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Maps represent powerful tools to show the spatial variation of a variable in a straightforward manner. A crucial aspect in map rendering for its interpretation by users is the gamut of colours used for displaying data. One part of this problem is linked to the proportion of the human population that is colour blind and, therefore, highly sensitive to colour palette selection. The aim of this paper is to present the cblindplot R package and its founding function - cblind.plot() - which enables colour blind people to just enter an image in a coding workflow, simply set their colour blind deficiency type, and immediately get as output a colour blind friendly plot. We will first describe in detail colour blind problems, and then show a step by step example of the function being proposed. While examples exist to provide colour blind people with proper colour palettes, in such cases (i) the workflow include a separate import of the image and the application of a set of colour ramp palettes and (ii) albeit being well documented, there are many steps to be done before plotting an image with a colour blind friendly ramp palette. The function described in this paper, on the contrary, allows to (i) automatically call the image inside the function without any initial import step and (ii) explicitly refer to the colour blind deficiency type being experienced, to further automatically apply the proper colour ramp palette.

Maps represent powerful tools to show the spatial variation of a variable in a straightforward manner. A crucial aspect in map rendering for its interpretation by users is the gamut of colours used for displaying data. One part of this problem is linked to the proportion of the human population that is colour blind and, therefore, highly sensitive to colour palette selection. The aim of this paper is to present a function in R - \texttt{cblind.plot} - which enables colour blind people to just enter an image in a coding workflow, simply set their colour blind deficiency type, and immediately get as output a colour blind friendly plot. We will first describe in detail colour blind problems, and then show a step by step example of the function being proposed. While examples exist to provide colour blind people with proper colour palettes, in such cases (i) the workflow include a separate import of the image and the application of a set of colour ramp palettes and (ii) albeit being well documented, there are many steps to be done before plotting an image with a colur blind friendly ramp palette. The function described in this paper (\ttt{cblind.plot}), on the contrary, allows to (i) automatically call the image inside the function without any initial import step and (ii) explicitly refer to the colour blind deficiency type being experienced, to further automatically apply the proper colour ramp palette.

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.

Additional file 1: Figure S1. Overview of the temperature-dependent functions used in the model for the four Aedes species. Figure S2. Overview of the temperature-dependent functions used in the model for Ae. aegypti. Figure S3. Overview of the temperature-dependent functions used in the model for Ae. albopictus. Figure S4. Overview of the temperature-dependent functions used in the model for Ae. japonicus. Figure S5. Overview of the temperature-dependent functions used in the model for Ae. koreicus. Figure S6. Overview of the photoperiod-dependent diapause function used to in the model for Ae. albopictus and Ae. japonicus. The Ae. japonicus function was used forAe. koreicus as well. Figure S7. Sensitivity analysis on the effect of (A) the variability of introduced propagules and juvenile-habitat water volume on the percentage of successful introduction; (B) the variability of the juvenile-habitat water volume on the median individual abundance. Figure S8. Predicted percentage of establishment of Ae. aegypti, Ae. albopictus in California (USA) for the years 2011–2016 and 2013–2018, respectively. Only pixels having a probability of successful introduction > 0 are shown. The red dots represent the counties where the species have been found. Table S1. Description of mechanistic models for invasive Aedes available as software or scripts (online or on request). Table S2. Other model features. Table S3. Species-specific temperature-dependent physiological parameters. Table S4. Species-specific dispersal parameters.Table S5. Species-specific photoperiod parameters. Table S6. Validation for Ae. koreicus model in Trento (NE Italy).

Spatio-ecological heterogeneity is strongly linked to many ecological processes and functions such as plant species diversity patterns and change, metapopulation dynamics, and gene flow. Remote sensing is particularly useful for measuring spatial heterogeneity of ecosystems over wide regions with repeated measurements in space and time. Besides, developing free and open source algorithms for ecological modelling from space is vital to allow to prove workflows of analysis reproducible. From this point of view, NASA developed programs like the Surface Biology and Geology (SBG) to support the development of algorithms for exploiting spaceborne remotely sensed data to provide a relatively fast but accurate estimate of ecological properties in vast areas over time. Most of the indices to measure heterogeneity from space are point descriptors : they catch only part of the whole heterogeneity spectrum. Under the SBG umbrella, in this paper we provide a new R function part of therasterdivR package which allows to calculate spatio-ecological heterogeneity and its variation over time by considering all its possible facets. The new function was tested on two different case studies, on multi- and hyperspectral images, proving to be an effective tool to measure heterogeneity and detect its changes over time.

Research on social learning has centered around vertebrates, but evidence is accumulating that small-brained, non-social arthropods also learn from others. Social learning can lead to social inheritance when socially acquired behaviors are transmitted to subsequent generations. Here, we first highlight the complementarities between social and classical genetic inheritance, using oviposition site selection, a behavior critical for many non-social arthropods, as a hypothetical example. We then discuss the relevance of studying social learning and transmission in non-social arthropods and document known cases in the literature, including examples of social learning from con and hetero-specifics. We subsequently highlight under which conditions social learning can be adaptive or not. We conclude that non-social arthropods and the study of oviposition behavior offer unparalleled opportunities to increase our understanding of social learning and inheritance.

