Spatiotemporal Variability of Dendroecological Indicators in Pedunculate Oak (Quercus robur L.) Tree‐Rings Across Europe in Relation to Species Distribution Models

Lowland forest loss and climate-only species distribution models exaggerate a forest-dependent species' vulnerability to climate change

Global environmental change and the uncertain fate of biodiversity

Selecting tree species to restore forest under climate change conditions: Complementing species distribution models with field experimentation

A species distribution model for Paracentrotus lividus: predicted projections of habitat suitability

Climate change’s impact on biodiversity is expected to be significant in the twenty-first century. Climate change will influence ecologically sensitive areas, and managing these changes will be critical. This chapter focuses on the utilization of species distribution models (SDMs) in assessing climate change impacts and its associated variables on species distribution, leading to population shift, migration, and species vulnerability. The review concentrates on several species distribution models (SDMs), its application in various ecosystems and their management, the gaps in the models and modelling techniques, and the challenges in their applicability. To investigate the variables utilized for modelling the future projections of the species distribution, several SDMs were explored. Additionally, the most commonly used SDM parameters are assessed in relation to their data inputs. However, the applicability of this metric is also evaluated for various ecosystems. Further, different SDMs were contrasted regarding how their algorithms utilized the input variables. A conventional review was conducted to examine the applicability of various SDMs in relation to climate change. The assessment concentrates on (1) climate change impacts on biodiversity and related ecologically sensitive hotspots, (2) various SDMs employed for biodiversity management, (4) SDM variables used to account for climate change, (5) the parameters and factors that influence the outcomes of SDMs, (6) how SDMs are applied in different ecosystems, and (7) a comparative of different SDMs currently used with the algorithms and variables they employ. Our research includes the discussion of gaps and challenges with the use of different SDM models, such as the lack of appropriate data and the noninclusion of biotic factors. But it also discusses the future perspectives and direction of research that needs to be conducted. Given our analysis, the use of SDMs will be critical in comprehending the future effect of climate change on species dispersal and distribution in the future; however there is a need to improve the robustness of these models so accurate assessments and predictions can be made.

Species distribution models (SDMs) are the main tool to predict global change impacts on species ranges. Climate change alone is frequently considered, but in freshwater ecosystems, hydrology is a key driver of the ecology of aquatic species. At large scale, hydrology is however rarely accounted for, owing to the lack of detailed stream flow data. In this study, we developed an integrated modelling approach to simulate stream flow using the hydrological Soil and Water Assessment Tool (SWAT). Simulated stream flow was subsequently included as an input variable in SDMs along with topographic, hydrographic, climatic and land-cover descriptors. SDMs were applied to two temporally-distinct surveys of the distribution of the endangered Pyrenean desman (Galemys pyrenaicus) in the French Pyrenees: a historical one conducted from 1985 to 1992 and a current one carried out between 2011 and 2013. The model calibrated on historical data was also forecasted onto the current period to assess its ability to describe the distributional change of the Pyrenean desman that has been modelled in the recent years. First, we found that hydrological and climatic variables were the ones influencing the most the distribution of this species for both periods, emphasizing the importance of taking into account hydrology when SDMs are applied to aquatic species. Secondly, our results highlighted a strong range contraction of the Pyrenean desman in the French Pyrenees over the last 25 years. Given that this range contraction was under-estimated when the historical model was forecasted onto current conditions, this finding suggests that other drivers may be interacting with climate, hydrology and land-use changes. Our results imply major concerns for the conservation of this endemic semi-aquatic mammal since changes in climate and hydrology are expected to become more intense in the future.

