Comparing sample bias correction methods for species distribution modeling using virtual species

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

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

Species' geographical range, environmental range and traits lead to specimen collection preference of dominant plant species of grasslands in Northern China

Many different factors, such as species traits, socio-economic factors, geographical and environmental factors, can lead to specimen collection preference. This study aims to determine whether grassland specimen collection in China is preferred by species traits (i.e., plant height, flowering and fruiting period), environmental range (i.e., the temperature and precipitation range) and geographical range (i.e., distribution range and altitudinal range). Ordinary least squares models and phylogenetic generalized linear mixed models were used to analyze the relationships between specimen number and the explanatory variables. Random Forest models were then used to find the most parsimonious multivariate model. The results showed that interannual variation in specimen number between 1900 and 2020 was considerable. Specimen number of these species in southeast China was notably lower than that in northwest China. Environmental range and geographical range of species had significant positive correlations with specimen number. In addition, there were relatively weak but significant associations between specimen number and species trait (i.e., plant height and flowering and fruiting period). Random Forest models indicated that distribution range was the most important variable, followed by flowering and fruiting period, and altitudinal range. These findings suggest that future floristic surveys should pay more attention to species with small geographical range, narrow environmental range, short plant height, and short flowering and fruiting period. The correction of specimen collection preference will also make the results of species distribution model, species evolution and other works based on specimen data more accurate.

Setting nutrient boundaries to protect aquatic communities: The importance of comparing observed and predicted classifications using measures derived from a confusion matrix

Defining nutrient thresholds that protect and support the ecological integrity of aquatic ecosystems is a fundamental step in maintaining their natural biodiversity and preserving their resilience. With increasing catchment pressures and climate change, it is more important than ever to develop clear methods to establish thresholds for status classification and management of waters. This must often be achieved using complex data and should be robust to interference from additional pressures as well as ameliorating or confounding conditions. We use both artificial and real data to examine challenges in setting nutrient thresholds in unbalanced and skewed data. We found significant advantages to using binary logistic regression over other techniques. However, one of the key challenges is objectively selecting a probability from which to derive the nutrient threshold. For this purpose, the examination of the proportions of matching and mismatching status classifications of nutrients and a biological quality element using a confusion matrix is a key step that should be more widely adopted in threshold selection. We examined a large array of statistical measures of classification accuracy and their performance over combinations of skewness and imbalance in the data. The most appropriate threshold probability is a compromise between maximising overall classification accuracy and reducing mismatches expressed as commission (false positives) without excessive omission (false negatives). An application to a lake type indicated total phosphorus thresholds that would be around 50 μg l-1 lower than the threshold achieved by an 'unguided' approach, indicating that this approach is a very significant development meriting attention from national authorities responsible for water management.

Loss of fungal symbionts and changes in pollinator availability caused by climate change will affect the distribution and survival chances of myco-heterotrophic orchid species

The first comprehensive species distribution models for orchid, its fungal symbionts and pollinator are presented. To evaluate impact of global warming on these organisms three different projections and four various climate change scenarios were analysed. The niche modelling was based on presence-only records of Limodorum abortivum, two species of Russula and three insects pollinating orchid (Anthophora affinis, Bombus terrestris, Rhodanthidium septemdentatum). Two sets of orchid predictions were examined-the first one included only climatic data and the second one was based on climate data and data on future distribution of orchid fungal symbionts. Overall, a poleward range shift is predicted to occur as a result of climate change and apparently global warming will be favorable for L. abortivum and its potential geographical range will expand. However, due to the negative effect of global warming on fungal symbionts of L. abortivum, the actual extension of the suitable niches of the orchid will be much limited. Considering future possibility of cross-pollination, the availability of A. affinis for L. abortivum will decrease and this bee will be available in the worst case scenarios only for 21% of orchid populations. On the other hand, the overlap of orchid and the buff-tailed bumblebee will increase and as much as 86.5% of plant populations will be located within B. terrestris potential range. Also the availability of R. septemdentatum will be higher than currently observed in almost all analysed climate change projections. This study showed the importance of inclusion of ecological factors in species distribution models as the climate data itself are not enough to estimate the future distribution of plant species. Moreover, the availability of pollen vectors which is crucial for long-term survival of orchid populations should be analysed in context of climate changes.

