Elizabeth E. Holmes

A LARGE-SCALE, MULTISPECIES STATUS ASSESSMENT: ANADROMOUS SALMONIDS IN THE COLUMBIA RIVER BASIN

Twelve salmonid evolutionarily significant units (ESUs) throughout the Columbia River Basin are currently listed as threatened or endangered under the Endangered Species Act; these ESUs are affected differentially by a variety of human activities. We present a standardized quantitative status and risk assessment for 152 listed salmonid stocks in these ESUs and 24 nonlisted stocks. Using data from 1980–2000, which represents a time of stable conditions in the Columbia River hydropower system and a period of ocean conditions generally regarded as poor for Columbia Basin salmonids, we estimated the status of these stocks under two different assumptions: that hatchery-reared spawners were not reproducing during the period of the censuses, or that hatchery-reared spawners were reproducing and thus that reproduction from hatchery inputs was masking population trends. We repeated the analyses using a longer time period containing both “good” and “bad” ocean conditions (1965–2000) as a first step toward determini...

VALIDATING POPULATION VIABILITY ANALYSIS FOR CORRUPTED DATA SETS

Diffusion approximation (DA) methods provide a powerful tool for popu- lation viability analysis (PVA) using simple time series of population counts. These methods have a strong theoretical foundation based on stochastic age-structured models, but their application to data with high sampling error or age-structure cycles has been problematic. Recently, a new method was developed for estimating DA parameters from highly corrupted time series. We conducted an extensive cross-validation of this new method using 189 long- term time series of salmon counts with very high sampling error and nonstable age-structure fluctuations. Parameters were estimated from one segment of a time series, and a subsequent segment was used to evaluate the predictions regarding the risk of crossing population thresholds. We also tested the theoretical distributions of the estimated parameters. The distribution of parameter estimates is an essential aspect of a PVA because it allows one to calculate confidence levels for risk metrics. This study is the first data-based cross- validation of these theoretical distributions. Our cross-validation analyses found that, when parameterization methods designed for corrupted data sets are used, DA predictions are very robust even for problematic data. Estimates of the probability of crossing population thresholds were unbiased, and the estimated parameters closely followed the expected theoretical distributions.

AGE-STRUCTURED MODELING REVEALS LONG-TERM DECLINES IN THE NATALITY OF WESTERN STELLER SEA LIONS

Since the mid-1970s, the western Steller sea lion (Eumetopias jubatus), inhabiting Alaskan waters from Prince William Sound west through the Aleutian Islands, has declined by over 80%. Changing oceanographic conditions, competition from fishing operations, direct human-related mortality, and predators have been suggested as factors driving the decline, but the indirect and interactive nature of their effects on sea lions have made it difficult to attribute changes in abundance to specific factors. In part, this is because only changes in abundance, not changes in vital rates, are known. To determine how vital rates of the western Steller sea lion have changed during its 28-year decline, we first estimated the changes in Steller sea lion age structure using measurements of animals in aerial photographs taken during population surveys since 1985 in the central Gulf of Alaska (CGOA). We then fit an age-structured model with temporally varying vital rates to the age-structure data and to total population and pup counts. The model fits indicate that birth rate in the CGOA steadily declined from 1976 to 2004. Over the same period, survivorship first dropped severely in the early 1980s, when the population collapsed, and then survivorship steadily recovered. The best-fitting model indicates that in 2004, the birth rate in the central Gulf of Alaska was 36% lower than in the 1970s, while adult and juvenile survivorship were close to or slightly above 1970s levels. These predictions and other model predictions concerning population structure match independent field data from mark-recapture studies and photometric analyses. The dominant eigenvalue for the estimated 2004 Leslie matrix is 1.0014, indicating a stable population. The stability, however, depends on very high adult survival, and the shift in vital rates results in a population that is more sensitive to changes in adult survivorship. Although our modeling analysis focused exclusively on the central Gulf of Alaska, the western Gulf of Alaska and eastern Aleutians show a similar pattern of declining pup fraction with no increase in the juvenile, or pre-breeding, fraction. This suggests that declining birth rate may be a problem for western Steller sea lions across the Gulf of Alaska and into the Aleutian Islands.

