Christophe F Randin

Michael B Ashcroft

Janine Bolliger

Jeannine Cavender-Bares

Nicholas C Coops

Stefan S Dullinger

Thomas T Dirnböck

Sandra Eckert

Erle Ellis

Nestor Fernández

Gregory Giuliani

Antoine Guisan

Walter Jetz

Stéphane Joost

Dirk Karger

Jonas Lembrechts

Jonathan Lenoir

Miska Luoto

Xavier Morin

Bronwyn Price

Duccio Rocchini

Michael Schaepman

Peter Verburg

Adam Wilson

Paul Woodcock

Nigel Yoccoz

Davnah Payne
email: davnah.payne@ips.unibe.ch

Monitoring biodiversity in the Anthropocene using remote sensing in species distribution models

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Human societies in the Anthropocene have emerged as a global force that is rapidly transforming ecosystems at local, regional, and global scales [START_REF] Ellis | Ecology in an anthropogenic biosphere[END_REF][START_REF] Ellis | Anthropogenic transformation of the terrestrial biosphere[END_REF][START_REF] Waters | The Anthropocene is functionally and stratigraphically distinct from the Holocene[END_REF]. Anthropogenic transformations affect the distribution of species, populations, and habitats through a range of processes including land use and cover change, climate change, pollution, (over-)exploitation, and invasions [START_REF] Chaudhary | The evolution of ecosystem services: A time series and discourse-centered analysis[END_REF][START_REF] Lenzen | Effects of land use on threatened species[END_REF][START_REF] Newbold | Global patterns of terrestrial assemblage turnover within and among land uses[END_REF][START_REF] Newbold | Global effects of land use on local terrestrial biodiversity[END_REF][START_REF] Pekin | Global land use intensity and the endangerment status of mammal species[END_REF][START_REF] Pereira | Global Biodiversity Change: The Bad, the Good, and the Unknown[END_REF]. Importantly, the existence of global supply chains that interconnect human societies implies that local anthropogenic changes in habitats and populations can also be triggered by consumptive demands thousands of kilometers away [START_REF] Chaudhary | Land use biodiversity impacts embodied in international food trade[END_REF][START_REF] Meyfroidt | Globalization of land use: distant drivers of land change and geographic displacement of land use[END_REF][START_REF] Rudel | Changing agents of deforestation: from state-initiated to enterprise driven processes, 1970-2000[END_REF][START_REF] Verburg | Land system science and sustainable development of the earth system: a global land project perspective[END_REF]. In addition, anthropogenic changes to the global environment may also trigger novel disturbance regimes such as altered frequency and intensity of extreme climate events [START_REF] Ipcc ; Field | Summary for policymakers[END_REF][START_REF] Mahecha | Detecting impacts of extreme events with ecological in situ monitoring networks[END_REF][START_REF] Ummenhofer | Extreme weather and climate events with ecological relevance : a review[END_REF] including temperature and precipitation extremes, causing heatwaves and droughts, or wildfires.

Such events impact the state, structure, functionality, and evolution of biological systems at different scales, revealing significant vulnerability to current climate variability [START_REF] Dirzo | Defaunation in the Anthropocene[END_REF][START_REF] Ipcc ; Field | Summary for policymakers[END_REF].

The challenges posed by anthropogenic impacts on the environment and by environmental changes on people are increasingly recognized at national and international levels, which has resulted in the formulation of various monitoring and reporting frameworks. At a global level, the United Nations' Sustainable Development Goals (SDGs; 2030 Agenda) and the Aichi biodiversity targets of the Convention for Biological Diversity (ATs, Strategic Plan for Biodiversity 2011-2020) provide a basis for safeguarding biodiversity and natural resources. For example, UN SDG 15.5 aims to: 'Take urgent and significant action to … halt the loss of biodiversity and, by 2020, protect and prevent the extinction of threatened species', whilst the closely related Aichi Target 12 focuses on improving the conservation status of threatened species. These targets can then be used in regional assessments reviewing trends in biodiversity to evaluate the alternative options for governance and decision-making required to achieve them. Meeting such global goals requires a suite of strategies that include the regular monitoring of specific species and a thorough understanding of current and emerging pressures acting on them [START_REF] Chen | Rapid range shifts of species associated with high levels of climate warming[END_REF][START_REF] Mirtl | Genesis, goals and achievements of Long-Term Ecological Research at the global scale: a critical review of ILTER and future directions[END_REF]. Monitoring programs should in turn inform conservation and management strategies based on explanatory as well as predictive models and support the regular evaluation of the effectiveness of policy interventions [START_REF] Haase | The next generation of site-based long-term ecological monitoring: linking essential biodiversity variables and ecosystem integrity[END_REF].

