Biology subjects

Rocchini, D.

Publications and source records attributed to Rocchini, D..

8 recordsLinked to original sources

bioRxiv2024

The rasterdiv package for measuring diversityfrom space

This book chapter provides an extensive overview of the rasterdiv package which provides a comprehensive suite of functions designed to calculate diversity indices from numerical matrices, including those derived from optical remote sensing imagery. It facilitates a deeper analysis of spatial heterogeneity and ecosystem complexity by translating pixel values into ecological indicators. This package serves as a valuable tool for researchers and practitioners in the fields of landscape ecology, biodiversity monitoring, and environmental management, allowing for an enhanced understanding of landscape patterns and their ecological implications through advanced quantitative measures.

ecology↗

bioRxiv2023

"Flower power": how flowering affects spectral diversity metrics and their relationship with plant diversity

Biodiversity monitoring is constrained by cost- and labour-intensive field sampling methods. Increasing evidence suggests that remotely sensed spectral diversity (SD) is linked to plant diversity, holding promise for monitoring applications. However, studies testing such a relationship reported conflicting findings, especially in challenging ecosystems such as grasslands, due to their high temporal dynamism and variety. It follows that a thorough investigation of the key factors, such as the metrics applied (i.e., continuous, categorical) and phenology (e.g., flowering), influencing such a relationship is necessary. Thus, this study aims to assess the applicability of SD for plant diversity monitoring at the local scale by testing six different SD metrics while considering the effect of the presence of flowering on the relationship and resampling the original data to assess how spatial resolution affects the results. Taxonomic diversity was calculated based on data collected in 159 plots with 1.5 m x1.5 m experimental mesic grassland communities. Spectral information was collected using a UAV-borne sensor measuring reflectance across six bands in the visible and near-infrared range at [~]2 cm spatial resolution. Our results show that, in the presence of flowering, the relationship is significant and positive only when SD is calculated using categorical metrics. Despite the observed significance, the variance explained by the models had very low values, with no evident differences when resampling spectral data to coarser pixel sizes. Such findings suggest that new insights into the possible confounding effects on the SD[~]plant diversity in grassland communities are needed to use SD for monitoring purposes.

ecology↗

bioRxiv2023

Plant spectral diversity from high-resolution multispectral imagery detects functional diversity patterns in coastal dune communities.

O_LIRemote sensing is a fundamental tool to monitor biodiversity over large spatial extents. However, it is still not clear whether spectral diversity (SD - variation of spectral response across a set of pixels) may represent a fast and reliable proxy for different biodiversity facets such as taxonomic (TD) and functional diversity (FD) across different spatial scales. C_LIO_LIWe used fine resolution (3 cm) multispectral imagery on coastal dune communities in Italy to explore SD patterns across spatial scales and assess SD relationships with TD and FD along the environmental gradient. C_LIO_LIWe measured TD as species richness, while SD and FD were computed using probability densities functions based on pixels and species position in multivariate spaces based on pixel values and traits, respectively. We assessed how SD is related to TD and FD, we compared SD and FD patterns in multivariate space occupation, and we explored diversity patterns across spatial scales using additive partitioning (i.e., plot, transect, and study area). C_LIO_LIWe found a strong correspondence between the patterns of occupation of the functional and spectral spaces and significant relationships were found along the environmental gradient. TD showed no significant relationships with SD. However, TD and SD showed higher variation at broader scale while most of FD variation occurred at plot level. C_LIO_LIBy measuring FD and SD with a common methodological framework, we demonstrate the potential of SD in approximating functional patterns in plant communities. We show that SD can retrieve information about FD at very small scale, which would otherwise require very intensive sampling efforts. Overall, we show that SD retrieved using high resolution images is able to capture different aspects of FD, so that the occupation of the spectral space is analogous to the occupation of the functional space. Studying the occupation of both spectral and functional space brings a more comprehensive understanding of the factors that influence the distribution and abundance of plant species across environmental gradients. C_LI

ecology↗

bioRxiv2022

The relationship between spectral and plant diversity: disentangling the influence of metrics and habitat types

Biodiversity monitoring is crucial for ecosystem conservation, yet field data collection is limited by costs, time, and extent. Remote sensing represents a convenient approach providing frequent, near-real-time information over wide areas. According to the Spectral Variation Hypothesis (SVH), spectral diversity (SD) is an effective proxy of environmental heterogeneity, which ultimately relates to plant diversity. So far, studies testing the relationship between SD and biodiversity have reported contradictory findings, calling for a thorough investigation of the key factors (e.g., metrics applied, ecosystem type) and the conditions under which such a relationship holds true. This study investigates the applicability of the SVH for plant diversity monitoring at the landscape scale by comparing the performance of three different types of SD metrics. Species richness and functional diversity were calculated for more than 2000 cells forming a grid covering the Czech Republic. Within each cell, we quantified SD using a Landsat-8 "greenest pixel" composite by applying: i) the standard deviation of NDVI, ii) Raos Q entropy index, and iii) richness of "spectral communities". Habitat type (i.e., land cover) was included in the models describing the relationship between SD and ground biodiversity. Both species richness and functional diversity show positive and significant relationships with each SD metric tested. However, SD alone accounts for a small fraction of the deviance explained by the models. Furthermore, the strength of the relationship depends significantly on habitat type and is highest in natural transitional areas. Our results underline that, despite the stability in the significance of the link between SD and plant diversity at this scale, the applicability of SD for biodiversity monitoring is context-dependent and the factors mediating such a relationship must be carefully considered to avoid drawing misleading conclusions. HighlightsO_LIPlant species richness and functional diversity show significant and positive relationships with spectral diversity C_LIO_LISpectral diversity alone explains a small fraction of the total variability in ground biodiversity C_LIO_LISlight differences among the performances of the spectral diversity metrics tested C_LIO_LIThe relationship between spectral and plant diversity is context-dependent C_LI

