precipitation.

RESULTS

Our results revealed that about 0.5% of Angola's area is classified as of "Very high" threat, 23.9% as "High" threat, and 66.5% as "Moderate" threat. Three of the studied species require special conservation efforts, namely B. spiciformis and I. angolensis, which have a large fraction of predicted distribution in areas of high threat, and G. coleosperma since it has a restricted distribution area and is one of the most valuable species in international markets. The priority areas for the conservation of Leguminosae timber species were found in Benguela and Huíla.

MAIN CONCLUSIONS

This study provides updated data that should be applied to inform policymakers, contributing to national conservation planning and protection of native flora in Angola. Moreover, it presents a methodological approach for the predictions of species distribution and for the creation of a threat index map that can be applied in other poorly surveyed tropical regions.

Phylogeographic analysis of shrubby beardtongues reveals range expansions during the Last Glacial Maximum and implicates the Klamath Mountains as a hotspot for hybridization

Quaternary glacial cycles often altered species' geographic distributions, which in turn altered the geographic structure of species' genetic diversity. In many cases, glacial expansion forced species in temperate climates to contract their ranges and reside in small pockets of suitable habitat (refugia), where they were likely to interact closely with other species, setting the stage for potential gene exchange. These introgression events, in turn, would have degraded species boundaries, making the inference of phylogenetic relationships challenging. Using high-throughput sequence data, we employed a combination of species distribution models and hybridization tests to assess the effect of glaciation on the geographic distributions, phylogenetic relationships, and patterns of gene flow of five species of Penstemon subgenus Dasanthera, long-lived shrubby angiosperms distributed throughout the Pacific Northwest of North America. Surprisingly, we found that rather than reducing their ranges to small refugia, most Penstemon subgenus Dasanthera species experienced increased suitable habitat during the Last Glacial Maximum relative to the present day. We also found substantial evidence for gene exchange between species, with the bulk of introgression events occurring in or near the Klamath Mountains of southwestern Oregon and northwestern California. Subsequently, our phylogenetic inference reveals blurred taxonomic boundaries in the Klamath Mountains, where introgression is most prevalent. Our results question the classical paradigm of temperate species' responses to glaciation and highlight the importance of contextualizing phylogenetic inference with species' histories of introgression.

John HK, Richards AL, Ryan SJ. Exploring the Niche of Rickettsia montanensis (Rickettsiales: Rickettsiaceae) Infection of the American Dog Tick (Acari: Ixodidae), Using Multiple Species Distribution Model Approaches

The American dog tick, Dermacentor variabilis (Say) (Acari: Ixodidae), is a vector for several human disease-causing pathogens such as tularemia, Rocky Mountain spotted fever, and the understudied spotted fever group rickettsiae (SFGR) infection caused by Rickettsia montanensis. It is important for public health planning and intervention to understand the distribution of this tick and pathogen encounter risk. Risk is often described in terms of vector distribution, but greatest risk may be concentrated where more vectors are positive for a given pathogen. When assessing species distributions, the choice of modeling framework and spatial layers used to make predictions are important. We first updated the modeled distribution of D. variabilis and R. montanensis using maximum entropy (MaxEnt), refining bioclimatic data inputs, and including soil variables. We then compared geospatial predictions from five species distribution modeling frameworks. In contrast to previous work, we additionally assessed whether the R. montanensis positive D. variabilis distribution is nested within a larger overall D. variabilis distribution, representing a fitness cost hypothesis. We found that 1) adding soil layers improved the accuracy of the MaxEnt model; 2) the predicted 'infected niche' was smaller than the overall predicted niche across all models; and 3) each model predicted different sizes of suitable niche, at different levels of probability. Importantly, the models were not directly comparable in output style, which could create confusion in interpretation when developing planning tools. The random forest (RF) model had the best measured validity and fit, suggesting it may be most appropriate to these data.