Monitoring design, models, and strategies that account for the scale, pace, and complexity of anthropogenic impacts on species and ecosystems [START_REF] Ceballos | Biological annihilation via the ongoing sixth mass extinction signaled by vertebrate population losses and declines[END_REF][START_REF] Dirzo | Defaunation in the Anthropocene[END_REF] require knowledge of past and current species distributions as well as robust projections of their potential future distributions (i.e., satisfactory accuracy and precision of models transferred to novel conditions [START_REF] Yates | Outstanding challenges in the transferability of ecological models[END_REF]. However, two key aspects limit our ability to decide on a global strategy. One is a substantial gap in available global data on functional traits and associated change driven by anthropogenic pressure, outpacing our knowledge gain by (slow) in situ sampling [START_REF] Jetz | Monitoring plant functional diversity from space[END_REF]. The second is the challenge of forecasting ecological dynamics into the future, and in particular in deciding at what temporal distance to forecast [START_REF] Petchey | The ecological forecast horizon, and examples of its uses and determinants[END_REF]. Species Distribution Models (SDMs, sensu [START_REF] Guisan | Predicting species distribution: offering more than simple habitat models[END_REF][START_REF] Guisan | Predictive habitat distribution models in ecology[END_REF] are a potentially powerful tool in this context. Two categories of SDMs can be distinguished: correlative and process-based models. Correlative models (sensu [START_REF] Guisan | Predicting species distribution: offering more than simple habitat models[END_REF] statistically relate descriptors of the environment to the response variable (e.g. occurrences, abundance or species' traits). In such models, processes are inferred from observed empirical relationships. In contrast, process-based models build upon explicit causal relationships determined experimentally. In these models, processes are explicitly described (e.g. process-based models of phenology and distribution; (see [START_REF] Chuine | Process-based models of phenology for plants and a nimals[END_REF], which increases the confidence in extrapolating beyond the known spatiotemporal extent [START_REF] Zurell | Benchmarking novel approaches for modelling species range dynamics[END_REF]. The continuum between these two modeling approaches includes hybrid (e.g. [START_REF] Dullinger | Extinction debt of high-mountain plants under twenty-first-century climate change[END_REF], dynamic range (DRM, e.g. [START_REF] Cotto | A dynamic eco-evolutionary model predicts slow response of alpine plants to climate warming[END_REF][START_REF] Engler | The MIGCLIM R package -seamless integration of dispersal constraints into projections of species distribution models[END_REF][START_REF] Pagel | Forecasting species ranges by statistical estimation of ecological niches and spatial population dynamics[END_REF], and integrated models [START_REF] Pagel | Forecasting species ranges by statistical estimation of ecological niches and spatial population dynamics[END_REF].

Applications of SDMs in conservation and decision-making are numerous (e.g. [START_REF] Guisan | Predicting species distributions for conservation decisions[END_REF][START_REF] Loiselle | Avoiding pitfalls of using species distribution models in conservation planning[END_REF]. SDMs can be used as explanatory tools (sensu [START_REF] Shmueli | To explain or to predict?[END_REF] to identify critical environmental variables for species or communities (e.g. [START_REF] Droz | Moderately urbanized areas as a conservation opportunity for an endangered songbird[END_REF], and as tools for converting non-systematically assessed survey locations into potential geographic ranges of species or communities, extrapolated to the entire target landscape [START_REF] Mcshea | What are the roles of species distribution models in conservation planning?[END_REF].

