Applying spatiotemporal models to monitoring data to quantify fish population responses to the Deepwater Horizon oil spill in the Gulf of Mexico.

Quantifying the impacts of disturbances such as oil spills on marine species can be challenging. Natural environmental variability, human responses to the disturbance (e.g., fisheries closures), the complex life histories of the species being monitored, and limited pre-spill data can make detection of effects of oil spills difficult. Using long-term monitoring data from the state of Louisiana (USA), we applied novel spatiotemporal approaches to identify anomalies in species occurrence and catch rates. We included covariates (salinity, temperature, turbidity) to help isolate unusual events. While some species showed evidence of unlikely temporal anomalies in occurrence or catch rates, we found that the majority of the observed anomalies were also before the Deepwater Horizon event. Several species-gear combinations suggested upticks in the spatial variability immediately following the spill, but most species indicated no trend. Across species-gear combinations, there was no clear evidence for synchronous or asynchronous responses in occurrence or catch rates across sites following the spill. Our results are in general agreement to other analyses of monitoring data that detected small impacts, but in contrast to recent results from ecological modeling that showed much larger effects of the oil spill on fish and shellfish.

Population assessment using multivariate time-series analysis: A case study of rockfishes in Puget Sound.

Estimating a population's growth rate and year-to-year variance is a key component of population viability analysis (PVA). However, standard PVA methods require time series of counts obtained using consistent survey methods over many years. In addition, it can be difficult to separate observation and process variance, which is critical for PVA. Time-series analysis performed with multivariate autoregressive state-space (MARSS) models is a flexible statistical framework that allows one to address many of these limitations. MARSS models allow one to combine surveys with different gears and across different sites for estimation of PVA parameters, and to implement replication, which reduces the variance-separation problem and maximizes informational input for mean trend estimation. Even data that are fragmented with unknown error levels can be accommodated. We present a practical case study that illustrates MARSS analysis steps: data choice, model set-up, model selection, and parameter estimation. Our case study is an analysis of the long-term trends of rockfish in Puget Sound, Washington, based on citizen science scuba surveys, a fishery-independent trawl survey, and recreational fishery surveys affected by bag-limit reductions. The best-supported models indicated that the recreational and trawl surveys tracked different, temporally independent assemblages that declined at similar rates (an average of -3.8% to -3.9% per year). The scuba survey tracked a separate increasing and temporally independent assemblage (an average of 4.1% per year). Three rockfishes (bocaccio, canary, and yelloweye) are listed in Puget Sound under the US Endangered Species Act (ESA). These species are associated with deep water, which the recreational and trawl surveys sample better than the scuba survey. All three ESA-listed rockfishes declined as a proportion of recreational catch between the 1970s and 2010s, suggesting that they experienced similar or more severe reductions in abundance than the 3.8-3.9% per year declines that were estimated for rockfish populations sampled by the recreational and trawl surveys.

Reducing bias and improving precision in species extinction forecasts.

Forecasting the risk of population decline is crucial in the realm of biological conservation and figures prominently in population viability analyses (PVA). A common form of available data for a PVA is population counts through time. Previous research has suggested that improving estimates of population trends and risk from count data depends on longer observation periods, but that is often impractical or undesirable. Making multiple observations within a single time step is an alternative way to gather more data without extending the observation period. In this paper, we examine the trade-off between the length of the time period over which observations of the population have been taken and the total number of observations or samples that have been recorded through an analysis of simulated data. We found that when the ratio of process error to measurement error variance is high, more precise estimates of quasi-extinction risks can be obtained if replicated observations are taken at each time step, but when the ratio is low, replicated observations add little benefit in improving precision. These results can be used to efficiently design effective monitoring schemes for species of conservation concern.

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.

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.

Effects of multiple levels of social organization on survival and abundance.

Identifying how social organization shapes individual behavior, survival, and fecundity of animals that live in groups can inform conservation efforts and improve forecasts of population abundance, even when the mechanism responsible for group-level differences is unknown. We constructed a hierarchical Bayesian model to quantify the relative variability in survival rates among different levels of social organization (matrilines and pods) of an endangered population of killer whales (Orcinus orca). Individual killer whales often participate in group activities such as prey sharing and cooperative hunting. The estimated age-specific survival probabilities and survivorship curves differed considerably among pods and to a lesser extent among matrilines (within pods). Across all pods, males had lower life expectancy than females. Differences in survival between pods may be caused by a combination of factors that vary across the population's range, including reduced prey availability, contaminants in prey, and human activity. Our modeling approach could be applied to demographic rates for other species and for parameters other than survival, including reproduction, prey selection, movement, and detection probabilities.

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

BACKGROUND: Menopause 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. RESULTS: We 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 daughter's 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. CONCLUSION: Although 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.

Shifts in a Pacific ocean fish assemblage: the potential influence of exploitation.

As in many regions of the world, marine fishes and invertebrates along the Pacific coast of the United States have long been subjected to overexploitation. Despite this history, however we lack basic information on the current status of many fishes along this coastline. We used data from a quarter century of fishery-independent, coast-wide trawl surveys to study systematically the demersal fish assemblages along the U.S. Pacific coast. We documented fundamental shifts in this fish assemblage. Average fish size, across a diversity of species, has declined 45% in 21 years. There have been major shifts in the constituent species of the assemblage, with some species achieving annual population growth rates of >10% and others declining in excess of 10% per year Annual rate of change in population size appeared to be a function of life history interacting with fishing pressure. Negative trends in population size were particularly apparent in rockfish (Sebastes spp.). However across all taxa examined, trends in population size were associated with size of maturity, maximum size, and growth rate. Trends in population size were associated inversely with harvest levels, but stocks that mature late tended to decline faster than would be predicted by catch rates alone. Our results are disquieting because they raise the possibility that fishing-induced phase shifts in fish communities may affect the recovery offishes, even after the implementation of severe fishing restrictions.