Estimating scale-specific and localized spatial patterns in allele frequency.

Characterizing spatial patterns in allele frequencies is fundamental to evolutionary biology because these patterns contain evidence of underlying processes. However, the spatial scales at which gene flow, changing selection, and drift act are often unknown. Many of these processes can operate inconsistently across space, causing nonstationary patterns. We present a wavelet approach to characterize spatial pattern in allele frequency that helps solve these problems. We show how our approach can characterize spatial patterns in relatedness at multiple spatial scales, i.e. a multilocus wavelet genetic dissimilarity. We also develop wavelet tests of spatial differentiation in allele frequency and quantitative trait loci (QTL). With simulation, we illustrate these methods under different scenarios. We also apply our approach to natural populations of Arabidopsis thaliana to characterize population structure and identify locally adapted loci across scales. We find, for example, that Arabidopsis flowering time QTL show significantly elevated genetic differentiation at 300-1,300 km scales. Wavelet transforms of allele frequencies offer a flexible way to reveal geographic patterns and underlying evolutionary processes.

A mechanistic statistical approach to infer invasion characteristics of human-dispersed species with complex life cycle.

The rising introduction of invasive species through trade networks threatens biodiversity and ecosystem services. Yet, we have a limited understanding of how transportation networks determine patterns of range expansion. This is partly because current analytical models fail to integrate the invader's life-history dynamics with heterogeneity in human-mediated dispersal patterns. And partly because classical statistical methods often fail to provide reliable estimates of model parameters due to spatial biases in the presence-only records and lack of informative demographic data. To address these gaps, we first formulate an age-structured metapopulation model that uses a probability matrix to emulate human-mediated dispersal patterns. The model reveals that an invader spreads along the shortest network path, such that the inter-patch network distances decrease with increasing traffic volume and reproductive value of hitchhikers. Next, we propose a Bayesian statistical method to estimate model parameters using presence-only data and prior demographic knowledge. To show the utility of the statistical approach, we analyze zebra mussel (Dreissena polymorpha) expansion in North America through the commercial shipping network. Our analysis underscores the importance of correcting spatial biases and leveraging priors to answer questions, such as where and when the zebra mussels were introduced and what life-history characteristics make these mollusks successful invaders.

The Mismatch between Range and Niche Limits due to Source-Sink Dynamics Can Be Greater than Species Mean Dispersal Distance.

AbstractSpecies distribution models assume that at broad spatial scales, environmental conditions determine species ranges and, as such, source-sink dynamics can be ignored. A rationale behind this assumption is that source-sink dynamics manifest at length scales comparable to species mean dispersal distance, which is much smaller than length scales of species distribution and variation in climate. Using a two-dimensional reaction-diffusion model, we show that species can use sink habitats near the niche limit as stepping-stones to occupy sink habitats much further than the mean dispersal distance, thereby extending the distribution far beyond the environmental niche limit. This mismatch between range and niche limits is mediated by the shape (local curvature) of the niche limit. These curvature effects may be significant for a highly dispersive species with low per capita growth rate sensitivity to changes in the environment. These findings underscore the potential importance of stepping-stone dispersal in determining range limits.

Fine-scale monitoring and mapping of biodiversity and ecosystem services reveals multiple synergies and few tradeoffs in urban green space management.

