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101. Phylogeny, distribution and speciation in the endemic diving beetle genus Pachynectes on Madagascar

Author

Bukontaite, Rasa, Bergsten, Johannes, Naimi, Babak, Svensson, Erik I., Bukontaite, Rasa, Bergsten, Johannes, Naimi, Babak, and Svensson, Erik I.

Abstract

Madagascar is well known for high levels of endemism in both the flora and fauna. More specifically most of Madagascars' endemic species have extremely restricted regional distributions, which is a characteristic of this large island’s biodiversity. Biologists are still struggling to understand the speciation processes that have produced this pattern of diversity. Several general hypotheses have been developed trying to explain the origin and microendemism of Malagasy species. Two main hypotheses focus on watersheds and climate gradients. Here, we use a phylogeny in combination with data on spatial distribution of the endemic diving beetle genus Pachynectes (Coleoptera: Dytiscidae: Bidessini) to critically evaluate the watershed versus the climatic gradient hypotheses of this river dwelling insect radiation. Based on field sampling from expeditions between 2006 and 2014, we first show that the species diversity in this genus is at least three times higher than previously believed. We reconstructed a molecular phylogeny based on five gene fragments and recovered three main clades: a western clade, an eastern clade and a "keeled" clade diagnosable based on an elytral keel. The western and keeled clades both occur mainly in the drier western parts of the country, while species in the eastern clade are typical of rivers in the eastern humid rainforest. Notably, sister species were always allopatrically distributed along either latitudinal or longitudinal-altitudinal gradients. Based on species distribution modelling (SDM) and our phylogenetic data, we conclude that the microendemic patterns of Pachynectes species are unlikely to be explained by the watershed hypothesis. In contrast, the four main bioclimatic regions are more likely to have been causally involved in the past speciation processes and might explain the current distribution pattern in this insect genus.

102. ELSA: a new local indicator for spatial association

Author

Hamm, Nicholas A.S., Naimi, Babak, Groen, Thomas, Hamm, Nicholas A.S., Naimi, Babak, and Groen, Thomas

Abstract

There are several local indicators of spatial association (LISA) that allow exploration of local patterns in spatial data. Despite numerous situations where categorical variables are encountered, few attempts have been devoted to the development of methods to explore the local spatial pattern in categorical data. To our knowledge, there is no indicator of local spatial association that can be used for both continuous and categorical data. We introduce ELSA, which can be used for exploring and testing local spatial association for continuous and categorical variables. We provide the R-package elsa for making these computations.

103. ELSA: Entropy-based local indicator of spatial association

Author

Naimi, Babak, Hamm, Nicholas A.S., Groen, Thomas, Skidmore, Andrew K., Toxopeus, Albertus G., Alibakhshi, Sara, Naimi, Babak, Hamm, Nicholas A.S., Groen, Thomas, Skidmore, Andrew K., Toxopeus, Albertus G., and Alibakhshi, Sara

Abstract

Research on spatial data analysis has developed a number of local indicators of spatial association (LISA), which allow exploration of local patterns in spatial data. These include local Moran’s I and local Geary’s c, as well as Gi and Gi* that can be used for continuous or interval variables only. Despite numerous situations where qualitative (nominal/categorical) variables are encountered, few attempts have been devoted to the development of methods to explore the local spatial pattern in categorical data. To our knowledge, there is no indicator of local spatial association that can be used for both continuous and categorical data at the same time. In this paper, we propose a new local indicator of spatial association, called the entropy-based local indicator of spatial association (ELSA), that can be used for both categorical and continuous spatial data. ELSA quantifies the degree of spatial association of a variable at each location relative to the same variable at the neighbouring locations. This indicator simultaneously incorporates both spatial and attribute aspects of spatial association into account. The values of ELSA vary between 0 and 1, which denote highest and lowest spatial association, respectively. We compare ELSA to existing statistics such as Local Moran’s I and test the power and size of the new statistic. We also introduce the "entrogram", a novel approach for exploring the global spatial structure within the entire area (like a variogram). This study showed that the ELSA is consistent and robust, and is therefore suitable for applications in a wide range of disciplines. The ELSA algorithm is made available as an R-package (elsa).

