Chris Wilcox

Competing harvesting strategies in a simulated population under uncertainty

We present a case study of the use of simulation modelling to develop and test strategies for managing populations under uncertainty. Strategies that meet a stock conservation criterion under a base case scenario are subjected to a set of robustness trials, including biased and highly variable abundance estimates and poaching. Strategy performance is assessed with respect to a conservation criterion, the revenues achieved and their variability. Strategies that harvest heavily, even when the population is apparently very large, perform badly in the robustness trials. Setting a threshold below which harvesting does not take place, and above which all individuals are harvested, does not provide effective protection against over-harvesting. Strategies that rely on population growth rates rather than estimates of population size are more robust to biased estimates. The strategies that are most robust to uncertainty are simple, involving harvesting a relatively small proportion of the population each year. The simulation modelling approach to exploring harvesting strategies is suggested as a useful tool for the assessment of the performance of competing strategies under uncertainty.

THE ROLE OF HABITAT DISTURBANCE AND RECOVERY IN METAPOPULATION PERSISTENCE

Classical metapopulation theory assumes a static landscape. However, empirical evidence indicates many metapopulations are driven by habitat succession and disturbance. We develop a stochastic metapopulation model, incorporating habitat disturbance and recovery, coupled with patch colonization and extinction, to investigate the effect of habitat dynamics on persistence. We discover that habitat dynamics play a fundamental role in metapopulation dynamics. The mean number of suitable habitat patches is not adequate for characterizing the dynamics of the metapopulation. For a fixed mean number of suitable patches, we discover that the details of how disturbance affects patches and how patches recover influences metapopulation dynamics in a fundamental way. Moreover, metapopulation persistence is dependent not only on the average lifetime of a patch, but also on the variance in patch lifetime and the synchrony in patch dynamics that results from disturbance. Finally, there is an interaction between the habitat and metapopulation dynamics, for instance declining metapopulations react differently to habitat dynamics than expanding metapopulations. We close, emphasizing the importance of using performance measures appropriate to stochastic systems when evaluating their behavior, such as the probability distribution of the state of the metapopulation, conditional on it being extant (i.e., the quasistationary distribution).

Habitat selection and population regulation in temporally fluctuating environments.

Understanding and predicting the distribution of organisms in heterogeneous environments lies at the heart of ecology, and the theory of density‐dependent habitat selection (DDHS) provides ecologists with an inferential framework linking evolution and population dynamics. Current theory does not allow for temporal variation in habitat quality, a serious limitation when confronted with real ecological systems. We develop both a stochastic equivalent of the ideal free distribution to study how spatial patterns of habitat use depend on the magnitude and spatial correlation of environmental stochasticity and also a stochastic habitat selection rule. The emerging patterns are confronted with deterministic predictions based on isodar analysis, an established empirical approach to the analysis of habitat selection patterns. Our simulations highlight some consistent patterns of habitat use, indicating that it is possible to make inferences about the habitat selection process based on observed patterns of habitat use. However, isodar analysis gives results that are contingent on the magnitude and spatial correlation of environmental stochasticity. Hence, DDHS is better revealed by a measure of habitat selectivity than by empirical isodars. The detection of DDHS is but a small component of isodar theory, which remains an important conceptual framework for linking evolutionary strategies in behavior and population dynamics.

Do Commercial Fishers Aggregate around Marine Reserves? Evidence from Big Creek Marine Ecological Reserve, Central California

Abstract Marine reserves have been widely touted as a promising strategy for managing fisheries and protecting marine biodiversity. However, their establishment can involve substantial social conflict and may not produce the anticipated biological and economic benefits. A crucial factor associated with the success of marine reserves for enhancing fisheries and protecting biodiversity is the spatial distribution of fishing activity. Fishers may be attracted to the perimeter of a reserve in expectation of spillover of adult fishes. This concentration of effort can reduce “spillover” of fish to the surrounding fishery and has major implications for the effectiveness of reserves in achieving ecological and socioeconomic goals. We examined the spatial distribution of fishing activity relative to Californias Big Creek Marine Ecological Reserve and found no aggregation near the reserve. We discuss the factors driving the spatial distribution of fishing activity relative to the reserve and the relevance of that ...

Fine scale patterns of migration and gene flow in the endangered mound spring snail, Fonscochlea accepta (Mollusca:Hydrobiidae) in arid Australia

Naturally patchy ecosystems are models for other systems currently undergoing anthropogenic habitat fragmentation. Understanding patterns of gene flow in these model systems can help us manage species and ecosystems threatened by human impacts. The mound springs of central Australia represent such a natural model ecosystem, supporting a unique aquatic fauna distributed within an inhospitable arid landscape. Moreover, these springs are being impacted by over extraction of groundwater, providing a unique opportunity to look at dispersal in a patchy habitat that is changing. The present study represents the first fine scale analysis of gene flow under different scenarios of habitat connectivity for the endangered mound spring snail, Fonscochlea accepta. Within a single spring group pairwise estimates of FST between springs were very low (ave 0.015) with no association found between genetic distance and a series of geographical distance matrices based on the degree of habitat connectivity among the springs: results implying unstructured dispersal and limited population isolation. However, results from Bayesian assignment tests showed that on average approximately 97% of snails were assigned to their spring of origin. In a preliminary analysis at broader geographic scales (among spring groups) the results from FST estimates, Mantel correlation analyses and assignment tests all suggest much stronger and geographically correlated population structuring. While varying results from F-statistics and Bayesian analyses stem from the different information they utilise, together they provide data on contemporary and historical estimates of gene flow and the influence of landscape dynamics on the spatial genetic patterning of the springs.

DO LIFE HISTORY TRAITS AFFECT THE ACCURACY OF DIFFUSION APPROXIMATIONS FOR MEAN TIME TO EXTINCTION

Estimating the risk of extinction is one of the central foci of conservation biology, but such estimates are often constrained by the available data. In the face of limited data, several authors have suggested using diffusion approximations to estimate the mean time to extinction as a means of quantifying the risk of extinction. However, the accuracy of the diffusion approximation for characterizing extinction processes has not been well tested. We develop a simulation model that includes life history parameters and incorporates both demographic and environmental stochasticity in population dynamics. We use the simulation model to study the effects of stochasticity on the accuracy of the diffusion approximation across different life history strategies. Our results show that predictions for mean time to extinction from the diffusion approximation may differ significantly from simulated ones, and that the amount and direction of the error in the approximation is not systematic. Given that this error is unpredictable and may be very sensitive to the exact demographic rates, we believe that the diffusion approximation should be used with caution as a basis for assessing extinction risk or making management decisions.