Urban watersheds can play a critical role in supporting biodiversity and ecosystem services in a rapidly changing world. However, managing for multiple environmental and social objectives in urban landscapes is challenging, especially if the optimization of one ecosystem service conflicts with another. Urban ecology research has frequently been limited to a few indicators - typically either biodiversity or ecosystem service indices - making tradeoffs and synergies difficult to assess. Through a recently established watershed-scale monitoring network in Central Texas, we address this gap by evaluating biodiversity (flora and fauna), habitat quality, and ecosystem service indices of urban green spaces across the watershed. Our results reveal substantial heterogeneity in biodiversity and ecosystem service levels and multiple synergies (stacked benefits or "win-wins"). For example, we found that carbon sequestration positively correlated with tree species richness and the proportion of native trees in a green space, indicating that biodiversity goals for increased tree diversity can also provide carbon sequestration benefits. We also documented correlations between green spaces with greater riparian forest cover and lower particulate matter (PM2.5) concentrations and cooler temperatures. In addition, we found that bee and wasp species richness was positively correlated with carbon sequestration and human visitation rates, meaning that urban green spaces can optimize carbon sequestration goals without losing pollinator habitat or access opportunities for city residents. Overall, our results indicate that many aspects of habitat quality, biodiversity, and ecosystem services can be simultaneously supported in urban green spaces. We conclude that urban design and management can optimize nature-based solutions and strategies to have distinct positive impacts on both people and nature.

Altitudinal limits of Eastern Himalayan birds are created by competition past and present.

The degree to which interspecific competition structures diverse communities is an oft-debated topic. An approach to answering this question is to examine spatial patterns of coexistence among putatively competing species. The degree to which interspecies competition predominates in a community can have important effects on our ability predict the response of that community to perturbations, most notably climate change, when shifting species' ranges may result in novel species assemblages. We present a study on the avifauna of the Eastern Himalayas. We hypothesize that in a community where competitive interactions predominate, there will be a relationship between pairwise altitudinal overlaps and morphological differences between species. Moreover, we hypothesize that both morphological traits and altitudinal traits depart from a Brownian motion evolution model, resulting in species trait covariances having a phylogenetic component. We find a significant relationship between morphological dissimilarity and altitudinal overlaps of species pairs. We also find that closely related species are significantly more altitudinally stratified than a null model would predict. However, as more distantly related species pairs are included in the analysis, this pattern disappears, indicating that competitive interactions predominate only in closely related species. This is further suggested by the fact that altitudinal ranges themselves are phylogenetically overdispersed at the genus level, as are morphological traits. This effect disappears when the entire phylogeny is examined, with morphology and altitude being phylogenetically underdispersed. Model fitting suggests that individual clades have evolved towards local clade-specific fitness peaks, while within-clade results show evidence of niche partitioning. We interpret these results as a tension between competition on shorter time scales and selection on longer time scales, where competition forces closely-related species away from fitness peaks in order to allow for niche separation and hence coexistence, suggesting that this effect is partially responsible for the recent diversification of Eastern Himalayan avifauna.

Iterative near-term ecological forecasting: Needs, opportunities, and challenges.

Two foundational questions about sustainability are "How are ecosystems and the services they provide going to change in the future?" and "How do human decisions affect these trajectories?" Answering these questions requires an ability to forecast ecological processes. Unfortunately, most ecological forecasts focus on centennial-scale climate responses, therefore neither meeting the needs of near-term (daily to decadal) environmental decision-making nor allowing comparison of specific, quantitative predictions to new observational data, one of the strongest tests of scientific theory. Near-term forecasts provide the opportunity to iteratively cycle between performing analyses and updating predictions in light of new evidence. This iterative process of gaining feedback, building experience, and correcting models and methods is critical for improving forecasts. Iterative, near-term forecasting will accelerate ecological research, make it more relevant to society, and inform sustainable decision-making under high uncertainty and adaptive management. Here, we identify the immediate scientific and societal needs, opportunities, and challenges for iterative near-term ecological forecasting. Over the past decade, data volume, variety, and accessibility have greatly increased, but challenges remain in interoperability, latency, and uncertainty quantification. Similarly, ecologists have made considerable advances in applying computational, informatic, and statistical methods, but opportunities exist for improving forecast-specific theory, methods, and cyberinfrastructure. Effective forecasting will also require changes in scientific training, culture, and institutions. The need to start forecasting is now; the time for making ecology more predictive is here, and learning by doing is the fastest route to drive the science forward.

Ontogeny constrains phenology: opportunities for activity and reproduction interact to dictate potential phenologies in a changing climate.