Assessing the vulnerability of Thailand's forest birds to global change

Species distribution models (SDMs) are widely used to hindcast or forecast suitable habitat conditions during climate change. Although distant populations of a given species may show local adaptations to diverging environmental conditions, traditional SDMs disregard intraspecific variation. Yet, incorporating genetic information into SDMs could improve predictions. Here we aimed at investigating whether genetically informed SDMs would outperform traditional SDMs. Using published information on the spatial genetic structure of the European Beech Fagus sylvatica L. (1753), we built lineage-specific SDMs for each phylogenetic group of the species. We then combined all lineage-specific SDMs into a single genetically informed SDM that we compared against a traditional SDM approach. We finally compared SDMs’ predictions against independent datasets of present-day distribution as well as fossil distribution data from the Mid-Holocene, using six metrics of model performance. We found that aggregating lineage-specific SDMs into a single genetically informed SDM increased model performances to identify suitable areas currently occupied by F. sylvatica. In comparison to a traditional SDM, the genetically informed SDM we built for F. sylvatica assigned higher probabilities of occurrence during the Mid-Holocene at locations where fossil records were found. Aggregating lineage-specific SDMs into a single genetically informed SDM seems to outperform the traditional SDM approach, especially so when the aim is to identify potentially suitable areas of occupancy. This could be particularly useful for the identification of cryptic refugia that remain undetected by traditional SDMs. Genetically informed SDMs have the potential to improve our understanding of species redistribution under climate change.

What determines plant species diversity in Central Africa?

Global climate change has profound impacts on the habitats of marine organisms, and predicting the habitat changes of species under climate change conditions is crucial for species sustainability. Boleophthalmus pectinirostris is an intertidal fish species that holds significant ecological and economic value. To better protect and manage its resources, this study aimed to predict its current potential distribution and habitat changes under different climate scenarios in the future. This study firstly quantified the hypervolume niches of the three lineages (AE1, AE2, and AES lineages) and compared the niche differentiation among them. Furthermore, this study constructed species-level and lineage-level species distribution models (SDMs) to assess the impact of climate change on the habitat suitability of B. pectinirostris. The result of the niche differentiation assessment showed that there was marked differentiation in niches among the three lineages. The responses of different lineages to environmental variables were different, suggesting that lineage-level models may provide more accurate prediction results. According to the model predictions, the AES may have greater resilience to climate change and may experience habitat expansion in the future, while the AE1 and the AE2 may face habitat loss in some regions. Climate change-driven shifts in oceanic conditions were anticipated to affect the distribution and community structure of marine organisms. This study assessed the impact of climate change on the suitable habitat range of three lineages of B. pectinirostris using SDMs. Consistent with previous studies, the results of our study indicated that lineage-level SDMs may be more reliable than species-level SDMs for species with population differentiation in terms of the accuracy of predictions. In addition, considering the vulnerability of the AE1 and AE2 lineages to climate change, conserving these two lineages should be given a higher priority. The results of this study will provide important information for the future management and conservation of this species.

Climate change risk assessments are essential for the successful conservation and management of species under future climates. However, many management plans fail to include such assessments when evaluating threats to species or ecological communities. Species distribution models (SDMs) are versatile tools that can estimate species exposure to shifts in climate, but they lack information on the response capacity of species to climate change. Functional traits can be used in conjunction with SDMs to understand aspects of species biology that would suggest sensitivity to climate change. We illustrate how exposure to climate change and sensitivity of species based on their traits can be combined to generate predictions of climate change risk in four species listed as threatened (under state and federal legislation) and four unlisted plant species from the endemic Australian genus Persoonia (Proteaceae). We develop distribution models for each species using two emissions scenarios and assess four traits and two range metrics that are recognized as limiting factors in four potential climate change responses (reproduction, movement capability, habitat specialization, and spatial coverage) to assess the sensitivity of these eight species to climate change. Listed and unlisted species varied in niche breadth, environmental envelopes, and the percent contribution of variables to the model building process. Our models project a significant decline in habitat suitability under future climates at current occurrence locations for all species, and a 32–95% reduction in the total area (km 2 ) of suitable habitat for all species by 2060, with significantly greater decline under the higher emissions scenario. Seven out of the eight species assessed ranked high for overall climate sensitivity therefore listing status may be a poor indicator of climate change risk to plant species. This has important implications for the continued focus on threatened taxa in conservation planning, particularly for hyper-diverse groups like plants and invertebrates where a species listing status is influenced by an often-slow progress towards comprehensive assessments of threat status. • Projected climate change exposure was high for all Persoonia we assessed. • Species traits revealed high sensitivity to climate change among most Persoonia we assessed. • Exposure and sensitivity did not differ based on listing status. • A species listing status may be a poor indicator of climate change risk. • Suitable habitat loss for all species was higher under the higher emissions scenario.