Projected Shifts in Bird Distribution in India under Climate Change

Global climate change is causing unprecedented impacts on biodiversity. In India, there is little information available regarding how climate change affects biodiversity at the taxon/group level, and large-scale ecological analyses have been lacking. In this study, we demonstrated the applicability of eBird and GBIF (Global Biodiversity Information Facility), and produced national-scale forecasts to examine the possible impacts of climate change on terrestrial avifauna in India. Using data collected by citizen scientists, we developed fine-tuned Species Distribution Models (SDMs) and predicted 1091 terrestrial bird species that would be distributed in India by 2070 on two climatic surfaces (RCP 4.5 and 8.5), using Maximum Entropy-based species distribution algorithms. Of the 1091 species modelled, our findings indicate that 66–73% of bird species in India will shift to higher elevations or shift northward, and 58–59% of bird species (RCP 4.5 and 8.5) would lose a portion of their distribution ranges. Furthermore, distribution ranges of 41–40% of bird species would increase. Under both RCP scenarios (RCP 4.5 and 8.5), bird species diversity will significantly increase in regions above 2500 m in elevation. Both RCP scenarios predict extensive changes in the species richness of the western Himalayas, Sikkim, northeast India, and the western Ghats regions by 2070. This study has resulted in novel, high-resolution maps of terrestrial bird species richness across India, and we predict predominantly northward shifts in species ranges, similar to predictions made for avifauna in other regions, such as Europe and the USA.

Sampling from commercial vessel routes can capture marine biodiversity distributions effectively

Collecting fine-scale occurrence data for marine species across large spatial scales is logistically challenging but is important to determine species distributions and for conservation planning. Inaccurate descriptions of species ranges could result in designating protected areas with inappropriate locations or boundaries. Optimizing sampling strategies therefore is a priority for scaling up survey approaches using tools such as environmental DNA (eDNA) to capture species distributions. In a marine context, commercial vessels, such as ferries, could provide sampling platforms allowing access to undersampled areas and repeatable sampling over time to track community changes. However, sample collection from commercial vessels could be biased and may not represent biological and environmental variability. Here, we evaluate whether sampling along Mediterranean ferry routes can yield unbiased biodiversity survey outcomes, based on perfect knowledge from a stacked species distribution model (SSDM) of marine megafauna derived from online data repositories. Simulations to allocate sampling point locations were carried out representing different sampling strategies (random vs regular), frames (ferry routes vs unconstrained), and number of sampling points. SSDMs were remade from different sampling simulations and compared with the "perfect knowledge" SSDM to quantify the bias associated with different sampling strategies. Ferry routes detected more species and were able to recover known patterns in species richness at smaller sample sizes better than unconstrained sampling points. However, to minimize potential bias, ferry routes should be chosen to cover the variability in species composition and its environmental predictors in the SSDMs. The workflow presented here can be used to design effective sampling strategies using commercial vessel routes globally for eDNA and other biodiversity survey techniques. This approach has potential to provide a cost-effective method to access remote oceanic areas on a regular basis and can recover meaningful data on spatiotemporal biodiversity patterns.

We need to talk about nonprobability samples

In most circumstances, probability sampling is the only way to ensure unbiased inference about population quantities where a complete census is not possible. As we enter the era of 'big data', however, nonprobability samples, whose sampling mechanisms are unknown, are undergoing a renaissance. We explain why the use of nonprobability samples can lead to spurious conclusions, and why seemingly large nonprobability samples can be (effectively) very small. We also review some recent controversies surrounding the use of nonprobability samples in biodiversity monitoring. These points notwithstanding, we argue that nonprobability samples can be useful, provided that their limitations are assessed, mitigated where possible and clearly communicated. Ecologists can learn much from other disciplines on each of these fronts.

