Expansion of bioenergy production is part of a global effort to reduce greenhouse gas emissions and mitigate climate change. Dedicated biomass crops will compete with other land uses as most high quality arable land is already used for agriculture, urban development, and biodiversity conservation.

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.

The emergence of macrosystems ecology (MSE), which focuses on regional- to continental-scale ecological patterns and processes, builds upon a history of long-term and broad-scale studies in ecology. Scientists face the difficulty of integrating the many elements that make up macrosystems, which consist of hierarchical processes at interacting spatial and temporal scales. Researchers must also identify the most relevant scales and variables to be considered, the required data resources, and the appropriate study design to provide the proper inferences. The large volumes of multi-thematic data often associated with macrosystem studies typically require validation, standardization, and assimilation. Finally, analytical approaches need to describe how cross-scale and hierarchical dynamics and interactions relate to macroscale phenomena. Here, we elaborate on some key methodological challenges of MSE research and discuss existing and novel approaches to meet them.

Macrosystems ecology is the study of diverse ecological phenomena at the scale of regions to continents and their interactions with phenomena at other scales. This emerging subdiscipline addresses ecological questions and environmental problems at these broad scales. Here, we describe this new field, show how it relates to modern ecological study, and highlight opportunities that stem from taking a macrosystems perspective. We present a hierarchical framework for investigating macrosystems at any level of ecological organization and in relation to broader and finer scales. Building on well-established theory and concepts from other subdisciplines of ecology, we identify feedbacks, linkages among distant regions, and interactions that cross scales of space and time as the most likely sources of unexpected and novel behaviors in macrosystems. We present three examples that highlight the importance of this multiscaled systems perspective for understanding the ecology of regions to continents.

Migratory stopover habitats are often not part of planning for conservation or new development projects. We identified potential stopover habitats within an avian migratory flyway and demonstrated how this information can guide the site-selection process for new development. We used the random forests modeling approach to map the distribution of predicted stopover habitat for the Whooping Crane (Grus americana), an endangered species whose migratory flyway overlaps with an area where wind energy development is expected to become increasingly important. We then used this information to identify areas for potential wind power development in a U.S. state within the flyway (Nebraska) that minimize conflicts between Whooping Crane stopover habitat and the development of clean, renewable energy sources. Up to 54% of our study area was predicted to be unsuitable as Whooping Crane stopover habitat and could be considered relatively low risk for conflicts between Whooping Cranes and wind energy development. We suggest that this type of analysis be incorporated into the habitat conservation planning process in areas where incidental take permits are being considered for Whooping Cranes or other species of concern. Field surveys should always be conducted prior to construction to verify model predictions and understand baseline conditions.

Abstract 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.

* Individual performance is a function of an individual's traits and its environment. This function, known as an environmental filter, varies in space and affects community composition. However, filters are poorly characterized because dispersal patterns can obscure environmental effects, and few studies utilize longitudinal data linking individual performance to environment. * We model the effects of environmental filters on demographic rates of nearly all tree species (99) in a 25-ha subtropical rain forest plot. We develop a hierarchical Bayesian model of environmental filtering, drawing inspiration from classic studies of intraspecific natural selection. We characterize the specific environmental gradients and trait axes most important in filtering of demographic rates across species. * We found that stronger filtering along a given trait axis corresponded to less spatial variation in the value of favoured traits. * Environmental gradients associated with filtering were different for growth versus survivorship. * Species maximum height was under the strongest filtering for growth, with shorter species favoured on convex ridges. Shorter stature species may be favoured on ridges because trees on ridges experience higher wind damage and lower soil moisture. * Wood density filtering had the strongest effects on survival. Steep slopes and high available P in the soil favoured species with low-density wood. Such sites may be favourable for fast-growing species that exploit resource-rich environments. * Synthesis: We characterized trait-mediated environmental filters that may underlie spatial niche differentiation and life-history trade-offs, which can promote species coexistence. Filtering along trait axes with the strongest effects on local community composition, that is, traits with the strongest filtering, may necessarily have a weaker potential to promote species coexistence across the plot. The weak spatial variation in filters with strong effects on demography may result from long-term processes affecting the species pool that favour habitat generalist strategies.

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.

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.

Historically, the migration of birds has been poorly understood in comparison to other life stages during the annual cycle. The goal of our research is to present a novel approach to predict the migratory movement of birds. Using a blue-winged teal case study, our process incorporates not only constraints on habitat (temperature, precipitation, elevation, and depth to water table), but also approximates the likely bearing and distance traveled from a starting location. The method allows for movement predictions to be made from unsampled areas across large spatial scales. We used USGS’ Bird Banding Laboratory database as the source of banding and recovery locations. We used recovery locations from banding sites with multiple within-30-day recoveries were used to build core maximum entropy models. Because the core models encompass information regarding likely habitat, distance, and bearing, we used core models to project (or forecast) probability of movement from starting locations that lacked sufficient data for independent predictions. The final model for an unsampled area was based on an inverse-distance weighted averaged prediction from the three nearest core models. To illustrate this approach, three unsampled locations were selected to probabilistically predict where migratory blue-wing teals would stopover. These locations, despite having little or none data, are assumed to have populations. For the blue-winged teal case study, 104 suitable locations were identified to generate core models. These locations ranged from 20 to 228 within-30-day recoveries, and all core models had AUC scores greater than 0.80. We can infer based on model performance assessment, that our novel approach to predicting migratory movement is well-grounded and provides a reasonable approximation of migratory movement.

Ecosystem development is mediated by coupled synthesis–decomposition cycles that capture, store and release energy necessary for maintenance and growth. I present a minimal ecosystem model with explicit energy and matter conservation. Energy is captured and stored via synthesis and release through decomposition. This energy is used for biomass production and maintenance. I examine materially closed systems where growth is limited by nutrient availability. I present two key findings. First, maximum biomass production does not occur under conditions of equal nutrient concentrations. Instead, production is maximized when the initial environmental concentration of the energy carrying substrate is increased. Second, the system is characterized by an abrupt collapse when the concentration of the energy carrying substrate is increased above a threshold. This model indicates that in the region of maximum biomass production, ecosystems are fragile rather than resilient.