Assessing population viability while accounting for demographic and environmental uncertainty

Abstract

Abstract. Predicting the future trend and viability of populations is an essential task in ecology. Because many populations respond to changing environments, uncertainty surrounding environmental responses must be incorporated into population assessments. However, understanding the effects of environmental variation on population dynamics requires information on several important demographic parameters that are often difficult to estimate. Integrated population models facilitate the integration of time series data on population size and all existing demographic information from a species, allowing the estimation of demographic parameters for which limited or no empirical data exist. Although these models are ideal for assessments of population viability, they have so far not included environmental uncertainty. We incorporated environmental variation in an integrated population model to account for both demographic and environmental uncertainty in an assessment of population viability. In addition, we used this model to estimate true juvenile survival, an important demographic parameter for population dynamics that is difficult to estimate empirically. We applied this model to assess the past and future population trend of a rare island endemic songbird, the Montserrat Oriole Icterus oberi, which is threatened by volcanic activity. Montserrat Orioles experienced lower survival in years with volcanic ashfall, causing periodic population declines that were compensated by higher seasonal fecundity in years with high pre-breeding season rainfall. Due to the inclusion of both demographic and environmental uncertainty in the model, the estimated population growth rate in the immediate future was highly imprecise (95% credible interval 0.844-1.105), and the probability of extinction after three generations (in the year 2028) was low (2.1%). This projection demonstrates that accounting for both demographic and environmental sources of uncertainty provides a more realistic assessment of the viability of populations under unknown future environmental conditions.

Studying the demographic drivers of an increasing Imperial Eagle population to inform conservation management

“…Although such poor precision may be undesirable for decision makers, the great advantage of an integrated population model is the appropriate consideration of all sources of uncertainty in a single modelling framework which often leads to more realistic error margins around parameter estimates (Kéry and Schaub 2012; Oppel et al 2014). Although our estimates of immigration are too imprecise to evaluate what proportion of the population increase in Bulgaria is due to immigration, they provide evidence that the population increase is likely a consequence of both better protection of breeding grounds and some immigration of eagles from adjacent regions.…”

“…Active conservation management that included permanent nest guarding and supplementary feeding was initiated in 2001 and intensified after 2004 (Stoychev et al 2004;Demerdzhiev et al 2011a;2014). Between 2001 and 2014, 94 nests were guarded during the breeding season-18 in the period 2001-2004 and 76 in the period 2005-2014.…”

“…For that purpose, we included a 10-year projection in the model run (Kéry and Schaub 2012; Oppel et al 2014) and estimated the probability of a population decline occurring as the proportion of simulations that yielded a future population size smaller than the population size recorded in 2014. We used a Bayesian framework for inference and parameter estimation because it provided more flexibility and allowed for the incorporation of existing information to inform prior distributions for demographic parameters (Wade 2000;Brooks et al 2004;Schaub et al 2007).…”

Managing individual nests promotes population recovery of a top predator

“…To do so, we developed Integrated Population Models (IPM) capable of combining disparate data sources into a joint likelihood that estimates real and latent parameters at multiple biological levels, providing realistic and robust assessments of population dynamics and model uncertainty (Abadi, Gimenez, Arlettaz, & Schaub, ; Schaub & Abadi, ). Since their recent development, IPM have been used to evaluate ongoing threats to rare species (Tenan, Adrover, Muñoz Navarro, Sergio, & Tavecchia, ), assess population viability (Oppel et al., ), improve inference from conservation monitoring (Tempel, Peery, & Gutiérrez, ), guide management planning (Coates et al., ), and evaluate population‐level management (Ketz, Johnson, Monello, & Hobbs, ). Our approach combines observations of nest occupancy and reproductive output to estimate demographic parameters at the individual‐ and population‐level.…”

Population dynamics reveal conservation priorities of the threatened New Zealand sea lion Phocarctos hookeri

“…However, the quantification of environmental variability can increase our understanding of processes that could cause a population decline (Melbourne and Hastings 2008). In this regard, stochastic population models are reliable for detecting a population decline (Oppel et al 2014) and to measure the proportional change of the population growth rate in response to changes in demographic parameters with an elasticity analysis (Benton and Grant 1999;Caswell 2001).…”