A tool for simulating and communicating uncertainty when modelling species distributions under future climates

Biodiversity modeling advances will improve predictions of nature's contributions to people

Accurate predictions of ecosystem functions and nature's contributions to people (NCP) are needed to prioritize environmental protection and restoration in the Anthropocene. However, our ability to predict NCP is undermined by approaches that rely on biophysical variables and ignore those describing biodiversity, which have strong links to NCP. To foster predictive mapping of NCP, we should harness the latest methods in biodiversity modeling. This field advances rapidly, and new techniques with promising applications for predicting NCP are still underutilized. Here, we argue that employing recent advances in biodiversity modeling can enhance the accuracy and scope of NCP maps and predictions. This enhancement will contribute significantly to the achievement of global objectives to preserve NCP, for both the present and an unpredictable future.

Mapping multiscale breeding bird species distributions across the United States and evaluating their conservation applications

Species distribution models are vital to management decisions that require understanding habitat use patterns, particularly for species of conservation concern. However, the production of distribution maps for individual species is often hampered by data scarcity, and existing species maps are rarely spatially validated due to limited occurrence data. Furthermore, community-level maps based on stacked species distribution models lack important community assemblage information (e.g., competitive exclusion) relevant to conservation. Thus, multispecies, guild, or community models are often used in conservation practice instead. To address these limitations, we aimed to generate fine-scale, spatially continuous, nationwide maps for species represented in the North American Breeding Bird Survey (BBS) between 1992 and 2019. We developed ensemble models for each species at three spatial resolutions-0.5, 2.5, and 5 km-across the conterminous United States. We also compared species richness patterns from stacked single-species models with those of 19 functional guilds developed using the same data to assess the similarity between predictions. We successfully modeled 192 bird species at 5-km resolution, 160 species at 2.5-km resolution, and 80 species at 0.5-km resolution. However, the species we could model represent only 28%-56% of species found in the conterminous US BBSs across resolutions owing to data limitations. We found that stacked maps and guild maps generally had high correlations across resolutions (median = 84%), but spatial agreement varied regionally by resolution and was most pronounced between the East and West at the 5-km resolution. The spatial differences between our stacked maps and guild maps illustrate the importance of spatial validation in conservation planning. Overall, our species maps are useful for single-species conservation and can support fine-scale decision-making across the United States and support community-level conservation when used in tandem with guild maps. However, there remain data scarcity issues for many species of conservation concern when using the BBS for single-species models.

Species-specific thermal classification schemes can improve climate related marine resource decisions

Global climate change increasingly contributes to large changes in ecosystem structure. Timely management of rapidly changing marine ecosystems must be matched with methods to rapidly quantify and assess climate driven impacts to ecological communities. Here we create a species-specific, classification system for fish thermal affinities, using three quantifiable datasets and expert opinion. Multiple sources of information limit potential data bias and avoid misclassification. Using a temperate kelp forest fish community in California, USA as a test case for this new methodology, we found the majority of species had high classification agreement across all four data sources (n = 78) but also a number of low agreement species (2 sources disagree from the others, n = 47). For species with low agreement, use of just one dataset to classify species, as is commonly done, would lead to high risk of misclassification. Differences in species classification between individual datasets and our composite classification were apparent. Applying different thermal classifications, lead to different conclusions when quantifying 'warm' and 'cool' species density responses to a marine heatwave. Managers can use this classification approach as a tool to generate accurate, timely and simple information for resource management.

Modeling the Potential Global Distribution of Phenacoccus madeirensis Green under Various Climate Change Scenarios

The Madeira mealybug, Phenacoccus madeirensis Green, is a serious invasive pest that does significant damage to more than 120 genera of host plants from 51 families in more than 81 countries. However, the potential distribution range of this pest is unclear, which could hamper control and eradication efforts. In the current study, MaxEnt models were developed to forecast the current and future distribution of the Madeira mealybug around the world. Moreover, the future potential distribution of this invasive species was projected for the 2050s and 2070s under three different climate change scenarios (HADGEM2-AO, GFDL-CM3, and MIROC5) and two representative concentration pathways (RCP-2.6 and RCP-8.5). The final model indicates that the Madeira mealybug has a highly suitable range for the continents of Asia, Europe, and Africa, as well as South America and North America, where this species has already been recorded. Potential expansions or reductions in distribution were also simulated under different future climatic conditions. Our study also suggested that the mean temperature of the driest quarter (Bio9) was the most important factor and explained 46.9% of the distribution model. The distribution model from the current and future predictions can enhance the strategic planning of agricultural and forestry organization by identifying regions that will need to develop integrated pest management programs to manage Madeira mealybug, especially for some highly suitable areas, such as South Asia and Europe. Moreover, the results of this research will help governments to optimize investment in the control and management of the Madeira mealybug by identifying regions that are or will become suitable for infestations.

Visualizing uncertainty in habitat suitability models with the hyper-envelope modeling interface, version 2

Habitat suitability models (HSMs) are popular and used for a wide variety of applications but most do not include analysis of the uncertainty of the model outputs. Additionally, some overfit the data and few allow the ability to fill data gaps with expert opinion. HEMI 1 addressed issues with overfitting data and allowed models to incorporate both occurrence data and expert opinion. HEMI 2 improves on HEMI 1 with a simplified interface and the ability to inject random noise into occurrence locations and environmental variable values to generate uncertainty maps. HEMI 2 uses Monte Carlo methods to perform uncertainty, validation, and sensitivity testing and generates mean and standard deviation habitat suitability maps.

