Palaeontologists characterize mass extinctions as times when the Earth loses more than three-quarters of its species in a geologically short interval, as has happened only five times in the past 540 million years or so. Biologists now suggest that a sixth mass extinction may be under way, given the known species losses over the past few centuries and millennia. Here we review how differences between fossil and modern data and the addition of recently available palaeontological information influence our understanding of the current extinction crisis. Our results confirm that current extinction rates are higher than would be expected from the fossil record, highlighting the need for effective conservation measures.
There is an urgent need to understand species and community responses to climatic and ecological changes to predict biodiversity patterns given anticipated global change. The current distribution of species and the environment provide a limited perspective to study and predict ecological responses; therefore, biodiversity responses to past environmental changes must be examined. The rapid development of ecological niche models (ENMs) and their use in reconstructing past species distributions has facilitated inclusion of past observations into predictive models. Paleodata offer an opportunity to test the predictive ability of ENMs and their underlying assumptions. However, paleodata remain underutilized despite the rapidly growing field of paleoinformatics. New modeling methods that incorporate species associations, coupled with paleodata, provide more robust approaches to studying species and community responses, especially given the predicted emergence of no-analog climates and communities in the future.
Species distribution models (SDMs) assume species exist in isolation and do not influence one another's distributions, thus potentially limiting their ability to predict biodiversity patterns. Community-level models (CLMs) capitalize on species co-occurrences to fit shared environmental responses of species and communities, and therefore may result in more robust and transferable models. Here, we conduct a controlled comparison of five paired SDMs and CLMs across changing climates, using palaeoclimatic simulations and fossilpollen records of eastern North America for the past 21 000 years. Both SDMs and CLMs performed poorly when projected to time periods that are temporally distant and climatically dissimilar from those in which they were fit; however, CLMs generally outperformed SDMs in these instances, especially when models were fit with sparse calibration datasets. Additionally, CLMs did not over-fit training data, unlike SDMs. The expected emergence of novel climates presents a major forecasting challenge for all models, but CLMs may better rise to this challenge by borrowing information from co-occurring taxa.
Future climates are projected to be highly novel relative to recent climates. Climate novelty challenges models that correlate ecological patterns to climate variables and then use these relationships to forecast ecological responses to future climate change. Here, we quantify the magnitude and ecological significance of future climate novelty by comparing it to novel climates over the past 21,000 years in North America. We then use relationships between model performance and climate novelty derived from the fossil pollen record from eastern North America to estimate the expected decrease in predictive skill of ecological forecasting models as future climate novelty increases. We show that, in the high emissions scenario (RCP 8.5) and by late 21st century, future climate novelty is similar to or higher than peak levels of climate novelty over the last 21,000 years. The accuracy of ecological forecasting models is projected to decline steadily over the coming decades in response to increasing climate novelty, although models that incorporate co-occurrences among species may retain somewhat higher predictive skill. In addition to quantifying future climate novelty in the context of late Quaternary climate change, this work underscores the challenges of making reliable forecasts to an increasingly novel future, while highlighting the need to assess potential avenues for improvement, such as increased reliance on geological analogs for future novel climates and improving existing models by pooling data through time and incorporating assemblage-level information.
Community‐level models (CLMs) consider multiple, co‐occurring species in model fitting and are lesser known alternatives to species distribution models (SDMs) for analysing and predicting biodiversity patterns. Community‐level models simultaneously model multiple species, including rare species, while reducing overfitting and implicitly considering drivers of co‐occurrence. Many CLMs are direct extensions of well‐known SDMs and therefore should be familiar to ecologists. However, CLMs remain underutilized, and there have been few tests of their potential benefits and no systematic reviews of their assumptions and implementations. Here, we review this emerging field and provide examples in r to fit common CLMs. Our goal is to introduce CLMs to a broader audience, and discuss their attributes, benefits and limitations relative to SDMs. We review (1) statistical implementations and applications of CLMs, (2) their advantages and limitations, and (3) comparative analyses of CLMs and SDMs. We also suggest directions for future research. We identify seven CLM algorithms with similar data structures and predictive outputs as SDMs that should be most accessible to ecologists familiar with species‐level modelling, including five methods that predict assemblage composition and individual species distributions and two methods that model compositional turnover along environmental gradients. Community‐level models have been applied to numerous taxa, regions, and spatial scales, and a variety of topics (e.g. studying drivers of community structure or assessing relationships between community composition and functional traits). Studies suggest that the relative benefits of CLMs and SDMs may be case specific, especially in terms of predicting species distributions and community composition. However, CLMs may offer advantages in terms of computational efficiency, modelling rare species, and projecting to no‐analog climates. A major shortcoming of CLMs is their reliance on presence–absence community composition data. Studies are needed to assess the relative merits of SDMs and CLMs, and different CLM algorithms, with a focus on three key areas: (1) under which circumstances CLMs improve predictions for rare species, (2) how CLMs perform under different community compositions (e.g. relative abundance of rare vs. common species), including the extent to which co‐occurrence patterns are structured by biotic interactions, and (3) ability to project across time/space.