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.

Spatially-explicit dataset of plant species occurrences collected in the Province of Siena (Central Italy) is analysed, with the aim of investigating a) the relative role of environmental factors in shaping spatial patterns of plant species richness, and b) how the spatial scale at which predictors have been sampled determines the explicative power of species richness models. The optimal spatial resolution of analysis was evaluated with respect to the total deviance explained by models, using a set of environmental and remotely sensed derived predictors calculated at different spatial scales. Results confirm the hypothesis that the predictive power of landscape structure is influenced by the spatial scale at which predictor variables have been sampled. Furthermore, the relevance of identifying a proper geographical scale of investigation, hence minimizing the redundancy in the predictor variables and maximising the explanatory power of the single groups of predictor variables, is highlighted as well.

BackgroundRefugia are island-like habitats that are linked to long-term environmental stability and, as a result, high endemism. Conservation of refugia and endemism hotspots should be based on a deep ecological and evolutionary understanding of their functioning, which remains limited. Although functional traits can provide such insights, a corresponding, coherent framework is lacking.Proposed frameworkPlant communities in refugia and endemism hotspots should, due to long-term environmental stability, display unique functional characteristics linked to distinct phylogenetic patterns. Therefore, such communities should be characterized by a functional signature that exhibits: (1) distinct values and combinations of traits, (2) higher functional diversity and (3) a prevalence of similar traits belonging to more distantly related lineages inside, compared to outside, of endemism hotspots and refugia. While the limited functional trait data available from refugia and endemism hotspots do not allow these predictions to be tested rigorously, three potential applications of the functional signature in biogeography and conservation planning are highlighted. Firstly, it allows the functional characteristics of endemism hotspots and refugia to be identified. Secondly, the strength of the functional signature can be compared among these entities, and with the surrounding landscape, to provide an estimate of the capacity of endemism hotspots and refugia to buffer environmental changes. Finally, the pattern of the functional signature can reveal ecological and evolutionary processes driving community assembly and functioning, which can assist in predicting the effect of environmental changes (e.g. climate, land-use) on communities in endemism hotspots and refugia.ConclusionThe proposed functional signature concept allows the systematic integration of plant functional traits and phylogeny into the study of endemism hotspots and refugia, but more data on functional traits in these entities are urgently needed. Overcoming this limitation would facilitate rigorous testing of the proposed predictions for the functional signature, advancing the eco-evolutionary understanding of endemism hotspots and refugia.

Good estimates of ecosystem complexity are essential for a number of ecological tasks: from biodiversity estimation, to forest structure variable retrieval, to feature extraction by edge detection and generation of multifractal surface as neutral models for e.g. feature change assessment. Hence, measuring ecological complexity over space becomes crucial in macroecology and geography. Many geospatial tools have been advocated in spatial ecology to estimate ecosystem complexity and its changes over space and time. Among these tools, free and open source options especially offer opportunities to guarantee the robustness of algorithms and reproducibility. In this paper we will summarize the most straightforward measures of spatial complexity available in the Free and Open Source Software GRASS GIS, relating them to key ecological patterns and processes.

Anticipating species distributions in space and time is necessary for effective biodiversity conservation and for prioritising management interventions. This is especially true when considering invasive species. In such a case, anticipating their spread is important to effectively plan management actions. However, considering uncertainty in the output of species distribution models is critical for correctly interpreting results and avoiding inappropriate decision-making. In particular, when dealing with species inventories, the bias resulting from sampling effort may lead to an over- or under-estimation of the local density of occurrences of a species. In this paper we propose an innovative method to i) map sampling effort bias using cartogram models and ii) explicitly consider such uncertainty in the modeling procedure under a Bayesian framework, which allows the integration of multilevel input data with prior information to improve the anticipation species distributions.

