Assessing methods for comparing species diversity from disparate data sources: the case of urban and peri‐urban forests. Issue 10 (8th October 2018)

• Record Type: Journal Article
• Authors: Staudhammer, Christina L.; Escobedo, Francisco J.; Blood, Amy
• Is Part Of: Ecosphere. Volume 9:Issue 10(2018)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-10-08
• Subjects: ecological homogenization -- Forest Inventory and Analysis -- forest sampling -- i‐Tree Eco -- species richness; Ecology -- Periodicals; Ecology; Periodicals; 577.05
• DOI: 10.1002/ecs2.2450
• Languages: English
• ISSNs: 2150-8925

Abstract

Abstract: Multi‐scale forest inventory and monitoring data are increasingly being used in studies assessing forest diversity, structure, disturbance, and carbon dynamics. Also, local‐level urban forest inventories are providing plot data and protocols to study tree diversity and ecosystem services in urban forests worldwide. But, differences in the sampling methods underlying these disparate protocols and data sources is a non‐trivial concern in formulating comparative analyses. We assess commonly used methods for comparing tree diversity in peri‐urban and urban forests when available data have different sample sizes, plot sizes, and sampling intensities. We present methods for appropriately evaluating species richness, as well as methods for comparing species distributions via community data matrices. Using permanent plot data from the southeastern United States, we present a case study comparing urban and peri‐urban forests along a north–south gradient, and assessing species richness and the ecological homogenization hypothesis. Our findings indicate that comparisons of tree species richness among communities, or forest types, are often inconclusive since commonly used sample sizes do not provide precise estimates of the number of species present. While the ecological homogenization hypotheses can be tested under conditions of unequal sampling effort, we suggest robust methods such as PERMANOVA and the Raup‐Crick dissimilarity index. A framework for selecting appropriate methods is also discussed. As forests are increasingly being altered by anthropogenic drivers, future studies using disparate data sources must account for differences in measurements and sampling protocols in order to produce results that are both statistically defensible and useful for science‐based management.