Unique responses to climate change can occur across intraspecific levels, resulting in individualistic adaptation or movement patterns among populations within a given species. Thus, the need to model potential responses among genetically distinct populations within a species is increasingly recognized. However, predictive models of future distributions are regularly fit at the species level, often because intraspecific variation is unknown or is identified only within limited sample locations. In this study, we considered the role of intraspecific variation to shape the geographic distribution of ponderosa pine (Pinus ponderosa), an ecologically and economically important tree species in North America. Morphological and genetic variation across the distribution of ponderosa pine suggest the need to model intraspecific populations: the two varieties (var. ponderosa and var. scopulorum) and several haplotype groups within each variety have been shown to occupy unique climatic niches, suggesting populations have distinct evolutionary lineages adapted to different environmental conditions. We utilized a recently available, geographically widespread dataset of intraspecific variation (haplotypes) for ponderosa pine and a recently devised lineage distance modeling approach to derive additional, likely intraspecific occurrence locations. We confirmed the relative uniqueness of each haplotype-climate relationship using a niche-overlap analysis, and developed ecological niche models (ENMs) to project the distribution for two varieties and eight haplotypes under future climate forecasts. Future projections of haplotype niche distributions generally revealed greater potential range loss than predicted for the varieties. This difference may reflect intraspecific responses of distinct evolutionary lineages. However, directional trends are generally consistent across intraspecific levels, and include a loss of distributional area and an upward shift in elevation. Our results demonstrate the utility in modeling intraspecific response to changing climate and they inform management and conservation strategies, by identifying haplotypes and geographic areas that may be most at risk, or most secure, under projected climate change.
The subfamily Equinae in the Great Plains region of North America underwent a dramatic radiation and subsequent decline as climate changed from warm and humid in the middle Miocene to cooler and more arid conditions during the late Miocene. Here we use ecological niche modeling (ENM), specifically the GARP (Genetic Algorithm using Rule-set Prediction) modeling system, to reconstruct the geographic distribution of individual species during two time slices from the middle Miocene through early Pliocene. This method combines known species occurrence points with environmental parameters inferred from sedimentological variables to model each species' fundamental niche. The geographic range of each species is then predicted to occupy the geographic area within the study region wherever the set of environmental parameters that constrain the fundamental niche occurs. We analyze changes in the predicted distributions of individual species between time slices in relation to Miocene/Pliocene climate change. Specifically, we examine and compare distribution patterns for two time slices that span the period from the mid-Miocene (Barstovian) Climatic Optimum into the early Pliocene (Blancan) to determine whether habitat fragmentation led to speciation within the clade and whether species survival was related to geographic range size. Patchy geographic distributions were more common in the middle Miocene when speciation rates were high. During the late Miocene, when speciation rates were lower, continuous geographic ranges were more common. Equid species tracked their preferred habitat within the Great Plains region as well as regionally throughout North America. Species with larger predicted ranges preferentially survived the initial cooling event better than species with small geographic ranges. As climate continued to deteriorate in the late Miocene, however, range size became irrelevant to survival, and extinction rates increased for species of all range sizes. This is the first use of ENM and GARP in the continental fossil record. This powerful quantitative biogeographic method offers great promise for studies of other taxa and geologic intervals.
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