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104. ELSA: a new local indicator for spatial association

Author

Hamm, Nicholas A.S., Naimi, Babak, Groen, Thomas, Hamm, Nicholas A.S., Naimi, Babak, and Groen, Thomas

Abstract

There are several local indicators of spatial association (LISA) that allow exploration of local patterns in spatial data. Despite numerous situations where categorical variables are encountered, few attempts have been devoted to the development of methods to explore the local spatial pattern in categorical data. To our knowledge, there is no indicator of local spatial association that can be used for both continuous and categorical data. We introduce ELSA, which can be used for exploring and testing local spatial association for continuous and categorical variables. We provide the R-package elsa for making these computations.

105. ELSA: Entropy-based local indicator of spatial association

Author

Naimi, Babak, Hamm, Nicholas A.S., Groen, Thomas, Skidmore, Andrew K., Toxopeus, Albertus G., Alibakhshi, Sara, Naimi, Babak, Hamm, Nicholas A.S., Groen, Thomas, Skidmore, Andrew K., Toxopeus, Albertus G., and Alibakhshi, Sara

Abstract

Research on spatial data analysis has developed a number of local indicators of spatial association (LISA), which allow exploration of local patterns in spatial data. These include local Moran’s I and local Geary’s c, as well as Gi and Gi* that can be used for continuous or interval variables only. Despite numerous situations where qualitative (nominal/categorical) variables are encountered, few attempts have been devoted to the development of methods to explore the local spatial pattern in categorical data. To our knowledge, there is no indicator of local spatial association that can be used for both continuous and categorical data at the same time. In this paper, we propose a new local indicator of spatial association, called the entropy-based local indicator of spatial association (ELSA), that can be used for both categorical and continuous spatial data. ELSA quantifies the degree of spatial association of a variable at each location relative to the same variable at the neighbouring locations. This indicator simultaneously incorporates both spatial and attribute aspects of spatial association into account. The values of ELSA vary between 0 and 1, which denote highest and lowest spatial association, respectively. We compare ELSA to existing statistics such as Local Moran’s I and test the power and size of the new statistic. We also introduce the "entrogram", a novel approach for exploring the global spatial structure within the entire area (like a variogram). This study showed that the ELSA is consistent and robust, and is therefore suitable for applications in a wide range of disciplines. The ELSA algorithm is made available as an R-package (elsa).

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106. ELSA: a new local indicator for spatial association

Author

Hamm, Nicholas A.S., Naimi, Babak, Groen, Thomas, Hamm, Nicholas A.S., Naimi, Babak, and Groen, Thomas

Abstract

There are several local indicators of spatial association (LISA) that allow exploration of local patterns in spatial data. Despite numerous situations where categorical variables are encountered, few attempts have been devoted to the development of methods to explore the local spatial pattern in categorical data. To our knowledge, there is no indicator of local spatial association that can be used for both continuous and categorical data. We introduce ELSA, which can be used for exploring and testing local spatial association for continuous and categorical variables. We provide the R-package elsa for making these computations.

107. ELSA: Entropy-based local indicator of spatial association

Author

Naimi, Babak, Hamm, Nicholas A.S., Groen, Thomas, Skidmore, Andrew K., Toxopeus, Albertus G., Alibakhshi, Sara, Naimi, Babak, Hamm, Nicholas A.S., Groen, Thomas, Skidmore, Andrew K., Toxopeus, Albertus G., and Alibakhshi, Sara

Abstract

Research on spatial data analysis has developed a number of local indicators of spatial association (LISA), which allow exploration of local patterns in spatial data. These include local Moran’s I and local Geary’s c, as well as Gi and Gi* that can be used for continuous or interval variables only. Despite numerous situations where qualitative (nominal/categorical) variables are encountered, few attempts have been devoted to the development of methods to explore the local spatial pattern in categorical data. To our knowledge, there is no indicator of local spatial association that can be used for both continuous and categorical data at the same time. In this paper, we propose a new local indicator of spatial association, called the entropy-based local indicator of spatial association (ELSA), that can be used for both categorical and continuous spatial data. ELSA quantifies the degree of spatial association of a variable at each location relative to the same variable at the neighbouring locations. This indicator simultaneously incorporates both spatial and attribute aspects of spatial association into account. The values of ELSA vary between 0 and 1, which denote highest and lowest spatial association, respectively. We compare ELSA to existing statistics such as Local Moran’s I and test the power and size of the new statistic. We also introduce the "entrogram", a novel approach for exploring the global spatial structure within the entire area (like a variogram). This study showed that the ELSA is consistent and robust, and is therefore suitable for applications in a wide range of disciplines. The ELSA algorithm is made available as an R-package (elsa).

