Andrew M. Latimer

BUILDING STATISTICAL MODELS TO ANALYZE SPECIES DISTRIBUTIONS

Models of the geographic distributions of species have wide application in ecology. But the nonspatial, single-level, regression models that ecologists have often employed do not deal with problems of irregular sampling intensity or spatial dependence, and do not adequately quantify uncertainty. We show here how to build statistical models that can handle these features of spatial prediction and provide richer, more powerful inference about species niche relations, distributions, and the effects of human disturbance. We begin with a familiar generalized linear model and build in additional features, including spatial random effects and hierarchical levels. Since these models are fully specified statistical models, we show that it is possible to add complexity without sacrificing interpretability. This step-by-step approach, together with attached code that implements a simple, spatially explicit, regression model, is structured to facilitate self-teaching. All models are developed in a Bayesian framework. We assess the performance of the models by using them to predict the distributions of two plant species (Proteaceae) from South Africas Cape Floristic Region. We demonstrate that making distribution models spatially explicit can be essential for accurately characterizing the environmental response of species, predicting their probability of occurrence, and assessing uncertainty in the model results. Adding hierarchical levels to the models has further advantages in allowing human transformation of the landscape to be taken into account, as well as additional features of the sampling process.

Ants sow the seeds of global diversification in flowering plants

Background The extraordinary diversification of angiosperm plants in the Cretaceous and Tertiary periods has produced an estimated 250,000–300,000 living angiosperm species and has fundamentally altered terrestrial ecosystems. Interactions with animals as pollinators or seed dispersers have long been suspected as drivers of angiosperm diversification, yet empirical examples remain sparse or inconclusive. Seed dispersal by ants (myrmecochory) may drive diversification as it can reduce extinction by providing selective advantages to plants and can increase speciation by enhancing geographical isolation by extremely limited dispersal distances. Methodology/Principal Findings Using the most comprehensive sister-group comparison to date, we tested the hypothesis that myrmecochory leads to higher diversification rates in angiosperm plants. As predicted, diversification rates were substantially higher in ant-dispersed plants than in their non-myrmecochorous relatives. Data from 101 angiosperm lineages in 241 genera from all continents except Antarctica revealed that ant-dispersed lineages contained on average more than twice as many species as did their non-myrmecochorous sister groups. Contrasts in species diversity between sister groups demonstrated that diversification rates did not depend on seed dispersal mode in the sister group and were higher in myrmecochorous lineages in most biogeographic regions. Conclusions/Significance Myrmecochory, which has evolved independently at least 100 times in angiosperms and is estimated to be present in at least 77 families and 11 000 species, is a key evolutionary innovation and a globally important driver of plant diversity. Myrmecochory provides the best example to date for a consistent effect of any mutualism on large-scale diversification.

Monitoring plant functional diversity from space

The world’s ecosystems are losing biodiversity fast. A satellite mission designed to track changes in plant functional diversity around the globe could deepen our understanding of the pace and consequences of this change and how to manage it.

Long-term climate and competition explain forest mortality patterns under extreme drought

Rising temperatures are amplifying drought-induced stress and mortality in forests globally. It remains uncertain, however, whether tree mortality across drought-stricken landscapes will be concentrated in particular climatic and competitive environments. We investigated the effects of long-term average climate [i.e. 35-year mean annual climatic water deficit (CWD)] and competition (i.e. tree basal area) on tree mortality patterns, using extensive aerial mortality surveys conducted throughout the forests of California during a 4-year statewide extreme drought lasting from 2012 to 2015. During this period, tree mortality increased by an order of magnitude, typically from tens to hundreds of dead trees per km2 , rising dramatically during the fourth year of drought. Mortality rates increased independently with average CWD and with basal area, and they increased disproportionately in areas that were both dry and dense. These results can assist forest managers and policy-makers in identifying the most drought-vulnerable forests across broad geographic areas.

