Statistical eco-demography:

a. Bayesian Survival Trajectory Analysis (BaSTA).

Understanding age-specific survival in wild animal populations is crucial to the study of population dynamics and is therefore an essential component of several fields including evolution, management and conservation. BaSTA (Colchero et al. 2012) is a tool under active development which allows users to analyse age patterns of survival in long-term studies with mark-capture-recapture data where recapture probabilities may be low, and where birth and death times and age are unknown. We have extended this model to account for dispersal out of the study site (i.e. apparent mortality) (Barthold et al. 2016a and b, Colchero et al. 2016) and for the combined 

b. Age-structured stochastic population modeling:

The demography of animal populations is strongly influenced by the way individuals use their landscape. Movement is a key determinant of an individual’s fitness, allowing it to forage for resources, find mates and disperse. We are developing statistical models for animal movement to understand how animals move over the landscape, what triggers their preferences and how different landscape and environmental features affect their movement decisions. Our models combine a state-space approach to handle missing movement records, together with what we have described as Markov Chain Resource Selection (MCRS) functions to determine what features increase the probability of moving to a specific point.

We are exploring how populations with different age-specific demographic rates (i.e. survival and fecundity) respond to the environment and how this affects stochastic population growth rates. In a paper published in Ecology Letters (Colchero et al. 2019), we have found that stochastic population growth rates and their long-term averages vary dramatically as a function of the age-trajectories in survival and fecundity. Furthermore, we found that modelling these populations by grouping ages into general stages (e.g. juveniles, sub-adutls, and adults), will produce markedly different stochastic population growth rates than models that account for the full age-dependency in demographic rates.

c. Inference on animal movement:

The demography of animal populations is strongly influenced by the way individuals use their landscape. Movement is a key determinant of an individual’s fitness, allowing it to forage for resources, find mates and disperse. We are developing statistical models for animal movement to understand how animals move over the landscape, what triggers their preferences and how different landscape and environmental features affect their movement decisions. Our models (Colchero et al 2011, Plante et al. 2014) combine a state-space approach to handle missing movement records, together with what we have described as Markov Chain Resource Selection (MCRS) functions to determine what features increase the probability of moving to a specific point.