Estimating tree species diversity across geographic scales

Abstract

The relationship between number of species and area observed has been described using numerous approaches and has been discussed for more than a century. The general objectives of our study were fourfold: (1) to evaluate the behaviour of species-area curves across geographic scales, (2) to determine sample sizes necessary to produce acceptably precise estimates of tree species diversity, (3) to evaluate relationships among estimates of tree species diversity for local to large geographic scales, and (4) to determine whether the proportion of native tree species may be precisely estimated using sample sizes smaller than those necessary to estimate total tree species diversity. Such investigations are necessary to improve biodiversity monitoring at large scales. The analytical approaches included Monte Carlo bootstrap simulations and two geospatial methods and relied on a database populated with data for more than 12,000 national forest inventory plots (NFI) from 16 regional units, 13 European countries and three ecoprovinces of the United States of America (USA). Four primary results were obtained. First, tree species diversity may be precisely estimated using observations for a random subsample of 2,000-4,000 NFI plots. Second, large sample sizes are necessary to estimate tree species diversity for regional units or forest categories, except possibly for boreal forests for which the number of tree species is small. Third, estimates of the proportion of native tree species may be precisely estimated using tree species information for a random sample of approximately 30 NFI plots. Finally, our estimated species-area curves show that the curve shapes and relationships among estimates of tree species diversity at large scales clearly depend on the relative geographic location of the anchor regional unit (a European country or ecoprovince of the USA) relative to the other regional units. None of the well-known models for species-area curves adequately describes our results. The conclusion was that the uncertainties of the estimates reflect the unfavourable state of global biodiversity monitoring of species groups. The total numbers of tree species in our regional units, which cannot be adequately estimated, are small relative to other tree species-rich regions. Consequently, monitoring of tree species diversity is currently a highly uncertain enterprise at large scales.

Congruent spatial patterns of species richness and phylogenetic diversity in karst flora: Case study of Primulina (Gesneriaceae)

“…Phylogenetic endemism (PE; Rosauer et al, 2009) provides an alternative evolutionary metric by integrating PD and geographical distribution of species, allowing identification of geographic regions where species are both phylogenetically and spatially restricted. These measures have been increasingly used in conjunction with the traditional species‐based metrics in identifying areas or lineages of conservation concern (Klein et al, 2009; Fritz & Rahbek, 2012; Winter et al, 2012; Costion et al, 2015; Rosauer & Jetz, 2015). Nonetheless, preserving all facets of biodiversity simultaneously remains difficult due to limited resources.…”

Assessing the Relationship between Tree Growth, Crown Size, and Neighboring Tree Species Diversity in Mixed Coniferous and Broad Forests Using Crown Size Competition Indices

“…Scholars have noted that it is the local assemblage of species (small-scale mixing) that affects tree growth, rather than the number or diversity of species in the stand as a whole, and that diversity at the neighborhood level improves productivity at the stand level [26]. In our study, CL provides shade for PB, significantly enhancing its productivity [61].…”

“…Competition among trees is a critical aspect of forest ecology, commonly analyzed through competition indices [24][25][26][27]. These indices serve as quantitative indicators, delineating the intensity of tree competition [28][29][30], influenced by both stand characteristics (such as diameter at breast height (DBH), tree height, crown size) and environmental factors [31][32][33].…”

Effects of outliers on remote sensing‐assisted forest biomass estimation: A case study from the United States national forest inventory

“…National forest inventories (NFIs) have been established globally to support forest resource estimation for greenhouse gas reporting and economic valuations, but NFIs also support other ecological purposes (Chirici et al, 2012;EPA, 2022;Romijn et al, 2015;Tinkham et al, 2018;Tomppo et al, 2010). Applications of NFI data include ecological indicators of climate change (Horn et al, 2018;Knott et al, 2020;Will-Wolf et al, 2017;Zhu et al, 2012); below-ground mycorrhizal associations with tree species (Averill et al, 2022;Carteron et al, 2022;Jo et al, 2019); invasion biology of plants, pests and pathogens (Baer & Gray, 2022;Fei et al, 2019;Iannone et al, 2016); and biodiversity patterns and processes (Chirici et al, 2012;Fei et al, 2018;Winter et al, 2012;Zeller et al, 2018), among others.…”

“…National forest inventories (NFIs) have been established globally to support forest resource estimation for greenhouse gas reporting and economic valuations, but NFIs also support other ecological purposes (Chirici et al, 2012; EPA, 2022; Romijn et al, 2015; Tinkham et al, 2018; Tomppo et al, 2010). Applications of NFI data include ecological indicators of climate change (Horn et al, 2018; Knott et al, 2020; Will‐Wolf et al, 2017; Zhu et al, 2012); below‐ground mycorrhizal associations with tree species (Averill et al, 2022; Carteron et al, 2022; Jo et al, 2019); invasion biology of plants, pests and pathogens (Baer & Gray, 2022; Fei et al, 2019; Iannone et al, 2016); and biodiversity patterns and processes (Chirici et al, 2012; Fei et al, 2018; Winter et al, 2012; Zeller et al, 2018), among others. In addition to characterizing observed ecological patterns and processes, NFI data have also supported the parameterization of ecological models including spatially explicit landscape models (Purves et al, 2007; Suárez‐Muñoz et al, 2021; Wang et al, 2013), tree growth models (Lichstein et al, 2010; Giebink, DeRose, et al, 2022; Heilman et al, 2022) and species distribution models (Crookston et al, 2010; Iverson et al, 2019; Prasad et al, 2020), which aim to predict forest responses to changing climate and disturbances.…”