DOI: 10.5061/dryad.v41ns1rw2
title: Monitoring small mammal abundance using NEON data: Are calibrated indices useful?

description: Small mammals are important to the functioning of ecological communities
 with changes to their abundances used to track impacts of environmental
 change. While capture-recapture estimates of absolute abundance are
 preferred, indices of abundance continue to be used in cases of limited
 sampling, rare species with little data, or unmarked individuals.
 Improvement to indices can be achieved by calibrating them to absolute
 abundance but their reliability across years, sites, or species is
 unclear. To evaluate this, we used the US National Ecological Observatory
 Network (NEON) capture-recapture data for 63 small mammal species over 46
 sites from 2013–2019. We generated 17,155 absolute abundance estimates
 using capture-recapture analyses and compared these to two standard
 abundance indices, and three types of calibrated indices. We found that
 neither raw abundance indices nor index calibrations were reliable
 approximations of absolute abundance, with raw indices less correlated
 with absolute abundance than index calibrations (raw indices overall R2
 &lt; 0.5, index calibration overall R2 &gt; 0.6). Performance of
 indices and index calibrations varied by species, with those having higher
 and less variable capture probabilities performing best. We conclude that
 indices and index calibration methods should be used with caution with a
 count of individuals being the best index to use, especially if it can be
 calibrated with capture probability. None of the indices we tested should
 be used for comparing different species due to high variation in capture
 probabilities.  Hierarchical models that allow for sharing of
 capture probabilities over species or plots (i.e., joint likelihood
 models) may offer a better solution to mitigate the cost and effort of
 large-scale small mammal sampling while still providing robust estimates
 of abundance.
description: The NEON dataset we used contains capture-recapture data for 63
 species over 46 sites from 2013–2019, representing 39% of North American
 small mammal species. Each site contains 3-8 (mean = 6) replicate trapping
 arrays of 100 traps set in grids with 10m spacing. Field scientists used
 baited Sherman live traps for animal capture, checked daily and set within
 10 days before or after the new moon. Traps were typically run at monthly
 intervals for 6 months during the growing season at a subset of sites
 (core sites), and 3-4 months for the rest of the sites. At each site, half
 of the trap arrays were run for multiple nights (mean = 3), and the other
 half were run for a single night. Trapped individuals were tagged with
 either an individually identifiable ear tag or PIT tag. NEON trapping
 targets small rodents including cricetids (New World rats and mice,
 lemmings, voles), dipodids (birch mice, jumping mice), heteromyids
 (kangaroo rats), small sciurids (flying squirrels), and introduced murids
 (Old World rats and mice, gerbils). Sampling did not target lagomorphs
 (rabbits, hares, pikas), mustelids (weasels), large squirrels, or soricids
 (shrews). Although some of these taxa were incidentally captured, they
 were removed from this analysis. Some captured individuals (n = 453) were
 incorrectly identified at the species level in the field, and we used NEON
 DNA barcoding data to correct those identifications before
 analysis. <em>Capture-recapture
 analysis.</em>—Under the NEON sampling protocol, about half of the
 trapping grids at each site were sampled multiple nights per trapping
 session, while other grids were sampled only a single night per month. 
 Traditional capture-recapture abundance estimates require that multiple
 nights of sampling are conducted, omitting data from grids that are
 sampled only a single night.  To accommodate all the data, we used a
 Bayesian hierarchical approach wherein trapping arrays sampled for
 multiple nights share a likelihood with arrays that ran only one night,
 thus providing information on capture probability that exploits the
 information available both in repeat- and single-night counts.  The
 approach of Royle et al. (2012) was adapted to estimate abundance at
 replicate trapping arrays within each geographic site during each month
 and year of sampling. We use data augmentation to fix the dimensionality
 of the model (i.e., make an unknown sample size “known”) by choosing an
 arbitrarily large super population size <em>M</em> of 700
 individuals for each species at each trapping array during each closed
 session, representing a density of roughly 70,000
 individuals/km<sup>2</sup>. The capture histories of all
 individuals of a given species captured at a geographic site are pooled
 (i.e., capture histories for each individual over all arrays at the site)
 and we then add an individual-level covariate describing the array
 membership, latent for augmented individuals.  Estimates of abundance at
 each replicate array within the geographic site can be obtained by summing
 over the individuals associated with each array.  Because sampling at each
 trapping array took place over a restricted time period (1–3 days), the
 assumption of constant detection probability <em>p</em> over
 time is reasonable.  While accounting for individual heterogeneity in
 capture probabilities has been shown important in small mammal studies,
 because we are making inference about individuals for which we have no
 individual detection data (i.e., those captured in arrays running single
 nights only) we could not adequately model individual heterogeneity in
 capture probabilities. We ran our model separately for each species within
 a site/month/year closed session in JAGS (Plummer 2003) via package
 runjags (Denwood 2016) in Program R (Version 3.5.3; R Core Team 2017).  We
 skipped species during closed sessions with &lt; 10 individuals
 captured at a geographic site and those site/month/years with &lt; 3
 trapping arrays running, for a total of 9,226 models.  We assessed model
 fit for each species closed session with posterior predictive checks (PPC)
 by calculating the sum of squared Pearson residuals. We calculated a
 Bayesian p-value from posterior simulations and assumed adequate fit if
 0.1 &lt; <em>p</em> &lt; 0.9. Most models converged
 (i.e., Gelman-Rubin statistic &lt; 1.1, confirmed by examining
 traceplots) after a burn-in of 5,000 iterations and sampling of 300,000
 iterations, thinning every 100 samples.  Those that did not converge after
 that number of iterations were discarded (n = 114). 
 <em>Datasets used: </em> 
 NEON. 2020a. Small mammal box trapping RELEASE-2021
 (DP1.10072.001). Downloaded from https://data.neonscience.org March 26, 2020. DOI: https://doi.org/10.48443/j1g9-2j27. NEON. 2020b. Small mammal sequences DNA barcode (DP1.10076.00).  (https://data.neonscience.org ed.). <em>References:</em> Royle J.A., Converse S.J. 2014. Hierarchical spatial capture–recapture models: modelling population density in stratified populations. Methods in Ecology and Evolution 5:37-43. Royle J.A., Converse S.J., Link W.A. 2012. Data augmentation for hierarchical capture-recapture models. arXiv preprint arXiv:1211.5706. Sollmann R., White A.M., Gardner B., Manley P.N. 2015. Investigating the effects of forest structure on the small mammal community in frequent-fire coniferous forests using capture-recapture models for stratified populations. Mammalian Biology 80:247-254.