Evidence for Range Expansion and Origins of an Invasive Hornet Vespa bicolor (Hymenoptera, Vespidae) in Taiwan, with Notes on Its Natural Status

Simple Summary

The invasive hornet Vespa bicolor Fabricius was first discovered in Taiwan in 2003 and was not confirmed to have been established until 2014. This study was conducted in order to (1) assess the current status of V. bicolor abundance, dispersal, seasonality, and possible impact on honeybee (Apis mellifera Linnaeus) in Taiwan; (2) and to trace the origins of Taiwan’s V. bicolor population. To assess V. bicolor abundance, we used visual surveys, sweep netting, and hornet traps in four known ranges in northern and central Taiwan from 2016 to 2020. Additionally, to understand V. bicolor dispersion, we mapped environmental data using ArcGIS, and to predict future V. bicolor range, we used ecological niche modeling. The results show that V. bicolor has stable populations in three areas in northern and central Taiwan, and mainly preys on Apis mellifera. Our analyses suggest samples from Southeastern China as having the closest relation in DNA sequences with Taiwan’s V. bicolor population. Due to the negative economic and ecological impacts of V. bicolor in Taiwan, our findings shed light on the value of monitoring and controlling its populations, rather than working exclusively towards elimination.

Abstract

The invasive alien species (IAS) Vespa bicolor is the first reported hornet that has established in Taiwan and is concerning as they prey on honeybee Apis mellifera, which leads to colony losses and public concerns. Thus, the aim of this study was to assess the current status of V. bicolor abundance, dispersal, and impact and to trace the origins of Taiwan’s V. bicolor population. Our studies took place in five areas in northern to central Taiwan. We used mtDNA in the phylogenetic analyses. Field survey and ecological niche modeling (ENM) were used to understand the origins and current range of the invasive species. Two main subgroups of V. bicolor in the phylogenetic tree were found, and a clade with short branch lengths in Southeastern China and Taiwan formed a subgroup, which shows that the Taiwan population may have invaded from a single event. Evidence shows that V. bicolor is not a severe pest to honeybees in the study area; however, using ENM, we predict the rapid dispersion of this species to the cooler and hilly mountain areas of Taiwan. The management of V. bicolor should also involve considering it a local pest to reduce loss by beekeepers and public fear in Taiwan. Our findings highlight how the government, beekeepers, and researchers alike should be aware of the implications of V. bicolor’s rapid range expansion in Taiwan, or in other countries.

Convolutional neural networks improve species distribution modelling by capturing the spatial structure of the environment

Convolutional Neural Networks (CNNs) are statistical models suited for learning complex visual patterns. In the context of Species Distribution Models (SDM) and in line with predictions of landscape ecology and island biogeography, CNN could grasp how local landscape structure affects prediction of species occurrence in SDMs. The prediction can thus reflect the signatures of entangled ecological processes. Although previous machine-learning based SDMs can learn complex influences of environmental predictors, they cannot acknowledge the influence of environmental structure in local landscapes (hence denoted "punctual models"). In this study, we applied CNNs to a large dataset of plant occurrences in France (GBIF), on a large taxonomical scale, to predict ranked relative probability of species (by joint learning) to any geographical position. We examined the way local environmental landscapes improve prediction by performing alternative CNN models deprived of information on landscape heterogeneity and structure ("ablation experiments"). We found that the landscape structure around location crucially contributed to improve predictive performance of CNN-SDMs. CNN models can classify the predicted distributions of many species, as other joint modelling approaches, but they further prove efficient in identifying the influence of local environmental landscapes. CNN can then represent signatures of spatially structured environmental drivers. The prediction gain is noticeable for rare species, which open promising perspectives for biodiversity monitoring and conservation strategies. Therefore, the approach is of both theoretical and practical interest. We discuss the way to test hypotheses on the patterns learnt by CNN, which should be essential for further interpretation of the ecological processes at play.

The role of heterogenous environmental conditions in shaping the spatiotemporal distribution of competing Aedes mosquitoes in Panama: implications for the landscape of arboviral disease transmission