BEYOND THEORY TO APPLICATION AND EVALUATION: DIFFUSION APPROXIMATIONS FOR POPULATION VIABILITY ANALYSIS

Census data on endangered species are often plagued by problems that make quantitative population viability analysis (PVA) a challenge. This paper addresses four such problems: sampling error, density dependence, nonstable age structure, and population supplementation that masks the true population status. Estimating trends and extinction risks using such corrupted data presents serious parameter estimation difficulties. Here I review diffusion approximation (DA) methods for estimating population status and risks from time series data. A variety of parameterization methods are available for DA models; some correct for data corruption and others do not. I illustrate how stochastic Leslie matrix models can be used to evaluate the performance of a proposed DA model and to select among different DA parameterization methods for a given application. Presenting the un- certainty in estimated risks is critical, especially when the data are highly corrupted and estimated parameters are more uncertain. Using a Bayesian approach, I demonstrate how the level of data support for different risk levels can be calculated using DA parameter likelihood functions.

EFFICACY OF SIMPLE VIABILITY MODELS IN ECOLOGICAL RISK ASSESSMENT: DOES DENSITY DEPENDENCE MATTER?

One commonly used PVA (population viability analysis) approach applies a diffusion approximation (DA) of population growth to time series of abundance data to estimate population parameters and various metrics of extinction risk. The simplest versions of this PVA assume density-independent population growth, an assumption that is commonly called into question for populations experiencing self-limitation. Using time series data generated from simulations of populations limited by three commonly used forms of density dependence (ceiling, Beverton-Holt, and Ricker) we asked the question: “When do simple density-independent PVA models provide useful guidelines for prioritizing extinction risk despite density-dependence inherent in the underlying real populations?” Simple DA methods severely underestimated maximum growth rates (μmax) used to generate time series data for all three forms of density dependence. These methods also underestimated the intrinsic environmental variability in growth rates, or process ...

The role of menopause and reproductive senescence in a long-lived social mammal

BackgroundMenopause is a seemingly maladaptive life-history trait that is found in many long-lived mammals. There are two competing evolutionary hypotheses for this phenomenon; in the adaptive view of menopause, the cessation of reproduction may increase the fitness of older females; in the non-adaptive view, menopause may be explained by physiological deterioration with age. The decline and eventual cessation of reproduction has been documented in a number of mammalian species, however the evolutionary cause of this trait is unknown.ResultsWe examined a unique 30-year time series of killer whales, tracking the reproductive performance of individuals through time. Killer whales are extremely long-lived, and may have the longest documented post-reproductive lifespan of any mammal, including humans. We found no strong support for either of the adaptive hypotheses of menopause; there was little support for the presence of post-reproductive females benefitting their daughters reproductive performance (interbirth interval and reproductive lifespan of daughters), or the number of mature recruits to the population. Oldest mothers (> 35) did appear to have a small positive impact on calf survival, suggesting that females may gain experience with age. There was mixed support for the grandmother hypothesis – grandoffspring survival probabilities were not influenced by living grandmothers, but grandmothers may positively influence survival of juveniles at a critical life stage.ConclusionAlthough existing data do not allow us to examine evolutionary tradeoffs between survival and reproduction for this species, we were able to examine the effect of maternal age on offspring survival. Our results are consistent with similar studies of other mammals – oldest mothers appear to be better mothers, producing calves with higher survival rates. Studies of juvenile survival in humans have reported positive benefits of grandmothers on newly weaned infants; our results indicate that 3-year old killer whales may experience a positive benefit from helpful grandmothers. While our research provides little support for menopause evolving to provide fitness benefits to mothers or grandmothers, our work supports previous research showing that menopause and long post-reproductive lifespans are not a human phenomenon.

Commentary on Holmes et al. (2007): resolving the debate on when extinction risk is predictable

We reconcile the findings of Holmes et al. (Ecology Letters, 10, 2007, 1182) that 95% confidence intervals for quasi-extinction risk were narrow for many vertebrates of conservation concern, with previous theory predicting wide confidence intervals. We extend previous theory, concerning the precision of quasi-extinction estimates as a function of population dynamic parameters, prediction intervals and quasi-extinction thresholds, and provide an approximation that specifies the prediction interval and threshold combinations where quasi-extinction estimates are precise (vs. imprecise). This allows PVA practitioners to define the prediction interval and threshold regions of safety (low risk with high confidence), danger (high risk with high confidence), and uncertainty.

