As recommended by Hortal et al. Community differences were quantified using the Raup‐Crick metric and the R function raupcrick (Oksanen et al. These studies explicitly recognize that species accumulation models indicate that not all species are seen in any sampled site, and hence use species pool functions to better estimate the number of unseen species (Colwell and Coddington 1994). 0000008825 00000 n On the other hand, when such studies utilize datasets with disparate sampling schemes or select trees within plots with different probabilities, comparisons must be adjusted for unequal plot sizes using a bootstrap process to construct means and standard errors (McPherson et al. However, we see only very weak evidence in support of the hypothesis (Table 3), at least in terms of tree diversity using both tree counts and tree basal area. The use of available yet disparate national‐level and local‐level plot data, using different measurement and sampling protocols, would therefore need to assume that species are part of the same regional species pool. Cahill, Jr. 2011. Evidence of such urban homogenization has been reported across major cities in the United States and China, with hypothesized continental‐scale consequences, including effects on carbon sequestration, microclimate, and other ecosystem properties (Groffman et al. (2013), for example, used sample‐based rarefaction curves to compare urban and peri‐urban forest diversity by considering the number of accumulated samples. Comparative analyses of tree and forest data collected from different ecosystems often use different inventory and sampling protocols. 2006)—even with standardized sampling techniques—which can bias estimates of species diversity. Atlanta's UF was very similar to its PF, and also that of Falls Church, while it had fairly high dissimilarity with all other forests. Independent evolution of leaf and root traits within and among temperate grassland plant communities. (2006), we present results from the Bootstrap and Jackknife1 estimators. $\alpha$ Diversity $\beta$ Diversity $\gamma$ Diversity; Isolation Diversity; Relative Species Abundance; The literature is important on the question of measuring species diversity, ecosystemic function diversity and genetic diversity. A framework for selecting appropriate methods is also discussed. However, this limits the scope of inference of any associated hypotheses tested and reduces the sample size, especially in comparisons with urban trees where smaller trees are measured on the entirety of the 0.0404‐ha plot. 2018). 2016). (2014). For example, unequal sampling intensity of smaller trees in the FIA protocol requires development of a differential measure of uncertainty in richness and composition estimates. However, you cannot compare the two index values using classic hypothesis tests because you do not have replicated data. 2003) “classical” estimators: the Abundance‐based Coverage Estimator (Chao and Lee 1992), the Abundance‐based Chao Estimator (Chao 1984), Bootstrap (Smith and van Belle 1984), Jackknife1 (Burnham and Overton 1979), and Jackknife2 estimators (Smith and van Belle 1984). Nonmetric multidimensional scaling is an ordination technique that finds the best rank‐order agreement between actual similarities and computed distances, representing a coordinate system in the ordination space (Fasham 1977). While the inclusion of plots with no trees would not alter the conclusions associated with comparisons of species richness among communities and/or forest types, the type of estimator used is critical. and you may need to create a new Wiley Online Library account. Using a matrix of comparisons between all pairs of associations, the Raup‐Crick index compares observed numbers of species with the distribution of co‐occurrences generated from 999 Monte Carlo random replicates (Chase et al. However, traditional multivariate analysis methods, such as MANOVA, make stringent assumptions which are untenable for most ecological datasets (McArdle and Anderson 2001). 2016, Yang et al. To further visualize the results, we created a nonmetric multidimensional scaling (NMDS) plot utilizing the Raup‐Crick dissimilarity metric to compare sites. Results were also similar when examining differences among locations within forest type. The standardizing of species richness data using extrapolation or rarefaction techniques is frequently performed in inter‐ and intra‐site comparisons (Gotelli and Colwell 2001). Try to compare pairwise. Comparison of specific forest characteristics, such as mortality or height, when plot sizes differ has been accomplished using statistical procedures, such as weighting (Flewelling and Monserud 2002). Since i‐Tree Eco species codes are comprised of the first two letters of the genus and species as well as other regional user‐created codes, the same species code can appear in different regions to represent multiple species. Nonetheless, comparisons between these datasets will become increasingly important to better understand how anthropogenic impacts affect urban and peri‐urban forest structure, diversity, and even ecosystem services across multiple scales, regions, and continents. Woody plants with dbh >12.7 cm were recorded within the entire 0.0675‐ha area, but trees with dbh between 2.54 and 12.7 cm were measured only in microplots. Previous urban ecology studies that test the hypothesis of ecological homogenization have used Jaccard's index (McKinney 2006), Sørensen's index (Pearse et al. 2006, Jonnes 2016). For example, Chazdon et al. 3A) was on average ~50% higher than observed, the Jackknife estimates (Fig. 2014, Livesley et al. Such an approach using data from different sampling methods—but within the same general geographic study areas—allows for the evaluation of quantitative methods while isolating variability associated with geography and climate. 