An Evaluation of Management Objectives for Canada's Commercial Harp Seal Hunt, 1996-1998

Abstract: The largest existing hunt for marine mammals is Canada's commercial hunt for Northwest Atlantic harp seals ( Pagophilus groenlandicus ). From 1995 to 1998, the total allowable catch was set at a level that the Canadian Department of Fisheries and Oceans calculated would not cause the population to decline, consistent both with its stated management objectives of maintaining stable seal populations while allowing a sustainable harvest and with its stated policy of taking a precautionary approach to management. During those years, Canada's total allowable catch was progressively increased from 186,000 harp seals per year (1995) to 250,000 (1996) to 275,000 (1997 & 1998). We examined whether the government's management objectives were achieved using the conventional approach of comparing landed catches with the replacement yield estimated from a biological population model. We then conducted a second assessment, using a more modern and precautionary approach recently implemented for marine mammal management in the United States which incorporates uncertainty into management models to estimate sustainable "potential biological removal levels." From 1996 to 1998, landed catches from Canada and Greenland exceeded Canada's estimated replacement yield. Over the same period, estimated total human-caused mortality exceeded potential biological removal levels by 1.5 to 5.9 times. Given such levels of reported catches and estimated total human-caused mortality, Canada's management of its harp seal hunt did not achieve its objectives. It is likely, therefore, that the population is now declining and, if recent levels of killing continue, the population will stabilize only at levels below (and possibly far below) its maximum net productivity level. Viewed from this perspective, Canada's approach to harp seal management between 1996 and 1998 cannot be deemed precautionary or risk-averse.     

Accounting for Uncertainty in Risk Assessment: Case Study of Hector's Dolphin Mortality due to Gillnet Entanglement

Abstract: Uncertainties about biological data and human effects often delay decisions on management of endangered species. Some decision makers argue that uncertainty about the risk posed to a species should lead to precautionary decisions, whereas others argue for delaying protective measures until there is strong evidence that a human activity is having a serious effect on the species. We have developed a method that incorporates uncertainty into the estimate of risk so that delays in action can be reduced or eliminated. We illustrate our method with an actual situation of a deadlock over how to manage Hector's dolphin ( Cephalorhychus hectori ). The management question is whether sufficient risk is posed to the dolphins by mortalities in gillnets to warrant regulating the fisheries. In our quantitative risk assessment, we use a population model that incorporates both demographic ( between-individual) and environmental ( between-year) stochasticity. We incorporate uncertainty in estimates of model parameters by repeatedly running the model for different combinations of survival and reproductive rates. Each value is selected at random from a probability distribution that represents the uncertainty in estimating that parameter. Before drawing conclusions, we perform sensitivity analyses to see whether model assumptions alter conclusions and to recommend priorities for future research. In this example, uncertainty did not alter the conclusion that there is a high risk of population decline if current levels of gillnet mortality continue. Sensitivity analyses revealed this to be a robust conclusion. Thus, our analysis removes uncertainty in the scientific data as an excuse for inaction.     

Practical management of cumulative anthropogenic impacts with working marine examples

Human pressure on the environment is expanding and intensifying, especially in coastal and offshore areas. Major contributors to this are the current push for offshore renewable energy sources, which are thought of as environmentally friendly sources of power, as well as the continued demand for petroleum. Human disturbances, including the noise almost ubiquitously associated with human activity, are likely to increase the incidence, magnitude, and duration of adverse effects on marine life, including stress responses. Stress responses have the potential to induce fitness consequences for individuals, which add to more obvious directed takes (e.g., hunting or fishing) to increase the overall population‐level impact. To meet the requirements of marine spatial planning and ecosystem‐based management, many efforts are ongoing to quantify the cumulative impacts of all human actions on marine species or populations. Meanwhile, regulators face the challenge of managing these accumulating and interacting impacts with limited scientific guidance. We believe there is scientific support for capping the level of impact for (at a minimum) populations in decline or with unknown statuses. This cap on impact can be facilitated through implementation of regular application cycles for project authorization or improved programmatic and aggregated impact assessments that simultaneously consider multiple projects. Cross‐company collaborations and a better incorporation of uncertainty into decision making could also help limit, if not reduce, cumulative impacts of multiple human activities. These simple management steps may also form the basis of a rudimentary form of marine spatial planning and could be used in support of future ecosystem‐based management efforts.     