Biological invasions are a side effect of globalisation and human modification of natural systems. Invasive mosquitoes and the pathogens that they carry are leading causes of human suffering. Mosquito vectors of pathogens are often ecologically plastic and able to exploit human movements to disperse. As such they spread across wide geographical areas and are among the most successful species to have become invasive. Mosquito-borne pathogens have complex life cycles which involve a rich set of reservoir and vector hosts. They are evolutionarily very dynamic, ready to take advantage of new opportunities offered by anthropogenic environmental changes. Many pathogens associated with mosquitoes are indeed changing their ecology or modifying their geographical and biological distribution, being considered as invasive species. Despite this impressive plasticity, invasive mosquitoes and mosquito-borne pathogens are still part of a natural system, and as such are limited by environmental conditions. The main target of my research was to apply ecological theory and environmental modelling to gain knowledge on these limiting conditions, by establishing mechanistic and correlative associations between the environment, invasive vectors and pathogen demographics, as well as physiological and spatial dynamics. Understanding these mechanisms is key to design effective health policies which aim to mitigate the emergence of new pathogens, mainly by hindering the population that vectors them. The core of this thesis is formed by a series of stand-alone scientific papers, published, submitted or close to submission to peer-reviewed journals; the core chapters are backed by the General Introduction, and the General Summary and Outlook. Chapter 1 (General Introduction) depicts the state of the knowledge of the thesis research topic, delineating the research context and objectives. The relevance of mosquitoes as vector species and of emerging pathogens, combined with the need to use environmental modelling in investigating their ecology, is exposed; Chapter 2 looks at the environmental factors associated with the re-emergence of West Nile Virus (WNV) in the Old World. The association between WNV incidence in humans and a wide range of predictors was assessed through an automatic statistical approach. By identifying key environmental drivers of WNV, this chapter aims to both lay the foundations for the development of models able to predict WNV risk at a continental scale and to highlight the weakness of available data on WNV in the Old World; Chapter 3 deals with the estimation of habitat suitability and potential spread velocities of Ae. koreicus in Italy, through statistical modelling. The integration of the developed suitability map with transportation network data and the observed species dispersal rate, allowed to characterise Ae. koreicus potential pattern of range expansion. Results showed that the study area is widely suitable for Ae. koreicus, and this mosquito was predicted to fill the suitable niche in a relatively short time. This scenario opens the way to an enriched entomological landscape with three container-breeding mosquitoes interacting and thus complicating the epidemiological status of vector-borne pathogens; Chapter 4 presents a new model framework to simulate, with ecologically robust foundations, the introduction, life cycle, population and spatial dynamics of Ae. albopictus in urban landscapes. The developed model was applied to investigate the mechanisms underlying Ae. albopictus invasion dynamics in a newly invaded urban area. We found that Ae. albopictus introduction in the study area was more likely to have happened in spring or summer, and that the derived population remained at low density for a long time. These findings support the hypothesis that the recent Ae. albopictus invasion of the study area may derive from an old introduction event; Chapter 5 explores Ae. albopictus invasion dynamics as a function of the configuration of urban vegetation. Vegetation is an important component of urban areas and it has also been suggested as important for Ae. albopictus invasion dynamics. We applied the model developed and proposed in chapter 4 to synthetic urban landscapes with different vegetation configurations. Results supported the expectation that vegetation affects Ae. albopictus invasion dynamics, with a spread vegetation configuration causing increased mosquito dispersal and density in urban areas; Chapter 6 presents data from an experimental design implemented to acquire information on the thermal characteristics of aboveground Ae. albopictus micro-habitats. The acquired data was used to estimate temperature difference distributions between micro-habitats which are generalisable to other locations. We investigated how the use of micro-habitat data rather than generic temperature changes estimated Ae. albopictus life cycle durations. We found that the latter may be sub-optimal when modelling Ae. albopictus population dynamics. The reported temperature dataset can be exploited to make opportunistic temperature data appropriate for Ae. albopictus population dynamics modelling; Chapter 7 (General Summary and Outlook) lay the key findings of the core chapters forming the thesis into a general context, and outlines directions for future research for using environmental modelling in studies of emergent mosquito-borne pathogens and invasive mosquito species. Biologische Invasionen sind ein Nebeneffekt der Globalisierung und der menschlichen Modifizierung von natürlichen Systemen. Invasive Stechmücken und die Krankheitserreger, die sie in sich tragen, sind die Hauptursache menschlichen Leidens. Moskito-Vektoren von Krankheitserregern zeigen oft ökologische Plastizität und können die menschliche Mobilität ausnutzen, um sich zu verteilen. Aus diesen Gründen sind sie über weite geographische Gebiete verteilt und gelten als einige der erfolgreichsten invasiven Arten weltweit. Stechmücken, die Krankheitserreger