Elise F. Zipkin

When can efforts to control nuisance and invasive species backfire

Population control through harvest has the potential to reduce the abundance of nuisance and invasive species. However, demographic structure and density-dependent processes can confound removal efforts and lead to undesirable consequences, such as overcompensation (an increase in abundance in response to harvest) and instability (population cycling or chaos). Recent empirical studies have demonstrated the potential for increased mortality (such as that caused by harvest) to lead to overcompensation and instability in plant, insect, and fish populations. We developed a general population model with juvenile and adult stages to help determine the conditions under which control harvest efforts can produce unintended outcomes. Analytical and simulation analyses of the model demonstrated that the potential for overcompensation as a result of harvest was significant for species with high fecundity, even when annual stage-specific survivorship values were fairly low. Population instability as a result of harvest occurred less frequently and was only possible with harvest strategies that targeted adults when both fecundity and adult survivorship were high. We considered these results in conjunction with current literature on nuisance and invasive species to propose general guidelines for assessing the risks associated with control harvest based on life history characteristics of target populations. Our results suggest that species with high per capita fecundity (over discrete breeding periods), short juvenile stages, and fairly constant survivorship rates are most likely to respond undesirably to harvest. It is difficult to determine the extent to which overcompensation and instability could occur during real-world removal efforts, and more empirical removal studies should be undertaken to evaluate population-level responses to control harvests. Nevertheless, our results identify key issues that have been seldom acknowledged and are potentially generic across taxa.

Strategies for fitting nonlinear ecological models in R, AD Model Builder, and BUGS

1. Ecologists often use nonlinear fitting techniques to estimate the parameters of complex ecological models, with attendant frustration. This paper compares three open-source model fitting tools and discusses general strategies for defining and fitting models. 2. R is convenient and (relatively) easy to learn, AD Model Builder is fast and robust but comes with a steep learning curve, while BUGS provides the greatest flexibility at the price of speed. 3. Our model-fitting suggestions range from general cultural advice (where possible, use the tools and models that are most common in your subfield) to specific suggestions about how to change the mathematical description of models to make them more amenable to parameter estimation. 4. A companion web site (https://groups.nceas.ucsb.edu/nonlinear-modeling/projects) presents detailed examples of application of the three tools to a variety of typical ecological estimation problems; each example links both to a detailed project report and to full source code and data.

Overcompensatory response of a smallmouth bass (Micropterus dolomieu) population to harvest: release from competition?

An intensive seven-year removal of adult, juvenile, and young-of-the-year smallmouth bass (Micropterus dolo- mieu) from a north temperate lake (Little Moose Lake, New York, USA) resulted in an increase in overall population abundance, primarily due to increased abundance of immature individuals. We developed a density-dependent, stage-struc- tured model to examine conditions under which population control through harvest could result in the increase of a tar- geted species. Parameter values were derived from a 54-year data set collected from another north temperate lake (Lake Opeongo, Ontario, Canada) smallmouth bass population. Sensitivity analyses identified the demographic conditions that could lead to increased abundance in response to harvest. An increase in population abundance with harvest was most likely to occur when either (i) per capita recruitment at low levels of spawner abundance was large, juvenile survivorship was high, and maturation of age-4 and older juveniles was moderately high or (ii) per capita recruitment at low levels of spawner abundance was slightly lower, yet the maturation rate of age-3 juveniles and adult survivorship were high. Our modeling results together with empirical evidence further demonstrate the importance of overcompensation as a substantial factor to consider in efforts to regulate population abundance through harvest.

Modeling structured population dynamics using data from unmarked individuals

The study of population dynamics requires unbiased, precise estimates of abundance and vital rates that account for the demographic structure inherent in all wildlife and plant populations. Traditionally, these estimates have only been available through approaches that rely on intensive mark-recapture data. We extended recently developed N-mixture models to demonstrate how demographic parameters and abundance can be estimated for structured populations using only stage-structured count data. Our modeling framework can be used to make reliable inferences on abundance as well as recruitment, immigration, stage-specific survival, and detection rates during sampling. We present a range of simulations to illustrate the data requirements, including the number of years and locations necessary for accurate and precise parameter estimates. We apply our modeling framework to a population of northern dusky salamanders (Desmognathus fuscus) in the mid-Atlantic region (USA) and find that the population is unexpectedly declining. Our approach represents a valuable advance in the estimation of population dynamics using multistate data from unmarked individuals and should additionally be useful in the development of integrated models that combine data from intensive (e.g., mark-recapture) and extensive (e.g., counts) data sources.