As global warming has lengthened the active seasons of many species, we need a framework for predicting how advances in phenology shape the life history and the resulting fitness of organisms. Using an individual-based model, we show how warming differently affects annual cycles of development, growth, reproduction and activity in a group of North American lizards. Populations in cold regions can grow and reproduce more when warming lengthens their active season. However, future warming of currently warm regions advances the reproductive season but reduces the survival of embryos and juveniles. Hence, stressful temperatures during summer can offset predicted gains from extended growth seasons and select for lizards that reproduce after the warm summer months. Understanding these cascading effects of climate change may be crucial to predict shifts in the life history and demography of species.

Resolving the life cycle alters expected impacts of climate change.

Recent models predict contrasting impacts of climate change on tropical and temperate species, but these models ignore how environmental stress and organismal tolerance change during the life cycle. For example, geographical ranges and extinction risks have been inferred from thermal constraints on activity during the adult stage. Yet, most animals pass through a sessile embryonic stage before reaching adulthood, making them more susceptible to warming climates than current models would suggest. By projecting microclimates at high spatio-temporal resolution and measuring thermal tolerances of embryos, we developed a life cycle model of population dynamics for North American lizards. Our analyses show that previous models dramatically underestimate the demographic impacts of climate change. A predicted loss of fitness in 2% of the USA by 2100 became 35% when considering embryonic performance in response to hourly fluctuations in soil temperature. Most lethal events would have been overlooked if we had ignored thermal stress during embryonic development or had averaged temperatures over time. Therefore, accurate forecasts require detailed knowledge of environmental conditions and thermal tolerances throughout the life cycle.

Resilience vs. historical contingency in microbial responses to environmental change.

How soil processes such as carbon cycling will respond to future climate change depends on the responses of complex microbial communities, but most ecosystem models assume that microbial functional responses are resilient and can be predicted from simple parameters such as biomass and temperature. Here, we consider how historical contingencies might alter those responses because function depends on prior conditions or biota. Functional resilience can be driven by physiological, community or adaptive shifts; historical contingencies can result from the influence of historical environments or a combination of priority effects and biotic resistance. By modelling microbial population responses to environmental change, we demonstrate that historical environments can constrain soil function with the degree of constraint depending on the magnitude of change in the context of the prior environment. For example microbial assemblages from more constant environments were more sensitive to change leading to poorer functional acclimatisation compared to microbial assemblages from more fluctuating environments. Such historical contingencies can lead to deviations from expected functional responses to climate change as well as local variability in those responses. Our results form a set of interrelated hypotheses regarding soil microbial responses to climate change that warrant future empirical attention.

The role of functional traits and individual variation in the co-occurrence of Ficus species.

The processes that structure assemblages of species in hyper-diverse genera, such as Ficus (Moraceae), are not well understood. Functional diversity of co-occurring species can reveal evidence for assembly processes; however, intraspecific variation may weaken species-level patterns. We studied whether functional and phylogenetic diversity of Ficus species indicated the effects of spatial variation in filters associated with topography or niche partitioning related to resource use and biotic interactions. We also asked whether individual trait patterns supported species-level patterns. We studied six traits (leaf area, succulence, specific leaf area [SLA], maximum diameter breast high [dbh], fruit size, and latex exudation) for 22 Ficus species and 335 individuals > or = 10 cm dbh on a 20-ha forest plot in China. We found that higher elevation was correlated to changes in mean and reduced diversity of five traits, possibly due to frequent disturbances at higher elevations that favored fast-growing, poorly defended species with high SLA. Maximum dbh showed phylogenetic conservatism but high diversity among co-occurring species, suggesting adult stature is an important axis of within-quadrat niche partitioning. At the individual level, trait patterns were qualitatively consistent but were stronger than species-level patterns, especially for the leaf traits with the greatest intraspecific variation (SLA and succulence). Individual-level SLA exhibited the strongest evidence for both traits among and within-quadrat niche partitioning and indicated elevational filtering. Local niche partitioning and elevational filtering likely play an important role in maintaining species and functional diversity in the most speciose genus at our study site. Our results highlight the importance of individual variation, as it may reveal otherwise obscured niche effects.

