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The relative influence of history, climate, topography and vegetation structure on local animal richness varies among taxa and spatial grains

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Carrasco, Luis; Giam, Xingli; Sheldon, Kimberly S.; Papes, Mona (2022). The relative influence of history, climate, topography and vegetation structure on local animal richness varies among taxa and spatial grains [Dataset]. Dryad.


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. 


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.