Person:

Ellison, Aaron

Quantifying and assessing changes in biological diversity are central aspects of many ecological studies, yet accurate methods of estimating biological diversity from sampling data have been elusive. Hill numbers, or the effective number of species, are increasingly used to characterize the taxonomic, phylogenetic or functional diversity of an assemblage. However, empirical estimates of Hill numbers, including species richness, tend to be an increasing function of sampling effort and thus tend to increase with sample completeness. Integrated curves based on sampling theory that smoothly link rarefaction (interpolation) and prediction (extrapolation) standardize samples on the basis of sample size or sample completeness and facilitate the comparison of biodiversity data. Here we extend previous rarefaction and extrapolation models for species richness (Hill number (^q)D, where q = 0) to measures of taxon diversity incorporating relative abundance (i.e., for any Hill number (^q)D, q > 0) and present a unified approach for both individual-based (abundance) data and sample-based (incidence) data. Using this unified sampling framework, we derive both theoretical formulas and analytic estimators for seamless rarefaction and extrapolation based on Hill numbers. Detailed examples are provided for the first three Hill numbers: q = 0 (species richness), q = 1 (the exponential of Shannon's entropy index) and q = 2 (the inverse of Simpson's concentration index). We develop a bootstrap method for constructing confidence intervals around Hill numbers, facilitating the comparison of multiple assemblages of both rarefied and extrapolated samples. The proposed estimators are accurate for both rarefaction and short-range extrapolation. For long-range extrapolation, the performance of the estimators depends on both the value of q and on the extrapolation range. We tested our methods on simulated data generated from species abundance models and on data from large species inventories. We also illustrate the formulas and estimators using empirical datasets from biodiversity surveys of temperate forest spiders and tropical ants.

Given the multitude of ecosystem services provided by mangroves, it is important to understand their potential responses to global climate change. Extensive reviews of the literature and manipulative experiments suggest that mangroves will be impacted by climate change, but few studies have tested these predictions over large scales using statistical models. We provide the first example of applying species and community distribution models (SDMs and CDMs, respectively) to coastal mangroves worldwide. Species distributions were modeled as ensemble forecasts using BIOMOD. Distributions of mangrove communities with high species richness were modeled in three ways: as the sum of the separate SDM outputs, as binary hotspots (with >3 species) using a generalized linear model, and continuously using a general boosted model. Individual SDMs were projected for 12 species with sufficient data and CDMs were projected for 30 species into 2080 using global climate model outputs and a range of sea-level rise projections. Species projected to shift their ranges polewards by at least 2 degrees of latitude consistently experience a decrease in the amount of suitable coastal area available to them. Central America and the Caribbean are forecast to lose more mangrove species than other parts of the world. We found that the extent and grain size, at which continuous CDM outputs are examined, independent of the grain size at which the models operate, can dramatically influence the number of pseudo-absences needed for optimal parameterization. The SDMs and CDMs presented here provide a first approximation of how mangroves will respond to climate change given simple correlative relationships between occurrence records and environmental data. Additional, precise georeferenced data on mangrove localities and concerted efforts to collect data on ecological processes across large-scale climatic gradients will enable future research to improve upon these correlative models.

Eastern hemlock (Tsuga canadensis) is a foundation species in eastern North American forests. Because eastern hemlock is a foundation species, it often is assumed that the diversity of associated species is high. However, the herbaceous layer of eastern hemlock stands generally is sparse, species-poor, and lacks unique species or floristic assemblages. The rapidly spreading, nonnative hemlock woolly adelgid (Adelges tusgae) is causing widespread death of eastern hemlock. Loss of individual hemlock trees or whole stands rapidly leads to increases in species richness and cover of shrubs, herbs, graminoids, ferns, and fern-allies. Naively, one could conclude that the loss of eastern hemlock has a net positive effect on biodiversity. What is lost besides hemlock, however, is landscape-scale variability in the structure and composition of the herbaceous layer. In the Harvard Forest Hemlock Removal Experiment, removal of hemlock by either girdling (simulating adelgid infestation) or logging led to a proliferation of early-successional and disturbance-dependent understory species. In other declining hemlock stands, nonnative plant species expand and homogenize the flora. While local richness increases in former eastern hemlock stands, between-site and regional species diversity will be further diminished as this iconic foundation species of eastern North America succumbs to hemlock woolly adelgid.