The Use of Bayesian Model Averaging to Better Represent Uncertainty in Ecological Models

Abstract:   In conservation biology, uncertainty about the choice of a statistical model is rarely considered. Model-selection uncertainty occurs whenever one model is chosen over plausible alternative models to represent understanding about a process and to make predictions about future observations. The standard approach to representing prediction uncertainty involves the calculation of prediction (or confidence) intervals that incorporate uncertainty about parameter estimates contingent on the choice of a "best" model chosen to represent truth. However, this approach to prediction based on statistical models tends to ignore model-selection uncertainty, resulting in overconfident predictions. Bayesian model averaging (BMA) has been promoted in a range of disciplines as a simple means of incorporating model-selection uncertainty into statistical inference and prediction. Bayesian model averaging also provides a formal framework for incorporating prior knowledge about the process being modeled. We provide an example of the application of BMA in modeling and predicting the spatial distribution of an arboreal marsupial in the Eden region of southeastern Australia. Other approaches to estimating prediction uncertainty are discussed.     

Testing the robustness of management decisions to uncertainty: Everglades restoration scenarios.

To effectively manage large natural reserves, resource managers must prepare for future contingencies while balancing the often conflicting priorities of different stakeholders. To deal with these issues, managers routinely employ models to project the response of ecosystems to different scenarios that represent alternative management plans or environmental forecasts. Scenario analysis is often used to rank such alternatives to aid the decision making process. However, model projections are subject to uncertainty in assumptions about model structure, parameter values, environmental inputs, and subcomponent interactions. We introduce an approach for testing the robustness of model-based management decisions to the uncertainty inherent in complex ecological models and their inputs. We use relative assessment to quantify the relative impacts of uncertainty on scenario ranking. To illustrate our approach we consider uncertainty in parameter values and uncertainty in input data, with specific examples drawn from the Florida Everglades restoration project. Our examples focus on two alternative 30-year hydrologic management plans that were ranked according to their overall impacts on wildlife habitat potential. We tested the assumption that varying the parameter settings and inputs of habitat index models does not change the rank order of the hydrologic plans. We compared the average projected index of habitat potential for four endemic species and two wading-bird guilds to rank the plans, accounting for variations in parameter settings and water level inputs associated with hypothetical future climates. Indices of habitat potential were based on projections from spatially explicit models that are closely tied to hydrology. For the American alligator, the rank order of the hydrologic plans was unaffected by substantial variation in model parameters. By contrast, simulated major shifts in water levels led to reversals in the ranks of the hydrologic plans in 24.1-30.6% of the projections for the wading bird guilds and several individual species. By exposing the differential effects of uncertainty, relative assessment can help resource managers assess the robustness of scenario choice in model-based policy decisions.     

A Strategy for Monitoring and Managing Declines in an Amphibian Community

Although many taxa have declined globally, conservation actions are inherently local. Ecosystems degrade even in protected areas, and maintaining natural systems in a desired condition may require active management. Implementing management decisions under uncertainty requires a logical and transparent process to identify objectives, develop management actions, formulate system models to link actions with objectives, monitor to reduce uncertainty and identify system state (i.e., resource condition), and determine an optimal management strategy. We applied one such structured decision‐making approach that incorporates these critical elements to inform management of amphibian populations in a protected area managed by the U.S. National Park Service. Climate change is expected to affect amphibian occupancy of wetlands and to increase uncertainty in management decision making. We used the tools of structured decision making to identify short‐term management solutions that incorporate our current understanding of the effect of climate change on amphibians, emphasizing how management can be undertaken even with incomplete information. Estrategia para Monitorear y Manejar Disminuciones en una Comunidad de Anfibios     