Possible Effects of Climate Change on the Occurrence and Distribution of the Rare Moss Buxbaumia viridis in Serbia (SE Europe)

The distribution range and occurrence of the rare and threatened epixylic moss Buxbaumia viridis have been reviewed in Serbia. Climatic conditions of its recent distribution in Serbia were involved in species distribution modeling and analyzed with the aim of obtaining a projection of unknown potential sites and future scenarios of its distribution dynamics. The results achieved suggest potential distribution range of the species will be significantly reduced. According to the climate change models, the habitat changes including the range loss of this species are predicted to be drastic, i.e., between 93% and 97% by the year 2050, and between 98% and 99.9% by the year 2070, affecting primarily lower elevations of its current range in Serbia. A major reason for the projected decline of the species is climate change combined with continued poor forest management.

ROBITT: A tool for assessing the risk-of-bias in studies of temporal trends in ecology

Aggregated species occurrence and abundance data from disparate sources are increasingly accessible to ecologists for the analysis of temporal trends in biodiversity. However, sampling biases relevant to any given research question are often poorly explored and infrequently reported; this can undermine statistical inference. In other disciplines, it is common for researchers to complete 'risk-of-bias' assessments to expose and document the potential for biases to undermine conclusions. The huge growth in available data, and recent controversies surrounding their use to infer temporal trends, indicate that similar assessments are urgently needed in ecology.We introduce ROBITT, a structured tool for assessing the 'Risk-Of-Bias In studies of Temporal Trends in ecology'. ROBITT has a similar format to its counterparts in other disciplines: it comprises signalling questions designed to elicit information on the potential for bias in key study domains. In answering these, users will define study inferential goal(s) and relevant statistical target populations. This information is used to assess potential sampling biases across domains relevant to the research question (e.g. geography, taxonomy, environment), and how these vary through time. If assessments indicate biases, then users must clearly describe them and/or explain what mitigating action will be taken.Everything that users need to complete a ROBITT assessment is provided: the tool, a guidance document and a worked example. Following other disciplines, the tool and guidance document were developed through a consensus-forming process across experts working in relevant areas of ecology and evidence synthesis.We propose that researchers should be strongly encouraged to include a ROBITT assessment when publishing studies of biodiversity trends, especially when using aggregated data. This will help researchers to structure their thinking, clearly acknowledge potential sampling issues, highlight where expert consultation is required and provide an opportunity to describe data checks that might go unreported. ROBITT will also enable reviewers, editors and readers to establish how well research conclusions are supported given a dataset combined with some analytical approach. In turn, it should strengthen evidence-based policy and practice, reduce differing interpretations of data and provide a clearer picture of the uncertainties associated with our understanding of reality.

VenomMaps: Updated species distribution maps and models for New World pitvipers (Viperidae: Crotalinae)

Beyond providing critical information to biologists, species distributions are useful for naturalists, curious citizens, and applied disciplines including conservation planning and medical intervention. Venomous snakes are one group that highlight the importance of having accurate information given their cosmopolitan distribution and medical significance. Envenomation by snakebite is considered a neglected tropical disease by the World Health Organization and venomous snake distributions are used to assess vulnerability to snakebite based on species occurrence and antivenom/healthcare accessibility. However, recent studies highlighted the need for updated fine-scale distributions of venomous snakes. Pitvipers (Viperidae: Crotalinae) are responsible for >98% of snakebites in the New World. Therefore, to begin to address the need for updated fine-scale distributions, we created VenomMaps, a database and web application containing updated distribution maps and species distribution models for all species of New World pitvipers. With these distributions, biologists can better understand the biogeography and conservation status of this group, researchers can better assess vulnerability to snakebite, and medical professionals can easily discern species found in their area.

Measurement(s)	Species Distributions
Technology Type(s)	Geographic Information System • Species Distribution Model (MaxEnt/kuenm)
Factor Type(s)	Occurrence Records • Environmental Data
Sample Characteristic - Organism	Crotalinae
Sample Characteristic - Location	North America • South America