Geology topics

Len Thomas

Publications and source records attributed to Len Thomas.

3 recordsLinked to original sources

USGS2026

Improved abundance trajectories with Bayesian population dynamics model: Case study with a Hawaiian honeycreeper

Many wildlife monitoring programmes collect annual data on population abundance. The resulting abundance estimates fluctuate over time partly because of true population change and partly because of observation error. These two components of variation can be separated by fitting the estimates to a population dynamics model within a Bayesian state-space modelling framework. By constraining the population trajectory to be biologically realistic, more precise estimates can be obtained. Independent biological knowledge can be incorporated through choice of model structure and by specifying informative prior distributions on demographic parameters. We illustrate the approach using a 31-year point transect study of the Hawai’i ’ākepa ( Loxops coccineus ). We fitted five models, each making different assumptions about how population change, recruitment and/or adult survival varied over time. Overall, the ’ākepa geometric mean growth rate was 1.02, indicating an increasing population over the 31-year time series, although there were periods of slow decline potentially associated with low recruitment and more rapid recovery associated with pulses of high recruitment. Abundance estimates derived from the population models were substantially more precise than the ‘raw’ point transect estimates: 95% credible interval (CrI) was on average 51.7% (s.d. = 14.1%) narrower.

Hawaii↗

USGS2020

Using density surface models to estimate spatio-temporal changes in population densities and trend

Precise measures of population abundance and trend are needed for species conservation; these are most difficult to obtain for rare and rapidly changing populations. We compare uncertainty in densities estimated from spatio–temporal models with that from standard design‐based methods. Spatio–temporal models allow us to target priority areas where, and at times when, a population may most benefit. Generalised additive models were fitted to a 31‐year time series of point‐transect surveys of an endangered Hawaiian forest bird, the Hawai‘i ‘ākepa Loxops coccineus . This allowed us to estimate bird densities over space and time. We used two methods to quantify uncertainty in density estimates from the spatio–temporal model: the delta method (which assumes independence between detection and distribution parameters) and a variance propagation method. With the delta method we observed a 52% decrease in the width of the design‐based 95% confidence interval (CI), while we observed a 37% decrease in CI width when propagating the variance. We mapped bird densities as they changed across space and time, allowing managers to evaluate management actions. Integrating detection function modelling with spatio–temporal modelling exploits survey data more efficiently by producing finer‐grained abundance estimates than are possible with design‐based methods as well as producing more precise abundance estimates. Model‐based approaches require switching from making assumptions about the survey design to assumptions about bird distribution. Such a switch warrants consideration. In this case the model‐based approach benefits conservation planning through improved management efficiency and reduced costs by taking into account both spatial shifts and temporal changes in population abundance and distribution.

Hawaii↗

USGS2017

Model-based approaches to deal with detectability: a comment on Hutto (2016)

In a recent paper, Hutto (2016a) challenges the need to account for detectability when interpreting data from point counts. A number of issues with model-based approaches to deal with detectability are presented, and an alternative suggested: surveying an area around each point over which detectability is assumed certain. The article contains a number of false claims and errors of logic, and we address these here. We provide suggestions about appropriate uses of distance sampling and occupancy modeling, arising from an intersection of design- and model-based inference.

Ecological Applications↗