Population Viability Estimates: Theory and Practice for a Wild Gorilla Population

The logic of demographic modeling, the apparent simplicity of its quantifiably substantiated answers, and the ready availability of software correlate with increasing use of demographic modeling as the means of applying biology to the conservation of potentially endangered populations. I investigated that use by considering a small population (about 300 individuals) of a large, forest-dwelling mammal of the tropics, the Virunga gorilla ( Gorilla gorilla ) of Zaire, Uganda, and Rwanda. Because censuses of forest populations are so inaccurate and data on variance of some parameters takes so long to collect, models might not be broadly applicable. Therefore, simple demographic indices of potential extinction should replace sophisticated models. The current best index could be problematic, however, because it is based on detecting adult mortality, perhaps the most difficult demographic parameter to measure. Models of the Virunga gorilla population that incorporate aspects of demographic heterogeneity valuably indicate genetic and demographic persistence for several hundred years. Deterministic change in habitat is a greater threat than stochastic demographic variation, and yet our ecological ignorance is such that we could not begin to model the consequences of removal of even the main food plant. We must add to our ability to model outcomes of demographic perturbation a far greater understanding of the processes by which the perturbations occur. Demography allows us to model demographic response to demographic change, but we usually need ecology to tell us how the threat produced the demographic change in the first place. In a time of change, accurate prediction requires ecological understanding of process as well as demographic understanding of outcome.     

Implications of different population model structures for management of threatened plants

Population viability analysis (PVA) is a reliable tool for ranking management options for a range of species despite parameter uncertainty. No one has yet investigated whether this holds true for model uncertainty for species with complex life histories and for responses to multiple threats. We tested whether a range of model structures yielded similar rankings of management and threat scenarios for 2 plant species with complex postfire responses. We examined 2 contrasting species from different plant functional types: an obligate seeding shrub and a facultative resprouting shrub. We exposed each to altered fire regimes and an additional, species‐specific threat. Long‐term demographic data sets were used to construct an individual‐based model (IBM), a complex stage‐based model, and a simple matrix model that subsumes all life stages into 2 or 3 stages. Agreement across models was good under some scenarios and poor under others. Results from the simple and complex matrix models were more similar to each other than to the IBM. Results were robust across models when dominant threats are considered but were less so for smaller effects. Robustness also broke down as the scenarios deviated from baseline conditions, likely the result of a number of factors related to the complexity of the species’ life history and how it was represented in a model. Although PVA can be an invaluable tool for integrating data and understanding species’ responses to threats and management strategies, this is best achieved in the context of decision support for adaptive management alongside multiple lines of evidence and expert critique of model construction and output.     

Fooled by local robustness: an applied ecology perspective

In this short discussion, we point out that it is apparently as easy to be fooled by robustness as it is to be fooled by randomness. Our objective is to bring to the attention of applied ecologists that radius-of-stability robustness models are models of local robustness. As such, these models are utterly unsuitable for the treatment/management of a severe uncertainty characterized by a vast uncertainty space and a likelihood-free quantification of the uncertainty. This observation is particularly pertinent to applications of info-gap decision theory in ecology, conservation biology, and environmental management, where the objective is to identify decisions that are robust against a severe uncertainty of this type.     