Modeling large scale species abundance with latent spatial processes

Modeling species abundance patterns using local environmental features is an important, current problem in ecology. The Cape Floristic Region (CFR) in South Africa is a global hot spot of diversity and endemism, and provides a rich class of species abundance data for such modeling. Here, we propose a multi-stage Bayesian hierarchical model for explaining species abundance over this region. Our model is specified at areal level, where the CFR is divided into roughly 37,000 one minute grid cells; species abundance is observed at some locations within some cells. The abundance values are ordinally categorized. Environmental and soil-type factors, likely to influence the abundance pattern, are included in the model. We formulate the empirical abundance pattern as a degraded version of the potential pattern, with the degradation effect accomplished in two stages. First, we adjust for land use transformation and then we adjust for measurement error, hence misclassification error, to yield the observed abundance classifications. An important point in this analysis is that only 28% of the grid cells have been sampled and that, for sampled grid cells, the number of sampled locations ranges from one to more than one hundred. Still, we are able to develop potential and transformed abundance surfaces over the entire region. In the hierarchical framework, categorical abundance classifications are induced by continuous latent surfaces. The degradation model above is built on the latent scale. On this scale, an areal level spatial regression model was used for modeling the dependence of species abundance on the environmental factors. To capture anticipated similarity in abundance pattern among neighboring regions, spatial random effects with a conditionally autoregressive prior (CAR) were specified. Model fitting is through familiar Markov chain Monte Carlo methods. While models with CAR priors are usually efficiently fitted, even with large data sets, with our modeling and the large number of cells, run times became very long. So a novel parallelized computing strategy was developed to expedite fitting. The model was run for six different species. With categorical data, display of the resultant abundance patterns is a challenge and we offer several different views. The patterns are of importance on their own, comparatively across the region and across species, with implications for species competition and, more generally, for planning and conservation.

A jungle in there: bacteria in belly buttons are highly diverse, but predictable.

The belly button is one of the habitats closest to us, and yet it remains relatively unexplored. We analyzed bacteria and arachaea from the belly buttons of humans from two different populations sampled within a nation-wide citizen science project. We examined bacterial and archaeal phylotypes present and their diversity using multiplex pyrosequencing of 16S rDNA libraries. We then tested the oligarchy hypothesis borrowed from tropical macroecology, namely that the frequency of phylotypes in one sample of humans predicts its frequency in another independent sample. We also tested the predictions that frequent phylotypes (the oligarchs) tend to be common when present, and tend to be more phylogenetically clustered than rare phylotypes. Once rarefied to four hundred reads per sample, bacterial communities from belly buttons proved to be at least as diverse as communities known from other skin studies (on average 67 bacterial phylotypes per belly button). However, the belly button communities were strongly dominated by a few taxa: only 6 phylotypes occurred on >80% humans. While these frequent bacterial phylotypes (the archaea were all rare) are a tiny part of the total diversity of bacteria in human navels (<0.3% of phylotypes), they constitute a major portion of individual reads (∼1/3), and are predictable among independent samples of humans, in terms of both the occurrence and evolutionary relatedness (more closely related than randomly drawn equal sets of phylotypes). Thus, the hypothesis that “oligarchs” dominate diverse assemblages appears to be supported by human-associated bacteria. Although it remains difficult to predict which species of bacteria might be found on a particular human, predicting which species are most frequent (or rare) seems more straightforward, at least for those species living in belly buttons.

Data-model fusion to better understand emerging pathogens and improve infectious disease forecasting

Ecologists worldwide are challenged to contribute solutions to urgent and pressing environmental problems by forecasting how populations, communities, and ecosystems will respond to global change. Rising to this challenge requires organizing ecological information derived from diverse sources and formally assimilating data with models of ecological processes. The study of infectious disease has depended on strategies for integrating patterns of observed disease incidence with mechanistic process models since John Snow first mapped cholera cases around a London water pump in 1854. Still, zoonotic and vector‐borne diseases increasingly affect human populations, and methods used to successfully characterize directly transmitted diseases are often insufficient. We use four case studies to demonstrate that advances in disease forecasting require better understanding of zoonotic host and vector populations, as well of the dynamics that facilitate pathogen amplification and disease spillover into humans. In each case study, this goal is complicated by limited data, spatiotemporal variability in pathogen transmission and impact, and often, insufficient biological understanding. We present a conceptual framework for data–model fusion in infectious disease research that addresses these fundamental challenges using a hierarchical state‐space structure to (1) integrate multiple data sources and spatial scales to inform latent parameters, (2) partition uncertainty in process and observation models, and (3) explicitly build upon existing ecological and epidemiological understanding. Given the constraints inherent in the study of infectious disease and the urgent need for progress, fusion of data and expertise via this type of conceptual framework should prove an indispensable tool.