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108. ELSA: Entropy-based local indicator of spatial association

Author

Naimi, Babak, Hamm, Nicholas A.S., Groen, Thomas, Skidmore, Andrew K., Toxopeus, Albertus G., Alibakhshi, Sara, Naimi, Babak, Hamm, Nicholas A.S., Groen, Thomas, Skidmore, Andrew K., Toxopeus, Albertus G., and Alibakhshi, Sara

Abstract

Research on spatial data analysis has developed a number of local indicators of spatial association (LISA), which allow exploration of local patterns in spatial data. These include local Moran’s I and local Geary’s c, as well as Gi and Gi* that can be used for continuous or interval variables only. Despite numerous situations where qualitative (nominal/categorical) variables are encountered, few attempts have been devoted to the development of methods to explore the local spatial pattern in categorical data. To our knowledge, there is no indicator of local spatial association that can be used for both continuous and categorical data at the same time. In this paper, we propose a new local indicator of spatial association, called the entropy-based local indicator of spatial association (ELSA), that can be used for both categorical and continuous spatial data. ELSA quantifies the degree of spatial association of a variable at each location relative to the same variable at the neighbouring locations. This indicator simultaneously incorporates both spatial and attribute aspects of spatial association into account. The values of ELSA vary between 0 and 1, which denote highest and lowest spatial association, respectively. We compare ELSA to existing statistics such as Local Moran’s I and test the power and size of the new statistic. We also introduce the "entrogram", a novel approach for exploring the global spatial structure within the entire area (like a variogram). This study showed that the ELSA is consistent and robust, and is therefore suitable for applications in a wide range of disciplines. The ELSA algorithm is made available as an R-package (elsa).

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109. ELSA: a new local indicator for spatial association

Author

Hamm, Nicholas A.S., Naimi, Babak, Groen, Thomas, Hamm, Nicholas A.S., Naimi, Babak, and Groen, Thomas

Abstract

There are several local indicators of spatial association (LISA) that allow exploration of local patterns in spatial data. Despite numerous situations where categorical variables are encountered, few attempts have been devoted to the development of methods to explore the local spatial pattern in categorical data. To our knowledge, there is no indicator of local spatial association that can be used for both continuous and categorical data. We introduce ELSA, which can be used for exploring and testing local spatial association for continuous and categorical variables. We provide the R-package elsa for making these computations.

110. ELSA: Entropy-based local indicator of spatial association

Author

Naimi, Babak, Hamm, Nicholas A.S., Groen, Thomas, Skidmore, Andrew K., Toxopeus, Albertus G., Alibakhshi, Sara, Naimi, Babak, Hamm, Nicholas A.S., Groen, Thomas, Skidmore, Andrew K., Toxopeus, Albertus G., and Alibakhshi, Sara

Abstract

Research on spatial data analysis has developed a number of local indicators of spatial association (LISA), which allow exploration of local patterns in spatial data. These include local Moran’s I and local Geary’s c, as well as Gi and Gi* that can be used for continuous or interval variables only. Despite numerous situations where qualitative (nominal/categorical) variables are encountered, few attempts have been devoted to the development of methods to explore the local spatial pattern in categorical data. To our knowledge, there is no indicator of local spatial association that can be used for both continuous and categorical data at the same time. In this paper, we propose a new local indicator of spatial association, called the entropy-based local indicator of spatial association (ELSA), that can be used for both categorical and continuous spatial data. ELSA quantifies the degree of spatial association of a variable at each location relative to the same variable at the neighbouring locations. This indicator simultaneously incorporates both spatial and attribute aspects of spatial association into account. The values of ELSA vary between 0 and 1, which denote highest and lowest spatial association, respectively. We compare ELSA to existing statistics such as Local Moran’s I and test the power and size of the new statistic. We also introduce the "entrogram", a novel approach for exploring the global spatial structure within the entire area (like a variogram). This study showed that the ELSA is consistent and robust, and is therefore suitable for applications in a wide range of disciplines. The ELSA algorithm is made available as an R-package (elsa).