übertragen, haben komplexe Lebenszyklen, die viele Reservoir- und Vektor-Wirte beinhalten. Sie sind in Bezug auf ihre Evolution sehr dynamisch und nutzen die Vorteile, die ihnen anthropogene Umweltveränderungen bieten. Viele Krankheitserreger, die mit Stechmücken in Verbindung gebracht werden, sind dabei, ihre Ökologie oder ihre geografische und biologische Verteilung zu verändern und werden deshalb als invasive Arten betrachtet. Trotz dieser beeindruckenden Plastizität sind invasive Stechmücken und übertragene Krankheitserreger immer noch Teil eines natürlichen Systems, und dementsprechend immer noch durch Umweltbedingungen eingeschränkt. Das Hauptziel meiner Untersuchungen war es, ökologische Theorie und Umweltmodellierung anzuwenden, um Erkenntnisse über diese einschränkenden Bedingungen zu gewinnen. Hierzu wurden mechanistische und korrelative Assoziationen zwischen Umwelt, invasiven Vektoren und der Demographie der Krankheitserreger hergestellt sowie die physiologische und räumliche Dynamik analysiert. Das Verständnis dieser Mechanismen ist der Schlüssel dazu, effektive Strategien einer Gesundheitspolitik zu entwickeln, die das Auftreten neuer Krankheitserreger vermindern sollen, indem sie vor allem die Krankheitsvektoren an der Ausbreitung hindern. Der Kern dieser Arbeit besteht aus eigenständigen wissenschaftlichen Arbeiten (scientific papers), die bei Zeitschriften mit peer-review-Verfahren eingereicht wurden oder sich kurz vor der Einreichung befinden. Die Kernkapitel werden von einer allgemeinen Einführung und Zusammenfassung und einem Ausblick begleitet. Kapitel 1 (Allgemeine Einführung) stellt den Kenntnisstand zum Forschungsthema der Doktorarbeit dar und umreißt den Forschungskontext und die Ziele. Die Relevanz von Mücken als Vektoren von neu auftretenden Krankheitserregern werden dargelegt, kombiniert mit der Notwendigkeit der Verwendung von Umweltmodellierung, um die Ökologie der Mücken zu untersuchen. Kapitel 2 befasst sich mit den Umweltfaktoren, die mit dem Wiederauftreten von West-Nil-Virus (WNV) in der Alten Welt verbunden sind. Die Verbindung von WNV-Vorkommen bei Menschen und einer Vielzahl von Vorhersage-Anzeichen wurde durch einen automatischen statistischer Ansatz bestimmt. Durch die Identifizierung der wichtigsten ökologischen Faktoren von WNV zielt dieses Kapitel sowohl auf die Grundlagen für die Entwicklung von Modellen, die das Risiko für WNV auf kontinentaler Ebene voraussagen sollen, als auch die Schwäche der verfügbaren Daten über WNV in der Alten Welt ab. Kapitel 3 befasst sich mit der Einschätzung der Habitateignung und möglicher Ausbreitungsgeschwindigkeiten von Aedes koreicus in Italien mittels statistischer Modellierung. Die Einbindung von Transportnetzwerkdaten und Ausbreitungsraten der beobachteten Arten in die hier entwickelte Eignungskarte erlaubte es, das Potentialmuster der Ausbreitung von Aedes koreicus zu charakterisieren. Die Ergebnisse zeigten, dass das Untersuchungsgebiet für Aedes albopictus weitestgehend geeignet ist, und es wurde vorhergesagt, dass diese Stechmückenart die entsprechende Nische in relativ kurzer Zeit füllen wird. Dieses Szenario zeigt das Potential einer reicheren entomologischen Landschaft auf mit drei sich in Behältern vermehrenden Stechmückenarten, die in Wechselwirkung zueinander stehen und den epidemiologischen Status der vektorübertragenen Pathogene verkomplizieren wird. Kapitel 4 stellt ein neues Rahmenmodell vor, welches mit ökologisch stabilen Kenntnissen die Einführung, den Lebenszyklus, die Bevölkerungs- und räumliche Dynamik von Aedes albopictus in urbanen Landschaften simuliert. Das entwickelte Modell wurde eingesetzt, um die der Dynamik der Invasion von Aedes albopictus zugrunde liegenden Mechanismen in einem neu befallenen Stadtgebiet zu untersuchen. Wir fanden heraus, dass die Einbringung von Aedes albopictus in das Untersuchungsgebiet vermutlich eher im Frühjahr oder Sommer geschehen war und dass die daraus entstandene Population lange bei niedriger Dichte blieb. Diese Ergebnisse stützen die Hypothese, dass sich die neueste Aedes albopictus Ausbreitung im Untersuchungsgebiet von einer alten Einführung abgeleitet werden kann. Kapitel 5 untersucht die Aedes albopictus Invasionsdynamik in Abhängigkeit von der Zusammensetzung der Stadtvegetation. Vegetation ist ein wichtiger Bestandteil städtischer Gebiete und wurde auch als wichtig für die Invasionsdynamik von Aedes albopictus angenommen. Wir verwendeten das in Kapitel 4 entwickelte und vorgeschlagene Modell für synthetische Stadtlandschaften mit verschiedenen Vegetations-Konfigurationen. Die Ergebnisse unterstützten die Erwartung, dass die Vegetation die Aedes albopictus Invasionsdynamik beeinflusst, und zwar mit einer sich ausbreitenden Vegetationszusammensetzung, die eine erhöhte Moskitoausbreitung und -dichte in städtischen Gebieten verursacht. Kapitel 6 stellt Daten aus einem experimentellen Design dar, das Informationen über die thermischen Eigenschaften der überirdischen Mikrohabitate von Aedes albopictus implementieren sollte. Die erfassten Daten wurden verwendet, um die Verteilung von Temperaturdifferenzen zwischen Mikrohabitaten zu schätzen, die auf andere Standorte übertragen werden können. Wir untersuchten, wie der Einsatz von Mikrohabitat-Daten die Lebenszyklusdauer von Aedes albopictus besser als generelle Temperaturmessungen schätzen kann. Wir fanden heraus, dass letztere zur Modellierung der Populationsdynamik von Aedes albopictus suboptimal sind. Der dargestellte Temperatur-Datensatz kann genutzt werden, um opportunistische Temperaturdaten für eine dynamische Modellierung der Aedes albopictus Population geeignet zu machen. Kapitel 7 (Allgemeine Zusammenfassung und Ausblick) stellt die wichtigsten Ergebnisse der Kernkapitel dieser Doktorarbeit in einen allgemeinen Kontext und umreißt Ziele (Zielrichtungen) für zukünftige Forschung unter Verwendung von Umweltmodellierung in Studien zu neu auftretenden Stechmücken-übertragener Pathogene und invasiver Mückenarten.