Quantifying effects of abiotic and biotic drivers on community dynamics with multivariate autoregressive (MAR) models

Long-term ecological data sets present opportunities for identifying drivers of community dynamics and quantifying their effects through time series analysis. Multivariate autoregressive (MAR) models are well known in many other disciplines, such as econometrics, but widespread adoption of MAR methods in ecology and natural resource management has been much slower despite some widely cited ecological examples. Here we review previous ecological applications of MAR models and highlight their ability to identify abiotic and biotic drivers of population dynamics, as well as community-level stability metrics, from long-term empirical observations. Thus far, MAR models have been used mainly with data from freshwater plankton communities; we examine the obstacles that may be hindering adoption in other systems and suggest practical modifications that will improve MAR models for broader application. Many of these modifications are already well known in other fields in which MAR models are common, although they are frequently described under different names. In an effort to make MAR models more accessible to ecologists, we include a worked example using recently developed R packages (MAR1 and MARSS), freely available and open-access software.

Anomalous droughts, not invasion, decrease persistence of native fishes in a desert river

Changing climate extremes and invasion by non-native species are two of the most prominent threats to native faunas. Predicting the relationships between global change and native faunas requires a quantitative toolkit that effectively links the timing and magnitude of extreme events to variation in species abundances. Here, we examine how discharge anomalies--unexpected floods and droughts--determine covariation in abundance of native and non-native fish species in a highly variable desert river in Arizona. We quantified stochastic variation in discharge using Fourier analyses on >15,000 daily observations. We subsequently coupled maximum annual spectral anomalies with a 15-year time series of fish abundances (1994-2008), using Multivariate Autoregressive State-Space (MARSS) models. Abiotic drivers (discharge anomalies) were paramount in determining long-term fish abundances, whereas biotic drivers (species interactions) played only a secondary role. As predicted, anomalous droughts reduced the abundances of native species, while floods increased them. However, in contrast to previous studies, we observed that the non-native assemblage was surprisingly unresponsive to extreme events. Biological trait analyses showed that functional uniqueness was higher in native than in non-native fishes. We also found that discharge anomalies influenced diversity patterns at the meta-community level, with nestedness increasing after anomalous droughts due to the differential impairment of native species. Overall, our results advance the notion that discharge variation is key in determining community trajectories in the long term, predicting the persistence of native fauna even in the face of invasion. We suggest this variation, rather than biotic interactions, may commonly underlie covariation between native and non-native faunas, especially in highly variable environments. If droughts become increasingly severe due to climate change, and floods increasingly muted due to regulation, fish assemblages in desert rivers may become taxonomically and functionally impoverished and dominated by non-native taxa.

Shifting regimes and changing interactions in the Lake Washington, U.S.A., plankton community from 1962-1994.

Understanding how changing climate, nutrient regimes, and invasive species shift food web structure is critically important in ecology. Most analytical approaches, however, assume static species interactions and environmental effects across time. Therefore, we applied multivariate autoregressive (MAR) models in a moving window context to test for shifting plankton community interactions and effects of environmental variables on plankton abundance in Lake Washington, U.S.A. from 1962–1994, following reduced nutrient loading in the 1960s and the rise of Daphnia in the 1970s. The moving-window MAR (mwMAR) approach showed shifts in the strengths of interactions between Daphnia, a dominant grazer, and other plankton taxa between a high nutrient, Oscillatoria-dominated regime and a low nutrient, Daphnia-dominated regime. The approach also highlighted the inhibiting influence of the cyanobacterium Oscillatoria on other plankton taxa in the community. Overall community stability was lowest during the period of elevated nutrient loading and Oscillatoria dominance. Despite recent warming of the lake, we found no evidence that anomalous temperatures impacted plankton abundance. Our results suggest mwMAR modeling is a useful approach that can be applied across diverse ecosystems, when questions involve shifting relationships within food webs, and among species and abiotic drivers.