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111. ELSA: a new local indicator for spatial association

Author

Hamm, Nicholas A.S., Naimi, Babak, Groen, Thomas, Hamm, Nicholas A.S., Naimi, Babak, and Groen, Thomas

Abstract

There are several local indicators of spatial association (LISA) that allow exploration of local patterns in spatial data. Despite numerous situations where categorical variables are encountered, few attempts have been devoted to the development of methods to explore the local spatial pattern in categorical data. To our knowledge, there is no indicator of local spatial association that can be used for both continuous and categorical data. We introduce ELSA, which can be used for exploring and testing local spatial association for continuous and categorical variables. We provide the R-package elsa for making these computations.

113. Spread of SARS-CoV-2 Coronavirus likely constrained by climate

Author

Babak Naimi, Miguel B. Araújo, Araújo, Miguel B. [0000-0002-5107-7265], Naimi, Babak [0000-0001-5431-2729], Araújo, Miguel B., and Naimi, Babak

Subjects
Ecological niche, 0303 health sciences, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), fungi, Outbreak, medicine.disease_cause, Arid, 3. Good health, law.invention, 03 medical and health sciences, 0302 clinical medicine, Geography, Transmission (mechanics), 13. Climate action, law, Climatology, medicine, Temperate climate, 030212 general & internal medicine, South east asia, 030304 developmental biology, Coronavirus
Abstract

As new cases of COVID-19 are being confirmed pressure is mounting to increase understanding of the factors underlying the spread the disease. Using data on local transmissions until the 23rd of March 2020, we develop an ensemble of 200 ecological niche models to project monthly variation in climate suitability for spread of SARS-CoV-2 throughout a typical climatological year. Although cases of COVID-19 are reported all over the world, most outbreaks display a pattern of clustering in relatively cool and dry areas. The predecessor SARS-CoV-1 was linked to similar climate conditions. Should the spread of SARS CoV-2 continue to follow current trends, asynchronous seasonal global outbreaks could be expected. According to the models, temperate warm and cold climates are more favorable to spread of the virus, whereas arid and tropical climates are less favorable. However, model uncertainties are still high across much of sub-Saharan Africa, Latin America and South East Asia. While models of epidemic spread utilize human demography and mobility as predictors, climate can also help constrain the virus. This is because the environment can mediate human-to-human transmission of SARS-CoV-2, and unsuitable climates can cause the virus to destabilize quickly, hence reducing its capacity to become epidemic.

Published
2020
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114. Response of an Afro-Palearctic bird migrant to glaciation cycles

Author

Kasper Thorup, Lykke Pedersen, Rute R. da Fonseca, Babak Naimi, David Nogués-Bravo, Mario Krapp, Andrea Manica, Mikkel Willemoes, Sissel Sjöberg, Shaohong Feng, Guangji Chen, Alba Rey-Iglesia, Paula F. Campos, Robert Beyer, Miguel B. Araújo, Anders J. Hansen, Guojie Zhang, Anders P. Tøttrup, Carsten Rahbek, Thorup, Kasper [0000-0002-0320-0601], da Fonseca, Rute R [0000-0002-2805-4698], Naimi, Babak [0000-0001-5431-2729], Krapp, Mario [0000-0002-2599-0683], Manica, Andrea [0000-0003-1895-450X], Willemoes, Mikkel [0000-0001-9363-6304], Feng, Shaohong [0000-0002-2462-7348], Chen, Guangji [0000-0002-9441-1155], Campos, Paula F [0000-0003-1285-4671], Araújo, Miguel B [0000-0002-5107-7265], Hansen, Anders J [0000-0002-1890-2702], Zhang, Guojie [0000-0001-6860-1521], Tøttrup, Anders P [0000-0001-8776-9629], Rahbek, Carsten [0000-0003-4585-0300], and Apollo - University of Cambridge Repository