Multimethod,multistate Bayesian hierarchical modeling approach for use in regional monitoring of wolves

In many cases, the first step in large‐carnivore management is to obtain objective, reliable, and cost‐effective estimates of population parameters through procedures that are reproducible over time. However, monitoring predators over large areas is difficult, and the data have a high level of uncertainty. We devised a practical multimethod and multistate modeling approach based on Bayesian hierarchical‐site‐occupancy models that combined multiple survey methods to estimate different population states for use in monitoring large predators at a regional scale. We used wolves (Canis lupus) as our model species and generated reliable estimates of the number of sites with wolf reproduction (presence of pups). We used 2 wolf data sets from Spain (Western Galicia in 2013 and Asturias in 2004) to test the approach. Based on howling surveys, the naïve estimation (i.e., estimate based only on observations) of the number of sites with reproduction was 9 and 25 sites in Western Galicia and Asturias, respectively. Our model showed 33.4 (SD 9.6) and 34.4 (3.9) sites with wolf reproduction, respectively. The number of occupied sites with wolf reproduction was 0.67 (SD 0.19) and 0.76 (0.11), respectively. This approach can be used to design more cost‐effective monitoring programs (i.e., to define the sampling effort needed per site). Our approach should inspire well‐coordinated surveys across multiple administrative borders and populations and lead to improved decision making for management of large carnivores on a landscape level. The use of this Bayesian framework provides a simple way to visualize the degree of uncertainty around population‐parameter estimates and thus provides managers and stakeholders an intuitive approach to interpreting monitoring results. Our approach can be widely applied to large spatial scales in wildlife monitoring where detection probabilities differ between population states and where several methods are being used to estimate different population parameters.     

Estimating Sustainable Bycatch Rates for California Sea Lion Populations in the Gulf of California

Abstract:  Commercial and subsistence fisheries pressure is increasing in the Gulf of California, Mexico. One consequence often associated with high levels of fishing pressure is an increase in bycatch of marine mammals and birds. Fisheries bycatch has contributed to declines in several pinniped species and may be affecting the California sea lion ( Zalophus californianus ) population in the Gulf of California. We used data on fisheries and sea lion entanglement in gill nets to estimate current fishing pressure and fishing rates under which viable sea lion populations could be sustained at 11 breeding sites in the Gulf of California. We used 3 models to estimate sustainable bycatch rates: a simple population-growth model, a demographic model, and an estimate of the potential biological removal. All models were based on life history and census data collected for sea lions in the Gulf of California. We estimated the current level of fishing pressure and the acceptable level of fishing required to maintain viable sea lion populations as the number of fishing days (1 fisher/boat setting and retrieving 1 day's worth of nets) per year. Estimates of current fishing pressure ranged from 101 (0–405) fishing days around the Los Machos breeding site to 1887 (842–3140) around the Los Islotes rookery. To maintain viable sea lion populations at each site, the current level of fishing permissible could be augmented at some sites and should be reduced at other sites. For example, the area around San Esteban could support up to 1428 (935–2337) additional fishing days, whereas fishing around Lobos should be reduced by at least 165 days (107–268). Our results provide conservation practitioners with site-specific guidelines for maintaining sustainable sea lion populations and provide a method to estimate fishing pressure and sustainable bycatch rates that could be used for other marine mammals and birds .     

The Sensitivity of Population Viability Analysis to Uncertainty about Habitat Requirements: Implications for the Management of the Endangered Southern Brown Bandicoot

Abstract:  Whenever population viability analysis (PVA) models are built to help guide decisions about the management of rare and threatened species, an important component of model building is the specification of a habitat model describing how a species is related to landscape or bioclimatic variables. Model-selection uncertainty may arise because there is often a great deal of ambiguity about which habitat model structure best approximates the true underlying biological processes. The standard approach to incorporate habitat models into PVA is to assume the best habitat model is correct, ignoring habitat-model uncertainty and alternative model structures that may lead to quantitatively different conclusions and management recommendations. Here we provide the first detailed examination of the influence of habitat-model uncertainty on the ranking of management scenarios from a PVA model. We evaluated and ranked 6 management scenarios for the endangered southern brown bandicoot ( Isoodon obesulus ) with PVA models, each derived from plausible competing habitat models developed with logistic regression. The ranking of management scenarios was sensitive to the choice of the habitat model used in PVA predictions. Our results demonstrate the need to incorporate methods into PVA that better account for model uncertainty and highlight the sensitivity of PVA to decisions made during model building. We recommend that researchers search for and consider a range of habitat models when undertaking model-based decision making and suggest that routine sensitivity analyses should be expanded to include an analysis of the impact of habitat-model uncertainty and assumptions.     

