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1. The effects of habitat loss and fragmentation on plant functional traits and functional diversity : what do we know so far ?

Author

Zambrano, J., Garzon-Lopez, C. X., Yeager, L., Fortunel, Claire, Cordeiro, N. J., and Beckman, N. G.

Subjects
Beta diversity, Plant communities, Functional homogenization, Alpha diversity, Functional traits
Abstract

Habitat loss and fragmentation result in significant landscape changes that ultimately affect plant diversity and add uncertainty to how natural areas will respond to future global change. This uncertainty is important given that the loss of biodiversity often includes losing key ecosystem functions. Few studies have explored the effects of landscape changes on plant functional diversity and evidence so far has shown far more pervasive effects than previously reported by species richness and composition studies. Here we present a review on the impact of habitat loss and fragmentation on (1) individual functional traits-related to persistence, dispersal and establishment-and (2) functional diversity. We also discuss current knowledge gaps and propose ways forward. From the literature review we found that studies have largely focused on dispersal traits, strongly impacted by habitat loss and fragmentation, while traits related to persistence were the least studied. Furthermore, most studies did not distinguish habitat loss from spatial fragmentation and were conducted at the plot or fragment-level, which taken together limits the ability to generalize the scale-dependency of landscape changes on plant functional diversity. For future work, we recommend (1) clearly distinguishing the effects of habitat loss from those of fragmentation, and (2) recognizing the scale-dependency of predicted responses when functional diversity varies in time and space. We conclude that a clear understanding of the effects of habitat loss and fragmentation on functional diversity will improve predictions of the resiliency and resistance of plant communities to varying scales of disturbance.

Published
2019

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

Author

Kate S. He, Harini Nagendra, Michael Förster, Martin Wegmann, Thomas W. Gillespie, Vítĕzslav Moudrý, Birgit Kleinschmit, Duccio Rocchini, Carl Beierkuhnlein, Jean-Baptiste Féret, Marco Malavasi, Nicole Salvatori, Florian de Boissieu, Davnah Payne, Heidi C. Hauffe, Carol X. Garzon-Lopez, Alessandro Chiarucci, Petra Šímová, Jonathan Lenoir, Michele Torresani, University of Bologna/Università di Bologna, Università degli Studi di Udine - University of Udine [Italie], University of Bayreuth, Ecologie fonctionnelle et biogéochimie des sols et des agro-écosystèmes (UMR Eco&Sols), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Département Performances des systèmes de production et de transformation tropicaux (Cirad-PERSYST), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Technical University of Berlin / Technische Universität Berlin (TU), Universidad de los Andes [Bogota] (UNIANDES), University of California (UC), Fondazione Edmund Mach - Edmund Mach Foundation [Italie] (FEM), Ecologie et Dynamique des Systèmes Anthropisés - UMR CNRS 7058 (EDYSAN), Université de Picardie Jules Verne (UPJV)-Centre National de la Recherche Scientifique (CNRS), Czech University of Life Sciences Prague (CZU), PES University [Bengaluru], University of Bern, Free University of Bozen-Bolzano, Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Horizon 2020 Framework ProgrammeCentre National d’Etudes Spatiales, ANR-17-CE32-0001,BioCop,Suivi de la biodiversité tropicale avec les satellites Sentinel-2 du programme Copernicus(2017), Rocchini D., Salvatori N., Beierkuhnlein C., Chiarucci A., de Boissieu F., Forster M., Garzon-Lopez C.X., Gillespie T.W., Hauffe H.C., He K.S., Kleinschmit B., Lenoir J., Malavasi M., Moudry V., Nagendra H., Payne D., Simova P., Torresani M., Wegmann M., Feret J.-B., Rocchini, D., Salvatori, N., Beierkuhnlein, C., Chiarucci, A., de Boissieu, F., Forster, M., Garzon-Lopez, C. X., Gillespie, T. W., Hauffe, H. C., He, K. S., Kleinschmit, B., Lenoir, J., Malavasi, M., Moudry, V., Nagendra, H., Payne, D., Simova, P., Torresani, M., Wegmann, M., Feret, J. -B., University of Bologna, Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut de Recherche pour le Développement (IRD)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Technical University Berlin, and University of California