Subjects
Male, Asia, Climate, Climate Change, Population Dynamics, Models, Biological, Birds, Effective population size, Animals, Ice Cover, Ecosystem, Multidisciplinary, Ecology, paleoclimate reconstruction, Biological Sciences, Long-distance migration, long-distance migration, Europe, Paleoclimate reconstruction, hindcasting, Africa, Commentary, Animal Migration, Female, Hindcasting, effective population size, Algorithms
Abstract

Migration allows animals to exploit spatially separated and seasonally available resources at a continental to global scale. However, responding to global climatic changes might prove challenging, especially for long-distance intercontinental migrants. During glacial periods, when conditions became too harsh for breeding in the north, avian migrants have been hypothesized to retract their distribution to reside within small refugial areas. Here, we present data showing that an Afro-Palearctic migrant continued seasonal migration, largely within Africa, during previous glacial–interglacial cycles with no obvious impact on population size. Using individual migratory track data to hindcast monthly bioclimatic habitat availability maps through the last 120,000 y, we show altered seasonal use of suitable areas through time. Independently derived effective population sizes indicate a growing population through the last 40,000 y. We conclude that the migratory lifestyle enabled adaptation to shifting climate conditions. This indicates that populations of resource-tracking, long-distance migratory species could expand successfully during warming periods in the past, which could also be the case under future climate scenarios.

Published
2022
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115. rasterdiv ‐ an Information Theory tailored R package for measuring ecosystem heterogeneity from space: to the origin and back