Such information is increasingly applied in conservation planning to minimize the impacts of development [START_REF] Guisan | Predicting species distributions for conservation decisions[END_REF] and may be linked to biodiversity monitoring through frameworks such as Essential Biodiversity Variables (EBVs; [START_REF] Pereira | Essential Biodiversity Variables[END_REF]. SDMs have further evolved to provide scenarios of future landscapes based on known and projected environmental parameters such as land cover, climate, and biotic constraints. This allows stakeholders to identify the natural resources they want to sustain, assess the costs and benefits of environmental policy options in relation to their projected effects on the distribution of critical species (e.g. [START_REF] Cianfrani | More than range exposure: global otter vulnerability to climate change[END_REF][START_REF] Cianfrani | A spatial modelling framework for assessing climate change impacts on freshwater ecosystems: response of brown trout (Salmo trutta L.) biomass to warming water temperature[END_REF][START_REF] Esselman | Application of species distribution models and conservation planning software to the design of a reserve network for the riverine fishes of northeastern Mesoamerica[END_REF], and articulate the range of solutions they are willing to accept in alternative futures. SDM projections can also indicate whether current protected areas or networks of protected sites match with projected future species and community distributions (e.g. [START_REF] Araujo | Would climate change drive species out of reserves? An assessment of existing reserve-selection methods[END_REF][START_REF] Bolliger | Spatial sensitivity of species habitat patterns to scenarios of land use change (Switzerland)[END_REF][START_REF] Droz | Moderately urbanized areas as a conservation opportunity for an endangered songbird[END_REF]. Collectively, these applications illustrate the increasing importance of SDMs for biodiversity conservation and hence for meeting the SDGs and ATs. However, numerous criticisms have been formulated in the literature about SDMs, in particular when they are used to assist biodiversity monitoring. Such criticisms originate primarily from the fact that both correlative and process-based SDMs rely on long-term, averaged, and interpolated spatial climate variables routinely used without accounting for their temporal variability (Zimmermann et al., 2009), nor for their error and uncertainty [START_REF] Stoklosa | A climate of uncertainty: accounting for error in climate variables for species distribution models[END_REF]. Moreover, correlative models are calibrated on statistical relationships that fail to capture the actual biological processes underlying biodiversity distribution and ecosystem functioning [START_REF] Dormann | Correlation and process in species distribution models: bridging a dichotomy[END_REF].

Finally, projections from both correlative and process-based SDMs are often based on calibration datasets with limited spatial and temporal windows, which restricts transferability of model projections [START_REF] Werkowska | A practical overview of transferability in species distribution modeling[END_REF][START_REF] Yates | Outstanding challenges in the transferability of ecological models[END_REF]. Although hybrid and processbased distribution models address flaws such as the causality between the response and the predictors as well as the spatiotemporal transferability, these models are data intensive (and thus limited to few species) and typically rely on climate interpolations.