Spatially explicit biogeographic models are among the most used methods in conservation biogeography, with correlative species distribution models (SDMs) being the most popular among them. SDMs can identify the potential for species’ and community range shifts under climate change, and thus can inspire, inform, and guide complex and adaptive conservation management planning efforts such as collaborative transboundary conservation frameworks. However, SDMs are rarely developed collaboratively, which would be ideal for conservation applications of such models. Further, SDMs that are applied to conservation often do not follow best practices of the field, which are particularly important for applications in climate change contexts for which model extrapolation into potentially novel climates is necessary. Thus, while there is substantial promise, particularly among machine-learning based SDM approaches, there are also many pitfalls to consider when applying SDMs to conservation, and especially in the context of transboundary management under climate change. Here, we summarize these pitfalls and the key steps to mitigate them and maximize the promise of applying SDMs to facilitate transboundary conservation planning under climate change. We argue that conservation modeling capacity must be elevated among practitioners such that they can easily implement best practices when using SDMs, especially regarding: 1) avoiding model overcomplexity, 2) addressing input data bias, and 3) accounting for uncertainty in model extrapolations and projections. While our discussion centers mainly on the pitfalls and opportunities of applying the most popular correlative SDM algorithm, Maxent, our suggestions can also be generalized to a range of other SDM tools. Overall, improved training in, tools for, and implementation of best practices in biogeographic models such as SDMs hold great promise to facilitate and help guide complex, transboundary collaborations for long-term planning of conservation under climate change.

Using species distribution model to predict the impact of climate change on the potential distribution of Japanese whiting Sillago japonica

Global biodiversity declines, largely driven by climate and land‐use changes, urge the development of transparent guidelines for effective conservation strategies. Species distribution modeling (SDM) is a widely used approach for predicting potential shifts in species distributions, which can in turn support ecological conservation where environmental change is expected to impact population and community dynamics. Improvements in SDM accuracy through incorporating intra‐ and interspecific processes have boosted the SDM field forward, but simultaneously urge harmonizing the vast array of SDM approaches into an overarching, widely adoptable, and scientifically justified SDM framework. In this review, we first discuss how climate warming and land‐use change interact to govern population dynamics and species’ distributions, depending on species’ dispersal and evolutionary abilities. We particularly emphasize that both land‐use and climate change can reduce the accessibility to suitable habitat for many species, rendering the ability of species to colonize new habitat and to exchange genetic variation a crucial yet poorly implemented component of SDM. We then unite existing methodological SDM practices that aim to increase model accuracy through accounting for multiple global change stressors, dispersal, or evolution, while shifting our focus to model feasibility. We finally propose a roadmap harmonizing model accuracy and feasibility, applicable to both common and rare species, particularly those with poor dispersal abilities. This roadmap (a) paves the way for an overarching SDM framework allowing comparison and synthesis of different SDM studies and (b) could advance SDM to a level that allows systematic integration of SDM outcomes into effective conservation plans.

AimConservation planning requires the prioritization of a subset of taxa and geographical locations to focus monitoring and management efforts. Integration of the threats and opportunities posed by climate change often relies on predictions from species distribution models, particularly for assessments of vulnerability or invasion risk for multiple taxa. We evaluated whether species distribution models could reliably rank changes in species range size under climate and land use change.LocationConterminous U.S.A.Time period1977–2014.Major taxa studiedPasserine birds.MethodsWe estimated ensembles of species distribution models based on historical North American Breeding Bird Survey occurrences for 190 songbirds, and generated predictions to recent years given c. 35 years of observed land use and climate change. We evaluated model predictions using standard metrics of discrimination performance and a more detailed assessment of the ability of models to rank species vulnerability to climate change based on predicted range loss, range gain, and overall change in range size.ResultsSpecies distribution models yielded unreliable and misleading assessments of relative vulnerability to climate and land use change. Models could not accurately predict range expansion or contraction, and therefore failed to anticipate patterns of range change among species. These failures occurred despite excellent overall discrimination ability and transferability to the validation time period, which reflected strong performance at the majority of locations that were either always or never occupied by each species.Main conclusionsModels failed for the questions and at the locations of greatest interest to conservation and management. This highlights potential pitfalls of multi‐taxa impact assessments under global change; in our case, models provided misleading rankings of the most impacted species, and spatial information about range changes was not credible. As modelling methods and frameworks continue to be refined, performance assessments and validation efforts should focus on the measures of risk and vulnerability useful for decision‐making.