Correlation and persistence of hunting and logging impacts on tropical rainforest mammals.

Humans influence tropical rainforest animals directly via exploitation and indirectly via habitat disturbance. Bushmeat hunting and logging occur extensively in tropical forests and have large effects on particular species. But how they alter animal diversity across landscape scales and whether their impacts are correlated across species remain less known. We used spatially widespread measurements of mammal occurrence across Malaysian Borneo and recently developed multispecies hierarchical models to assess the species richness of medium- to large-bodied terrestrial mammals while accounting for imperfect detection of all species. Hunting was associated with 31% lower species richness. Moreover, hunting remained high even where richness was very low, highlighting that hunting pressure persisted even in chronically overhunted areas. Newly logged sites had 11% lower species richness than unlogged sites, but sites logged >10 years previously had richness levels similar to those in old-growth forest. Hunting was a more serious long-term threat than logging for 91% of primate and ungulate species. Hunting and logging impacts across species were not correlated across taxa. Negative impacts of hunting were the greatest for common mammalian species, but commonness versus rarity was not related to species-specific impacts of logging. Direct human impacts appeared highly persistent and lead to defaunation of certain areas. These impacts were particularly severe for species of ecological importance as seed dispersers and herbivores. Indirect impacts were also strong but appeared to attenuate more rapidly than previously thought. The lack of correlation between direct and indirect impacts across species highlights that multifaceted conservation strategies may be needed for mammal conservation in tropical rainforests, Earths most biodiverse ecosystems.

Tracking climate impacts on the migratory monarch butterfly

Understanding the impacts of climate on migratory species is complicated by the fact that these species travel through several climates that may be changing in diverse ways throughout their complete migratory cycle. Most studies are not designed to tease out the direct and indirect effects of climate at various stages along the migration route. We assess the impacts of spring and summer climate conditions on breeding monarch butterflies, a species that completes its annual migration cycle over several generations. No single, broad-scale climate metric can explain summer breeding phenology or the substantial year-to-year fluctuations observed in population abundances. As such, we built a Poisson regression model to help explain annual arrival times and abundances in the Midwestern United States. We incorporated the climate conditions experienced both during a spring migration/breeding phase in Texas as well as during subsequent arrival and breeding during the main recruitment period in Ohio. Using data from a state-wide butterfly monitoring network in Ohio, our results suggest that climate acts in conflicting ways during the spring and summer seasons. High spring precipitation in Texas is associated with the largest annual population growth in Ohio and the earliest arrival to the summer breeding ground, as are intermediate spring temperatures in Texas. On the other hand, the timing of monarch arrivals to the summer breeding grounds is not affected by climate conditions within Ohio. Once in Ohio for summer breeding, precipitation has minimal impacts on overall abundances, whereas warmer summer temperatures are generally associated with the highest expected abundances, yet this effect is mitigated by the average seasonal temperature of each location in that the warmest sites receive no benefit of above average summer temperatures. Our results highlight the complex relationship between climate and performance for a migrating species and suggest that attempts to understand how monarchs will be affected by future climate conditions will be challenging.

Seasonal weather patterns drive population vital rates and persistence in a stream fish

Climate change affects seasonal weather patterns, but little is known about the relative importance of seasonal weather patterns on animal population vital rates. Even when such information exists, data are typically only available from intensive fieldwork (e.g., mark-recapture studies) at a limited spatial extent. Here, we investigated effects of seasonal air temperature and precipitation (fall, winter, and spring) on survival and recruitment of brook trout (Salvelinus fontinalis) at a broad spatial scale using a novel stage-structured population model. The data were a 15-year record of brook trout abundance from 72 sites distributed across a 170-km-long mountain range in Shenandoah National Park, Virginia, USA. Population vital rates responded differently to weather and site-specific conditions. Specifically, young-of-year survival was most strongly affected by spring temperature, adult survival by elevation and per-capita recruitment by winter precipitation. Low fall precipitation and high winter precipitation, the latter of which is predicted to increase under climate change for the study region, had the strongest negative effects on trout populations. Simulations show that trout abundance could be greatly reduced under constant high winter precipitation, consistent with the expected effects of gravel-scouring flows on eggs and newly hatched individuals. However, high-elevation sites would be less vulnerable to local extinction because they supported higher adult survival. Furthermore, the majority of brook trout populations are projected to persist if high winter precipitation occurs only intermittently (≤3 of 5 years) due to density-dependent recruitment. Variable drivers of vital rates should be commonly found in animal populations characterized by ontogenetic changes in habitat, and such stage-structured effects may increase population persistence to changing climate by not affecting all life stages simultaneously. Yet, our results also demonstrate that weather patterns during seemingly less consequential seasons (e.g., winter precipitation) can have major impacts on animal population dynamics.

