Correction: Joint Effect of Habitat Identity and Spatial Distance on Spiders' Community Similarity in a Fragmented Transition Zone

[This corrects the article DOI: 10.1371/journal.pone.0168417.]

Measuring beta-diversity by remote sensing: a challenge for biodiversity monitoring

Biodiversity includes multiscalar and multitemporal structures and processes, with different levels of functional organization, from genetic to ecosystemic levels. One of the mostly used methods to infer biodiversity is based on taxonomic approaches and community ecology theories. However, gathering extensive data in the field is difficult due to logistic problems, overall when aiming at modelling biodiversity changes in space and time, which assumes statistically sound sampling schemes. In this view, airborne or satellite remote sensing allow to gather information over wide areas in a reasonable time. Most of the biodiversity maps obtained from remote sensing have been based on the inference of species richness by regression analysis. On the contrary, estimating compositional turnover (beta-diversity) might add crucial information related to relative abundance of different species instead of just richness. Presently, few studies have addressed the measurement of species compositional turnover from space. Extending on previous work, in this manuscript we propose novel techniques to measure beta-diversity from airborne or satellite remote sensing, mainly based on: i) multivariate statistical analysis, ii) the spectral species concept, iii) self-organizing feature maps, iv) multi- dimensional distance matrices, and the v) Rao's Q diversity. Each of these measures allow to solve one or several issues related to turnover measurement. This manuscript is the first methodological example encompassing (and enhancing) most of the available methods for estimating beta-diversity from remotely sensed imagery and potentially relate them to species diversity in the field

Measuring beta-diversity by remote sensing: a challenge for biodiversity monitoring

Biodiversity includes multiscalar and multitemporal structures and processes, with different levels of functional organization, from genetic to ecosystemic levels. One of the mostly used methods to infer biodiversity is based on taxonomic approaches and community ecology theories. However, gathering extensive data in the field is difficult due to logistic problems, overall when aiming at modelling biodiversity changes in space and time, which assumes statistically sound sampling schemes. In this view, airborne or satellite remote sensing allow to gather information over wide areas in a reasonable time. Most of the biodiversity maps obtained from remote sensing have been based on the inference of species richness by regression analysis. On the contrary, estimating compositional turnover (beta-diversity) might add crucial information related to relative abundance of different species instead of just richness. Presently, few studies have addressed the measurement of species compositional turnover from space. Extending on previous work, in this manuscript we propose novel techniques to measure beta-diversity from airborne or satellite remote sensing, mainly based on: i) multivariate statistical analysis, ii) the spectral species concept, iii) self-organizing feature maps, iv) multi- dimensional distance matrices, and the v) Rao's Q diversity. Each of these measures allow to solve one or several issues related to turnover measurement. This manuscript is the first methodological example encompassing (and enhancing) most of the available methods for estimating beta-diversity from remotely sensed imagery and potentially relate them to species diversity in the field

Joint Effect of Habitat Identity and Spatial Distance on Spiders’ Community Similarity in a Fragmented Transition Zone

<div><p>Understanding the main processes that affect community similarity have been the focus of much ecological research. However, the relative effects of environmental and spatial aspects in structuring ecological communities is still unresolved and is probably scale-dependent. Here, we examine the effect of habitat identity and spatial distance on fine-grained community similarity within a biogeographic transition zone. We compared four hypotheses: i) habitat identity alone, ii) spatial proximity alone, iii) non-interactive effects of both habitat identity and spatial proximity, and iv) interactive effect of habitat identity and spatial proximity. We explored these hypotheses for spiders in three fragmented landscapes located along the sharp climatic gradient of Southern Judea Lowlands (SJL), Israel. We sampled 14,854 spiders (from 199 species or morphospecies) in 644 samples, taken in 35 patches and stratified to nine different habitats. We calculated the Bray-Curtis similarity between all samples-pairs. We divided the pairwise values to four functional distance categories (same patch, different patches from the same landscape, adjacent landscapes and distant landscapes) and two habitat categories (same or different habitats) and compared them using non-parametric MANOVA. A significant interaction between habitat identity and spatial distance was found, such that the difference in mean similarity between same-habitat pairs and different-habitat pairs decreases with spatial distance. Additionally, community similarity decayed with spatial distance. Furthermore, at all distances, same-habitat pairs had higher similarity than different-habitats pairs. Our results support the fourth hypothesis of interactive effect of habitat identity and spatial proximity. We suggest that the environmental complexity of habitats or increased habitat specificity of species near the edge of their distribution range may explain this pattern. Thus, in transitions zones care should be taken when using habitats as surrogate of community composition in conservation planning since similar habitats in different locations are more likely to support different communities.</p></div