Monitoring the invasion process of the Asian tiger mosquito Aedes albopictus and its interaction with the contender Aedes aegypti, is critical to prevent and control the arthropod-borne viruses (i.e., Arboviruses) they transmit to humans. Generally, the superior ecological competitor Ae. albopictus displaces Ae. aegypti from most geographic areas, with the combining factors of biology and environment influencing the competitive outcome. Nonetheless, detailed studies asserting displacement come largely from sub-tropical areas, with relatively less effort being made in tropical environments, including no comprehensive research about Aedes biological interactions in Mesoamerica. Here, we examine contemporary and historical mosquito surveillance data to assess the role of shifting abiotic conditions in shaping the spatiotemporal distribution of competing Aedes species in the Republic of Panama. In accordance with prior studies, we show that Ae. albopictus has displaced Ae. aegypti under suboptimal wet tropical climate conditions and more vegetated environments within the southwestern Azuero Peninsula. Conversely, in the eastern Azuero Peninsula, Ae. aegypti persists with Ae. albopictus under optimal niche conditions in a dry and more seasonal tropical climate. While species displacement was stable over the course of two years, the presence of both species generally appears to fluctuate in tandem in areas of coexistence. Aedes albopictus was always more frequently found and abundant regardless of location and climatic season. The heterogenous environmental conditions of Panama shape the competitive outcome and micro-geographic distribution of Aedes mosquitoes, with potential consequences for the transmission dynamics of urban and sylvatic zoonotic diseases.

SUPPLEMENTARY INFORMATION

The online version of this article (10.1007/s10530-021-02482-y).

Spotting intruders: Species distribution models for managing invasive intertidal macroalgae

Invasive macroalgae represent one of the major threats to marine biodiversity, ecosystem functioning and structure, as well as being important drivers of ecosystem services depletion. Many such species have become well established along the west coast of the Iberian Peninsula. However, the lack of information about the distribution of the invaders and the factors determining their occurrence make bioinvasions a difficult issue to manage. Such information is key to enabling the design and implementation of effective management plans. The present study aimed to map the current probability of presence of six invasive macroalgae: Grateloupia turuturu, Asparagopsis armata, Colpomenia peregrina, Sargassum muticum, Undaria pinnatifida, and Codium fragile ssp. fragile. For this purpose, an extensive field survey was carried out along the coast of the north-western Iberian Peninsula. Species distribution models (SDMs) were then used to map the presence probability of these invasive species throughout the study region on the basis of environmental and anthropogenic predictor variables. The southern Galician rias were identified as the main hotspots of macroalgal invasion, with a high probability of occurrence for most of the species considered. Conversely, the probability of presence on the Portuguese coast was generally low. Physico-chemical variables were the most important factors for predicting the distribution of invasive macroalgae contributing between 57.27 and 85.24% to the ensemble models. However, anthropogenic factors (including size of vessels, number of shipping lines, distance from ports, population density, etc.) considerably improved the estimates of the probability of occurrence for most of the target species. This study is one of the few to include anthropogenic factors in SDMs for invasive macroalgae. The findings suggest that management actions aimed at controlling these species should strengthen control and surveillance at ports, particularly in southern Galician rias. Early detection should be of main concern for risk assessment plans on the Portuguese coast.

Scoping review of distribution models for selected Amblyomma ticks and rickettsial group pathogens

The rising prevalence of tick-borne diseases in humans in recent decades has called attention to the need for more information on geographic risk for public health planning. Species distribution models (SDMs) are an increasingly utilized method of constructing potential geographic ranges. There are many knowledge gaps in our understanding of risk of exposure to tick-borne pathogens, particularly for those in the rickettsial group. Here, we conducted a systematic scoping review of the SDM literature for rickettsial pathogens and tick vectors in the genus Amblyomma. Of the 174 reviewed articles, only 24 studies used SDMs to estimate the potential extent of vector and/or pathogen ranges. The majority of studies (79%) estimated only tick distributions using vector presence as a proxy for pathogen exposure. Studies were conducted at different scales and across multiple continents. Few studies undertook original data collection, and SDMs were mostly built with presence-only datasets from public database or surveillance sources. The reliance on existing data sources, using ticks as a proxy for disease risk, may simply reflect a lag in new data acquisition and a thorough understanding of the tick-pathogen ecology involved.

European cephalopods distribution under climate-change scenarios

In a context of increasing anthropogenic pressure, projecting species potential distributional shifts is of major importance for the sustainable exploitation of marine species. Despite their major economical (i.e. important fisheries) and ecological (i.e. central position in food-webs) importance, cephalopods literature rarely addresses an explicit understanding of their current distribution and the potential effect that climate change may induce in the following decades. In this study, we focus on three largely harvested and common cephalopod species in Europe: Octopus vulgaris, Sepia officinalis and Loligo vulgaris. Using a recently improved species ensemble modelling framework coupled with five atmosphere-ocean general circulation models, we modelled their contemporary and potential future distributional range over the twenty-first century. Independently of global warming scenarios, we observed a decreasing in the suitability of environmental conditions in the Mediterranean Sea and the Bay of Biscay. Conversely, we projected a rapidly increasing environmental suitability in the North, Norwegian and Baltic Seas for all species. This study is a first broad scale assessment and identification of the geographical areas, fisheries and ecosystems impacted by climate-induced changes in cephalopods distributional range.