The developments in Earth Observation (EO) data derived from remote sensing (RS) platforms provides opportunities for resolving some of the criticisms of SDMs. RS has long been applied to observe the state of and changes in natural systems as well as extremes affecting the land surface and its processes [START_REF] Mahecha | Detecting impacts of extreme events with ecological in situ monitoring networks[END_REF]. For example, a large variety of products from various sensors and with long time series are now available to assess the status of and changes in land use and cover (e.g. Landsat, MODIS, IKONOS or SPOT; [START_REF] Verburg | Challenges in using land use and land cover data for global change studies[END_REF], forest cover (e.g. AVHRR, Landsat; [START_REF] Hansen | A method for integrating MODIS and Landsat data for systematic monitoring of forest cover and change in the Congo Basin[END_REF][START_REF] Klein | Water availability predicts forest canopy height at the global scale[END_REF], vegetation structure (e.g ICEsat, Sentinel; LiDAR; [START_REF] Schneider | Simulating imaging spectrometer data: 3D forest modeling based on LiDAR and in situ data[END_REF], vegetation productivity and phenology (e.g. MODIS, Sentinel;[START_REF] De Jong | Shifts in global vegetation activity trends[END_REF][START_REF] Garonna | Shifting relative importance of climatic constraints on land surface phenology[END_REF][START_REF] Jolly | A generalized, bioclimatic index to predict foliar phenology in response to climate[END_REF], snow (e.g. AVHRR; [START_REF] Hüsler | A satellite-based snow cover climatology (1985-2011) for the European Alps derived from AVHRR data[END_REF][START_REF] Xie | Altitude-dependent influence of snow cover on alpine land surface phenology[END_REF]; temperature (e.g. Landsat, MODIS; [START_REF] Ibrahim | Estimating fractional cover of plant functional types in African savannah from harmonic analysis of MODIS timeseries data[END_REF], and precipitation (e.g. from TRMM; [START_REF] Naumann | Monitoring drought conditions and their uncertainties in Africa using TRMM data[END_REF]. RS data are also increasingly recommended for and applied to biodiversity monitoring and conservation (e.g. see [START_REF] Alleaume | A generic remote sensing approach to derive operational essential biodiversity variables (EBVs) for conservation planning[END_REF][START_REF] Lausch | Linking Earth Observation and taxonomic, structural and functional biodiversity: local to ecosystem perspectives[END_REF][START_REF] Rocchini | Satellite remote sensing to monitor species diversity: potential and pitfalls[END_REF][START_REF] Schneider | Mapping functional diversity from remotely sensed morphological and physiological forest traits[END_REF][START_REF] Schulte To Bühne | Better together: i ntegrating and fusing multispectral and radar satellite imagery to inform biodiversity monitoring, ecological research and conservation science[END_REF][START_REF] Vihervaara | How Essential Biodiversity Variables and remote sensing can help national biodiversity monitoring[END_REF]. In that context, they are used notably in the definition and monitoring of EBVs (Remotely Sensed or RS-enabled EBVs (e.g. see [START_REF] Alleaume | A generic remote sensing approach to derive operational essential biodiversity variables (EBVs) for conservation planning[END_REF][START_REF] Pettorelli | Framing the concept of satellite remote sensing essential biodiversity variables: challenges and future directions[END_REF] and adoption of systematic observation requirements is steadily improving [START_REF] Navarro | Monitoring biodiversity change through effective global coordination[END_REF][START_REF] Pettorelli | Framing the concept of satellite remote sensing essential biodiversity variables: challenges and future directions[END_REF][START_REF] Skidomore | Agree on biodiversity metrics to track from space[END_REF]. Some forms of RS data have also been extensively incorporated in SDMs as abiotic and biotic predictor variables and occasionally as response variable (see [START_REF] He | Will remote sensing shape the next generation of species distribution models ? Remote Sens[END_REF] for a comprehensive review). However, RS and species distribution modeling are still quite distinct fields that have not typically overlapped extensively, resulting in a lack of awareness of potential opportunities. Accordingly, we argue that RS-derived data are not yet used to their full potential and can make an important contribution when building SDMs for biodiversity monitoring and policy.

In this contribution, we1 first discuss if current RS-derived data suffice to understand and project species distributions in the Anthropocene. The discussion then focuses on where joint ventures between the ecological modeling and RS community are needed to fill data and conceptual gaps and develop remotely sensed data products that can effectively contribute to the monitoring and modeling of ecological systems and ultimately to guiding and informing conservation and management strategies towards sustainability. By structuring the main body of the text around key biophysical dimensions and processes underlying the distribution of species in the Anthropocene -as opposed to organizing it around existing RS technologies -this contribution uniquely complements exiting contributions on the potential of RS in biodiversity monitoring and conservation (e.g. [START_REF] He | Will remote sensing shape the next generation of species distribution models ? Remote Sens[END_REF]. Our review is aimed at species distribution modelers and RS specialists who jointly want to better inform and guide monitoring and conservation actions at different spatial and temporal scales.

Modeling species distribution using RS data: state-of-the-art

From a purely ecological point of view, key biophysical dimensions and processes for modeling the distribution of species are (1) climate and its variability from the global to the regional scale, (2) topo-and microclimate from the regional to the local scale, (3) water, food resources and nutrient availability, and (4) non-anthropogenic physical disturbance processes modulating distribution at various scales. From a social-ecological point of view, critical variables in species distributions are (5) changes in land cover and land use (Fig. 1).

