Viable population monitoring : risk-based population monitoring for threatened and endangered species with application to bull trout, Salvelinus confluentus

Population monitoring is a vital component for managing threatened and endangered (TE) species to demonstrate recovery, or alert managers if the status is deteriorating. Common methods for analyzing monitoring data, however, have poor power to detect changes in population status and do not directly address questions about population status as defined for threatened (likely to be endangered) or endangered (in danger of extinction) species. Population viability analysis (PVA) methods are used to estimate the risk of decline for population, and have been recommended to reconcile short-term management actions with the ultimate long-term goal of preserving the species. The research presented herein is concerned with how to use PVA for monitoring population status, with a general focus on TE species and specific application to a bull trout (Salvelinus confluentus) population. The bull trout population in the Flathead Lake and River System of NW Montana, USA provides an example for the motivation of VPM, and will serve as a test bed for developing and applying VPM for population management. Bull trout are listed as threatened under the Endangered Species Act, and the Flathead Lake population is of special concern because of a dramatic decline in the late 1980’s. Risk estimates are constrained by the ability to estimate model parameters from data. I develop methods to accommodate sampling error in population data and temporal correlations in population growth for count-based PVA models, and evaluate the effects of model extrapolation errors on risk estimates. Further, I present model structural adequacy analyses in which model evaluation criteria are not based on a model’s fit to data, but on how well the model answers the scientific question of interest concerning the population’s future status. This study suggests monitoring with a Gompertz density dependent model is likely the best available means for estimating average risk of decline for the bull trout population. Data on juvenile vital rates and abundances incorporated into a relatively simple demographic model could potentially enhance the ability to foresee imminent declines in adult abundances, though risk estimates can be detrimentally affected by uncertainty in sub-adult survival rates.

[1]  Brian Dennis,et al.  ESTIMATING POPULATION TREND AND PROCESS VARIATION FOR PVA IN THE PRESENCE OF SAMPLING ERROR , 2004 .

[2]  Michael J. Wisdom,et al.  Life Stage Simulation Analysis: Estimating Vital-Rate Effects on Population Growth for Conservation , 2000 .

[3]  S. Ellner,et al.  Stochastic matrix models for conservation and management: A comparative review of methods , 2001 .

[4]  F. Allendorf,et al.  Effective Population Size and Genetic Conservation Criteria for Bull Trout , 2001 .

[5]  J. Post,et al.  Density-dependent intercohort interactions and recruitment dynamics: models and a bull trout (Salvelinus confluentus) time series , 2000 .

[6]  Mark L. Taper,et al.  Impact of non-linearities in density dependence beyond the range of the data on predicting population extinction risk , 2006 .

[7]  Michael P. Hassell,et al.  DENSITY-DEPENDENCE IN SINGLE-SPECIES POPULATIONS , 1975 .

[8]  Y. Iwasa,et al.  Extinction risk of a density-dependent population estimated from a time series of population size. , 2000, Journal of theoretical biology.

[9]  C. Walters,et al.  Density-dependent growth and competitive asymmetries in size-structured fish populations: a theoretical model and recommendations for field experiments , 1993 .

[10]  Alan Hastings,et al.  FITTING POPULATION MODELS INCORPORATING PROCESS NOISE AND OBSERVATION ERROR , 2002 .

[11]  Mark L. Taper,et al.  Risk‐Based Viable Population Monitoring , 2005 .

[12]  Brian Dennis,et al.  DENSITY DEPENDENCE IN TIME SERIES OBSERVATIONS OF NATURAL POPULATIONS: ESTIMATION AND TESTING' , 1994 .

[13]  Ray Hilborn,et al.  The Influence of Model Structure on Conclusions about the Viability and Harvesting of Serengeti Wildebeest , 1997 .

[14]  Jerald B. Johnson,et al.  Model selection in ecology and evolution. , 2004, Trends in ecology & evolution.

[15]  J. Logan Toward an Expert System for Development of Pest Simulation Models , 1988 .

[16]  C. Hall,et al.  Ecosystem Modeling in Theory and Practice: An Introduction with Case Histories , 1990 .

[17]  K. Wiegand,et al.  The Role of Density Regulation in Extinction Processes and Population Viability Analysis , 2004, Biodiversity & Conservation.

[18]  Mark L. Taper,et al.  Observer Error Structure in Bull Trout Redd Counts in Montana Streams: Implications for Inference on True Redd Numbers , 2006 .

[19]  R L Tummala,et al.  Population Modeling: A Systems Approach , 1972, Science.

[20]  Jesse A. Logan,et al.  Derivation and Analysis of Composite Models for Insect Populations , 1989 .

[21]  S. Lele,et al.  ESTIMATING DENSITY DEPENDENCE, PROCESS NOISE, AND OBSERVATION ERROR , 2006 .

[22]  Alan A. Berryman,et al.  Population Theory: An Essential Ingredient in Pest Prediction, Management, and Policy-making , 1991 .

[23]  M. Taper,et al.  JOINT DENSITY DEPENDENCE , 1998 .

[24]  J. Logan,et al.  In Defense of Big Ugly Models , 1994 .

[25]  John Sabo,et al.  Morris, W. F., and D. F. Doak. 2003. Quantitative Conservation Biology: Theory and Practice of Population Viability Analysis. Sinauer Associates, Sunderland, Massachusetts, USA , 2003 .

[26]  Brian Dennis,et al.  Estimation of Growth and Extinction Parameters for Endangered Species , 1991 .

[27]  Armando Caballero,et al.  Developments in the prediction of effective population size , 1994, Heredity.