Biology subjects

Torresani, M.

Publications and source records attributed to Torresani, M..

5 recordsLinked to original sources

bioRxiv2024

From calamity to infestation: linking windstorm tree damage to bark beetle outbreak through forest structure and meteorological analysis

In recent years, we have witnessed worldwide, an increase in natural forest disturbances, particularly windstorms, which have caused significant direct and indirect forest damages, often triggering largescale bark beetle outbreaks. In this study, we investigated the interaction between windstorm-induced tree damage and subsequent bark beetle outbreaks in the northeastern Italian Alps (Province of Belluno and Bolzano), focusing on the 2018 Vaia windstorm and the successive bark beetle infestation started in 2021. Additionally, we aimed to determine whether this potential correlation is influenced by forest structural characteristics such as forest height heterogeneity (HH), forest density, and forest mean height using LiDAR data, or by meteorological factors (mean temperature and cumulative precipitation) through in-situ spatialized information. Our research findings, based on a methodology centered on spatial interactions, indicate a potential link between the bark beetle outbreaks and the windstorm event Vaia occurred three years before. Our results suggest that forest structural variables are, in most of the cases, significantly similar across all areas affected by the bark beetle. This similarity is observed both in forests impacted by the Vaia windstorm and in other Picea abies forests not affected by the windstorm, indicating that these forest structural variables may not be a trigger for the bark beetle outbreak. Our findings do not show a clear and consistently significant difference in meteorological conditions. This variability can be attributed to the specific areas affected by the Vaia windstorm, which are predominantly mountainous regions characterized by distinct temperatures and precipitation compared to the rest of the provinces. When analyzing the combined influence of structural and meteorological variables in both study areas, our results indicate that none of these factors were ultimately significant predictors of the interaction between bark beetle infestations and areas affected by the Vaia windstorm. Our study suggests that, as climate change increases the frequency and severity of these disturbances, adaptable forest management framework to enhance forest resilience and sustainability are needed, helping forests to better withstand and recover from future natural disturbances.

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↗

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↗

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↗