Climate and its variability

Climate has been consistently identified as the main determinant of species ranges at large scale [START_REF] Woodward | The impact of low temperatures in controlling the geographical distribution of plants[END_REF], whereas non-climate predictors (such as topography and habitat) are more important at smaller scales (e.g. [START_REF] Luoto | The role of land cover in bioclimatic models depends on spatial resolution[END_REF][START_REF] Normand | Importance of abiotic stress as a range-limit determinant for European plants: insights from species responses to climatic gradients[END_REF]. It is therefore common to build large-scale and coarse-resolution SDMs to characterize species geographic extents and spatial patterns of occurrence using only climate predictors (see e.g. [START_REF] Mod | What we use is not what we know: environmental predictors in plant distribution models[END_REF] for plants; [START_REF] Thuiller | Climate change threats to plant diversity in Europe[END_REF]. This approach has often been referred to as bioclimatic envelope modeling and climate predictor variables have been defined as direct or regulator predictors [START_REF] Austin | Improving species distribution models for climate change studies: variable selection and scale[END_REF][START_REF] Guisan | Predictive habitat distribution models in ecology[END_REF]. From a conservation perspective, bioclimatic envelop modeling is common in SDMs that are intended to inform largescale spatial planning and is also used to understand species distributions under future climates (e.g. for protected site designation or evaluation of alternative climate scenarios). However, spatial climate variables in SMDs are routinely used without accounting for their measurement errors and uncertainty, which can lead to biased estimates, erroneous inference, and poor performances when predicting to new environmental conditions [START_REF] Stoklosa | A climate of uncertainty: accounting for error in climate variables for species distribution models[END_REF]. In addition, temperature and precipitation interpolations from weather stations (e.g. Worldclim) capture neither temperature-related processes, such as inversion, air stagnation [START_REF] Vitasse | Intensity, frequency and spatial configuration of winter temperature inversions in the closed La Brevine valley, Switzerland[END_REF] or cold air pooling (e.g. [START_REF] Patsiou | The contribution of cold air pooling to the distribution of a rare and endemic plant of the Alps[END_REF], nor precipitation-related processes, such as orographic effects (but see CHELSA Karger et al., 2017). Such physical processes can be however captured by RS products such as Landsat8 or MODIS for temperature or TRMM and GPM for precipitation (e.g. at the scale of the Andes; [START_REF] Bookhagen | Orographic barriers, high-resolution TRMM rainfall, and relief variations along the eastern Andes[END_REF]. These products offer direct observations and have been successfully integrated in SDM studies [START_REF] Cord | Remote sensing time series for modeling invasive species distribution: a case study of Tamarix spp. in the US and Mexico[END_REF][START_REF] Estrada-Peña | Perspectives on modelling the distribution of ticks for large areas: so far so good?[END_REF][START_REF] Neteler | Is Switzerland suitable for the invasion of Aedes albopictus?[END_REF]. Alongside the development of improved RS products, considerable advances have also been achieved in terms of the algorithms needed to process RS data. For instance, algorithms for deriving land surface temperature are now sufficiently advanced that a typical accuracy of 1 deg K is possible. The spatial resolution is 120 m for Landsat 5 TIR and 100 m for Landsat 8, and data are resampled at 30 m by the US Geological Survey (USGS) agency. Such high spatial resolution data can ultimately be used to detect microclimatic features such as urban heat islands [START_REF] Liu | Urban heat island analysis using the Landsat TM data and ASTER data: a case study in Hong Kong[END_REF], which are key components for the persistence or extinction of plants and animals.

Although temperature obtained from RS derived datasets (e.g. MODIS) can substantially improve projections of SDMs, the gain from precipitation data derived from sensors still relies on the native resolution of the data and the addition of ground observations (e.g. TRMM at a 0.05° native resolution versus CHIRPS at a 0.25° resolution and calibrated with 45'707 weather stations worldwide). This prevents the capture of small-scale processes (e.g. orographic processes) that influence species distribution [START_REF] Deblauwe | Remotely sensed temperature and precipitation data improve species distribution modelling in the tropics[END_REF].