Species distribution in the three landscapes.

<p>Venn diagram indicating the number of species (Sp.) found exclusively in the south (So.), middle (Mi.) and north (No.) landscapes (LS), shared by any two landscapes, and shared by all three landscapes. In each area, the embedded tables indicate the species that occurred only in simple habitats (S), only on complex habitat (C) or in both simple and complex habitats (S+C).</p

Results of the two-way non parametric MANOVA analyses.

<p>Median, 25 and 75 percentiles (± 1.5 inter quantile range) and average (triangle) similarity in spider community structure between pairs of samples. Pairs of samples are divided to 4 distance categories and 2 habitats categories—same habitat (white) or different habitats (grey). The panels represent four stratifications: (a) all habitats, (b) two samples from simple habitats, (c) one sample from a simple habitat and one from a complex habitat (d) two samples from complex habitat. In each panel, results of two-way non-parametric MANOVA are given. Distance categories that did not differ in the post-hoc are labelled with a similar capital letters (same habitat) or lower-case letters (different habitats). Within a distance category, significant differences between same habitat pairs and different habitats pairs are given as *.</p

Hierarchical scales of the study system.

<p>(a) The sharp precipitation gradient of Israel and location of Southern Judea Lowland (SJL) along the gradient. (b) The north, middle and south landscapes within SJL. Note the sharp decline in mean annual precipitation along a short gradient of 30 km. (c) The north landscape—the distribution of remnant natural patches within the agricultural matrix. (d) A patch, with its specific attributes of area, shape, and internal heterogeneity. (e) One complex habitat (perennial shrub, low right corner) surrounded by a simple habitat (annual plants <15 cm tall), and the contrast with the agricultural matrix during the sampling period.</p

Summary ouput data - Wasteaware Cities Benchmark Indicators - WABI 2023 - Global data analytics - Machine learning vs. Non-linear Regression

This is the output dataset for the research publication "Socio-economic development drives solid waste management performance in cities: A global analysis using machine learning". It features Metadata info used by R codes Summary of results for two modelling approaches (machine learning: Conditional random-forest and non-linear regression) The independent variables dataset analysed here refer to specific indicators of the WABI methodology (https://www.sciencedirect.com/science/article/pii/S0956053X14004905) that generates solid waste management and resource recovery profiles for cities. It was applied here for 40 cities around the world. The data input are available here: 10.5281/zenodo.7570174We are grateful to UN-Habitat for funding the work on the initial version of the indicators, including 20 city profiles, and GIZ through their 'Operator Models' project which funded an intermediate version of the indicators and a further 5 city profiles. We acknowledge all the profilers of the individual cities – many are named in Table S2, and others in reference (Scheinberg et al. 2010). We thank past MSc students under the authors' supervision for offering preliminary partial data clearing and commentary: Henry Hickman (MSc dissertation at University of Leeds, supervised by C.A.V and D.C.W.) and Margaux Fargier (Final year MEng dissertation at Imperial College London, supervised by S.M.G, D.C.W and C.A.V.). We are grateful to Dr Josh Cottom and Mr Ed Cook at the University of Leeds for input on the GDP version selection. We acknowledge the support of Dr Ljiljana Rodic for contributing in data quality control