Abundance and population density are fundamental pieces of information for population ecology and species conservation, but they are difficult to estimate for rare and elusive species. Mark--resight models are popular for estimating population abundance because they are less invasive and expensive than traditional mark-recapture. However, density estimation using mark-resight is difficult because the area sampled must be explicitly defined, historically using ad hoc approaches. We developed a spatial mark--resight model for estimating population density that combines spatial resighting data and telemetry data. Incorporating telemetry data allows us to inform model parameters related to movement and individual location. Our model also allows <100% individual identification of marked individuals. We implemented the model in a Bayesian framework, using a custom-made Metropolis-within-Gibbs Markov chain Monte Carlo algorithm. As an example, we applied this model to a mark--resight study of raccoons (Procyon lotor) on South Core Banks, a barrier island in Cape Lookout National Seashore, North Carolina, USA. We estimated a population of 186.71 +/- 14.81 individuals, which translated to a density of 8.29 +/- 0.66 individuals/km2 (mean +/- SD). The model presented here will have widespread utility in future applications, especially for species that are not naturally marked.

Modeling population dynamics while accounting for imperfect detection is essential to monitoring programs. Distance sampling allows estimating population size while accounting for imperfect detection, but existing methods do not allow for estimation of demographic parameters. We develop a model that uses temporal correlation in abundance arising from underlying population dynamics to estimate demographic parameters from repeated distance sampling surveys. Using a simulation study motivated by designing a monitoring program for Island Scrub-Jays (Aphelocoma insularis), we investigated the power of this model to detect population trends. We generated temporally autocorrelated abundance and distance sampling data over six surveys, using population rates of change of 0.95 and 0.90. We fit the data generating Markovian model and a mis-specified model with a log-linear time effect on abundance, and derived post hoc trend estimates from a model estimating abundance for each survey separately. We performed these analyses for varying numbers of survey points. Power to detect population changes was consistently greater under the Markov model than under the alternatives, particularly for reduced numbers of survey points. The model can readily be extended to more complex demographic processes than considered in our simulations. This novel framework can be widely adopted for wildlife population monitoring.

Context Over the past two decades, an increase in the number of resident (non-migratory) Canada geese (Branta canadensis) in the United States has heightened the awareness of human–goose interactions. Aims Accordingly, baseline demographic estimates for goose populations are needed to help better understand the ecology of Canada geese in suburban areas. Methods As a basis for monitoring efforts, we estimated densities of adult resident Canada geese in a suburban environment by using a novel spatial mark–resight method. We resighted 763 neck- and leg-banded resident Canada geese two to three times per week in and around Greensboro, North Carolina, over an 18-month period (June 2008 – December 2009). We estimated the density, detection probabilities, proportion of male geese in the population, and the movements and home-range radii of the geese by season ((post-molt I 2008 (16 July – 31 October), post-molt II 2008/2009 (1 November – 31 January), breeding and nesting 2009 (1 February – 31 May), and post-molt I 2009). Additionally, we used estimates of the number of marked individuals to quantify apparent monthly survival. Key results Goose densities varied by season, ranging from 11.10 individuals per km2 (s.e. = 0.23) in breeding/nesting to 16.02 individuals per km2 (s.e. = 0.34) in post-molt II. The 95% bivariate normal home-range radii ranged from 2.60 to 3.86 km for males and from 1.90 to 3.15 km for females and female home ranges were smaller than those of male geese during the breeding/nesting and post-molt II seasons. Apparent monthly survival across the study was high, ranging from 0.972 (s.e. = 0.005) to 0.995 (s.e. = 0.002). Conclusions By using spatial mark–resight models, we determined that Canada goose density estimates varied seasonally. Nevertheless, the seasonal changes in density are reflective of the seasonal changes in behaviour and physiological requirements of geese. Implications Although defining the state–space of spatial mark–resight models requires careful consideration, the technique represents a promising new tool to estimate and monitor the density of free-ranging wildlife. Spatial mark–resight methods provide managers with statistically robust population estimates and allow insight into animal space use without the need to employ more costly methods (e.g. telemetry). Also, when repeated across seasons or other biologically important time periods, spatial mark–resight modelling techniques allow for inference about apparent survival.