Uncertainty, learning, and the optimal management of wildlife

Wildlife management is limited by uncontrolled and often unrecognized environmental variation, by limited capabilities to observe and control animal populations, and by a lack of understanding about the biological processes driving population dynamics. In this paper I describe a comprehensive framework for management that includes multiple models and likelihood values to account for structural uncertainty, along with stochastic factors to account for environmental variation, random sampling, and partial controllability. Adaptive optimization is developed in terms of the optimal control of incompletely understood populations, with the expected value of perfect information measuring the potential for improving control through learning. The framework for optimal adaptive control is generalized by including partial observability and non-adaptive, sample-based updating of model likelihoods. Passive adaptive management is derived as a special case of constrained adaptive optimization, representing a potentially efficient suboptimal alternative that nonetheless accounts for structural uncertainty.     

Using global sensitivity analysis of demographic models for ecological impact assessment

Population viability analysis (PVA) is widely used to assess population‐level impacts of environmental changes on species. When combined with sensitivity analysis, PVA yields insights into the effects of parameter and model structure uncertainty. This helps researchers prioritize efforts for further data collection so that model improvements are efficient and helps managers prioritize conservation and management actions. Usually, sensitivity is analyzed by varying one input parameter at a time and observing the influence that variation has over model outcomes. This approach does not account for interactions among parameters. Global sensitivity analysis (GSA) overcomes this limitation by varying several model inputs simultaneously. Then, regression techniques allow measuring the importance of input‐parameter uncertainties. In many conservation applications, the goal of demographic modeling is to assess how different scenarios of impact or management cause changes in a population. This is challenging because the uncertainty of input‐parameter values can be confounded with the effect of impacts and management actions. We developed a GSA method that separates model outcome uncertainty resulting from parameter uncertainty from that resulting from projected ecological impacts or simulated management actions, effectively separating the 2 main questions that sensitivity analysis asks. We applied this method to assess the effects of predicted sea‐level rise on Snowy Plover (Charadrius nivosus). A relatively small number of replicate models (approximately 100) resulted in consistent measures of variable importance when not trying to separate the effects of ecological impacts from parameter uncertainty. However, many more replicate models (approximately 500) were required to separate these effects. These differences are important to consider when using demographic models to estimate ecological impacts of management actions.     

A Behaviorally Explicit Demographic Model Integrating Habitat Selection and Population Dynamics in California Sea Lions

Abstract: Although there has been a call for the integration of behavioral ecology and conservation biology, there are few tools currently available to achieve this integration. Explicitly including information about behavioral strategies in population viability analyses may enhance the ability of conservation biologists to understand and estimate patterns of extinction risk. Nevertheless, most behavioral‐based PVA approaches require detailed individual‐based data that are rarely available for imperiled species. We present a mechanistic approach that incorporates spatial and demographic consequences of behavioral strategies into population models used for conservation. We developed a stage‐structured matrix model that includes the costs and benefits of movement associated with 2 habitat‐selection strategies (philopatry and direct assessment). Using a life table for California sea lions (Zalophus californianus), we explored the sensitivity of model predictions to the inclusion of these behavioral parameters. Including behavioral information dramatically changed predicted population sizes, model dynamics, and the expected distribution of individuals among sites. Estimated population sizes projected in 100 years diverged up to 1 order of magnitude among scenarios that assumed different movement behavior. Scenarios also exhibited different model dynamics that ranged from stable equilibria to cycles or extinction. These results suggest that inclusion of behavioral data in viability models may improve estimates of extinction risk for imperiled species. Our approach provides a simple method for incorporating spatial and demographic consequences of behavioral strategies into population models and may be easily extended to other species and behaviors to understand the mechanisms of population dynamics for imperiled populations.     