Detection biases yield misleading patterns of species persistence and colonization in fragmented landscapes

Species occurrence patterns, and related processes of persistence, colonization and turnover, are increasingly being used to infer habitat suitability, predict species distributions, and measure biodiversity potential. The majority of these studies do not account for observational error in their analyses despite growing evidence suggesting that the sampling process can significantly influence species detection and subsequently, estimates of occurrence. We examined the potential biases of species occurrence patterns that can result from differences in detectability across species and habitat types using hierarchical multispecies occupancy models applied to a tropical bird community in an agricultural fragmented landscape. Our results suggest that detection varies widely among species and habitat types. Not incorporating detectability severely biased occupancy dynamics for many species by overestimating turnover rates, producing misleading patterns of persistence and colonization of agricultural habitats, and misclassifying species into ecological categories (i.e., forest specialists and generalists). This is of serious concern, given that most research on the ability of agricultural lands to maintain current levels of biodiversity by and large does not correct for differences in detectability. We strongly urge researchers to apply an inferential framework which explicitly account for differences in detectability to fully characterize species-habitat relationships, correctly guide biodiversity conservation in human-modified landscapes, and generate more accurate predictions of species responses to future changes in environmental conditions.

Guidelines for a priori grouping of species in hierarchical community models

Recent methodological advances permit the estimation of species richness and occurrences for rare species by linking species-level occurrence models at the community level. The value of such methods is underscored by the ability to examine the influence of landscape heterogeneity on species assemblages at large spatial scales. A salient advantage of community-level approaches is that parameter estimates for data-poor species are more precise as the estimation process “borrows” from data-rich species. However, this analytical benefit raises a question about the degree to which inferences are dependent on the implicit assumption of relatedness among species. Here, we assess the sensitivity of community/group-level metrics, and individual-level species inferences given various classification schemes for grouping species assemblages using multispecies occurrence models. We explore the implications of these groupings on parameter estimates for avian communities in two ecosystems: tropical forests in Puerto Rico and temperate forests in northeastern United States. We report on the classification performance and extent of variability in occurrence probabilities and species richness estimates that can be observed depending on the classification scheme used. We found estimates of species richness to be most precise and to have the best predictive performance when all of the data were grouped at a single community level. Community/group-level parameters appear to be heavily influenced by the grouping criteria, but were not driven strictly by total number of detections for species. We found different grouping schemes can provide an opportunity to identify unique assemblage responses that would not have been found if all of the species were analyzed together. We suggest three guidelines: (1) classification schemes should be determined based on study objectives; (2) model selection should be used to quantitatively compare different classification approaches; and (3) sensitivity of results to different classification approaches should be assessed. These guidelines should help researchers apply hierarchical community models in the most effective manner.

Explaining Local-Scale Species Distributions: Relative Contributions of Spatial Autocorrelation and Landscape Heterogeneity for an Avian Assemblage

Understanding interactions between mobile species distributions and landcover characteristics remains an outstanding challenge in ecology. Multiple factors could explain species distributions including endogenous evolutionary traits leading to conspecific clustering and endogenous habitat features that support life history requirements. Birds are a useful taxon for examining hypotheses about the relative importance of these factors among species in a community. We developed a hierarchical Bayes approach to model the relationships between bird species occupancy and local landcover variables accounting for spatial autocorrelation, species similarities, and partial observability. We fit alternative occupancy models to detections of 90 bird species observed during repeat visits to 316 point-counts forming a 400-m grid throughout the Patuxent Wildlife Research Refuge in Maryland, USA. Models with landcover variables performed significantly better than our autologistic and null models, supporting the hypothesis that local landcover heterogeneity is important as an exogenous driver for species distributions. Conspecific clustering alone was a comparatively poor descriptor of local community composition, but there was evidence for spatial autocorrelation in all species. Considerable uncertainty remains whether landcover combined with spatial autocorrelation is most parsimonious for describing bird species distributions at a local scale. Spatial structuring may be weaker at intermediate scales within which dispersal is less frequent, information flows are localized, and landcover types become spatially diversified and therefore exhibit little aggregation. Examining such hypotheses across species assemblages contributes to our understanding of community-level associations with conspecifics and landscape composition.