Tree species richness predicted using a spatial environmental model including forest area and frost frequency, eastern USA

Abstract

Assessing geographic patterns of species richness is essential to develop biological conservation as well as to understand the processes that shape these patterns. We aim to improve geographic prediction of tree species richness (TSR) across eastern USA by using: 1) gridded point-sample data rather than spatially generalized range maps for the TSR outcome variable, 2) new predictor variables (forest area FA; mean frost day frequency MFDF) and 3) regression models that account for spatial autocorrelation. TSR was estimated in 50 km by 50 km grids using Forest Inventory and Analysis (FIA) point-sample data. Eighteen environmental predictor variables were employed, with the most effective set selected by a LASSO that reduced multicollinearity. Those predictors were then employed in Generalized linear models (GLMs), and in Eigenvector spatial filtering (ESF) models that accounted for spatial autocorrelation. Models were evaluated by model fit statistics, spatial patterns of TSR predictions, and spatial autocorrelation. Our results showed gridded TSR was best-predicted by the ESF model that used, in descending order of influence: precipitation seasonality, mean precipitation in the driest quarter, FA, and MFDF. ESF models, by accounting for spatial autocorrelation, outperformed GLMs regardless of the predictors employed, as indicated by percent deviance explained and spatial autocorrelation of residuals. Small regions with low TSR, such as the Midwest prairie peninsula, were successfully predicted by ESF models, but not by GLMs or other studies. Gridded TSR in Florida was only correctly predicted by the ESF model with FA and MFDF, and was over-predicted by all other models.

The vertical distribution of epiphytic orchids on Schima wallichii trees in a montane forest in West Java, Indonesia

“…The explanatory variables included were DBH, number of branches in crown zones (zone 3, 4, and 5) and crown depth of the host tree. GLM with a Poisson error distribution and a logarithmic link function was used since the factor generally satisfied the Poisson error distribution as a count variable (Bolker et al 2009, Kwon et al 2018. Models were ranked and those with the lowest AIC (Akaike's Information Criterion) value were selected.…”

Investigation of the Peninsula Effect Using the Latitudinal Abundance Pattern for Tree Species in Florida

“…This may mean that, first, the geometry hypothesis is not a valid explanation for tree species in Florida or, second, that population dynamics are difficult to trace by the current abundance pattern. Lastly, it could mean that tree species richness gradients in the Florida peninsula are merely driven by environmental factors specific to this region, such as forest patch size or other limiting factors (e.g., frost days, forest area, and precipitation in the driest quarter; see Kwon et al [33]).…”

“…This grid size was adopted from the size of estimation unit of FIA sampling framework [32]. Firstly, we estimated grid-level species richness (i.e., counts of unique tree species) following Kwon et al [33] in two steps: first, we selected grids containing more than three plots and, second, we applied a bootstrapping method of 1000 iterations to calculate the mean values for species counts after randomly selecting three plots for each grid. This method ensures that our sample-based richness estimates were not biased by the area sampled (i.e., the number of plots within a grid) because the relationship between the number of plots and the richness in a grid was linear (Pearson's r of 0.58, p < 0.01).…”

“…Thus, the history hypothesis can explain why there are many endemic species in the Florida peninsula, but it is not sufficient to address the causal factors for the peninsula effect. Regarding habitat hypotheses, climate conditions change from warm and temperate in the north of Florida to subtropical in the south, and it is likely that the climatic and habitat conditions control the peninsula effect in Florida [33]. The present study, however, attempted to test the geometry hypothesis only, as no other studies quantified the potential effect of peninsula geometry in tree species population dynamics due to limited empirical evidence [23,48].…”

“…However, their study combined data from multiple adjacent and small peninsulas in the region and focused on alpha diversity rather than gamma diversity (as was used in our study). Kwon et al [33] also found forest patch areas and frost days as the most determinative factors behind tree species richness in Florida using spatial modeling approaches, and the geometry hypothesis, as tested by the nearby waterbody area, was not a significant factor.…”