Author

Rocchini, Duccio, Thouverai, Elisa, Marcantonio, Matteo, Iannacito, Martina, Da Re, Daniele, Torresani, Michele, Bacaro, Giovanni, Bazzichetto, Manuele, Bernardi, Alessandra, Foody, Giles M., Furrer, Reinhard, Kleijn, David, Larsen, Stefano, Lenoir, Jonathan, Malavasi, Marco, Marchetto, Messori, Filippo, Montaghi, Alessandro, Moudr'y, V'itvezslav, Naimi, Babak, Ricotta, Carlo, Rossini, Micol, Santi, Francesco, Santos, Maria J., Schaepman, Michael E., Schneider, Fabian D., Schuh, Leila, Silvestri, Sonia, 'imov'a, Petra, Skidmore, Andrew K., Tattoni, Clara, Tordoni, Enrico, Vicario, Saverio, Zannini, Piero, Wegmann, Martin, University of Zurich, Rocchini, Duccio, Department of Natural Resources, UT-I-ITC-FORAGES, Faculty of Geo-Information Science and Earth Observation, Alma Mater Studiorum Università di Bologna [Bologna] (UNIBO), Czech University of Life Sciences Prague (CZU), University of California [Davis] (UC Davis), University of California (UC), High-End Parallel Algorithms for Challenging Numerical Simulations (HiePACS), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université Catholique de Louvain = Catholic University of Louvain (UCL), Free University of Bozen-Bolzano, Università degli studi di Trieste = University of Trieste, Ecosystèmes, biodiversité, évolution [Rennes] (ECOBIO), Université de Rennes (UR)-Institut Ecologie et Environnement (INEE), Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Observatoire des Sciences de l'Univers de Rennes (OSUR), Université de Rennes (UR)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Centre National de la Recherche Scientifique (CNRS), Università degli Studi di Trento (UNITN), University of Nottingham, UK (UON), Universität Zürich [Zürich] = University of Zurich (UZH), Wageningen University and Research [Wageningen] (WUR), Ecologie et Dynamique des Systèmes Anthropisés - UMR CNRS 7058 (EDYSAN), Université de Picardie Jules Verne (UPJV)-Centre National de la Recherche Scientifique (CNRS), Università degli Studi di Firenze = University of Florence (UniFI), Helsingin yliopisto = Helsingfors universitet = University of Helsinki, Università degli Studi di Roma 'La Sapienza' = Sapienza University [Rome] (UNIROMA), Università degli Studi di Milano-Bicocca = University of Milano-Bicocca (UNIMIB), California Institute of Technology (CALTECH), Jet Propulsion Laboratory (JPL), NASA-California Institute of Technology (CALTECH), Macquarie University, University of Twente, Università degli studi di Bari Aldo Moro = University of Bari Aldo Moro (UNIBA), Julius-Maximilians-Universität Würzburg (JMU), H2020 Project SHOWCASE [862480], H2020 COST Action European Cooperation in Science and Technology (COST) [CA17134], Jet Propulsion Laboratory, California Institute of Technology, National Aeronautics and Space Administration National Aeronautics and Space Administration (NASA) [80NM0018D0004], University of Zurich Research Priority Program on Global Change and Biodiversity (URPP GCB), Friuli Venezia Giulia Region, Swiss National Science Foundation Swiss National Science Foundation (SNSF) [SNSF-175529], Czech University of Life Sciences Prague, European Project: 862480,SHOWCASE(2020), University of California, Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Inria Bordeaux - Sud-Ouest, Università degli studi di Trieste, Centre National de la Recherche Scientifique (CNRS)-Observatoire des Sciences de l'Univers de Rennes (OSUR)-Institut Ecologie et Environnement (INEE), Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES), Centre National de la Recherche Scientifique (CNRS)-Université de Picardie Jules Verne (UPJV), Università degli Studi di Firenze = University of Florence [Firenze] (UNIFI), University of Helsinki, Università degli Studi di Roma 'La Sapienza' = Sapienza University [Rome], Università degli Studi di Milano-Bicocca [Milano] (UNIMIB), University of Twente [Netherlands], Università degli studi di Bari Aldo Moro (UNIBA), Julius-Maximilians-Universität Würzburg [Wurtzbourg, Allemagne] (JMU), Thouverai, Elisa, Marcantonio, Matteo, Iannacito, Martina, DA RE, Daniele, Torresani, Michele, Bacaro, Giovanni, Bazzichetto, Manuele, Bernardi, Alessandra, Foody, Giles M., Furrer, Reinhard, Kleijn, David, Larsen, Stefano, Lenoir, Jonathan, Malavasi, Marco, Marchetto, Elisa, Messori, Filippo, Montaghi, Alessandro, Moudrý, Vı́tězslav, Naimi, Babak, Ricotta, Carlo, Rossini, Micol, Santi, Francesco, Santos, Maria J., Schaepman, Michael, Schneider, Fabian, Schuh, Leila, Silvestri, Sonia, Šı́mová, Petra, Skidmore, Andrew K., Tattoni, Clara, Tordoni, Enrico, Vicario, Saverio, Zannini, Piero, Wegmann, Martin, Rocchini D., Thouverai E., Marcantonio M., Iannacito M., Da Re D., Torresani M., Bacaro G., Bazzichetto M., Bernardi A., Foody G.M., Furrer R., Kleijn D., Larsen S., Lenoir J., Malavasi M., Marchetto E., Messori F., Montaghi A., Moudry V., Naimi B., Ricotta C., Rossini M., Santi F., Santos M.J., Schaepman M.E., Schneider F.D., Schuh L., Silvestri S., Simova P., Skidmore A.K., Tattoni C., Tordoni E., Vicario S., Zannini P., Wegmann M., UCL - SST/ELI - Earth and Life Institute, UCL - SST/ELI/ELIC - Earth & Climate, Department of Geosciences and Geography, Rocchini, D, Thouverai, E, Marcantonio, M, Iannacito, M, Da Re, D, Torresani, M, Bacaro, G, Bazzichetto, M, Bernardi, A, Foody, G, Furrer, R, Kleijn, D, Larsen, S, Lenoir, J, Malavasi, M, Marchetto, E, Messori, F, Montaghi, A, Moudry, V, Naimi, B, Ricotta, C, Rossini, M, Santi, F, Santos, M, Schaepman, M, Schneider, F, Schuh, L, Silvestri, S, Simova, P, Skidmore, A, Tattoni, C, Tordoni, E, Vicario, S, Zannini, P, and Wegmann, M