Title of data: R function to perform logistic regression with Bayesian inference. Description of data: R function to perform logistic regression with Bayesian inference. (PDF 37 kb)

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.

International audience; Aim. The majority of work done to gather information on the Earth's biodiversity has been carried out using 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 Shannon’s information theory, namely on the differences in relative abundance of pixel reflectances in a certain area. Additional metrics like the Rao’s quadratic entropy allow the use of spectral distance beside abundance, but they are point descriptors of diversity, that is they can account only for a part of the whole diversity continuum. The aim of this paper is thus to generalize the Rao’s quadratic entropy by proposing its parameterization for the first time. Innovation. The parametric Rao’s quadratic entropy, coded in R, (a) allows the representation of the whole continuum of potential diversity indices in one formula, and (b) starting from the Rao’s quadratic entropy, allows the explicit use of distances among pixel reflectance values, together with relative abundances. Main conclusions. The 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. Being part of the rasterdiv R package, the proposed method is expected to ensure high robustness and reproducibility.

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.

Refereed/Peer-reviewed

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.

Predicting the geographical distribution of a species is a central topic in ecology, conservation and management of natural resources especially for invasive organisms. Invasive species can modify the structure and function of invaded ecosystems, altering their biodiversity, and causing significant economic losses locally and globally. Therefore, measuring and visualizing the uncertainty inherent in species’ potential distributions is fundamental for effective biodiversity monitoring and planning conservation interventions. This paper discusses a new Bayesian approach to mapping this uncertainty using cartograms, previously published knowledge, and presence/absence data.

Studies addressing multiple aspects of biodiversity simultaneously (i.e., multifaceted approaches) can quantify plant diversity-environment links comprehensively - this is because of the multidimensional nature of plant diversity. However, multifaceted studies are scant in forests. Here, we examined taxonomic, functional and phylogenetic diversity patterns in 19 beech forest understory plots in two areas belonging to a biodiversity monitoring plan in Tuscany, Italy. We performed linear mixed effect models to quantify the influence of elevation (proxy for macroclimate), aspect (affecting microclimate), and basal area (related to microclimate and stand maturity) on diversity facets of vascular plants. Elevation played a major role in shaping diversity: high-elevation plots were less rich in species and had a reduced functional diversity of storage organs that may promote cold-tolerance. Conversely, the diversity of flowering phenology increased with elevation, thus low-elevation vegetation converged functionally towards a common, short blooming period. This strategy may be advantageous for understory plants in the deciduous beech forests experiencing longer growing seasons, hence more extended canopy closure at lower elevations. Basal area negatively affected foliar and multiple traits functional diversity which may be associated with highly selective and competitive environment for light capture in closed canopy, mature stands. Slope aspect did not exert any significant effect on diversity facets, neither did interactions among predictors. Overall, these results confirm the usefulness of implementing multifaceted approaches to i) better understand the influence of environmental drivers on different aspects of plant diversity, and ii) inform the biodiversity monitoring plan that is in place in the study forests by systematically including functional diversity instead of taxonomic metrics only., GO was supported by the Grant Agency of the Czech Republic (Centre of Excellence PLADIAS, 14-36079G and 16-19245S). AC acknowledges the financial and administrative support of the Regional Administration of Tuscany and LAMMA consortium

Refugia are key environments in biogeography and conservation. Because of their unique eco-evolutionary formation and functioning, they should display distinct functional trait signatures. However, comparative trait-based studies of plants in refugia and non-refugia are lacking. Here, we provide a comparison between resource-rich (putative microrefugia for species preferring mesic habitats under increasing aridity) and resource-impoverished woodlands (non-refugia) around two granite outcrops in south-western Australia. We measured and compared six functional traits (bark thickness, foliar δ13C, foliar C:N, leaf dry matter content, plant height, specific leaf area) in four woody species. We performed multiple-trait, multiple-species and single-trait, within-species analyses to test whether plants in resource-rich habitats were functionally distinct and more diverse than those in the surrounding resource-impoverished woodlands. We found that species in resource-rich woodlands occupied larger and distinct multiple-trait functional spaces and showed distinct single-trait values (for specific leaf area and bark thickness). This suggests that plants in resource-rich woodlands can deploy unique and more diverse ecological strategies, potentially making these putative microrefugia more resilient to environmental changes. These findings suggest that species in microrefugia may be characterised by unique functional signatures, illustrating the utility of comparative trait-based approaches to improve understanding of the functioning of refugia. Refereed/Peer-reviewed

