Biology subjects

Chianucci, F.

Publications and source records attributed to Chianucci, F..

9 recordsLinked to original sources

bioRxiv2025

Coppice conversion of European beech (Fagus sylvatica L.): natural evolution, periodic thinning, regeneration cutting

Beech coppice forests have shaped the mountainous European landscape for centuries. The socio-economical changes occurred over the last 60 years have led to a progressive decline in coppice management, which has resulted in either the abandonment of traditional coppice management, or into active coppice conversion to high forests. Given the long-term process of this process, an ecological, long-term perspective is required to understand the ecological implication of different management practices in these forests. We investigated the influence of conversion management on canopy attributes (leaf litter and seed production, leaf area index). The management options considered were traditional conversion management, based on periodic thinning, and alternative conversion, based on anticipated seed cutting and final harvesting, which were compared against natural evolution (unthinned control). Results showed that the differences between natural evolution and traditional conversion were largest in the years immediately after thinning, and then reduced with full canopy (leaf litter and leaf area index) recovery after 10 years. Conversely, the alternative method with the anticipated seed cutting significantly enhanced canopy heterogeneity and further accelerates the transition to high forest, with dense beech saplings reaching an height over 8 meters eight years post-harvest.

plant biology↗

bioRxiv2024

Reliability of canopy photography for forest ecology and biodiversity studies

Understory is a key component of forest biodiversity. The structure of the forest stand and the horizontal composition of the canopy play a major role on the light regime of the understory, which in turn affects the abundance and the diversity of the understory plant community. Reliable assessments of canopy structural attributes are essential for forest research and biodiversity monitoring programs, as well as to study the relationship between canopy and understory plant communities. Canopy photography is a widely used method but it is still not clear which photographic techniques is better suited to capture canopy attributes at stand-level that can be relevant in forest biodiversity studies. For this purpose, we collected canopy structure and understory plant diversity data on 51 forest sites in the north-eastern Italian Alps, encompassing a diversity of forest types. Canopy images were acquired using both digital cover (DCP) and hemispherical (DHP) photography. Canopy structural attributes were then compared to tree species composition data to evaluate whether they were appropriate to differentiate between forest types. Additionally, we tested what canopy attributes derived from DCP and DHP best explained the species composition of vascular plants growing in the understory. We found that hemispherical canopy photography was most suitable to capture differences in forest types, which was best expressed by variables such as leaf inclination angle and canopy openness. On our sites, DHP-based canopy attributes were also able to better distinguish between different conifer forests. Leaf clumping was the most important attribute for determining plant species distribution of the understory, indicating that diverse gap structures create different microclimate conditions enhancing diverse plant species with different ecological strategies. This study supports the reliability of canopy photography in forest ecology and biodiversity monitoring, but also provide insights for increasing understory diversity in managed forests of high conservation value. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=100 SRC="FIGDIR/small/610276v1_ufig1.gif" ALT="Figure 1"> View larger version (49K): org.highwire.dtl.DTLVardef@15b706forg.highwire.dtl.DTLVardef@329571org.highwire.dtl.DTLVardef@f13605org.highwire.dtl.DTLVardef@11b816_HPS_FORMAT_FIGEXP M_FIG C_FIG

ecology↗

bioRxiv2024

One to rule them all? Assessing the performance of Forest Europe's biodiversity indicators against multitaxonomic data

Several regional initiatives and reporting efforts assess the state of forest biodiversity through broad-scale indicators based on data from national forest inventories. Although valuable, these indicators are essentially indirect and evaluate habitat quantity and quality rather than biodiversity per se. Therefore, their link to biodiversity may be weak, which decreases their usefulness for decision-making. For several decades, Forest Europe indicators assessed the state of European forests, in particular their biodiversity. However, no extensive study has been conducted to date to assess their performance - i.e. the capacity of the indicators to reflect variations in biodiversity - against multitaxonomic data. We hypothesized that no single biodiversity indicator from Forest Europe can represent overall forest biodiversity, but that several indicators would reflect habitat quality for at least some taxa in a comprehensive way. We tested the set of Forest Europes indicators against the species richness of six taxonomic and functional groups across several hundreds of sampling units over Europe. We showed that, while some indicators perform relatively well across groups (e.g. deadwood volume), no single indicator represented all biodiversity at once, and that a combination of several indicators performed better. Forest Europe indicators were chosen for their availability and ease of understanding for most people. However, we showed that gaps in the monitoring framework persist, and that surveying certain taxa along with stand structure is necessary to support policymaking and tackle forest biodiversity loss at the large scale. Adding context (e.g. forest type) may also contribute to increase the performance of biodiversity indicators. Impact statementBiodiversity indicators used to assess the state of Europes forests perform unequally; a combination of indicators is more informative

ecology↗

bioRxiv2023

treespat:an R package for spatial tree diversity analysis

Forest structure is a key element in understanding functionality and resilience of forest ecosystems. Analyses of forest structure and diversity have traditionally employed non-spatial measures, due to the simplicity of such approach. However, spatial structure can provide a deeper understanding of tree patterns, but widespread use of a spatially-explicit approach has been often limited by the complexity of theoretical equations and formulas, and the lack of freely distributed tools to calculate such spatial indices. To fill this gap we created the R package treespat, which allows to calculate some of the most diffuse stand-level spatial diversity indices. The document describes theory, basic definition and example application for using the treespat package. O_FIG O_LINKSMALLFIG WIDTH=174 HEIGHT=200 SRC="FIGDIR/small/541683v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@b631f7org.highwire.dtl.DTLVardef@18a85dcorg.highwire.dtl.DTLVardef@15a5f84org.highwire.dtl.DTLVardef@35146e_HPS_FORMAT_FIGEXP M_FIG C_FIG

ecology↗

bioRxiv2022

LAD: an R package to estimate leaf angle distribution from measured leaf inclination angles

