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PREMISE OF THE STUDY: Crop wild relatives (CWR) provide important traits for plant breeding, including pest, pathogen, and abiotic stress resistance. Therefore, their conservation and future availability are essential for food security. Despite this need, the world's genebanks are currently thought to conserve only a small fraction of the total diversity of CWR. METHODS: We define the eggplant genepool using the results of recent taxonomic and phylogenetic studies. We identify the gaps in germplasm accessions for menting species distribution models (SDM). Preliminary conservation assessments using IUCN criteria were done for all species and were combined with the gap analysis to pinpoint where under-collected and threatened CWR species coincide with high human disturbance and occur outside of protected areas. KEY RESULTS: We show that many eggplant CWR are poorly represented in genebanks compared to their native ranges. Priority areas for future collecting are concentrated in Africa, especially along the Kenya-Tanzania border. Fourteen species of eggplant CWR are assessed as threatened or near-threatened; these are also concentrated in eastern Africa. CONCLUSIONS: The knowledge base upon which conservation of wild relative germplasm depends must take into account both taxonomie and phylogenetic advances. Beyond traditional research focus on close relatives of crops, we emphasize the benefits of defining a broad CWR genepool, and the importance of assessing threats to wild species when targeting localities for future collection of CWR to improve crop breeding in the face of environmental change.

The location and timing of domestication of the olive tree, a key crop in Early Mediterranean societies, remain hotly debated. Here, we unravel the history of wild olives (oleasters), and then infer the primary origins of the domesticated olive. Phylogeography and Bayesian molecular dating analyses based on plastid genome profiling of 1263 oleasters and 534 cultivated genotypes reveal three main lineages of pre-Quaternary origin. Regional hotspots of plastid diversity, species distribution modelling and macrofossils support the existence of three long-term refugia; namely the Near East (including Cyprus), the Aegean area and the Strait of Gibraltar. These ancestral wild gene pools have provided the essential foundations for cultivated olive breeding. Comparison of the geographical pattern of plastid diversity between wild and cultivated olives indicates the cradle of first domestication in the northern Levant followed by dispersals across the Mediterranean basin in parallel with the expansion of civilizations and human exchanges in this part of the world.

Traditional species distribution modelling relies on the links between species and their environments, but often such information is unavailable or unreliable. The objective of our research is to take a machine learning (ML) approach to estimate ant species richness in data-poor countries based on published data on the broader distribution of described ant species. ML is a novel black box method that does not consider functional links between species and their environment. Its prediction accuracy is limited only by the quality and quantity of species records data. ML modelling is applied to calculate the global distribution of ant species richness and achieves 71.78% (decision tree), 70.62% (random forest), 71.09% (logistic regression), and 75.18% (neural network) testing accuracy. The results show that in some West African countries, the species predicted by ML are 1.99 times as many as the species currently recorded. These West African countries have many ant species but lack observational data, and policymakers may be overlooking areas that require protection.

Coffee is one of the most traded commodities in the world. It plays a significant role in the global economy, employing over 125 million people. However, it is possible that this vital crop is threatened by changing climate conditions and fungal infections. This paper reviews how suitable areas for coffee cultivation and the toxigenic fungi species of Aspergillus, Penicillium, and Fusarium will be affected due to climate change. By combining climate models with species distribution models, a number of studies have investigated the future distribution of coffee cultivation. Studies predict that suitable coffee cultivation area could drop by ~50% under representation concentration pathway (RCP) 6.0 by 2050 for both Arabica and Robusta. These findings agree with other studies which also see an altitudinal migration of suitable cultivation areas to cooler regions, but limited scope for latitudinal migration owing to coffee’s inability to tolerate seasonal temperature changes. Increased temperatures will see an overall increase in mycotoxin production such as aflatoxins, particularly in mycotoxigenic fungi (e.g., Aspergillus flavus) more suited to higher temperatures. Arabica and Robusta’s limited ability to relocate means both species will be grown in less suitable climates, increasing plant stress and making coffee more susceptible to fungal infection and mycotoxins. Information regarding climate change parameters with respect to mycotoxin concentrations in real coffee samples is provided and how the changed climate affects mycotoxins in non-coffee systems is discussed. In a few areas where relocating farms is possible, mycotoxin contamination may decrease due to the “parasites lost” phenomenon. More research is needed to include the effect of mycotoxins on coffee under various climate change scenarios, as currently there is a significant knowledge gap, and only generalisations can be made. Future modelling of coffee cultivation, which includes the influence of atmospheric carbon dioxide fertilisation and forest management, is also required; however, all indications show that climate change will have an extremely negative effect on future coffee production worldwide in terms of both a loss of suitable cultivation areas and an increase in mycotoxin contamination.

