Evaluating abundance and trends in a Hawaiian avian community using state-space analysis

Abstract

SummaryEstimating population abundances and patterns of change over time are important in both ecology and conservation. Trend assessment typically entails fitting a regression to a time series of abundances to estimate population trajectory. However, changes in abundance estimates from year-to-year across time are due to both true variation in population size (process variation) and variation due to imperfect sampling and model fit. State-space models are a relatively new method that can be used to partition the error components and quantify trends based only on process variation. We compare a state-space modelling approach with a more traditional linear regression approach to assess trends in uncorrected raw counts and detection-corrected abundance estimates of forest birds at Hakalau Forest National Wildlife Refuge, Hawai‘i. Most species demonstrated similar trends using either method. In general, evidence for trends using state-space models was less strong than for linear regression, as measured by estimates of precision. However, while the state-space models may sacrifice precision, the expectation is that these estimates provide a better representation of the real world biological processes of interest because they are partitioning process variation (environmental and demographic variation) and observation variation (sampling and model variation). The state-space approach also provides annual estimates of abundance which can be used by managers to set conservation strategies, and can be linked to factors that vary by year, such as climate, to better understand processes that drive population trends.

Bioacoustics for species management: two case studies with a Hawaiian forest bird

“…These results were in agreement with the density estimation at the same sites by Camp et al. (in press) that also identified a higher ‘Amakihi density at site 2 than at site 1 using the point‐count method (GLMM, ΔAIC with null model = 24.3, P < 0.001, Fig. B).…”

“…Then, we tested the performance of our relative abundance estimation comparing our results with an ‘Amakihi density estimation calculated at the same area in April–May 2012 using point counts by Camp et al. (in press). In their article, Camp et al.…”

“…We created a 500‐m buffer around each of our study points and averaged the ‘Amakihi density estimated by Camp et al. (in press) for all the locations that were inside the buffer.…”

“…We used linear mixed models (LMMs) in R to compare the calls per minute and the density estimation from Camp et al. (in press) among the two study sites. As the data were taken from two different points within each study site, we used point as a random variable in our models.…”

Evaluating community‐level response to management actions across a diverse Hawaiian forest bird community

“…The refuge supports critical populations of three endangered bird species endemic to Hawai'i Island, the ‘Akiapōlā’au ( Hemignathus munroi ), Hawai'i ‘Ākepa ( Loxops coccineus ), and Hawai'i Creeper ( Loxops mana ), as well as sizeable population of other native birds (Camp et al. ). While Hakalau currently provides a high‐elevation refuge from vector‐borne diseases such as avian malaria, which plays a major role in limiting the distribution and population size of native Hawaiian forest birds, global warming is predicted to facilitate the encroachment of mosquitoes and diseases into increasingly higher elevations of the refuge (Liao et al.…”