Natural variation in abiotic stress responsive gene expression and local adaptation to climate in Arabidopsis thaliana.

Gene expression varies widely in natural populations, yet the proximate and ultimate causes of this variation are poorly known. Understanding how variation in gene expression affects abiotic stress tolerance, fitness, and adaptation is central to the field of evolutionary genetics. We tested the hypothesis that genes with natural genetic variation in their expression responses to abiotic stress are likely to be involved in local adaptation to climate in Arabidopsis thaliana. Specifically, we compared genes with consistent expression responses to environmental stress (expression stress responsive, "eSR") to genes with genetically variable responses to abiotic stress (expression genotype-by-environment interaction, "eGEI"). We found that on average genes that exhibited eGEI in response to drought or cold had greater polymorphism in promoter regions and stronger associations with climate than those of eSR genes or genomic controls. We also found that transcription factor binding sites known to respond to environmental stressors, especially abscisic acid responsive elements, showed significantly higher polymorphism in drought eGEI genes in comparison to eSR genes. By contrast, eSR genes tended to exhibit relatively greater pairwise haplotype sharing, lower promoter diversity, and fewer nonsynonymous polymorphisms, suggesting purifying selection or selective sweeps. Our results indicate that cis-regulatory evolution and genetic variation in stress responsive gene expression may be important mechanisms of local adaptation to climatic selective gradients.

Reserve size and fragmentation alter community assembly, diversity, and dynamics.

Researchers have disputed whether a single large habitat reserve will support more species than many small reserves. However, relatively little is known from a theoretical perspective about how reserve size affects competitive communities structured by spatial abiotic gradients. We investigate how reserve size affects theoretical communities whose assembly is governed by dispersal limitation, abiotic niche differentiation, and source-sink dynamics. Simulations were conducted with varying scales of dispersal across landscapes with variable environmental spatial autocorrelation. Landscapes were inhabited by simulated trees with seedling and adult stages. For a fixed total area in reserves, we found that small reserve systems increased the distance between environments dominated by different species, diminishing the effects of source-sink dynamics. As reserve size decreased, environmental limitations to community assembly became stronger, α species richness decreased, and γ richness increased. When dispersal occurred across short distances, a large reserve strategy caused greater stochastic community variation, greater α richness, and lower γ richness than in small reserve systems. We found that reserve size variation trades off between preserving different aspects of natural communities, including α diversity versus γ diversity. Optimal reserve size will depend on the importance of source-sink dynamics and the value placed on different characteristics of natural communities. Anthropogenic changes to the size and separation of remnant habitats can have far-reaching effects on community structure and assembly.

Spatial forecasting of switchgrass productivity under current and future climate change scenarios.

Evaluating the potential of alternative energy crops across large geographic regions, as well as over time, is a necessary component to determining if biofuel production is feasible and sustainable in the face of growing production demands and climatic change. Switchgrass (Panicum virgatum L.), a native perennial herbaceous grass, is a promising candidate for cellulosic feedstock production. In this study, current and future (from 2080 to 2090) productivity is estimated across the central and eastern United States using ALMANAC, a mechanistic model that simulates plant growth over time. The ALMANAC model was parameterized for representative ecotypes of switchgrass. Our results indicate substantial variation in switchgrass productivity both within regions and over time. States along the Gulf Coast, southern Atlantic Coast, and in the East North Central Midwest have the highest current biomass potential. However, these areas also contain critical wetland habitat necessary for the maintenance of biodiversity and agricultural lands necessary for food production. The southern United States is predicted to have the largest decrease in future biomass production. The Great Plains are expected to experience large increases in productivity by 2080-2090 due to climate change. In general, regions where future temperature and precipitation are predicted to increase are also where larger future biomass production is expected. In contrast, regions that show a future decrease in precipitation are associated with smaller future biomass production. Switchgrass appears to be a promising biofuel crop for the central and eastern United States, with local biomass predicted to be high (>10 Mg/ha) for approximately 50% of the area studied for each climate scenario. In order to minimize land conversion and loss of biodiversity, areas that currently have and maintain high productivity under climate change should be targeted for their long-term growth potential.