Marine extinctions and conservation

In contrast to the large number of terrestrial extinctions that have taken place over the past 12,000 years, there have apparently been very few marine extinctions. But these small losses should not be reason for complacency. During the past 50 years, government supported, commercial fishing has resulted in the collapse of about a thousand populations that once supplied most of the world’s seafood. For the collapsed species, now existing as small remnants of their former population sizes, the future is bleak. They suffer from loss of genetic diversity, inbreeding depression, and depensation. Because marine species were eliminated by historic climatic changes, continued global warming is likely to result in the extinction of small populations that already have a precarious existence. They may be considered evidence of an extinction debt that must be paid as the climate change becomes more severe. For some of the remnant species, extinction can be avoided if there is a rapid management conversion to the use of more marine protected areas (MPAs) and extensive ocean zoning where fishing is prohibited.     

Choosing ecosystem service investments that are robust to uncertainty across multiple parameters

Info-gap decision theory facilitates decision making for problems in which uncertainty is large and probability distributions of uncertain variables are unknown. The info-gap framework allows the decision maker to maximize robustness to failure in the presence of uncertainty, where uncertainty is in the parameters of the model and failure is defined as the model output falling below some minimally acceptable performance threshold. Info-gap theory has found particular application to problems in conservation biology and ecological economics. In this study, we applied info-gap theory to an ecosystem services tradeoff case study in which a decision maker aiming to maximize ecosystem service investment returns must choose between two alternative land uses: native vegetation conservation or the establishment of an exotic timber plantation. The uncertain variables are the carbon price and the water price. With a "no-information" uncertainty model that assumes equal relative uncertainty across both variables, info-gap theory identifies a minimally acceptable reward threshold above which conservation is preferred, but below which plantation establishment is preferred. However, with an uncertainty model that allows the carbon price to be substantially more uncertain than the water price, conservation of native vegetation becomes an economically more robust investment option than establishing alien pine plantations. We explored the sensitivity of the results to the use of alternative uncertainty models, including asymmetric uncertainty in individual variables. We emphasize the general finding that the results of info-gap analyses can be sensitive to the choice of uncertainty model and that, therefore, future applications to ecological problems should be careful to incorporate all available qualitative and quantitative information relating to uncertainties or should at least justify the no-information uncertainty model.     

Cost-Effective Management Alternatives for Snake River Chinook Salmon: a Biological-Economic Synthesis

Abstract: The mandate to increase endangered salmon populations in the Columbia River Basin of North America has created a complex, controversial resource‐management issue. We constructed an integrated assessment model as a tool for analyzing biological‐economic trade‐offs in recovery of Snake River spring‐ and summer‐run chinook salmon (Oncorhynchus tshawytscha). We merged 3 frameworks: a salmon‐passage model to predict migration and survival of smolts; an age‐structured matrix model to predict long‐term population growth rates of salmon stocks; and a cost‐effectiveness analysis to determine a set of least‐cost management alternatives for achieving particular population growth rates. We assessed 6 individual salmon‐management measures and 76 management alternatives composed of one or more measures. To reflect uncertainty, results were derived for different assumptions of effectiveness of smolt transport around dams. Removal of an estuarine predator, the Caspian Tern (Sterna caspia), was cost‐effective and generally increased long‐term population growth rates regardless of transport effectiveness. Elimination of adult salmon harvest had a similar effect over a range of its cost estimates. The specific management alternatives in the cost‐effective set depended on assumptions about transport effectiveness. On the basis of recent estimates of smolt transport effectiveness, alternatives that discontinued transportation or breached dams were prevalent in the cost‐effective set, whereas alternatives that maximized transportation dominated if transport effectiveness was relatively high. More generally, the analysis eliminated 80–90% of management alternatives from the cost‐effective set. Application of our results to salmon management is limited by data availability and model assumptions, but these limitations can help guide research that addresses critical uncertainties and information. Our results thus demonstrate that linking biology and economics through integrated models can provide valuable tools for science‐based policy and management.     