Experimental biogeography: the role of environmental gradients in high geographic diversity in Cape Proteaceae

One of the fundamental dimensions of biodiversity is the rate of species turnover across geographic distance. The Cape Floristic Region of South Africa has exceptionally high geographic species turnover, much of which is associated with groups of closely related species with mostly or completely non-overlapping distributions. A basic unresolved question about biodiversity in this global hotspot is the relative importance of ecological gradients in generating and maintaining high geographic turnover in the region. We used reciprocal transplant experiments to test the extent to which abiotic environmental factors may limit the distributions of a group of closely related species in the genus Protea (Proteaceae), and thus elevate species turnover in this diverse, iconic family. We tested whether these species have a “home site advantage” in demographic rates (germination, growth, mortality), and also parameterized stage-structured demographic models for the species. Two of the three native species were predicted to have a demographic advantage at their home sites. The models also predicted, however, that species could maintain positive population growth rates at sites beyond their current distribution limits. Thus the experiment suggests that abiotic limitation under current environmental conditions does not fully explain the observed distribution limits or resulting biogeographic pattern. One potentially important mechanism is dispersal limitation, which is consistent with estimates based on genetic data and mechanistic dispersal models, though other mechanisms including competition may also play a role.

Cattle grazing and conservation of a meadow-dependent amphibian species in the Sierra Nevada.

World-wide population declines have sharpened concern for amphibian conservation on working landscapes. Across the Sierra Nevadas national forest lands, where almost half of native amphibian species are considered at risk, permitted livestock grazing is a notably controversial agricultural activity. Cattle (Bos taurus) grazing is thought to degrade the quality, and thus reduce occupancy, of meadow breeding habitat for amphibian species of concern such as the endemic Yosemite toad (Anaxyrus [ = Bufo] canorus). However, there is currently little quantitative information correlating cattle grazing intensity, meadow breeding habitat quality, and toad use of meadow habitat. We surveyed biotic and abiotic factors influencing cattle utilization and toad occupancy across 24 Sierra Nevada meadows to establish these correlations and inform conservation planning efforts. We utilized both traditional regression models and Bayesian structural equation modeling to investigate potential drivers of meadow habitat use by cattle and Yosemite toads. Cattle use was negatively related to meadow wetness, while toad occupancy was positively related. In mid and late season (mid July–mid September) grazing periods, cattle selected for higher forage quality diets associated with vegetation in relatively drier meadows, whereas toads were more prevalent in wetter meadows. Because cattle and toads largely occupied divergent zones along the moisture gradient, the potential for indirect or direct negative effects is likely minimized via a partitioning of the meadow habitat. During the early season, when habitat use overlap was highest, overall low grazing levels resulted in no detectable impacts on toad occupancy. Bayesian structural equation analyses supported the hypothesis that meadow hydrology influenced toad meadow occupancy, while cattle grazing intensity did not. These findings suggest cattle production and amphibian conservation can be compatible goals within this working landscape.

Forest disturbance accelerates thermophilization of understory plant communities

Summary 1. Climate change is likely to shift plant communities towards species from warmer regions, a process termed ‘thermophilization’. In forests, canopy disturbances such as fire may hasten this process by increasing temperature and moisture stress in the understory, yet little is known about the mechanisms that might drive such shifts, or the consequences of these processes for plant diversity. 2. We sampled understory vegetation across a gradient of disturbance severity from a large-scale natural experiment created by the factorial combination of forest thinning and wildfire in California. Using information on evolutionary history and functional traits, we tested the hypothesis that disturbance severity should increase community dominance by species with southern-xeric biogeographic affinities. We also analysed how climatic productivity mediates the effect of disturbance severity, and quantified the functional trait response to disturbance, to investigate potential mechanisms behind thermophilization. 3. The proportion of north-temperate flora decreased, while the proportion of southern-xeric flora increased, with greater disturbance severity and less canopy closure. Disturbance caused a greater reduction of north-temperate flora in productive (wetter) forests, while functional trait analyses suggested that species colonizing after severe disturbance may be adapted to increased water stress. Forests with intermediate disturbance severity, where abundances of northern and southern species were most equitable, had the highest stand-scale understory diversity. 4. Synthesis: Canopy disturbance is likely to accelerate plant community shifts towards species from warmer regions, via its effects on understory microclimate at small scales. Understory diversity can be enhanced by intermediate disturbance regimes that promote the coexistence of species with different biogeographic affinities.