Subjects
0106 biological sciences, 010504 meteorology & atmospheric sciences, Computer science, UFSP13-8 Global Change and Biodiversity, ecological informatic, 340 Law, Information theory, Space (mathematics), computer.software_genre, 01 natural sciences, remote sensing, Feature (machine learning), biodiversity, education.field_of_study, Ecology, Ecological Modeling, Spatial Heterogeneity, PE&RC, 10123 Institute of Mathematics, 10122 Institute of Geography, ecological informatics, modeling, satellite imagery, Plantenecologie en Natuurbeheer, Data mining, 1171 Geosciences, Application, Evolution, Ecology (disciplines), Population, Metapopulation, Plant Ecology and Nature Conservation, 610 Medicine & health, 85 satellite imagery, 010603 evolutionary biology, ITC-HYBRID, modelling, 510 Mathematics, 2302 Ecological Modeling, Behavior and Systematics, Settore BIO/07 - ECOLOGIA, Ecosystem, education, Ecology, Evolution, Behavior and Systematics, 0105 earth and related environmental sciences, biodiversity, ecological informatics, modelling, remote sensing, satellite imagery, Ecological indicator, Ecological Modelling, 1105 Ecology, Evolution, Behavior and Systematics, Spatial Ecology, ITC-ISI-JOURNAL-ARTICLE, 10231 Institute for Computational Science, Key (cryptography), [SDE.BE]Environmental Sciences/Biodiversity and Ecology, Scale (map), computer
Abstract

Ecosystem 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.In 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.The rasterdiv package allows calculating multiple indices, robustly rooted in Information Theory, and based on reproducible open source algorithms.

Published
2020
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116. A quixotic view of spatial bias in modelling the distribution of species and their diversity.

Author

Rocchini D, Tordoni E, Marchetto E, Marcantonio M, Barbosa AM, Bazzichetto M, Beierkuhnlein C, Castelnuovo E, Gatti RC, Chiarucci A, Chieffallo L, Da Re D, Di Musciano M, Foody GM, Gabor L, Garzon-Lopez CX, Guisan A, Hattab T, Hortal J, Kunin WE, Jordán F, Lenoir J, Mirri S, Moudrý V, Naimi B, Nowosad J, Sabatini FM, Schweiger AH, Šímová P, Tessarolo G, Zannini P, and Malavasi M

Abstract

Ecological processes are often spatially and temporally structured, potentially leading to autocorrelation either in environmental variables or species distribution data. Because of that, spatially-biased in-situ samples or predictors might affect the outcomes of ecological models used to infer the geographic distribution of species and diversity. There is a vast heterogeneity of methods and approaches to assess and measure spatial bias; this paper aims at addressing the spatial component of data-driven biases in species distribution modelling, and to propose potential solutions to explicitly test and account for them. Our major goal is not to propose methods to remove spatial bias from the modelling procedure, which would be impossible without proper knowledge of all the processes generating it, but rather to propose alternatives to explore and handle it. In particular, we propose and describe three main strategies that may provide a fair account of spatial bias, namely: (i) how to represent spatial bias; (ii) how to simulate null models based on virtual species for testing biogeographical and species distribution hypotheses; and (iii) how to make use of spatial bias - in particular related to sampling effort - as a leverage instead of a hindrance in species distribution modelling. We link these strategies with good practice in accounting for spatial bias in species distribution modelling., (© 2023. The Author(s).)

Published
2023
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117. Response of an Afro-Palearctic bird migrant to glaciation cycles.

Author

Thorup K, Pedersen L, da Fonseca RR, Naimi B, Nogués-Bravo D, Krapp M, Manica A, Willemoes M, Sjöberg S, Feng S, Chen G, Rey-Iglesia A, Campos PF, Beyer R, Araújo MB, Hansen AJ, Zhang G, Tøttrup AP, and Rahbek C

Subjects
Africa, Algorithms, Animals, Asia, Ecosystem, Europe, Female, Ice Cover, Male, Models, Biological, Animal Migration physiology, Birds physiology, Climate, Climate Change, Population Dynamics
Abstract