Background: Refugia are island-like habitats that are linked to long-term environmental stability and, as a result, high endemism. Conservation of refugia and endemism hotspots should be based on a deep ecological and evolutionary understanding of their functioning, which remains limited. Although functional traits can provide such insights, a corresponding, coherent framework is lacking. Proposed Framework: Plant communities in refugia and endemism hotspots should, due to long-term environmental stability, display unique functional characteristics linked to distinct phylogenetic patterns. Therefore, such communities should be characterized by a functional signature that exhibits: (1) distinct values and combinations of traits, (2) higher functional diversity and (3) a prevalence of similar traits belonging to more distantly related lineages inside, compared to outside, of endemism hotspots and refugia. While the limited functional trait data available from refugia and endemism hotspots do not allow these predictions to be tested rigorously, three potential applications of the functional signature in biogeography and conservation planning are highlighted. Firstly, it allows the functional characteristics of endemism hotspots and refugia to be identified. Secondly, the strength of the functional signature can be compared among these entities, and with the surrounding landscape, to provide an estimate of the capacity of endemism hotspots and refugia to buffer environmental changes. Finally, the pattern of the functional signature can reveal ecological and evolutionary processes driving community assembly and functioning, which can assist in predicting the effect of environmental changes (e.g. climate, land-use) on communities in endemism hotspots and refugia. Conclusion: The proposed functional signature concept allows the systematic integration of plant functional traits and phylogeny into the study of endemism hotspots and refugia, but more data on functional traits in these entities are urgently needed. Overcoming this limitation would facilitate rigorous testing of the proposed predictions for the functional signature, advancing the eco-evolutionary understanding of endemism hotspots and refugia. Refereed/Peer-reviewed

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.

International audience; 1. Biodiversity includes multiscalar and multitemporal structures and processes, with different levels of functional organization, from genetic to ecosystemic levels. One of the mostly used methods to infer biodiversity is based on taxonomic approaches and community ecology theories. However, gathering extensive data in the field is difficult due to logistic problems, especially when aiming at modelling biodiversity changes in space and time, which assumes statistically sound sampling schemes. In this context, airborne or satellite remote sensing allows information to be gathered over wide areas in a reasonable time. 2. Most of the biodiversity maps obtained from remote sensing have been based on the inference of species richness by regression analysis. On the contrary, estimating compositional turnover (beta‐diversity) might add crucial information related to relative abundance of different species instead of just richness. Presently, few studies have addressed the measurement of species compositional turnover from space. 3. Extending on previous work, in this manuscript, we propose novel techniques to measure beta‐diversity from airborne or satellite remote sensing, mainly based on: (1) multivariate statistical analysis, (2) the spectral species concept, (3) self‐organizing feature maps, (4) multidimensional distance matrices, and the (5) Rao's Q diversity. Each of these measures addresses one or several issues related to turnover measurement. This manuscript is the first methodological example encompassing (and enhancing) most of the available methods for estimating beta‐diversity from remotely sensed imagery and potentially relating them to species diversity in the field.

Land-cover change, a major driver of the distribution and functioning of ecosystems, is characterized by a high diversity of patterns of change across space and time. Thus, a large amount of information is necessary to analyse change and develop plans for proper management of natural resources. In this work we tested MaxEnt algorithm in a completely remote land-cover classification and change analysis. In order to provide an empirical example, we selected south-eastern Italian Alps, manly Trentino-South Tyrol, as test region. We classified two Landsat images (1976 and 2001) in order to forecast probability of occurrence for unsampled locations and to determine the best subset of predictors (spectral bands). A difference map for each land cover class, representing the difference between 1976 and 2001 probability of occurrence values, was built. In order to better address the patterns of change analysis, we put together difference maps and topographic variables. The latter are considered, at least in the study area, as the main environmental drivers of land-use change, in connection with climate change. Our results indicate that the selected algorithm, applied to land cover classes, can provide reliable data, especially when referring to classes with homogeneous texture properties and surface reflectance. The performed models had satisfactory predictive performance, showing relatively clear patterns of difference between the two considered time steps. The development of a methodology that, in the absence of field data, allow to obtain data on land use change dynamics, is of extreme importance for land planning and management.

