Abstract
Wildlife populations consist of individuals that contribute disproportionately to growth and viability. Understanding a population's spatial and temporal dynamics requires estimates of abundance and demographic rates that account for this heterogeneity. Estimating these quantities can be difficult, requiring years of intensive data collection. Often, this is accomplished through the capture and recapture of individual animals, which is generally only feasible at a limited number of locations. In contrast, N-mixture models allow for the estimation of abundance, and spatial variation in abundance, from count data alone. We extend recently developed multistate, open population N-mixture models, which can additionally estimate demographic rates based on an organism's life history characteristics. In our extension, we develop an approach to account for the case where not all individuals can be assigned to a state during sampling. Using only state-specific count data, we show how our model can be used to estimate local population abundance, as well as density-dependent recruitment rates and state-specific survival. We apply our model to a population of black-throated blue warblers (Setophaga caerulescens) that have been surveyed for 25 years on their breeding grounds at the Hubbard Brook Experimental Forest in New Hampshire, USA. The intensive data collection efforts allow us to compare our estimates to estimates derived from capture–recapture data. Our model performed well in estimating population abundance and density-dependent rates of annual recruitment/immigration. Estimates of local carrying capacity and per capita recruitment of yearlings were consistent with those published in other studies. However, our model moderately underestimated annual survival probability of yearling and adult females and severely underestimates survival probabilities for both of these male stages. The most accurate and precise estimates will necessarily require some amount of intensive data collection efforts (such as capture–recapture). Integrated population models that combine data from both intensive and extensive sources are likely to be the most efficient approach for estimating demographic rates at large spatial and temporal scales.
Highlights
A key objective of wildlife research is to determine how demographic parameters change over space and time
Rodenhouse et al (2003; Fig. 2) estimated that approximately 2 fledglings were produced per breeding pair at similar densities (10 breeding individuals per 10 hectares). This would indicate that approximately onethird of individuals survive from fledging to their first breeding season, similar to the 0.3 value that Sillett and Holmes (2005) used to approximate observed warbler dynamics
The state-structured models developed in Zipkin et al (2014) and presented in this article represent an extension of the Dail and Madsen (2011) open population model as well as an extension of multistate models to populations for which data on individual encounter histories are not available (Table 1)
Summary
A key objective of wildlife research is to determine how demographic parameters change over space and time. Understanding reproduction, survival, and dispersal of animals is necessary to determine the status and viability of populations, and to identify critical or sensitive portions of a population. These vital rate estimates can be used to parameterize matrix models, or more complicated life cycle-based models, to assess whether populations are declining and determine the life stages that have the greatest influences on population growth a 2014 The Authors. This information can help researchers and wildlife managers to determine optimal approaches for management and conservation activities including the life stages and demographic rates to target, when action is warranted (Williams et al 2002)
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