Accounting for the indirect area effect in stacked species distribution models to map species richness in a montane biodiversity hotspot

How does spatial extent and environmental limits affect the accuracy of species richness estimates from ecological niche models? A case study with North American Pinaceae and Cactaceae

Measuring species richness at varying spatial extents can be challenging, especially at large extents where exhaustive species surveys are difficult or impossible. Our work aimed at determining the reliability of species richness estimates from stacked ecological niche models at different spatial extents for taxonomic groups with vastly different environmental dependencies and interactions. To accomplish this, we generated ecological niche models for the species of Cactaceae and Pinaceae that occur within 180 published floras from North America north of Mexico. We overlaid or stacked the resulting species' potential distribution estimates over the bounding boxes representing each of the 180 floras to generate predictions of species richness. In general, our stacked models of Cactaceae and Pinaceae were poor predictors of species richness. The relationships between observed and predicted values improved noticeably with the size of spatial extents. However, the stacked models tended to overpredict the richness of Cactaceae and over- and underpredict the richness of Pinaceae. Cactaceae stacked models showed higher sensitivity and lower specificity than those for Pinaceae. We conclude that stacked ecological niche models may be somewhat poor predictors of species richness at smaller spatial extents and should be used with caution for this purpose. Perhaps more importantly, abilities to compensate for their limitations or apply corrections to their reliability may vary with taxonomic groups.

Species richness and turnover patterns for tropical and temperate plants on the elevation gradient of the eastern Himalayan Mountains

Understanding species’ elevational distributions in mountain ecosystems is needed under climate change, but remote biodiverse mountain areas may be poorly documented. National Forest Inventories (NFIs) offer a potential source of data. We used NFI records from Bhutan to ask three questions about elevational richness patterns of Himalayan woody plant species. First, does the mean elevation for all species differ from those species whose entire elevational distribution is recorded in the survey? Second, how does the elevation of maximum richness differ when combining species originating from temperate and tropical regions vs. analyzing them separately? And third, do the highest species turnover rates adjoin elevation zones of maximum species richness? We used 32,198 species records from 1685 forest plots along a 7570 m gradient to map species elevation ranges. Species whose entire range was documented were those whose lowest records are located above 400 m, while bare rock defined all species’ upper limits. We calculated species richness and turnover using 400 m elevation bands. Of 569 species, 79% of temperate and 61% of tropical species’ elevation ranges were fully sampled within the NFI data. Mean elevation of tree and shrub species differed significantly for temperate and tropical species. Maximum combined species richness is from 1300 to 1700 m (277 species), differing significantly from maximum tropical (900–1300 m, 169) and temperate species richness (2500–2900 m, 92). Temperate tree turnover rate was highest in the bands adjoining its maximum species richness (2500–2900 m). But turnover for tropical trees was highest several bands above their maximum species richness, where turnover and decrease in richness interact. Shrub species turnover patterns are similar, but rates were generally higher than for trees. Bhutan’s NFI records show that woody plant species are arrayed on the Himalaya in part according to floristic origins, and that combining temperate- and tropical-originating floras for gradient-based studies such as species richness and turnover obscures actual elevational patterns. In addition, species whose ranges extend below the Himalayan elevation gradient should be accounted for in future studies that correlate climate and environment factors with elevational species richness patterns.

Identifying appropriate protected areas for endangered fern species under climate change

The management of protected areas (PAs) is widely used in the conservation of endangered plant species under climate change. However, studies that have identified appropriate PAs for endangered fern species are rare. To address this gap, we must develop a workflow to plan appropriate PAs for endangered fern species that will be further impacted by climate change. Here, we used endangered fern species in China as a case study, and we applied conservation planning software coupled with endangered fern species distribution data and distribution modeling to plan conservation areas with high priority protection needs under climate change. We identified appropriate PAs for endangered fern species under climate change based on the IUCN protected area categories (from Ia to VI) and planned additional PAs for endangered fern species. The high priority regions for protecting the endangered fern species were distributed throughout southern China. With decreasing temperature seasonality, the priority ranking of all endangered fern species is projected to increase in existing PAs. Accordingly, we need to establish conservation areas with low climate vulnerability in existing PAs and expand the conservation areas for endangered fern species in the high priority conservation regions.

Species-free species distribution models describe macroecological properties of protected area networks

Among the greatest challenges facing the conservation of plants and animal species in protected areas are threats from a rapidly changing climate. An altered climate creates both challenges and opportunities for improving the management of protected areas in networks. Increasingly, quantitative tools like species distribution modeling are used to assess the performance of protected areas and predict potential responses to changing climates for groups of species, within a predictive framework. At larger geographic domains and scales, protected area network units have spatial geoclimatic properties that can be described in the gap analysis typically used to measure or aggregate the geographic distributions of species (stacked species distribution models, or S-SDM). We extend the use of species distribution modeling techniques in order to model the climate envelope (or “footprint”) of individual protected areas within a network of protected areas distributed across the 48 conterminous United States and managed by the US National Park System. In our approach we treat each protected area as the geographic range of a hypothetical endemic species, then use MaxEnt and 5 uncorrelated BioClim variables to model the geographic distribution of the climatic envelope associated with each protected area unit (modeling the geographic area of park units as the range of a species). We describe the individual and aggregated climate envelopes predicted by a large network of 163 protected areas and briefly illustrate how macroecological measures of geodiversity can be derived from our analysis of the landscape ecological context of protected areas. To estimate trajectories of change in the temporal distribution of climatic features within a protected area network, we projected the climate envelopes of protected areas in current conditions onto a dataset of predicted future climatic conditions. Our results suggest that the climate envelopes of some parks may be locally unique or have narrow geographic distributions, and are thus prone to future shifts away from the climatic conditions in these parks in current climates. In other cases, some parks are broadly similar to large geographic regions surrounding the park or have climatic envelopes that may persist into near-term climate change. Larger parks predict larger climatic envelopes, in current conditions, but on average the predicted area of climate envelopes are smaller in our single future conditions scenario. Individual units in a protected area network may vary in the potential for climate adaptation, and adaptive management strategies for the network should account for the landscape contexts of the geodiversity or climate diversity within individual units. Conservation strategies, including maintaining connectivity, assessing the feasibility of assisted migration and other landscape restoration or enhancements can be optimized using analysis methods to assess the spatial properties of protected area networks in biogeographic and macroecological contexts.

Applying the dark diversity concept to nature conservation

Linking diversity to biological processes is central for developing informed and effective conservation decisions. Unfortunately, observable patterns provide only a proportion of the information necessary for fully understanding the mechanisms and processes acting on a particular population or community. We suggest conservation managers use the often overlooked information relative to species absences and pay particular attention to dark diversity (i.e., a set of species that are absent from a site but that could disperse to and establish there, in other words, the absent portion of a habitat-specific species pool). Together with existing ecological metrics, concepts, and conservation tools, dark diversity can be used to complement and further develop conservation prioritization and management decisions through an understanding of biodiversity relativized by its potential (i.e., its species pool). Furthermore, through a detailed understanding of the population, community, and functional dark diversity, the restoration potential of degraded habitats can be more rigorously assessed and so to the likelihood of successful species invasions. We suggest the application of the dark diversity concept is currently an underappreciated source of information that is valuable for conservation applications ranging from macroscale conservation prioritization to more locally scaled restoration ecology and the management of invasive species.