2015, Jenerette et al. We then examine the results in terms of our ability to make definitive statements for urban forest management in light of species accumulation curves and pools. In this case study, we use data from both peri‐urban USDA FIA and the Southeastern Urban Tree Inventory and Canopy (SUTIC) database. In three instances, data were also extracted from surrounding states because the 25‐km buffer extended past state lines (Fig. (2018) collected data from 21 to 30 urban household yards and 3 to 6 natural area sites in their study of ecological homogenization across seven metropolitan areas. 2006), If samples are heterogenous among sites, three estimators are recommended: incidence‐based Chao (Chao and Lee 1992), Bootstrap (Smith and van Belle 1984), and Jackknife1 (Burnham and Overton 1979), This study provides one of the first assessments of statistical methods that are being used in an increasing number of studies of woody plant diversity, homogenization, and functionality. I described this data set in more detail in a recent paper:S.W. Trees were inventoried using randomly sampled 0.0404‐ha, circular, permanent plots established within city limit boundaries with the exception of east Orlando, where plots were placed within a 200‐km2 pre‐defined study area. When considering the pool of genera, similar patterns were observed, but with estimates approximately 10% closer to observed estimates (Table 2). However, we refer to these data with the name i‐Tree Eco hereafter. Nonmetric multidimensional scaling plot by location and forest type, utilizing the Raup‐Crick dissimilarity distances. Thus, we are able to identify that these multiple stems came from the same individual. (doi:10.1371/journ… Our study, which further incorporated disparate data sources, demonstrates that the Raup‐Crick dissimilarity indices, based on presence/absence data, are robust to sampling differences. Thus, appropriate and robust methods for making comparisons are necessary and important in making meaningful conclusions about the differences and similarities of these two different forest types. (Further information about the study sites can be found in Table 1 of Blood et al. In PERMANOVA, it is assumed that the observations are exchangeable under the null hypothesis, which implies that the observations are independent and have “similar” distributions. This results in the use of exact equations, without simulations, which would not take into account the unequal efforts in sampling in the two types of data. Observed and estimated peri‐urban (PF; FIA) and urban (UF; i‐Tree Eco) forest species richness by location including plots with zero tree counts (in urban areas only), utilizing (A) Chao estimator, (B) Bootstrap estimator, and (C) Jackknife estimator. Any queries (other than missing content) should be directed to the corresponding author for the article. Likewise, when using the Jackknife or Bootstrap estimator, many more pairs of locations were found to have non‐overlapping confidence intervals in terms of species and genus richness. A uniform population of a single species of plants adapted to a particular environment is more at … This becomes increasingly important as urban areas are used as a proxy for future conditions under climate change, and often are the epicenters of invasive species establishment and other socio‐ecological disturbances. (2008). (2016), we assumed that tree species were uniformly distributed across the 0.0675‐ha FIA plot sample area, but explicitly recognize the higher uncertainty associated with smaller trees not accounted for in this study. Moreover, errors due to multiple stems only occur when splits occur below 0.3 m, and thus, we assume this error to be small. 0000001574 00000 n 0000003242 00000 n However, Winchester and Falls Church, Virginia, indicate different species composition patterns from those of their regional peri‐urban counterparts. Recently, disparate data sources have been used for regional‐scale and even continental‐scale urban‐to‐rural analyses of woody plant community composition, similarity, species richness, and other questions such as ecological homogenization and ecosystem dynamics (Blood et al. While the Roanoke and Abingdon UFs were similar to all UFs and PFs in Virginia, the Falls Church UF was only dissimilar to the Virginia PFs. Therefore, Chase et al. Species diversity is the number of different species that are represented in a given community (a dataset). 0000006369 00000 n Beta diversity describes the species diversity between two communities or ecosystems. Quantifying biodiversity: procedures and pitfalls in the measurement and comparison of species richness, Biological diversity: frontiers in measurement and assessment, Explicit calculation of the rarefaction diversity measurement and the determination of sufficient sample size, Accelerated succession following an intense wind storm in an oak‐dominated forest, Evaluating the performance of species richness estimators: sensitivity to sample grain size, Impact of plot size and spatial pattern of forest attributes on sampling efficacy, Effects of selective logging on tree species diversity and composition of Bornean tropical rain forests at different spatial scales, Climate tolerances and trait choices shape continental patterns of urban tree biodiversity, Urban forests: a natural history of trees and people in the American cityscape, A global comparison of the climatic niches of urban and native tree populations. 