Potential of Airborne LiDAR Derived Vegetation Structure for the Prediction of Animal Species Richness at Mount Kilimanjaro

The monitoring of species and functional diversity is of increasing relevance for the development of strategies for the conservation and management of biodiversity. Therefore, reliable estimates of the performance of monitoring techniques across taxa become important. Using a unique dataset, this study investigates the potential of airborne LiDAR-derived variables characterizing vegetation structure as predictors for animal species richness at the southern slopes of Mount Kilimanjaro. To disentangle the structural LiDAR information from co-factors related to elevational vegetation zones, LiDAR-based models were compared to the predictive power of elevation models. 17 taxa and 4 feeding guilds were modeled and the standardized study design allowed for a comparison across the assemblages. Results show that most taxa (14) and feeding guilds (3) can be predicted best by elevation with normalized RMSE values but only for three of those taxa and two of those feeding guilds the difference to other models is significant. Generally, modeling performances between different models vary only slightly for each assemblage. For the remaining, structural information at most showed little additional contribution to the performance. In summary, LiDAR observations can be used for animal species prediction. However, the effort and cost of aerial surveys are not always in proportion with the prediction quality, especially when the species distribution follows zonal patterns, and elevation information yields similar results

The Relationship of Heteroptera Species Richness, Abundance, and Morphology to Elevation Gradients and Land-use Regimes on Mt. Kilimanjaro

Insect biodiversity is unevenly distributed on local, regional, and global scales. Elevation is a key factor in the uneven distribution of insect diversity, serving as a proxy for a host of environmental variables. My study examines the relationship of Heteroptera (true bugs) species diversity, abundance, and morphology to elevational gradients and land-use regimes on Mt. Kilimanjaro, Tanzania, East Africa. Heteroptera specimens were collected from 60 research sites covering an elevational range of 3684m (866-4550m above sea level). Thirty of the sites were classified as natural, while the remaining 30 were classified as disturbed (e.g., agricultural use or converted to grasslands). I measured aspects of the body size of adult specimens and recorded their location of origin. I used regression models to analyze the relationships of Heteroptera species richness, abundance, and body measurements to elevation and land-use regime. Richness and abundance declined with greater elevation, controlling for land use. The declines were linear or logarithmic in form, depending on the model. Richness and abundance were greater in natural than disturbed sites, controlling for elevation. According to an interaction, richness decreased more in natural than disturbed sites with rising elevation. Body length increased as a quadratic function of elevation, adjusting for land use. Body width X length decreased as a logarithmic function of elevation, while leg length/body length decreased as a quadratic function. Leg length/body length was greater in disturbed than natural sites. Interactions indicated that body length and body width X length were greater in natural than disturbed sites as elevation rose, although the general trend was downward. Future research should examine the relative importance of land area, temperature, and resource constraints for Heteroptera diversity and morphology on Mt. Kilimanjaro

Effects of compositional heterogeneity and spatial autocorrelation on richness and diversity in simulated landscapes

Abstract Landscape structure plays a key role in mediating a variety of ecological processes affecting biodiversity patterns; however, its precise effects and the mechanisms underpinning them remain unclear. While the effects of landscape structure have been extensively investigated both empirically and theoretically from a metapopulation perspective, the effects of spatial structure at the landscape scale remain poorly explored from a metacommunity perspective. Here, we attempt to address this gap using a spatially explicit, individual‐based metacommunity model to explore the effects of landscape compositional heterogeneity and per se spatial configuration on diversity at the landscape and patch levels via their influence on long‐term community assembly processes. Our model simulates communities composed of species of annual, asexual organisms living, reproducing, dispersing, and competing within grid‐based, fractal landscapes that vary in their magnitude of spatial environmental heterogeneity and in their degree of spatial environmental autocorrelation. Communities are additionally subject to temporal environmental fluctuations and external immigration, allowing for turnover in community composition. We found that compositional heterogeneity and spatial autocorrelation had differing effects on richness, diversity, and the landscape and patch scales. Landscape‐level diversity was driven by community dissimilarity at the patch level and increased with greater heterogeneity, while landscape richness was largely the result of the short‐term accumulation of immigrants and decreased with greater compositional heterogeneity. Both richness and diversity decreased in variance with greater compositional heterogeneity, indicating a reduction in community turnover over time. Patch‐level richness and diversity patterns appeared to be driven by overall landscape richness and local mass effects, resulting in maximum patch‐level richness and diversity at moderate levels of compositional heterogeneity and high spatial autocorrelation

Data file for Dryad repository

Sheet 1 of the data file contains data describing plant growth rates. Column A identifies the individual plant. Column B show the light treatment (0=shade, 1=sun). Column C indicates ant treatment (0=ants present, 1-ants absent. Columns D through X are measurements of plant growth rate as an estimate of increased biomass over each 2 week period. Sheet 2 of the data file contains data describing herbivory rates. Column A identifies the individual plant. Column B show the light treatment (0=shade, 1=sun). Column C indicates ant treatment (0=ants present, 1-ants absent. Columns D through X are measurements of plant herbivory rate as an estimate of % leaf area removal. Sheet 3 of the data file contains data describing plant size. Column A identifies the individual plant. Column B show the light treatment (0=shade, 1=sun). Column C indicates ant treatment (0=ants present, 1-ants absent. Columns D through AB are measurements of plant size, taken as the total number of growing stems. Sheet 4 of the data file contains data describing extrafloral nectar production. Column A identifies the individual plant. Column B show the light treatment (0=shade, 1=sun). Column C indicates ant treatment (0=ants present, 1-ants absent. Columns D through I are bi-monthly measurements of extrafloral nectar production, expressed as total sugar production from 5 leaves on each plant. Sheet 5 of the data file contains data from insect surveys conducted throughout the year long study. Column A shows the date that surveys were conducted. Column B identifies the individual plant. Column C shows the light treatment (0=shade, 1=sun). Column D indicates ant treatment (0=ants present, 1-ants absent. Column E indicates whether the survey was conducted during the day or at night (0=day, 1=night). Columns F through AY are different insects and they presence and abundance are indicated for each survey. Sheet 6 of the data file contains data describing plant reproductive fitness. Column A shows the date that data were collected. Column B identifies the individual plant. Column C shows the light treatment (0=shade, 1=sun). Column D indicates ant treatment (0=ants present, 1-ants absent. Columns E, F, G, and H indicate numbers of flowers, fruit set, mature fruit, and seeds respectively

Predictors of elevational biodiversity gradients change from single taxa to the multi-taxa community level

The factors determining gradients of biodiversity are a fundamental yet unresolved topic in ecology. While diversity gradients have been analysed for numerous single taxa, progress towards general explanatory models has been hampered by limitations in the phylogenetic coverage of past studies. By parallel sampling of 25 major plant and animal taxa along a 3.7 km elevational gradient on Mt. Kilimanjaro, we quantify cross-taxon consensus in diversity gradients and evaluate predictors of diversity from single taxa to a multi-taxa community level. While single taxa show complex distribution patterns and respond to different environmental factors, scaling up diversity to the community level leads to an unambiguous support for temperature as the main predictor of species richness in both plants and animals. Our findings illuminate the influence of taxonomic coverage for models of diversity gradients and point to the importance of temperature for diversification and species coexistence in plant and animal communities