Migration allows animals to exploit spatially separated and seasonally available resources at a continental to global scale. However, responding to global climatic changes might prove challenging, especially for long-distance intercontinental migrants. During glacial periods, when conditions became too harsh for breeding in the north, avian migrants have been hypothesized to retract their distribution to reside within small refugial areas. Here, we present data showing that an Afro-Palearctic migrant continued seasonal migration, largely within Africa, during previous glacial-interglacial cycles with no obvious impact on population size. Using individual migratory track data to hindcast monthly bioclimatic habitat availability maps through the last 120,000 y, we show altered seasonal use of suitable areas through time. Independently derived effective population sizes indicate a growing population through the last 40,000 y. We conclude that the migratory lifestyle enabled adaptation to shifting climate conditions. This indicates that populations of resource-tracking, long-distance migratory species could expand successfully during warming periods in the past, which could also be the case under future climate scenarios., Competing Interests: The authors declare no competing interest., (Copyright © 2021 the Author(s). Published by PNAS.)

Published
2021
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118. rasterdiv-An Information Theory tailored R package for measuring ecosystem heterogeneity from space: To the origin and back.

Author

Rocchini D, Thouverai E, Marcantonio M, Iannacito M, Da Re D, Torresani M, Bacaro G, Bazzichetto M, Bernardi A, Foody GM, Furrer R, Kleijn D, Larsen S, Lenoir J, Malavasi M, Marchetto E, Messori F, Montaghi A, Moudrý V, Naimi B, Ricotta C, Rossini M, Santi F, Santos MJ, Schaepman ME, Schneider FD, Schuh L, Silvestri S, Ŝímová P, Skidmore AK, Tattoni C, Tordoni E, Vicario S, Zannini P, and Wegmann M

Abstract

Ecosystem heterogeneity has been widely recognized as a key ecological indicator of several ecological functions, diversity patterns and change, metapopulation dynamics, population connectivity or gene flow.In 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.The rasterdiv package allows calculating multiple indices, robustly rooted in Information Theory, and based on reproducible open-source algorithms., (© 2021 The Authors. Methods in Ecology and Evolution published by John Wiley & Sons Ltd on behalf of British Ecological Society.)

Published
2021
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119. Land surface black-sky albedo at a fixed solar zenith angle and its relation to forest structure during peak growing season based on remote sensing data.

Author

Alibakhshi S, Crowther TW, and Naimi B

Abstract

Satellite data provide the opportunity to explore different land surface properties, such as albedo (reflectivity) and forest structure, for multidisciplinary purposes. We estimated land surface black-sky albedo at shortwave, near-infrared and visible spectral regions at a fixed solar zenith angle (i.e., 38 ∘ ) during peak growing season in 2005 on a global scale. In addition, we estimated the links between albedo and forest structure variables including forest density [the number of trees/km 2 ], tree cover [percent], and leaf area index [m 2 /m 2 ] over pure forest pixels during peak growing season in 2005 on a global scale. We acquired and processed remotely sensed variables from moderate resolution imaging spectroradiometer (MODIS) and Landsat satellite images. This article provides 1) dataset of black-sky albedo at fixed solar zenith angle at a 1-km spatial resolution, 2) comparison between black-sky albedos at fixed solar zenith angle and local noon at a 1-km spatial resolution that are grouped based on forest types with the classes of evergreen needleleaf, evergreen broadleaf, deciduous needleleaf, deciduous broadleaf, mixed and woody savannah forests, and also the major biome zones including boreal, mediterranean, temperate and tropical region. 3) the links between black-sky albedo at fixed solar zenith angle and forest structure using generalized additive models at a 0.5-degree spatial resolution during peak growing season in 2005. The pre-processing steps to enhance the accuracy of these datasets include: (1) identifying pure forest pixels, (2) excluding high slope pixels and those covered partially by water in the albedo product using high spatial resolution water (i.e., 30-m spatial resolution) and slope (i.e., 90-m spatial resolution) masks, and (3) using the most recent collection (collection 6) of MODIS satellite images. More details and interpretations of these datasets can be found in Alibakhshi et al. (2020) [1]., Competing Interests: None, (© 2020 The Authors.)

Published
2020
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