In modeling species distributions and population dynamics, spatially‐interpolated climatic data are often used as proxies for real, on‐the‐ground measurements. For shallow freshwater systems, this practice may be problematic as interpolations used for surface waters are generated from terrestrial sensor networks measuring air temperatures. Using these may therefore bias statistical estimates of species' environmental tolerances or population projections – particularly among pleustonic and epilimnetic organisms. Using a global database of millions of daily satellite‐derived lake surface water temperatures (LSWT), I trained machine learning models to correct for the correspondence between air and LSWT as a function of atmospheric and topographic predictors, resulting in the creation of monthly high‐resolution global maps of air‐LSWT offsets, corresponding uncertainty measures and derived LSWT‐based bioclimatic layers for use by the scientific community. I then compared the performance of these LSWT layers and air temperature‐based layers in population dynamic and ecological niche models (ENM). While generally high, the correspondence between air temperature and LSWT was quite variable and often nonlinear depending on the spatial context. These LSWT predictions were better able to capture the modeled population dynamics and geographic distributions of two common aquatic plant species. Further, ENM models trained with LSWT predictors more accurately captured lab‐measured thermal response curves. I conclude that these predicted LSWT temperatures perform better than raw air temperatures when used for population projections and environmental niche modeling, and should be used by practitioners to derive more biologically‐meaningful results. These global LSWT predictions and corresponding error estimates and bioclimatic layers have been made freely available to all researchers in a permanent archive.

Aim: In this paper, we investigate the role of climate and topography in shaping the distribution of Neanderthals (Homo neanderthalensis) at different spatial scales. To this end, we compiled the most comprehensive data set on the distribution of this species during the Last Interglacial optimum (MIS 5e) available to date. This was used to calibrate a palaeo-species distribution model, and analyse variable importance at continental and local scales. Location: Europe and Irano-Turanian region (20° N to 70° N, 10° W to 70° E). Methods: We used archaeological records and palaeoclimatic and topographic predictors to calibrate a model based on an ensemble of generalized linear models fitted with different combinations of predictors and weighted background data. Area under the curve scores computed by leave-one-out were used to assess variable importance at the continental scale, while local regression combined with recursive partition trees was used to assess variable importance at the local scale. Results: Annual rainfall and winter temperatures were the most important predictors at the continental scale, while topography and summer rainfall defined habitat suitability at the local scale. The highest habitat suitability scores were observed along the Mediterranean coastlines. Mountain ranges and continental plains showed low habitat suitability values. Main conclusions: The model results confirmed that abiotic drivers played an important role in shaping Neanderthals distribution during the Last Interglacial. The high suitability of the Mediterranean coastlines and the low suitability values of most sites at the northern and eastern distribution limits (Germany, Hungary, Ukraine) challenge the notion of Neanderthals as a species with preference for colder environments.

Aim To test statistical models used to predict species distributions under different shapes of occurrence-environment relationship. We addressed three questions: (1) Is there a statistical technique that has a consistently higher predictive ability than others for all kinds of relationships? (2) How does species prevalence influence the relative performance of models? (3) When an automated stepwise selection procedure is used, does it improve predictive modelling, and are the relevant variables being selected? Location We used environmental data from a real landscape, the state of California, and simulated species distributions within this landscape. Methods Eighteen artificial species were generated, which varied in their occurrence response to the environmental gradients considered (random, linear, Gaussian, threshold or mixed), in the interaction of those factors (no interaction vs. multiplicative), and on their prevalence (50% vs. 5%). The landscape was then randomly sampled with a large (n = 2000) or small (n = 150) sample size, and the predictive ability of each statistical approach was assessed by comparing the true and predicted distributions using five different indexes of performance (area under the receiver-operator characteristic curve, Kappa, correlation between true and predictive probability of occurrence, sensitivity and specificity). We compared generalized additive models (GAM) with and without flexible degrees of freedom, logistic regressions (general linear models, GLM) with and without variable selection, classification trees, and the genetic algorithm for rule-set production (GARP). Results Species with threshold and mixed responses, additive environmental effects, and high prevalence generated better predictions than did other species for all statistical models. In general, GAM outperforms all other strategies, although differences with GLM are usually not significant. The two variable-selection strategies presented here did not discriminate successfully between truly causal factors and correlated environmental variables. Main conclusions Based on our analyses, we recommend the use of GAM or GLM over classification trees or GARP, and the specification of any suspected interaction terms between predictors. An expert-based variable selection procedure was preferable to the automated procedures used here. Finally, for low-prevalence species, variability in model performance is both very high and sample-dependent. This suggests that distribution models for species with low prevalence can be improved through targeted sampling.