Anticipating species distributions in space and time is necessary for effective biodiversity conservation and for prioritising management interventions. This is especially true when considering invasive species. In such a case, anticipating their spread is important to effectively plan management actions. However, considering uncertainty in the output of species distribution models is critical for correctly interpreting results and avoiding inappropriate decision-making. In particular, when dealing with species inventories, the bias resulting from sampling effort may lead to an over- or under-estimation of the local density of occurrences of a species. In this paper we propose an innovative method to i) map sampling effort bias using cartogram models and ii) explicitly consider such uncertainty in the modeling procedure under a Bayesian framework, which allows the integration of multilevel input data with prior information to improve the anticipation species distributions.

Understanding biodiversity changes in time is crucial to promptly provide management practices against diversity loss. This is over- all true when considering global scales, since human-induced global change is expected to make signicant changes on the Earth's biota. Biodiversity management and planning is mainly based on eld ob- servations related to community diversity, considering dierent taxa. However, such methods are time and cost demanding and does not allow in most cases to get temporal replicates. In this view, remote sensing can provide for a wide data coverage in a short period of time. Recently, the use of Rao's Q diversity as a measure of spectral diversity has been proposed in order to explicitly taking into account dierences in a neighborhood considering abundance and relative distance among pixels. The aim of this paper was to extend such a measure over the temporal dimension and to present an innovative approach to calculate remotely sensed temporal diversity. We demonstrated that temporal beta-diversity (spectral turnover) can be calculated pixel-wise in terms of both slope and coecient of variation and further plotted over the whole matrix / image. From an ecological and operational point of view, for prioritisation practices in biodiversity protection, temporal variability could be benecial in order to plan more ecient conser- vation practices starting from spectral diversity hotspots in space andtime. In this paper we delivered a highly reproducible approach to cal- culate spatio-temporal diversity in a robust and straightforward man- ner. Since it is based on open source code, we expect that our method will be further used by several researchers and landscape managers. &nbsp

Rarefaction curves represent a powerful method for comparing species richness among habitats on an equal-effort basis. Three assumptions are required to correctly perform rarefaction analysis: (i) data collection should be a representative sample of the community under study, (ii) individuals are randomly dispersed, and (iii) species are independently dispersed. However, the community structure is spatially organized, and these criteria cannot be guaranteed. Recently, Chiarucci et al. (2009) proposed a new type of rarefaction, named Spatially Constrained Rarefaction (SCR), which allows to include the autocorrelated structure of the samples in the construction of a rarefaction curve. Here we present an easy-to-use procedure to calculate Spatially Constrained Rarefaction curve in the R environment.

The identification of shape and size of sampling units maximising species richness has major implications in conservation planning and monitoring actions. In this study we tested the effect of three sampling unit shapes (rectangles, squared, and randomly shaped sampling units) on the number of recorded plant species. We used a large dataset gathered in the network of protected areas of the Siena Province, Italy, as part of a joint project with the Italian National Forest Inventory. This dataset is composed of plant species occurrence data recorded from 604 plots (10m x10m), each divided in a grid of 16 contiguous subplot units (2.5 m x 2.5 m). Furthermore, we evaluated the effect of plot orientation along the main environmental gradient, related to slope, to understand how the selection of plot orientation (when elongated plots are used) influences the number of species collected. In total, 1041 plant species were recorded from the study plots. Species richness was significantly higher using randomly shaped sampling units compared to rectangular and squared units. Although rectangular units captured a significantly larger number of species than squared ones, plot orientation along the main environmental gradient (terrain slope) did not show a systematic effect on the number of recorded species. The decision of whether or not using rectangular versus squared plots should therefore depend upon the objectives of the survey and the characteristics of the area to be surveyed, with squared plots being more suitable for assessing species composition of more homogeneous vegetation units while rectangular plots being more suited for recording more species in the pooled sample of a large area. If the aim of a survey is to evaluate the biodiversity and conservation value of a ecosystem/community, sampling protocols that maximise the number of recorded species given the same sampling effort should be used. Furthermore, considerations on the orientation of rectangular plots should be carefully made to enhance reproducibility of collected data and correctly interpret the results of a survey. This information is crucial to develop cost-effective surveys and monitoring programs aimed at maximising the number of collected species.

The identification of shape and size of sampling units that maximises the number of plant species recorded in multiscale sampling designs has major implications in conservation planning and monitoring actions. In this paper we tested the effect of three sampling shapes (rectangles, squared, and randomly shaped sampling units) on the number of recorded species. We used a large dataset derived from the network of protected areas in the Siena Province, Italy. This dataset is composed of plant species occurrence data recorded from 604 plots (10 m × 10 m), each divided in a grid of 16 contiguous subplot units (2.5 m × 2.5 m). Moreover, we evaluated the effect of plot orientation along the main environmental gradient, to examine how the selection of plot orientation (when elongated plots are used) influences the number of species collected. In total, 1041 plant species were recorded from the study plots. A significantly higher species richness was recorded by the random arrangement of 4 subplots within each plot in comparison to the ‘rectangle’ and ‘square’ shapes. Although the rectangular shape captured a significant larger number of species than squared ones, plot orientation along the main environmental gradient did not show a systematic effect on the number of recorded species. We concluded that the choice of whether or not using elongated (rectangular) versus squared plots should dependent upon the objectives of the specific survey with squared plots being more suitable for assessing species composition of more homogeneous vegetation units and rectangular plots being more suited for recording more species in the pooled sample of a large area.