Predicting ecological responses in a changing ocean: the effects of future climate uncertainty

Predicting how species will respond to climate change is a growing field in marine ecology, yet knowledge of how to incorporate the uncertainty from future climate data into these predictions remains a significant challenge. To help overcome it, this review separates climate uncertainty into its three components (scenario uncertainty, model uncertainty, and internal model variability) and identifies four criteria that constitute a thorough interpretation of an ecological response to climate change in relation to these parts (awareness, access, incorporation, communication). Through a literature review, the extent to which the marine ecology community has addressed these criteria in their predictions was assessed. Despite a high awareness of climate uncertainty, articles favoured the most severe emission scenario, and only a subset of climate models were used as input into ecological analyses. In the case of sea surface temperature, these models can have projections unrepresentative against a larger ensemble mean. Moreover, 91% of studies failed to incorporate the internal variability of a climate model into results. We explored the influence that the choice of emission scenario, climate model, and model realisation can have when predicting the future distribution of the pelagic fish, Electrona antarctica. Future distributions were highly influenced by the choice of climate model, and in some cases, internal variability was important in determining the direction and severity of the distribution change. Increased clarity and availability of processed climate data would facilitate more comprehensive explorations of climate uncertainty, and increase in the quality and standard of marine prediction studies.

Potential effects of climate change on geographic distribution of the Tertiary relict tree species Davidia involucrata in China

This study, using species distribution modeling (involving a new approach that allows for uncertainty), predicts the distribution of climatically suitable areas prevailing during the mid-Holocene, the Last Glacial Maximum (LGM), and at present, and estimates the potential formation of new habitats in 2070 of the endangered and rare Tertiary relict tree Davidia involucrata Baill. The results regarding the mid-Holocene and the LGM demonstrate that south-central and southwestern China have been long-term stable refugia, and that the current distribution is limited to the prehistoric refugia. Given future distribution under six possible climate scenarios, only some parts of the current range of D. involucrata in the mid-high mountains of south-central and southwestern China would be maintained, while some shift west into higher mountains would occur. Our results show that the predicted suitable area offering high probability (0.5‒1) accounts for an average of only 29.2% among the models predicted for the future (2070), making D. involucrata highly vulnerable. We assess and propose priority protected areas in light of climate change. The information provided will also be relevant in planning conservation of other paleoendemic species having ecological traits and distribution ranges comparable to those of D. involucrata.

Forecasting Large-Scale Habitat Suitability of European Bustards under Climate Change: The Role of Environmental and Geographic Variables

We modelled the distribution of two vulnerable steppe birds, Otis tarda and Tetrax tetrax, in the Western Palearctic and projected their suitability up to the year 2080. We performed two types of models for each species: one that included environmental and geographic variables (space-included model) and a second one that only included environmental variables (space-excluded model). Our assumption was that ignoring geographic variables in the modelling procedure may result in inaccurate forecasting of species distributions. On the other hand, the inclusion of geographic variables may generate an artificial constraint on future projections. Our results show that space-included models performed better than space-excluded models. While distribution of suitable areas for T. tetrax in the future was approximately the same as at present in the space-included model, the space-excluded model predicted a pronounced geographic change of suitable areas for this species. In the case of O. tarda, the space-included model showed that many areas of current presence shifted to low or medium suitability in the future, whereas a northward expansion of intermediate suitable areas was predicted by the space-excluded one. According to the best models, current distribution of these species can restrict future distribution, probably due to dispersal constraints and site fidelity. Species ranges would be expected to shift gradually over the studied time period and, therefore, we consider it unlikely that most of the current distribution of these species in southern Europe will disappear in less than one hundred years. Therefore, populations currently occupying suitable areas should be a priority for conservation policies. Our results also show that climate-only models may have low explanatory power, and could benefit from adjustments using information on other environmental variables and biological traits; if the latter are not available, including the geographic predictor may improve the reliability of predicted results.

Using Species Distribution Models to Predict Potential Landscape Restoration Effects on Puma Conservation

A mosaic of intact native and human-modified vegetation use can provide important habitat for top predators such as the puma (Puma concolor), avoiding negative effects on other species and ecological processes due to cascade trophic interactions. This study investigates the effects of restoration scenarios on the puma's habitat suitability in the most developed Brazilian region (São Paulo State). Species Distribution Models incorporating restoration scenarios were developed using the species' occurrence information to (1) map habitat suitability of pumas in São Paulo State, Southeast, Brazil; (2) test the relative contribution of environmental variables ecologically relevant to the species habitat suitability and (3) project the predicted habitat suitability to future native vegetation restoration scenarios. The Maximum Entropy algorithm was used (Test AUC of 0.84 ± 0.0228) based on seven environmental non-correlated variables and non-autocorrelated presence-only records (n = 342). The percentage of native vegetation (positive influence), elevation (positive influence) and density of roads (negative influence) were considered the most important environmental variables to the model. Model projections to restoration scenarios reflected the high positive relationship between pumas and native vegetation. These projections identified new high suitability areas for pumas (probability of presence >0.5) in highly deforested regions. High suitability areas were increased from 5.3% to 8.5% of the total State extension when the landscapes were restored for ≥ the minimum native vegetation cover rule (20%) established by the Brazilian Forest Code in private lands. This study highlights the importance of a landscape planning approach to improve the conservation outlook for pumas and other species, including not only the establishment and management of protected areas, but also the habitat restoration on private lands. Importantly, the results may inform environmental policies and land use planning in São Paulo State, Brazil.