Biodiversity sampling is labor intensive and is especially challenging for myrmecologists, because the sampling units (individual workers) do not correspond in a simple way to the natural units of diversity (individual nests). Because it is usually not possible to reach a sampling asymptote for ants, comparisons of species richness among collections have to be carefully standardized for the number of individuals and number of samples examined. Asymptotic estimators allow for extrapolation to an estimated asymptote of species richness, and rarefaction curves permit meaningful comparisons of samples by interpolating data to a standardized number of sampling units. Winkler sacks of leaf litter and specialized traps for arboreal ants often yield distinctive sets of species in tropical and subtropical latitudes. These microhabitats are best treated as distinctive assemblages that can be compared with stratified sampling. Within other habitat types, different baiting and sampling methods yield similar, but not identical sets of species, and many authors have advocated using a diversity of methods to gain the greatest coverage of species. However, many of the distinctive species that are sampled by a particular method are rare, and are just as likely to have been found with other sampling methods. The estimated similarity in composition of ants sampled by different methods in the same habitat is probably more similar than has been appreciated. Recent published comparisons of sampling efficiency have shown that hand-collecting accumulates species more efficiently than the more commonly used pitfall traps or baits. However, if hand-collected samples are to be analyzed quantitatively, hand-sampling must be standardized to a constant plot size that is searched for a fixed amount of time, and all nests encountered must be sampled and counted. For comparative studies of ant assemblages across habitats, hand collecting may be superior to either litter sampling or pitfall traps because it can be used in sites that have no leaf litter or are too environmentally sensitive, too rocky, too steep, or contain too much human and domestic animal traffic for pitfall trapping. Data from hand-sampling and grids of pitfall traps lend themselves to slightly different kinds of analyses. Hand sampling probably gives the best estimates of the true frequency of nests of different species in an area, and the data can be used with individual-based rarefaction curves and many standard parametric statistical tests. Data from pitfall trap grids should usually be treated as sample-based occurrences, and analyzed with sample-based rarefaction and diversity estimators. For the purposes of estimating diversity, myrmecologists should avoid the temptation to analyze the underlying counts of individual workers. Data from replicated grids of pitfall traps can be analyzed with hierarchical occupancy models. These newly developed models provide useful estimates of probabilities of occurrence and probabilities of detection for each species in the assemblage. Biogeographic patterns of ant diversity, such as latitudinal gradients of species richness, are comparable to those seen in other taxa, and the data collected by myrmecologists can contribute in important ways to our general understanding of biodiversity patterns.

Tropical forest canopies house most of the globe’s diversity, yet little is known about global patterns and drivers of canopy diversity. Here we present models of ant species density, using climate, abundance and habitat (i.e., canopy v. litter) as predictors.  Ant species density is positively associated with temperature and precipitation, and negatively (or non‐significantly) associated with two metrics of seasonality, precipitation seasonality and temperature range. Ant species density was significantly higher in canopy samples, but this difference disappeared once abundance was considered.  Thus, the apparent differences in species density between canopy and litter samples are likely due to differences in abundance‐diversity relationships, not differences in climate‐diversity relationships.  Thus it appears that canopy and litter ant assemblages share a common abundance‐diversity relationship influenced by similar but not identical climatic drivers.

Aim: To use a fine-grained global model of ant diversity to identify the limits of our knowledge of diversity in the context of climate change. Location: Global. Methods: We applied generalized linear modelling to a global database of local ant assemblages to predict the species density of ants globally. Predictors evaluated include simple climate variables, combined temperature × precipitation variables, biogeographic region, elevation, and interactions between select variables. Areas of the planet identified as beyond the reliable prediction ability of the model were those having climatic conditions more extreme than what was represented in the ant database. Results: Temperature was the most important single predictor of ant species density, and a mix of climatic variables, biogeographic region, and interactions between climate and region yielded the best overall model. Broadly, geographic patterns of ant diversity match those of other taxa, with high species density in the wet tropics and in some, but not all, parts of the dry tropics. Uncertainty in model predictions appears to derive from the low amount of standardized sampling of ants in Asia, Africa, and in the most extreme (e.g. hottest) climates. Model residuals increase as a function of temperature. This suggests that our understanding of the drivers of ant diversity at high temperatures is incomplete, especially in hot and arid climates. In other words, our ignorance of how ant diversity relates to environment is greatest in those regions where most species occur—hot climates, both wet and dry. Main conclusions: Our results have two important implications. First, temperature is necessary, but not sufficient, to explain fully the patterns of ant diversity. Second, our ability to predict ant diversity is weakest exactly where we need to know the most, the warmest regions of a warming world. This includes significant parts of the tropics and some of the most biologically diverse areas in the world.