ecology↗

bioRxiv2021

rasterdiv - an Information Theory tailored R package for measuring ecosystem heterogeneity from space: to the origin and back

O_LIEcosystem heterogeneity has been widely recognized as a key ecological feature, influencing several ecological functions, since it is strictly related to several ecological functions like diversity patterns and change, metapopulation dynamics, population connectivity, or gene flow. C_LIO_LIIn this paper, we present a new R package - rasterdiv - to calculate heterogeneity indices based on remotely sensed data. We also provide an ecological application at the landscape scale and demonstrate its power in revealing potentially hidden heterogeneity patterns. C_LIO_LIThe rasterdiv package allows calculating multiple indices, robustly rooted in Information Theory, and based on reproducible open source algorithms. C_LI

ecology↗

bioRxiv2021

From zero to infinity: minimum to maximum diversity of the planet by spatio-parametric Rao's quadratic entropy

AimThe majority of work done to gather information on Earth diversity has been carried out by in-situ data, with known issues related to epistemology (e.g., species determination and taxonomy), spatial uncertainty, logistics (time and costs), among others. An alternative way to gather information about spatial ecosystem variability is the use of satellite remote sensing. It works as a powerful tool for attaining rapid and standardized information. Several metrics used to calculate remotely sensed diversity of ecosystems are based on Shannons Information Theory, namely on the differences in relative abundance of pixel reflectances in a certain area. Additional metrics like the Raos quadratic entropy allow the use of spectral distance beside abundance, but they are point descriptors of diversity, namely they can account only for a part of the whole diversity continuum. The aim of this paper is thus to generalize the Raos quadratic entropy by proposing its parameterization for the first time. InnovationThe parametric Raos quadratic entropy, coded in R, i) allows to represent the whole continuum of potential diversity indices in one formula, and ii) starting from the Raos quadratic entropy, allows to explicitly make use of distances among pixel reflectance values, together with relative abundances. Main conclusionsThe proposed unifying measure is an integration between abundance- and distance-based algorithms to map the continuum of diversity given a satellite image at any spatial scale.

ecology↗

bioRxiv2020

Measuring diversity from space: a global view of the free and open source rasterdiv R package under a coding perspective

The variation of species diversity over space and time has been widely recognised as a key challenge in ecology. However, measuring species diversity over large areas might be difficult for logistic reasons related to both time and cost savings for sampling, as well as accessibility of remote ecosystems. In this paper, we present a new R package - rasterdiv - to calculate diversity indices based on remotely sensed data, by discussing the theory beyond the developed algorithms. Obviously, measures of diversity from space should not be viewed as a replacement of in-situ data on biological diversity, but they are rather complementary to existing data and approaches. In practice, they integrate available information of Earth surface properties, including aspects of functional (structural, biophysical and bio-chemical), taxonomic, phylogenetic and genetic diversity. Making use of the rasterdiv package can result useful in making multiple calculations based on reproducible open source algorithms, robustly rooted in Information Theory.

ecology↗

bioRxiv2020

From local spectral species to global spectral communities: a benchmark for ecosystem diversity estimate by remote sensing

In the light of unprecedented change in global biodiversity, real-time and accurate ecosystem and biodiversity assessments are becoming increasingly essential. Nevertheless, estimation of biodiversity using ecological field data can be difficult for several reasons. For instance, for very large areas, it is challenging to collect data that provide reliable information. Some of these restrictions in Earth observation can be avoided through the use of remote sensing approaches. Various studies have estimated biodiversity on the basis of the Spectral Variation Hypothesis (SVH). According to this hypothesis, spectral heterogeneity over the different pixel units of a spatial grid reflects a higher niche heterogeneity, allowing more organisms to coexist. Recently, the spectral species concept has been derived, following the consideration that spectral heterogeneity at a landscape scale corresponds to a combination of subspaces sharing a similar spectral signature. With the use of high resolution remote sensing data, on a local scale, these subspaces can be identified as separate spectral entities, the so called "spectral species". Our approach extends this concept over wide spatial extents and to a higher level of biological organization. We applied this method to MODIS imagery data across Europe. Obviously, in this case, a spectral species identified by MODIS is not associated to a single plant species in the field but rather to a species assemblage, habitat, or ecosystem. Based on such spectral information, we propose a straightforward method to derive - (local relative abundance and richness of spectral species) and {beta}-diversity (turnover of spectral species) maps over wide geographical areas.

ecology↗