Subjects
0106 biological sciences, Earth observation, Ecological informatic, Biodiversity, Earth Observations, [SDU.STU]Sciences of the Universe [physics]/Earth Sciences, 010603 evolutionary biology, 01 natural sciences, Modelling, Settore BIO/07 - ECOLOGIA, Satellite imagery, Ecosystem diversity, Imaging Spectroscopy, Ecology, Evolution, Behavior and Systematics, Remote sensing, Ecological informatics, Spectral signature, Ecology, 010604 marine biology & hydrobiology, Applied Mathematics, Ecological Modeling, 15. Life on land, Field (geography), Computer Science Applications, Computational Theory and Mathematics, 13. Climate action, Modeling and Simulation, [SDE]Environmental Sciences, Environmental science, Species richness, Rapideye, Global biodiversity
Abstract

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 ofremote sensingapproaches. 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 similarspectral 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 toMODISimagery data across Europe. Obviously, in this case, a spectral species identified by MODIS is not associated to a singleplant speciesin the field but rather to a species assemblage, habitat, or ecosystem. Based on such spectral information, we propose a straightforward method to deriveα- (local relative abundance and richness of spectral species) andβ-diversity (turnover of spectral species) maps over wide geographical areas.

Published
2021
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5. Remotely sensed spatial heterogeneity as an exploratory tool for taxonomic and functional diversity study

Author

Edvinas Rommel, Gherardo Chirici, Sebastian Schmidtlein, Marta Galluzzi, Thomas W. Gillespie, Giovanni Bacaro, Martin Wegmann, Duccio Rocchini, Daniele Da Re, Giles M. Foody, Carlo Ricotta, Andrew K. Skidmore, Harini Nagendra, Benedetto Rugani, Matteo Marcantonio, Jonathan Lenoir, Kate S. He, Carol X. Garzon-Lopez, Ruben Van De Kerchove, Hannes Feilhauer, Rocchini, D, Bacaro, G, Chirici, G, Da Re, D, Feilhauer, H, Foody G., M, Galluzzi, M, Garzon-Lopez C., X, Gillespie T., W, He K., S, Lenoir, J, Marcantonio, M, Nagendra, H, Ricotta, C, Rommel, E, Schmidtlein, S, Skidmore A., K, Van De Kerchove, R, Wegmann, M, Rugani, B., Department of Biodiversity and Molecular Ecology, Fondazione Edmund Mach, Research and Innovation Centre, 38010, S. Micehle all’Adige (TN), Italy, Universidad de los Andes [Bogota] (UNIANDES), Department of Geography, Escuela Militar de Ingeniería (EMI), Ecologie et Dynamique des Systèmes Anthropisés - UMR CNRS 7058 (EDYSAN), Université de Picardie Jules Verne (UPJV)-Centre National de la Recherche Scientifique (CNRS), Dpt of Environmental Biology [Rome], Università degli Studi di Roma 'La Sapienza' = Sapienza University [Rome], Karlsruhe Institute of Technology (KIT), Institut des Géosciences de l’Environnement (IGE), Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut de Recherche pour le Développement (IRD)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Centre de Recherche Public Henri Tudor [Technoport] (CRP Henri Tudor), Centre de Recherche Public Henri-Tudor [Luxembourg] (CRP Henri-Tudor), Department of Natural Resources, UT-I-ITC-FORAGES, Faculty of Geo-Information Science and Earth Observation, Rocchini, Duccio, Bacaro, Giovanni, Chirici, Gherardo, Da Re, Daniele, Feilhauer, Hanne, Foody, Giles M., Galluzzi, Marta, Garzon-Lopez, Carol X., Gillespie, Thomas W., He, Kate S., Lenoir, Jonathan, Marcantonio, Matteo, Nagendra, Harini, Ricotta, Carlo, Rommel, Edvina, Schmidtlein, Sebastian, Skidmore, Andrew K., Van De Kerchove, Ruben, Wegmann, Martin, and Rugani, Benedetto

Subjects
0106 biological sciences, 010504 meteorology & atmospheric sciences, Taxonomic diversity, Computer science, [SDE.MCG]Environmental Sciences/Global Changes, Population, Biodiversity, Settore BIO/03 - BOTANICA AMBIENTALE E APPLICATA, General Decision Sciences, [SDV.BID]Life Sciences [q-bio]/Biodiversity, Functional diversity, 010603 evolutionary biology, 01 natural sciences, evolution, Sampling design, Satellite imagery, education, Cartograms, Functional diversity, Ecology, Evolution, Behavior and Systematics, biodiversity, 0105 earth and related environmental sciences, [SDV.EE]Life Sciences [q-bio]/Ecology, environment, education.field_of_study, Rao's quadratic diversity, Remote sensing, Spectral rarefaction, Ecology, Sampling (statistics), behavior and systematics, 15. Life on land, Biodiversity hotspot, Field (geography), Spatial heterogeneity, Cartograms, cartograms, functional diversity, remote sensing, satellite imagery, spectral rarefaction, taxonomic diversity, ecology, ITC-ISI-JOURNAL-ARTICLE, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, Cartography
Abstract