1982). 2018). But a study across climate and land use revealed that these targets are rarely met at the species level (Kendal et al. Since FIA plot locations are not reported as exact spatial coordinates to comply with privacy issues, extracted locations were between 0.8 and 1.6 km of the actual plot. Moreover, USDA FIA protocols utilize a set species list, and unlisted species will not receive a species code. Also, further information is needed on how the increasing use of available plot‐level data in both rural and urban forests can be used to address questions regarding ecological disturbance, functionality, and homogenization (Staudhammer et al. If species exhibit non‐random spatial patterns, such as within‐species clumping and segregation among species, which may occur with planted urban forests, estimates of species pools can be overestimated in small samples, and knowledge about spatial autocorrelation has not been found useful in correcting bias (Collins and Simberloff 2009). Diversity is often quantified in terms of richness and evenness, as well as community composition. 2017). We used an expansion factor to adjust tree counts within microplots for their smaller sample exposure, and thus, each recorded stem in a microplot is comparable to 0.0675/0.0054 = 12.5 stems in the larger plot. 3C) were 30% higher than observed for PF and 44% higher than observed for UF. Our PERMANOVA results utilizing basal area and tree counts were very similar, with both analyses indicating that species distributions were different depending on ecological province (P = 0.001) and forest type (UF vs. PF; P = 0.001; Table 3). To promote urban forest tree diversity and its management, metrics of species richness have been proposed. Regardless of forest type, estimated species richness was much higher than measured when considering the Chao and Jackknife estimators (Table 2). Other measurements included height, land use, crown width, crown light exposure, and tree location within plot. It refers to the variety of life and includes all living organisms such as plants, animals and microorganisms and their unique characteristics. They observed that the loss of β diversity is not only a consequence of compositional change, but that β diversity also declines if species found in polluted sites are consistently ranked in order of abundance; if the same species tend to dominate polluted assemblages and other species occur at moderate to … 0000008450 00000 n Washington, DC 20036phone 202-833-8773email: esajournals@esa.org. 1975). For example, our results indicate that the inclusion of plots with no trees, while having little impact on species pool estimates, can greatly affect the shape of the species accumulation curve, leading researchers to make different conclusions about the adequacy of sampling methods. This has often been referred to as an assumption of equal “multivariate spread” among groups, which is a multivariate analog to the assumption of homoscedasticity in univariate ANOVA. Second, we evaluate how these different methods can influence study findings and management implications. This can be problematic due to varying sampling intensities, plot shapes, and sizes (Laurance et al. 2011). Given current knowledge of the available methods for situations when there are differing sampling intensities and non‐homogeneous species distributions, and the results of our case study, we recommend specific analytical methods for quantifying diversity using disparate data sources in urban–rural forested contexts and across different scales (Table 5). For comparing datasets with different sampling intensities, PERMANOVA has been documented as being the most appropriate of these methods, as it allows for the formulation of multifactorial hypothesis tests and formulation of several types of distance metrics (Anderson 2001). 's (2008) protocol, where each tree or palm with dbh >2.54 cm was measured and its species name recorded within a 0.0404‐ha (0.1 acre) circular plot. The variety of life forms of a particular area are referred to as biodiversity. If the hypothesis of ecological homogenization was supported, we would expect a different outcome for urban forests; species composition across urban communities would not be significantly different by province. Plot‐based sampling requires different methods from those of individual‐based protocols (sensu Gotelli and Colwell 2001), as plots involve samples of multiple, grouped individuals as replicates, rather than single individuals (Speak et al. Number of times cited according to CrossRef: Comparative morphometric analysis of lungs of the semifossorial giant pouched rat (Cricetomys gambianus) and the subterranean Nigerian mole rat (Cryptomys foxi). ANOVA‐like test statistics are constructed from matrices of among‐sample resemblances, which may be distances, dissimilarities, or similarities, and P‐values are obtained with randomly generated permutations of observations among groups (Anderson and Walsh 2013). We used the function specaccum, which uses as its default method the sample‐based (i.e., plot‐based) exact method to estimate an expected species accumulation curve via sample‐based rarefaction (Chiarucci et al. While observed and estimated species richness values were very close between the two types of datasets when applying each of the estimators, there were larger, detectable differences in the standard error of these bootstrap estimates. 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