Aim: We explore the utility of newly available optical and microwave remote sensing data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and QuikSCAT (QSCAT) instruments for species distribution modelling at regional to continental scales. Using eight Neotropical species from three taxonomic groups, we assess the extent to which remote sensing data can improve predictions of their geographic distributions. For two bird species, we investigate the specific contributions of different types of remote sensing variables to the predictions and model accuracy at the regional scale, where the benefits of the MODIS and QSCAT satellite data are expected to be most significant. Location: South America, with a focus on the tropical and subtropical Andes and the Amazon Basin. Methods: Potential geographic distributions of eight species, namely two birds, two mammals and four trees, were modelled with the MAXENT algorithm at 1-km resolution over the South American continent using climatic and remote sensing data separately and combined. For each species and model scenario, we assess model performance by testing the agreement between observed and simulated distributions across all thresholds and, in the case of the two focal bird species, at selected thresholds. Results: Quantitative performance tests showed that models built with remote sensing and climatic layers in isolation performed well in predicting species distributions, suggesting that each of these data sets contains useful information. However, predictions created with a combination of remote sensing and climatic layers generally resulted in the best model performance across the three taxonomic groups. In Ecuador, the inclusion of remote sensing data was critical in resolving the known geographically isolated populations of the two focal bird species along the steep Amazonian-Andean elevational gradients. Within remote sensing subsets, microwave-based data were more important than optical data in the predictions of the two bird species. Main conclusions: Our results suggest that the newly available remote sensing data (MODIS and QSCAT) have considerably utility in modelling the contemporary geographical distributions of species at both regional and continental scales and in predicting range shifts as a result of large-scale land-use change.

The environment, the on-going global climate change and the ecology of animal species determine the localisation of habitats and the geographical distribution of the various species in nature. The aim of this study was to explore the effects of such changes on snail species not only of interest to naturalists but also of importance to human and animal health. The spatial distribution of freshwater snail intermediate hosts involved in the transmission of schistosomiasis, fascioliasis and paramphistomiasis (i.e. Bulinus globosus, Biomphalaria pfeifferi and Lymnaea natalensis) were modelled by the use of a maximum entropy algorithm (Maxent). Two snail observation datasets from Zimbabwe, from 1988 and 2012, were compared in terms of geospatial distribution and potential distributional change over this 24-year period investigated. Climate data, from the two years were identified and used in a species distribution modelling framework to produce maps of predicted suitable snail habitats. Having both climate- and snail observation data spaced 24 years in time represent a unique opportunity to evaluate biological response of snails to changes in climate variables. The study shows that snail habitat suitability is highly variable in Zimbabwe with foci mainly in the central Highveld but also in areas to the South and West. It is further demonstrated that the spatial distribution of suitable habitats changes with variation in the climatic conditions, and that this parallels that of the predicted climate change.

Conservation interventions for threatened species must be based on accurate assessments of the effects of anthropogenic pressures on habitat suitability. We used multiscale multivariable species-distribution modeling to evaluate habitat suitability for an Asian elephant (Elephas maximus) population in Shangyong Reserve, Yunnan Province, southwestern China. We investigated the scales at which measurements of environmental variables best reflected elephant habitat selection, and examined whether these responses changed over 2 decades (2000–2010 and 2011–2020) in response to 20 environmental variables, including 14 variables reflecting landscape fragmentation, the extent of buildings, and transport infrastructure. Elephant presence was sensitive to the scale of each variable, and the effects differed among variables within and between decades. More than half of the variables influenced elephant presence at coarse scales of 8 or 16 km, including 12 variables reflecting anthropogenic pressures in 2000–2010 and 10 in 2011–2020. Overall, multivariate models with variables at their optimal scales had higher discrimination than models at uniformly fine scales of 1 km or 2 km. The extent of suitable habitat for elephants declined by 24% over 2 decades. Less than half of elephant habitat was located within Shangyong Reserve (49% in 2000–2010, 40% in 2011–2020), indicating the importance of managing suitable habitat beyond reserve boundaries. Roads and buildings reduced the probability of elephant presence, with effects that extended beyond their immediate footprint. We advocate that infrastructure be planned with buffers, ≥8 km wide, between roads or buildings and core elephant habitat. Multiscale multivariable species-distribution modeling should be employed to ensure that all suitable habitat for the remaining fragmented elephant populations in Yunnan is identified, mapped, and protected.