Estimating the unobservable moose - converting index to population size using a Bayesian Hierarchical state space model

TL;DR

This paper introduces a Bayesian hierarchical state space model that accounts for temporal variability in the link between indirect ecological observations and actual population size, improving population estimates and management decisions.

Contribution

It presents a novel model incorporating time-varying countability to better estimate population dynamics from indirect data.

Findings

Countability explains 30-50% of data variability.

Ignoring countability leads to inaccurate population management.

Spatial variation in countability is significant and warrants further study.

Abstract

Indirect information on population size, like pellet counts or volunteer counts, is the main source of information in most ecological studies and applied population management situations. Often, such observations are treaded as if they were actual measurements of population size. This assumption results in incorrect conclusions about a population's size and its dynamics. We propose a model with a temporal varying link, denoted countability, between indirect observations and actual population size. We show that, when indirect measurement has high precision (for instance many observation hours) the assumption of temporal varying countability can have a crucial effect on the estimated population dynamic. We apply the model on two local moose populations in Sweden. The estimated population dynamics is found to explain 30-50 percent of the total variability in the observation data; thus, countability accounts for most of the variation. This unreliability of the estimated dynamics has a substantial negative impact on the ability to manage populations; for example, reducing (increasing) the number of animals that needs to be harvested in order to sustain the population above (below) a fixed level. Finally, large difference in countability between two study areas implies a substantial spatial variation in the countability; this variation in itself is highly worthy of study.