Characterizing genomic variation of Arabidopsis thaliana: the roles of geography and climate.

Arabidopsis thaliana inhabits diverse climates and exhibits varied phenology across its range. Although A. thaliana is an extremely well-studied model species, the relationship between geography, growing season climate and its genetic variation is poorly characterized. We used redundancy analysis (RDA) to quantify the association of genomic variation [214 051 single nucleotide polymorphisms (SNPs)] with geography and climate among 1003 accessions collected from 447 locations in Eurasia. We identified climate variables most correlated with genomic variation, which may be important selective gradients related to local adaptation across the species range. Climate variation among sites of origin explained slightly more genomic variation than geographical distance. Large-scale spatial gradients and early spring temperatures explained the most genomic variation, while growing season and summer conditions explained the most after controlling for spatial structure. SNP variation in Scandinavia showed the greatest climate structure among regions, possibly because of relatively consistent phenology and life history of populations in this region. Climate variation explained more variation among nonsynonymous SNPs than expected by chance, suggesting that much of the climatic structure of SNP correlations is due to changes in coding sequence that may underlie local adaptation.

Enhanced migratory waterfowl distribution modeling by inclusion of depth to water table data.

In addition to being used as a tool for ecological understanding, management and conservation of migratory waterfowl rely heavily on distribution models; yet these models have poor accuracy when compared to models of other bird groups. The goal of this study is to offer methods to enhance our ability to accurately model the spatial distributions of six migratory waterfowl species. This goal is accomplished by creating models based on species-specific annual cycles and introducing a depth to water table (DWT) data set. The DWT data set, a wetland proxy, is a simulated long-term measure of the point either at or below the surface where climate and geological/topographic water fluxes balance. For species occurrences, the USGS' banding bird data for six relatively common species was used. Distribution models are constructed using Random Forest and MaxEnt. Random Forest classification of habitat and non-habitat provided a measure of DWT variable importance, which indicated that DWT is as important, and often more important, to model accuracy as temperature, precipitation, elevation, and an alternative wetland measure. MaxEnt models that included DWT in addition to traditional predictor variables had a considerable increase in classification accuracy. Also, MaxEnt models created with DWT often had higher accuracy when compared with models created with an alternative measure of wetland habitat. By comparing maps of predicted probability of occurrence and response curves, it is possible to explore how different species respond to water table depth and how a species responds in different seasons. The results of this analysis also illustrate that, as expected, all waterfowl species are tightly affiliated with shallow water table habitat. However, this study illustrates that the intensity of affiliation is not constant between seasons for a species, nor is it consistent between species.

A sampling theory for asymmetric communities.

We introduce the first analytical model of asymmetric community dynamics to yield Hubbell's neutral theory in the limit of functional equivalence among all species. Our focus centers on an asymmetric extension of Hubbell's local community dynamics, while an analogous extension of Hubbell's metacommunity dynamics is deferred to an appendix. We find that mass-effects may facilitate coexistence in asymmetric local communities and generate unimodal species abundance distributions indistinguishable from those of symmetric communities. Multiple modes, however, only arise from asymmetric processes and provide a strong indication of non-neutral dynamics. Although the exact stationary distributions of fully asymmetric communities must be calculated numerically, we derive approximate sampling distributions for the general case and for nearly neutral communities where symmetry is broken by a single species distinct from all others in ecological fitness and dispersal ability. In the latter case, our approximate distributions are fully normalized, and novel asymptotic expansions of the required hypergeometric functions are provided to make evaluations tractable for large communities. Employing these results in a bayesian analysis may provide a novel statistical test to assess the consistency of species abundance data with the neutral hypothesis.