Greater topoclimatic control of above- versus below-ground communities

Assessing the degree to which climate explains the spatial distributions of different taxonomic and functional groups is essential for anticipating the effects of climate change on ecosystems. Most effort so far has focused on above-ground organisms, which offer only a partial view on the response of biodiversity to environmental gradients. Here including both above- and below-ground organisms, we quantified the degree of topoclimatic control on the occurrence patterns of >1,500 taxa and phylotypes along a c. 3,000 m elevation gradient, by fitting species distribution models. Higher model performances for animals and plants than for soil microbes (fungi, bacteria and protists) suggest that the direct influence of topoclimate is stronger on above-ground species than on below-ground microorganisms. Accordingly, direct climate change effects are predicted to be stronger for above-ground than for below-ground taxa, whereas factors expressing local soil microclimate and geochemistry are likely more important to explain and forecast the occurrence patterns of soil microbiota. Detailed mapping and future scenarios of soil microclimate and microhabitats, together with comparative studies of interacting and ecologically dependent above- and below-ground biota, are thus needed to understand and realistically forecast the future distribution of ecosystems.

Combining Deforestation and Species Distribution Models to Improve Measures of Chimpanzee Conservation Impacts of REDD: A Case Study from Ntakata Mountains, Western Tanzania

Projects to reduce emissions from deforestation and degradation (REDD) are designed to reduce carbon emissions through avoided deforestation and degradation, and in many cases, to produce additional community and biodiversity conservation co-benefits. While these co-benefits can be significant, quantifying conservation impacts has been challenging, and most projects use simple species presence to demonstrate positive biodiversity impact. Some of the same tools applied in the quantification of climate mitigation benefits have relevance and potential application to estimating co-benefits for biodiversity conservation. In western Tanzania, most chimpanzees live outside of national park boundaries, and thus face threats from human activity, including competition for suitable habitat. Through a case study of the Ntakata Mountains REDD project in western Tanzania, we demonstrate a combined application of deforestation modelling with species distribution models to assess forest conservation benefits in terms of avoided carbon emissions and improved chimpanzee habitat. The application of such tools is a novel approach that we argue permits the better design of future REDD projects for biodiversity co-benefits. This approach also enables project developers to produce the more manageable, accurate and cost-effective monitoring, reporting and verification of project impacts that are critical to verification under carbon standards.

Explicit integration of dispersal-related metrics improves predictions of SDM in predatory arthropods

Fishing spiders (Dolomedes spp.) make an interesting model to predict the impact of global changes because they are generalist, opportunistic predators, whose distribution is driven mostly by abiotic factors. Yet, the two European species are expected to react differently to forthcoming environmental changes, because of habitat specialization and initial range. We used an original combination of habitat and dispersal data to revisit these predictions under various climatic scenarios. We used the future range of suitable habitat, predicted with habitat variables only, as a base layer to further predict the range or reachable habitat by accounting for both dispersal ability and landscape connectivity. Our results confirm the northward shift in range and indicate that the area of co-occurrences should also increase. However, reachable habitat should expand less than suitable habitat, especially when accounting for landscape connectivity. In addition, the potential range expansion was further limited for the red-listed D. plantarius, which is more of a habitat specialist and has a lower ability to disperse. This study highlights the importance of looking beyond habitat variables to produce more accurate predictions for the future of arthropods populations.

Predictive ability of a process-based versus a correlative species distribution model

Species distribution modeling is a widely used tool in many branches of ecology and evolution. Evaluations of the transferability of species distribution models-their ability to predict the distribution of species in independent data domains-are, however, rare. In this study, we contrast the transferability of a process-based and a correlative species distribution model. Our case study uses 664 Australian eucalypt and acacia species. We estimate models for these species using data from their native Australia and then assess whether these models can predict the adventive range of these species. We find that the correlative model-MaxEnt-has a superior ability to describe the data in the training data domain (Australia) and that the process-based model-TTR-SDM-has a superior ability to predict the distribution of the study species outside of Australia. The implication of this analysis, that process-based models may be more appropriate than correlative models when making projections outside of the domain of the training data, needs to be tested in other case studies.