Multiple sources of predictive uncertainty in modeled estimates of net ecosystem CO2 exchange

Net ecosystem CO₂ exchange (NEE) is typically measured directly by eddy covariance towers or is estimated by ecosystem process models, yet comparisons between the data obtained by these two methods can show poor correspondence. There are three potential explanations for this discrepancy. First, estimates of NEE as measured by the eddy-covariance technique are laden with uncertainty and can potentially provide a poor baseline for models to be tested against. Second, there could be fundamental problems in model structure that prevent an accurate simulation of NEE. Third, ecosystem process models are dependent on ecophysiological parameter sets derived from field measurements in which a single parameter for a given species can vary considerably. The latter problem suggests that with such broad variation among multiple inputs, any ecosystem modeling scheme must account for the possibility that many combinations of apparently feasible parameter values might not allow the model to emulate the observed NEE dynamics of a terrestrial ecosystem, as well as the possibility that there may be many parameter sets within a particular model structure that can successfully reproduce the observed data. We examined the extent to which these three issues influence estimates of NEE in a widely used ecosystem process model, Biome-BGC, by adapting the generalized likelihood uncertainty estimation (GLUE) methodology. This procedure involved 400,000 model runs, each with randomly generated parameter values from a uniform distribution based on published parameter ranges, resulting in estimates of NEE that were compared to daily NEE data from young and mature Ponderosa pine stands at Metolius, Oregon. Of the 400,000 simulations run with different parameter sets for each age class (800,000 total), over 99% of the simulations underestimated the magnitude of net ecosystem CO₂ exchange, with only 4.07% and 0.045% of all simulations providing satisfactory simulations of the field data for the young and mature stands, even when uncertainties in eddy-covariance measurements are accounted for. Results indicate fundamental shortcomings in the ability of this model to produce realistic carbon flux data over the course of forest development, and we suspect that much of the mismatch derives from an inability to realistically model ecosystem respiration. However, difficulties in estimating historic climate data are also a cause for model-data mismatch, particularly in a highly ecotonal region such as central Oregon. This latter difficulty may be less prevalent in other ecosystems, but it nonetheless highlights a challenge in trying to develop a dynamic representation of the terrestrial biosphere.     

Using demography and movement behavior to predict range expansion of the southern sea otter

In addition to forecasting population growth, basic demographic data combined with movement data provide a means for predicting rates of range expansion. Quantitative models of range expansion have rarely been applied to large vertebrates, although such tools could be useful for restoration and management of many threatened but recovering populations. Using the southern sea otter (Enhydra lutris nereis) as a case study, we utilized integro-difference equations in combination with a stage-structured projection matrix that incorporated spatial variation in dispersal and demography to make forecasts of population recovery and range recolonization. In addition to these basic predictions, we emphasize how to make these modeling predictions useful in a management context through the inclusion of parameter uncertainty and sensitivity analysis. Our models resulted in hind-cast (1989-2003) predictions of net population growth and range expansion that closely matched observed patterns. We next made projections of future range expansion and population growth, incorporating uncertainty in all model parameters, and explored the sensitivity of model predictions to variation in spatially explicit survival and dispersal rates. The predicted rate of southward range expansion (median = 5.2 km/yr) was sensitive to both dispersal and survival rates; elasticity analysis indicated that changes in adult survival would have the greatest potential effect on the rate of range expansion, while perturbation analysis showed that variation in subadult dispersal contributed most to variance in model predictions. Variation in survival and dispersal of females at the south end of the range contributed most of the variance in predicted southward range expansion. Our approach provides guidance for the acquisition of further data and a means of forecasting the consequence of specific management actions. Similar methods could aid in the management of other recovering populations.