DOI: 10.5061/dryad.x0k6djhmx
title: The relative influence of history, climate, topography and vegetation structure on local animal richness varies among taxa and spatial grains

description: Understanding the spatial scales at which environmental factors drive
 species richness patterns is a major challenge in ecology. Due to the
 trade-off between spatial grain and extent, studies tend to focus on a
 single spatial scale, and the effects of multiple environmental variables
 operating across spatial scales on the pattern of local species richness
 have rarely been investigated. Here, we related variation in local species
 richness of ground beetles, landbirds, and small mammals to variation in
 vegetation structure and topography, regional climate, biome diversity,
 and glaciation history for 27 sites across the USA at two different
 spatial grains. We studied the relative influence of broad-scale
 (landscape) environmental conditions using variables estimated at the site
 level (climate, productivity, biome diversity, and glacial era ice cover)
 and fine-scale (local) environmental conditions using variables estimated
 at the plot level (topography and vegetation structure) to explain local
 species richness. We also examined whether plot-level factors scale up to
 drive continental scale richness patterns. We used Bayesian hierarchical
 models and quantified the amount of variance in observed richness that was
 explained by environmental factors at different spatial scales. For all
 three animal groups, our models explained much of the variation in local
 species richness (85-89%), but site-level variables explained a greater
 proportion of richness variance than plot-level variables. Temperature was
 the most important site-level predictor for explaining variance in
 landbirds and ground beetles richness. Some aspects of vegetation
 structure were the main plot-level predictors of landbird richness.
 Environmental predictors generally had poor explanatory power for small
 mammal richness, while glacial era ice cover was the most important
 site-level predictor. Relationships between plot-level factors and
 richness varied greatly among geographical regions and spatial grains, and
 most relationships did not hold when predictors were scaled up to
 continental scale. Our results suggest that the factors that determine
 richness may be highly dependent on spatial grain, geography, and animal
 group. We demonstrate that instead of artificially manipulating the
 resolution to study multi-scale effects, a hierarchical approach that uses
 fine grain data at broad extents could help solve the issue of scale
 selection in environment-richness studies. 
description: We
 estimated landbird richness at the plot level using the NEON dataset of
 breeding bird point counts (Carrasco et al. 2022: National Ecological
 Observatory Network 2016a). Birds associated with terrestrial habitats
 were sampled during the breeding season using point counts performed by
 one or more expert observers who recorded all species seen or heard within
 a 125 m radius during a 6-minute period. To allow comparison across plots
 with different number of point counts, we only used the central point
 count for plots with multiple point counts. To standardise the sampling
 effort among plots, we aggregated data from two survey dates for each
 plot, carried out during the years 2016, 2017 or 2018, which resulted in
 192 plots across 25 NEON sites for our analysis (Carrasco et al. 2022:
 Supplementary Material Table S1). We selected survey dates based on their
 temporal proximity to the available lidar data (for calculating
 topographical and vegetation structure indices). We obtained
 data on ground beetles from the NEON’s Ground Beetles Sampled from Pitfall
 Traps dataset (Carrasco et al. 2022: National Ecological Observatory
 Network 2016c), which provides counts of ground beetles (Coleoptera:
 Carabidae). The NEON sampling protocol uses four pitfall traps (473 mL
 deli containers filled with 150 or 250 mL of propylene glycol) placed 20 m
 from the centre of the plot in the four cardinal directions. Sampling
 occurred in a two-week duration (i.e., a bout) during the growing season.
 We used 10 bouts per year in each plot in order to standardise the
 sampling efforts. Unlike for landbirds and small mammals, we only selected
 plots that were sampled in 2018 (n = 110 plots across 19 NEON
 sites; Carrasco et al. 2022: Supplementary Material Table S1). We made
 this decision because the number of traps per bout changed from four to
 three from 2018 on, and therefore using data from different years would
 have led to inconsistent sampling efforts. We
 aggregated taxa identified to species across the 10 bouts to calculate
 plot-level species richness. For specimens sent for taxonomic validation,
 we used the species ID assigned by the expert taxonomist. For ground
 beetles, we used lidar data that were collected temporally closest to
 2018. We extracted data on small mammals from the
 NEON’s Small Mammal Box Trapping dataset (Carrasco et al. 2022: National
 Ecological Observatory Network 2016d). NEON defines a small mammal as any
 rodent that is nonvolant, nocturnally active, and an aboveground forager
 weighing 5-600 g (e.g., cricetids, heteromyds, small sciurids and murids,
 etc.). Mammals were sampled using box traps, which were configured in a 10
 m x 10 m grid (totalling 100 box traps) in most plots. Only
 species classified as “targeted” by NEON’s box traps were used in our
 analyses. To standardise sampling efforts, we included four bouts (each
 bout constitutes three consecutive nights of trapping) per plot and year.
 We estimated species richness based on individuals identified to the
 species level for each plot and year (2016-2018). We excluded
 opportunistic captures of non-targeted species. Lastly, for statistical
 modelling we retained from each plot only data from the year closest to
 the year of the lidar data (n = 89 plots across 23 NEON sites; Carrasco et
 al. 2022: Supplementary Material Table S1). For all
 three taxonomic groups, our plot-level species richness metric is
 equivalent to the species density metric described by Gotelli and Colwell
 (2011). We used the term richness in our paper because we combined
 multiple surveys for birds and sampling bouts for ground beetles and small
 mammals (each of which aggregates multiple traps), to maximise sampling
 completeness within each plot, while ensuring that the sampling effort is
 the same by using the same number of surveys and bouts across all plots
 within each taxonomic group. Despite these measures, sampling completeness
 may nevertheless differ across plots. We examined the correlation between
 our raw species richness metric and the estimated asymptotic richness
 (Carrasco et al. 2022: Chao et al. 2014) (calculated using the iNext
 package in R (Carrasco et al. 2022: Hsieh et al. 2016)) to assess if
 differences in sampling completeness might affect our analyses and results
 from section 2.4 below. We found moderate to high positive correlation
 (landbirds: Pearson r = 0.60; ground beetles: r = 0.93; small mammals: r =
 0.99) between our richness metric and estimated asymptotic richness
 (Carrasco et al. 2022: Supplementary Material Figure S1), suggesting that
 differences in sampling completeness is unlikely to affect the main
 conclusionsofouranalysis,especially for ground beetles and small
 mammals. Detailed information on animal surveys and richness estimation
 methodology can be found in Carrasco et al. 2022: Supplementary Material
 Appendix S1.