Species range shifts in response to climate change and human pressure for the world's largest amphibian

The Chinese giant salamander, Andrias davidianus, the world's largest amphibian, is critically endangered and has an extremely unique evolutionary history. Therefore, this species represents a global conservation priority and will be impacted by future climate and human pressures. Understanding the range and response to environmental change of this species is a priority for the identification of targeted conservation activities. We projected future range shifts of the Chinese giant salamander under the independent and combined impacts of climate change and human population density (HPD) variations by using ensemble species distribution models. We further evaluated the sustainability of existing nature reserves and identified priority areas for the mitigation or prevention of such pressures. Both climate change and increasing HPD tended to reduce the species range, with the latter leading to greater range losses and fragmentation of the range. Notably, 65.6%, 18.0% and 18.4% of the range loss were attributed solely to HPD change, solely to climate change and to their overlapping impacts, respectively. Overall, the average total and net losses of the species range were 52.5% and 23.4%, respectively, and HPD and climate changes were responsible for 71.4% and 28.6% of the net losses, respectively. We investigated the stability of the remaining species range and found that half of the nature reserves are likely vulnerable, with 57.1% and 66.7% of them likely to lose their conservation value in 2050 and 2070, respectively. To effectively protect this salamander, conservation policies should address both pressures simultaneously, especially considering the negative impact of human pressures in both contemporary periods and the near future. The species range shifts over space and time projected by this research could help guide long-term surveys and the sustainable conservation of wild habitats and populations of this ancient and endangered amphibian.

What and where? Predicting invasion hotspots in the Arctic marine realm

The risk of aquatic invasions in the Arctic is expected to increase with climate warming, greater shipping activity and resource exploitation in the region. Planktonic and benthic marine aquatic invasive species (AIS) with the greatest potential for invasion and impact in the Canadian Arctic were identified and the 23 riskiest species were modelled to predict their potential spatial distributions at pan-Arctic and global scales. Modelling was conducted under present environmental conditions and two intermediate future (2050 and 2100) global warming scenarios. Invasion hotspots-regions of the Arctic where habitat is predicted to be suitable for a high number of potential AIS-were located in Hudson Bay, Northern Grand Banks/Labrador, Chukchi/Eastern Bering seas and Barents/White seas, suggesting that these regions could be more vulnerable to invasions. Globally, both benthic and planktonic organisms showed a future poleward shift in suitable habitat. At a pan-Arctic scale, all organisms showed suitable habitat gains under future conditions. However, at the global scale, habitat loss was predicted in more tropical regions for some taxa, particularly most planktonic species. Results from the present study can help prioritize management efforts in the face of climate change in the Arctic marine ecosystem. Moreover, this particular approach provides information to identify present and future high-risk areas for AIS in response to global warming.

Potential Impact of Climate Change on the Forest Coverage and the Spatial Distribution of 19 Key Forest Tree Species in Italy under RCP4.5 IPCC Trajectory for 2050s

Forests provide a range of ecosystem services essential for human wellbeing. In a changing climate, forest management is expected to play a fundamental role by preserving the functioning of forest ecosystems and enhancing the adaptive processes. Understanding and quantifying the future forest coverage in view of climate changes is therefore crucial in order to develop appropriate forest management strategies. However, the potential impacts of climate change on forest ecosystems remain largely unknown due to the uncertainties lying behind the future prediction of models. To fill this knowledge gap, here we aim to provide an uncertainty assessment of the potential impact of climate change on the forest coverage in Italy using species distribution modelling technique. The spatial distribution of 19 forest tree species in the country was extracted from the last national forest inventory and modelled using nine Species Distribution Models algorithms, six different Global Circulation Models (GCMs), and one Regional Climate Models (RCMs) for 2050s under an intermediate forcing scenario (RCP 4.5). The single species predictions were then compared and used to build a future forest cover map for the country. Overall, no sensible variation in the spatial distribution of the total forested area was predicted with compensatory effects in forest coverage of different tree species, whose magnitude and patters appear largely modulated by the driving climate models. The analyses reported an unchanged amount of total land suitability to forest growth in mountain areas while smaller values were predicted for valleys and floodplains than high-elevation areas. Pure woods were predicted as the most influenced when compared with mixed stands which are characterized by a greater species richness and, therefore, a supposed higher level of biodiversity and resilience to climate change threatens. Pure softwood stands along the Apennines chain in central Italy (e.g., Pinus, Abies) were more sensitive than hardwoods (e.g., Fagus, Quercus) and generally characterized by pure and even-aged planted forests, much further away from their natural structure where admixture with other tree species is more likely. In this context a sustainable forest management strategy may reduce the potential impact of climate change on forest ecosystems. Silvicultural practices should be aimed at increasing the species richness and favoring hardwoods currently growing as dominating species under conifers canopy, stimulating the natural regeneration, gene flow, and supporting (spatial) migration processes.