The most pervasive threats to biological diversity are directly or indirectly linked to the road networks. For this reason, over the last few decades, interest in the study of the ecological characteristics of the edges associated with roads has increased. This work aims to investigate the effect of roads as a humaninduced disturbance on the plant diversity in two managed Mediterranean forest sites, focusing on the responses of plants species richness, evenness, composition and taxonomic diversity. A stratified random sampling was performed in two protected areas located in Tuscany, Central Italy. The species richness, composition and abundance were measured in 53$20 � 20 m plots. Ordinary Least Square and quantile regressions were used to study the effect of the roads on species richness, evenness and taxonomic distinctness, and redundancy analysis was used to examine the species composition. Generalized linear models in conjunction with an Information Criterion-based approach to model selection were used to test the role of road distance in the structure of forest plant biodiversity. Our findings indicated a clear relationship between road distance and different plant biodiversity facets, which showed its maximum effect in the first 0e20 m forest-to-road segment and a mitigation after the 200 m threshold. Furthermore, the presence and abundance of many key forest species, such as Fagus sylvatica and Abies alba, were influenced more by the road distance than by other environmental gradients. The few remnants of core forest habitats in the Mediterranean basin highlight the need to recognize that road construction and maintenance have several ecological implications and accordingly require long-term monitoring programs.

Effective conservation of biodiversity in the face of increasing human impacts and global environmental changes requires accurate measurement of key trends and alternative management actions at landscape scales. Past ecological conditions are certainly important key factors in determining the present species diversity patterns and the inclusion of such factors (e.g. by historical cartographic data) can dramatically improve the predictive power of ecological models. In this paper we applied a retrogressive approach with the aim of simulating secondary forest regrowth effects on plant species diversity using present field data and historic land-use maps. The field data from an extensive sample were here used to model the temporal species richness change among the forest areas in the last 60 years. In order to rebuild the past species pool matrix using present field data and historical land use map, we applied a nearest neighbour selection using spatial query. Species-based rarefaction curves were derived for the two dates (1954 and 2010); the two datasets have been interpolated using inverse distance weighted algorithm, obtaining two maps showing the distribution of plant species richness for the two dates. The results showed that the cessation of human pressure on semi-natural areas and the consequent forest recovery, resulted in a decrease of vascular plant as a woodland flora replaces the open habitats flora. This study also showed that secondary forest regrowth and its effect on plant species diversity may be revealed by a retrogressive analysis, which represent a valid support in case of high uncertainty or absence of historical data.

Forests cover about 30% of the Earth surface, they are among the most biodiverse terrestrial ecosystems and represent the bulk of many ecological processes and services. The assessment of biodiversity is an important and essential goal to achieve but it can results difficult, time consuming and expensive when based on field data. Remote sensing covers large areas and provides consistent quality and stan- dardized data, which can be used to estimate species diversity. One method to estimate species diversity from remote sensing data is based on the Spectral Variation Hypothesis (SVH), which assumes that the higher the spectral variation of an image, the higher the environmental heterogeneity and the species diversity of the considered area. SVH has been tested using different spectral heterogeneity (SH) indices and measures, recently the Rao’s Q index has been proposed as a new spec- tral variation measure to be applied to remote sensing data. In this paper, we tested the SVH in an alpine coniferous forest to estimate tree species diversity. We evaluated the performance of the Rao’s Q index and compared it with another widely used SH index, the Coef- ficient of Variation (CV), validating them against values of Shannon’s H (used as species diversity index) derived from in-situ collected data. A NDVI time-series (for 2016 and 2017) derived from the Sentinel-2A and 2B and Landsat 8 OLI satellites has been used to test the effect of the spatial grain of both the sensors and to understand the seasonality of the SVH. The results showed that the SVH is season and sensor de- pendent. For both years and satellites, the relation between Rao’s Q and field data reached the highest R2 between June and July, decreas- ing towards winter and spring similarly to the NDVI time-series. This relationship could be given because, when NDVI reaches its highest values, it is able to capture small variation in reflectance of different leaf traits typical of specific trees. The relation between field and spec- tral diversity reached a value of R2=0.70 (2017) and R2=0.48 (2016) for Sentinel-2 and of R2 =0.42 (2017) and R2 =0.47 (2016) for Landsat 8. CV showed the same NDVI temporal tendency. However, the rela- tion between field-derived Shannon’s H and CV was on average lower than that we found for Rao’s Q. This research underlined the goodness of the Rao’s Q index, the relevance of the NDVI in the study of the SVH and the importance of the multi-temporal approach. &nbsp