Leaf angle distribution (LAD) is an important factor for characterizing the optical features of vegetation canopies. The characterization of LAD requires direct measurements of leaf inclination angles, which can be obtained from manual clinometer measurements or leveled digital photography. Package LAD allows to calculate the Leaf angle distribution (LAD) function and the G-function from measured leaf inclination angles. Using the package, the LAD distribution and G-function is derived by fitting a two parameter Beta distribution. Summary leaf angle statistics and distribution type is also calculated, by comparing the obtained LAD against theoretical distribution by de Wit (1965).

ecology↗

bioRxiv2022

bRAW: an R package for digital raw canopy imagery

Digital photography is an increasingly popular tool to estimate forest canopy attributes. However, estimates of gap fraction, upon which calculations of canopy attributes are based, are sensitive to photographic exposure in upward-facing images. Recent studies have indicated that analyzing RAW imagery, rather than other inbuilt camera format (e.g. jpeg, png, tiff) allows to obtain largely-insensitive gap fraction retrieval from digital photography. The package bRaw implemented the method proposed by Macfarlane et al. (2014). They found that shooting raw with one stop of underexposure and applying a linear contrast stretch yielded largely insensitive results, thus providing a way for standardizing and optimizing photographic exposure. The package replicate the methodology and thus it provides an effective tool to use raw imagery in canopy photography.

plant biology↗

bioRxiv2022

hemispheR: an R package for fisheye canopy image analysis

Hemispherical photography is a relevant tool to estimate canopy attributes such as leaf area index (LAI). Advancements in digital photography and image processing tools have supported long-lasting use of digital hemispherical photography (DHP). While some open-source tools have been made available for DHP, very few solutions have been made available in R programming packages, and none of these allows a full processing workflow to retrieve LAI and other canopy attributes from fisheye images. To fill this gap, we developed an R package (hemispheR) to support the whole processing of DHP images in an automated, fast, and reproducible way. The package functions, which are designed for step-by-step single-image analysis, can be performed sequentially in a pipeline, while allowing inspecting the quality of each image processing step. The package allows to analyze both circular and fullframe fisheye images, collected either with upward facing (forest canopies) or downward facing (short canopies and crops) camera orientation. In addition, the package allows to implement two consolidated LAI methods (LAI-2000/2200 and 57{degrees} method). A case study is presented to demonstrate the reliability of canopy attributes derived from hemispheR in temperate deciduous forests with variable canopy density and structure. Canopy attributes were validated against either results obtained from a reference proprietary software, either by benchmarking measurements obtained from terrestrial laser scanning. Results indicated hemispheR provide reliable openness and leaf area index values in forest canopies as compared with reference values. By providing a simple, transparent, and flexible image processing procedure, hemispheR supported the use of DHP for routine measurements and monitoring of forest canopy attributes. Hosting the package in a Git repository will further support development of the package, through either collaborative coding or forking projects.

ecology↗

bioRxiv2022

coveR: An R package for processing Digital Cover Photography images to retrieve forest canopy attributes

O_LIDigital Cover Photography (DCP) is an increasingly popular tool for estimating canopy cover and leaf area index (LAI). However, existing solutions to process canopy images are predominantly tailored for fisheye photography, whereas open-access tools for DCP are lacking. C_LIO_LIWe developed an R package (coveR) to support the whole processing of DCP images in an automated, fast, and reproducible way. The package functions, which are designed for step-by-step single-image analysis, can be performed sequentially in a pipeline, while also allowing simple implementation for batch-processing bunches of images. C_LIO_LIA case study is presented to demonstrate the reliability of canopy attributes derived from coveR in pure beech (Fagus sylvatica L.) stands with variable canopy density and structure. Estimates of gap fraction and effective LAI from DCP were validated against reference measurements obtained from terrestrial laser scanning. C_LIO_LIBy providing a simple, transparent, and flexible image processing procedure, coveR supported the use of DCP for routine measurements and monitoring of forest canopy attributes. This, combined with the implementability of DCP in many devices, including smartphones, micro-cameras, and remote trail cameras, can greatly expand the accessibility of the method also by non-experts. C_LI

plant biology↗

bioRxiv2021

With great power comes great responsibility: an analysis of sustainable forest management quantitative indicators in the DPSIR framework

The monitoring of environmental policies in Europe has taken place since the 1980s and still remains a challenge for decision- and policy-making. For forests, it is concretized through the publication of a State Of Europes Forests every five years, the last report just been released. However, the process lacks a clear analytical framework and appears limited to orient and truly assess sustainable management of European forests. We classified the 34 quantitative sustainable forest management indicators in the Driver-Pressure-State-Impact-Response (DPSIR) framework to analyse gaps in the process. In addition, we classified biodiversity-related indicators in the simpler Pressure-State-Response (PSR) framework. We showed that most of the sustainable forest management indicators assess the state of European forests, but almost half could be classified in another DPSIR category. For biodiversity, most indicators describe pressures, while direct taxonomic state indicators are very few. Our expert-based classification show that sustainable forest management indicators are unbalanced regarding the DPSIR framework. However, completing this framework with other indicators would help to have a better view and more relevant tools for decision-making. The results for biodiversity were comparable, but we showed that some indicators from other criteria than the one dedicated to biodiversity could also help understanding threats and actions concerning it. Such classification helps in the decision process, but is not sufficient to fully support policy initiative. In particular, the next step would be to better understand the links between DPSIR and PSR categories.

ecology↗