Ensemble Modelling of Skipjack Tuna (Katsuwonus pelamis) Habitats in the Western North Pacific Using Satellite Remotely Sensed Data; a Comparative Analysis Using Machine-Learning Models

To examine skipjack tuna’s habitat utilization in the western North Pacific (WNP) we used an ensemble modelling approach, which applied a fisher- derived presence-only dataset and three satellite remote-sensing predictor variables. The skipjack tuna data were compiled from daily point fishing data into monthly composites and re-gridded into a quarter degree resolution to match the environmental predictor variables, the sea surface temperature (SST), sea surface chlorophyll-a (SSC) and sea surface height anomalies (SSHA), which were also processed at quarter degree spatial resolution. Using the sdm package operated in RStudio software, we constructed habitat models over a 9-month period, from March to November 2004, using 17 algorithms, with a 70:30 split of training and test data, with bootstrapping and 10 runs as parameter settings for our models. Model performance evaluation was conducted using the area under the curve (AUC) of the receiver operating characteristic (ROC), the point biserial correlation coefficient (COR), the true skill statistic (TSS) and Cohen’s kappa (k) metrics. We analyzed the response curves for each predictor variable per algorithm, the variable importance information and the ROC plots. Ensemble predictions of habitats were weighted with the TSS metric. Model performance varied across various algorithms, with the Support Vector Machines (SVM), Boosted Regression Trees (BRT), Random Forests (RF), Multivariate Adaptive Regression Splines (MARS), Generalized Additive Models (GAM), Classification and Regression Trees (CART), Multi-Layer Perceptron (MLP), Recursive Partitioning and Regression Trees (RPART), and Maximum Entropy (MAXENT), showing consistently high performance than other algorithms, while the Flexible Discriminant Analysis (FDA), Mixture Discriminant Analysis (MDA), Bioclim (BIOC), Domain (DOM), Maxlike (MAXL), Mahalanobis Distance (MAHA) and Radial Basis Function (RBF) had lower performance. We found inter-algorithm variations in predictor variable responses. We conclude that the multi-algorithm modelling approach enabled us to assess the variability in algorithm performance, hence a data driven basis for building the ensemble model. Given the inter-algorithm variations observed, the ensemble prediction maps indicated a better habitat utilization map of skipjack tuna than would have been achieved by a single algorithm.

Improving prediction of rare species' distribution from community data

Species distribution models (SDMs) have been increasingly used to predict the geographic distribution of a wide range of organisms; however, relatively fewer research efforts have concentrated on rare species despite their critical roles in biological conservation. The present study tested whether community data may improve modelling rare species by sharing information among common and rare ones. We chose six SDMs that treat community data in different ways, including two traditional single-species models (random forest and artificial neural network) and four joint species distribution models that incorporate species associations implicitly (multivariate random forest and multi-response artificial neural network) or explicitly (hierarchical modelling of species communities and generalized joint attribute model). In addition, we evaluated two approaches of data arrangement, species filtering and conditional prediction, to enhance the selected models. The model predictions were tested using cross validation based on empirical data collected from marine fisheries surveys, and the effects of community data were evaluated by comparing models for six selected rare species. The results demonstrated that the community data improved the predictions of rare species' distributions to certain extent but might also be unhelpful in some cases. The rare species could be appropriately predicted in terms of occurrence, whereas their abundance tended to be underestimated by most models. Species filtering and conditional predictions substantially benefited the predictive performances of multiple- and single-species models, respectively. We conclude that both the modelling algorithms and community data need to be carefully selected in order to deliver improvement in modelling rare species. The study highlights the opportunity and challenges to improve prediction of rare species' distribution by making the most of community data.