International audience; Assessing biodiversity from field-based data is difficult for a number of practical reasons: (i) establishing the total number of sampling units to be investigated and the sampling design (e.g. systematic, random, stratified) can be difficult; (ii) the choice of the sampling design can affect the results; and (iii) defining the focal population of interest can be challenging. Satellite remote sensing is one of the most cost-effective and comprehensive approaches to identify biodiversity hotspots and predict changes in species composition. This is because, in contrast to field-based methods, it allows for complete spatial coverages of the Earth's surface under study over a short period of time. Furthermore, satellite remote sensing provides repeated measures, thus making it possible to study temporal changes in biodiversity. While taxonomic diversity measures have long been established, problems arising from abundance related measures have not been yet disentangled. Moreover, little has been done to account for functional diversity besides taxonomic diversity measures. The aim of this manuscript is to propose robust measures of remotely sensed heterogeneity to perform exploratory analysis for the detection of hotspots of taxonomic and functional diversity of plant species.

Published
2018
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6. Potential of remote sensing to predict species invasions: a modelling perspective

Author

Michael Förster, Kate S. He, Matteo Marcantonio, R. Zorer, Marc Zebisch, Carlo Ricotta, Birgit Kleinschmit, Heidi C. Hauffe, Verónica Andreo, Luigi Ponti, Markus Metz, Duccio Rocchini, Harini Nagendra, Andrew Paul Gutierrez, Carol X. Garzon-Lopez, Thomas W. Gillespie, Gertrud Schaab, Markus Neteler, Paola Mairota, Sajid Pareeth, Annapaola Rizzoli, Ponti, L., Rocchini D., Andreo V., Forster M., Garzon-Lopez C. X., Gutierrez A. P., Gillespie T. W., Hauffe H. C., He K. S., Kleinschmit B., Mairota P., Marcantonio M., Metz M., Nagendra H., Pareeth S., Ponti L., Ricotta C., Rizzoli A., Schaab G., Zebisch M., Zorer R., and Neteler M.

Subjects
physiologically-based demographic models, ecological niche models, biological invasion, satellite imagery, species diversity, species distribution modelling, uncertainty, Ecology (disciplines), Geography, Planning and Development, Species distribution, Biology, pecies diversity, Invasive species, Settore BIO/07 - ECOLOGIA, Earth and Planetary Sciences (miscellaneous), Species distribution modelling, 14. Life underwater, pecies distribution modelling, Modelli di distribuzione delle specie, ecological niche model, ddc:910, Remote sensing, Species diversity, atellite imagery, Uncertainty, Satellite imagery, 15. Life on land, Ecological niche models, Physiologically-based demographic models, Telerilevamento, Environmental niche modelling, 13. Climate action, Remote sensing (archaeology), General Earth and Planetary Sciences, Conservation biology, physiologically-based demographic model, Biological invasion, Predictive modelling
Abstract

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich. This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively. Understanding the causes and effects of species invasions is a priority in ecology and conservation biology. One of the crucial steps in evaluating the impact of invasive species is to map changes in their actual and potential distribution and relative abundance across a wide region over an appropriate time span. While direct and indirect remote sensing approaches have long been used to assess the invasion of plant species, the distribution of invasive animals is mainly based on indirect methods that rely on environmental proxies of conditions suitable for colonization by a particular species. The aim of this article is to review recent efforts in the predictive modelling of the spread of both plant and animal invasive species using remote sensing, and to stimulate debate on the potential use of remote sensing in biological invasion monitoring and forecasting. Specifically, the challenges and drawbacks of remote sensing techniques are discussed in relation to: i) developing species distribution models, and ii) studying life cycle changes and phenological variations. Finally, the paper addresses the open challenges and pitfalls of remote sensing for biological invasion studies including sensor characteristics, upscaling and downscaling in species distribution models, and uncertainty of results.

Published
2015

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