Habitat conversion, extinction thresholds, and pollination services in agroecosystems.

Parallel declines of wild pollinators and pollinator-dependent plants have raised alarms over the loss of pollination services in agroecosystems. A spatially explicit approach is needed to develop specific recommendations regarding the design of agricultural landscapes to sustain wild pollinator communities and the services they provide. I modeled pollination services in agroecosystems using a pair of models: a stochastic individual-based simulation model of wild pollinators, pollinator-dependent plants, and crop pollination; and a set of coupled difference equations designed to capture the nonspatial component of the simulation model. Five spatially explicit models of habitat conversion to crops were simulated, and results for pollination services were compared. Mean-field behavior of the simulation model was in good agreement with analysis of the difference equations. A major feature of the models was the presence of a cusp leading to loss of stability and extinction of pollinators and pollinator-dependent plants beyond a critical amount of habitat loss. The addition of pollen obtained from crop visitation caused a breakdown of the cusp preventing extinction of pollinators, but not of wild pollinator-dependent plants. Spatially restricted foraging and dispersal also altered model outcomes relative to mean-field predictions, in some cases causing extinction under parameter settings that would otherwise lead to persistence. Different patterns of habitat conversion to crops resulted in different levels of pollination services. Most interesting was the finding that optimal pollination services occurred when the size of remnant habitat patches was equal to half the mean foraging and dispersal distance of pollinators and the spacing between remnant patches was equal to the mean foraging and dispersal distance. Conservation of wild pollinators and pollinator-dependent plants in agroecosystems requires careful attention to thresholds in habitat conversion and spatial pattern and scale of remnant habitats. Maximization of pollination services was generally incompatible with conservation of wild pollinator-dependent plants. My prediction is that pollination services will be maximized by providing islands of nesting habitat where interisland distance matches mean foraging distances of wild pollinators.

The role of demography and markets in determining deforestation rates near Ranomafana National Park, Madagascar.

The highland forests of Madagascar are home to some of the world's most unique and diverse flora and fauna and to some of its poorest people. This juxtaposition of poverty and biodiversity is continually reinforced by rapid population growth, which results in increasing pressure on the remaining forest habitat in the highland region, and the biodiversity therein. Here we derive a mathematical expression for the subsistence of households to assess the role of markets and household demography on deforestation near Ranomafana National Park. In villages closest to urban rice markets, households were likely to clear less land than our model predicted, presumably because they were purchasing food at market. This effect was offset by the large number of migrant households who cleared significantly more land between 1989-2003 than did residents throughout the region. Deforestation by migrant households typically occurred after a mean time lag of 9 years. Analyses suggest that while local conservation efforts in Madagascar have been successful at reducing the footprint of individual households, large-scale conservation must rely on policies that can reduce the establishment of new households in remaining forested areas.

Using circuit theory to model connectivity in ecology, evolution, and conservation.

Connectivity among populations and habitats is important for a wide range of ecological processes. Understanding, preserving, and restoring connectivity in complex landscapes requires connectivity models and metrics that are reliable, efficient, and process based. We introduce a new class of ecological connectivity models based in electrical circuit theory. Although they have been applied in other disciplines, circuit-theoretic connectivity models are new to ecology. They offer distinct advantages over common analytic connectivity models, including a theoretical basis in random walk theory and an ability to evaluate contributions of multiple dispersal pathways. Resistance, current, and voltage calculated across graphs or raster grids can be related to ecological processes (such as individual movement and gene flow) that occur across large population networks or landscapes. Efficient algorithms can quickly solve networks with millions of nodes, or landscapes with millions of raster cells. Here we review basic circuit theory, discuss relationships between circuit and random walk theories, and describe applications in ecology, evolution, and conservation. We provide examples of how circuit models can be used to predict movement patterns and fates of random walkers in complex landscapes and to identify important habitat patches and movement corridors for conservation planning.