From Nucleotides to Satellite Imagery: Approaches to Identify and Manage the Invasive Pathogen Xylella fastidiosa and Its Insect Vectors in Europe

Biological invasions represent some of the most severe threats to local communities and ecosystems. Among invasive species, the vector-borne pathogen Xylella fastidiosa is responsible for a wide variety of plant diseases and has profound environmental, social and economic impacts. Once restricted to the Americas, it has recently invaded Europe, where multiple dramatic outbreaks have highlighted critical challenges for its management. Here, we review the most recent advances on the identification, distribution and management of X. fastidiosa and its insect vectors in Europe through genetic and spatial ecology methodologies. We underline the most important theoretical and technological gaps that remain to be bridged. Challenges and future research directions are discussed in the light of improving our understanding of this invasive species, its vectors and host–pathogen interactions. We highlight the need of including different, complimentary outlooks in integrated frameworks to substantially improve our knowledge on invasive processes and optimize resources allocation. We provide an overview of genetic, spatial ecology and integrated approaches that will aid successful and sustainable management of one of the most dangerous threats to European agriculture and ecosystems.

State of the Art in Paleoenvironment Mapping for Modeling Applications in Archeology—Summary, Conclusions, and Future Directions from the PaleoMaps Workshop

In this report, we present the contributions, outcomes, ideas, discussions and conclusions obtained at the PaleoMaps Workshop 2019, that took place at the Institute of Geography of the University of Cologne on 23 and 24 September 2019. The twofold aim of the workshop was: (1) to provide an overview of approaches and methods that are presently used to incorporate paleoenvironmental information in human–environment interaction modeling applications, and building thereon; (2) to devise new approaches and solutions that might be used to enhance the reconstruction of past human–environmental interconnections. This report first outlines the presented papers, and then provides a joint protocol of the often extensive discussions that came up following the presentations or else during the refreshment intervals. It concludes by adressing the open points to be resolved in future research avenues, e.g., implementation of open science practices, new procedures for reviewing of publications, and future concepts for quality assurance of the often complex paleoenvironmental data. This report may serve as an overview of the state of the art in paleoenvironment mapping and modeling. It includes an extensive compilation of the basic literature, as provided by the workshop attendants, which will itself facilitate the necessary future research.

Modelling Distributions of Rove Beetles in Mountainous Areas Using Remote Sensing Data

Mountain ecosystems are biodiversity hotspots that are increasingly threatened by climate and land use/land cover changes. Long-term biodiversity monitoring programs provide unique insights into resulting adverse impacts on plant and animal species distribution. Species distribution models (SDMs) in combination with satellite remote sensing (SRS) data offer the opportunity to analyze shifts of species distributions in response to these changes in a spatially explicit way. Here, we predicted the presence probability of three different rove beetles in a mountainous protected area (Gran Paradiso National Park, GPNP) using environmental variables derived from Landsat and Aster Global Digital Elevation Model data and an ensemble modelling approach based on five different model algorithms (maximum entropy, random forest, generalized boosting models, generalized additive models, and generalized linear models). The objectives of the study were (1) to evaluate the potential of SRS data for predicting the presence of species dependent on local-scale environmental parameters at two different time periods, (2) to analyze shifts in species distributions between the years, and (3) to identify the most important species-specific SRS predictor variables. All ensemble models showed area under curve (AUC) of the receiver operating characteristics values above 0.7 and true skills statistics (TSS) values above 0.4, highlighting the great potential of SRS data. While only a small proportion of the total area was predicted as highly suitable for each species, our results suggest an increase of suitable habitat over time for the species Platydracus stercorarius and Ocypus ophthalmicus, and an opposite trend for Dinothenarus fossor. Vegetation cover was the most important predictor variable in the majority of the SDMs across all three study species. To better account for intra- and inter-annual variability of population dynamics as well as environmental conditions, a continuation of the monitoring program in GPNP as well as the employment of SRS with higher spatial and temporal resolution is recommended.