Matthew I. Pyne

A structured and dynamic framework to advance traits‐based theory and prediction in ecology

Predicting changes in community composition and ecosystem function in a rapidly changing world is a major research challenge in ecology. Traits-based approaches have elicited much recent interest, yet individual studies are not advancing a more general, predictive ecology. Significant progress will be facilitated by adopting a coherent theoretical framework comprised of three elements: an underlying trait distribution, a performance filter defining the fitness of traits in different environments, and a dynamic projection of the performance filter along some environmental gradient. This framework allows changes in the trait distribution and associated modifications to community composition or ecosystem function to be predicted across time or space. The structure and dynamics of the performance filter specify two key criteria by which we judge appropriate quantitative methods for testing traits-based hypotheses. Bayesian multilevel models, dynamical systems models and hybrid approaches meet both these criteria and have the potential to meaningfully advance traits-based ecology.

Developing linkages between species traits and multiscaled environmental variation to explore vulnerability of stream benthic communities to climate change

Abstract Forecasting responses of benthic community structure and function to anthropogenic climate change is an emerging scientific challenge. Characterizing benthic species by biological attributes (traits) that are responsive to temperature and streamflow conditions can support a mechanistic approach for assessing the potential ecological responses to climate change. However, nonclimatic environmental factors also structure benthic communities and may mitigate transient climatic conditions, and these must be considered in evaluating potential impacts of climate change. Here we used macroinvertebrate and environmental data for 279 reference-quality sites spanning 12 states in the western US. For each sampling location, we described 45 environmental variables that spanned reach to catchment scales and that represented contemporary climate drivers, hydrologic metrics, and nonclimatic habitat features, as well as purely spatial metrics. We described benthic community composition at each site in terms of 7 species traits, including those considered sensitive to temperature increases and streamflow changes. All combined environmental variables explained 67% of the total trait variation across the sites, and catchment-scale climatic and hydrologic variables independently accounted for 19%. Sites were clustered into 3 community types based on trait composition, and a classification-tree analysis confirmed that climatic and hydrologic variables were important in partitioning these groups. Sensitivity of benthic communities to projected climate change was assessed by quantifying the proportion of taxa at sites having the traits of either cold stenothermy or obligate rheophily. Regression-tree analysis showed that temperature and hydrologic variables mostly accounted for the differences in proportion of sensitivity traits across the sites. We examined the vulnerability of sites to climate change by superimposing regional-scale projections of late-21st-century temperature and runoff change on the spatial distribution of temperature- and runoff-sensitive assemblages. Sites with high proportions of cold stenotherms and obligate rheophiles occur throughout the western US, but the degree of temperature and runoff change is projected to be greatest for reference sites in the Upper Colorado River and Great Basin. Thus, our results suggest that traits-based sensitivity coupled with intraregional variation in projected changes in temperature and runoff will cause reference sites in the western US to be differentially vulnerable to future climate change.

Vulnerability of stream community composition and function to projected thermal warming and hydrologic change across ecoregions in the western United States

Shifts in biodiversity and ecological processes in stream ecosystems in response to rapid climate change will depend on how numerically and functionally dominant aquatic insect species respond to changes in stream temperature and hydrology. Across 253 minimally perturbed streams in eight ecoregions in the western USA, we modeled the distribution of 88 individual insect taxa in relation to existing combinations of maximum summer temperature, mean annual streamflow, and their interaction. We used a heat map approach along with downscaled general circulation model (GCM) projections of warming and streamflow change to estimate site-specific extirpation likelihood for each taxon, allowing estimation of whole-community change in streams across these ecoregions. Conservative climate change projections indicate a 30-40% loss of taxa in warmer, drier ecoregions and 10-20% loss in cooler, wetter ecoregions where taxa are relatively buffered from projected warming and hydrologic change. Differential vulnerability of taxa with key functional foraging roles in processing basal resources suggests that climate change has the potential to modify stream trophic structure and function (e.g., alter rates of detrital decomposition and algal consumption), particularly in warmer and drier ecoregions. We show that streamflow change is equally as important as warming in projected risk to stream community composition and that the relative threat posed by these two fundamental drivers varies across ecoregions according to projected gradients of temperature and hydrologic change. Results also suggest that direct human modification of streams through actions such as water abstraction is likely to further exacerbate loss of taxa and ecosystem alteration, especially in drying climates. Management actions to mitigate climate change impacts on stream ecosystems or to proactively adapt to them will require regional calibration, due to geographic variation in insect sensitivity and in exposure to projected thermal warming and hydrologic change.

Survival and Breeding Transitions for a Reintroduced Bison Population: a Multistate Approach

Abstract The iconic plains bison (Bison bison) have been reintroduced to many places in their former range, but there are few scientific data evaluating the success of these reintroductions or guiding the continued management of these populations. Relying on mark–recapture data, we used a multistate model to estimate bison survival and breeding transition probabilities while controlling for the recapture process. We tested hypotheses in these demographic parameters associated with age, sex, reproductive state, and environmental variables. We also estimated biological process variation in survival and breeding transition probabilities by factoring out sampling variation. The recapture rate of females and calves was high (0.78 ± 0.15 [SE]) and much lower for males (0.41 ± 0.23), especially older males (0.17 ± 0.15). We found that overall bison survival was high (>0.8) and that males (0.80 ± 0.13) survived at lower rates than females (0.94 ± 0.04), but as females aged survival declined (0.89 ± 0.05 for F ≥15 yr old). Lactating and non-lactating females survived at similar rates. We found that females can conceive early (approx. 1.5 yr of age) and had a high probability (approx. 0.8) of breeding in consecutive years, until age 13.5 years, when females that were non-lactating tended to stay in that state. Our results suggest senescence in reproduction and survival for females. We found little support for the effect of climatic covariates on demographic rates, perhaps because the parks current population management goals were predicated from drought-year conditions. This reintroduction has been successful, but continued culling actions will need to be employed and an adaptive management approach is warranted. Our demographic approach can be applied to other heavily managed large-ungulate systems with few or no natural predators.

Classification of California streams using combined deductive and inductive approaches: Setting the foundation for analysis of hydrologic alteration

Regional classification of streams is an early step in the Ecological Limits of Hydrologic Alteration framework. Many stream classifications are based on an inductive approach using hydrologic data from minimally disturbed basins, but this approach may underrepresent streams from heavily disturbed basins or sparsely gaged arid regions. An alternative is a deductive approach, using watershed climate, land use, and geomorphology to classify streams, but this approach may miss important hydrological characteristics of streams. We classified all stream reaches in California using both approaches. First, we used Bayesian and hierarchical clustering to classify reaches according to watershed characteristics. Streams were clustered into seven classes according to elevation, sedimentary rock, and winter precipitation. Permutation-based analysis of variance and random forest analyses were used to determine which hydrologic variables best separate streams into their respective classes. Stream typology (i.e., the class that a stream reach is assigned to) is shaped mainly by patterns of high and mean flow behavior within the streams landscape context. Additionally, random forest was used to determine which hydrologic variables best separate minimally disturbed reference streams from non-reference streams in each of the seven classes. In contrast to stream typology, deviation from reference conditions is more difficult to detect and is largely defined by changes in low-flow variables, average daily flow, and duration of flow. Our combined deductive/inductive approach allows us to estimate flow under minimally disturbed conditions based on the deductive analysis and compare to measured flow based on the inductive analysis in order to estimate hydrologic change.