Climate also enters SDM-based studies of species distribution in the form of long-term averaged variables used to define range limits. However, such averages overlook climate extremes of increasing frequencies, whose influence on range limits remains to be fully understood [START_REF] Ummenhofer | Extreme weather and climate events with ecological relevance : a review[END_REF]. Accordingly, Kollas and colleagues (2014) called for the use of temperature extremes during key phenological stages of focal species when attempting to explain range limits. Zimmermann and co-authors (2009) in turn showed that the primary effect of including information on climate variability and extremes is to correct local SDMs for overand underprediction. Such results speak in favor of the incorporation of targeted absolute climate values instead of long-term means that are only proxies of unknown relevance for the physiologically critical facets of climate that control species abundance and distribution. They also have important implications for projections of climate change impacts on species distributions that are based on correlative approaches only. Relevant data for deriving extremes are spectral time series. With such series approaching 20 years of records at a daily time steps, it is now for instance becoming possible to use surface temperature from RS to derive extreme climatic events. The Global Climate Observing System was specifically set up under the auspices of United Nations organizations and the International Council for Science to ensure the availability of so-called essential climate variables (ECVs, GCOS 2010), which are systematic and long-term observations of climate. An ECV is a physical, chemical, or biological variable or a group of linked variables that critically contributes to the characterization of the Earth's climate [START_REF] Bojinski | The concept of Essential Climate Variables in support of climate research, applications, and policy[END_REF]. Specific ECVs of interests for the SDMs community include parameters of temperature, precipitation, wind speed and direction, snow cover, glaciers and ice caps, permafrost, albedo, land cover (including vegetation type), fraction of absorbed photosynthetically active radiation (FAPAR), Leaf area index (LAI), above-ground biomass, soil carbon, fire disturbance and soil moisture. For the latter, a global ECV surface soil moisture data set has for instance been generated within the European Space Agence (ESA) Climate Change Initiative. This soil moisture dataset covers a 38-year period from 1978 to 2016 at a daily time step and at a 0.25° spatial resolution. Snow cover, high-resolution landcover, surface temperature and permaforst are other ECV currently elaborated by ESA (http://cci.esa.int/). Similar initiatives have also been developed at smaller scale. The Sentinel Alpine Observatory (SAO) of Eurac Research (http://sao.eurac.edu/sao/) and the satellite-based snow cover climatology [START_REF] Hüsler | A satellite-based snow cover climatology (1985-2011) for the European Alps derived from AVHRR data[END_REF] are two examples for the European Alps. Yet, although the temporal resolution might be appealing for the SDM community, typical spatial resolutions of 0.25° (at best 500 m) do not match the requirements for safe calibration and projections of SDMs for many organisms, and some form of downscaling would be further required.

Topography

Both model fit and predictive power of SDMs are often improved by incorporating non-climatic predictors at both small [START_REF] Pradervand | Very high resolution environmental predictors in species distribution models[END_REF] and large scale (e.g. [START_REF] Luoto | Disregarding topographical heterogeneity biases species turnover assessments based on bioclimatic models[END_REF], or by enhancing the climatic predictors to include finer-scale processes (; e.g. topoclimate [START_REF] Daly | Guidelines for assessing the suitability of spatial climate data sets[END_REF][START_REF] Karger | Climatologies at high resolution for the earth's land surface areas[END_REF]. One important non-climate predictor is the topography that locally controls biota, habitat structure, and growing conditions (albeit mostly indirectly [START_REF] Austin | Improving species distribution models for climate change studies: variable selection and scale[END_REF]). It does so primarily by affecting local climate (<1 km 2 ) through elevation (adiabatic lapse rate), exposure (to solar radiation and wind), and cold air pooling [START_REF] Böhner | Land-surface parameters specific to topo-climatology[END_REF], but also through its effect on soil development, causing spatial variability in soil depth and nutrient as well as water availability [START_REF] Fisk | Topographic pattersn of above-and belowground production and nitrogen cycling in alpine tundra[END_REF].

Topography-related indirect variables (sensu [START_REF] Guisan | Predictive habitat distribution models in ecology[END_REF], such as slope or topographic position, or more direct variables such as potential solar radiation are broadly used in SDMs and evolutionary ecology [START_REF] Kozak | Integrating GIS-based environmental data into evolutionary biology[END_REF][START_REF] Leempoel | Very high-resolution digital elevation models: are multi-scale derived variables ecologically relevant?[END_REF]. Topographic wetness index (TWI) is also a commonly used proxy for soil moisture (see e.g. le Roux et al., 2013a). Including these variables improves SDMs, but interpreting the actual drivers of species distributions related to these variables can be difficult. Topographic data are indeed only surrogates for direct environmental controls of occurrence and abundance and the effects of topographic variables on plant distributions are therefore distal (sensu [START_REF] Austin | Spatial prediction of species distribution: an interface between ecological theory and statistical modelling[END_REF][START_REF] Austin | Species distribution models and ecological theory: a critical assessment and some possible new approaches[END_REF][START_REF] Mod | What we use is not what we know: environmental predictors in plant distribution models[END_REF][START_REF] Moeslund | Topographically controlled soil moisture is the primary driver of local vegetation patterns across a lowland region[END_REF]. Improvements are also scale-dependent as topographic variables that make sense over a small geographic area can become problematic at larger scale if they are not linearly related to the environmental factors for which they serve as proxies.

Regardless of scale, the problem with using indirect (i.e. distal) predictors of topography or other environmental variables is that the identified relationships are inherently non-causal and therefore this dramatically reduces model transferability in space and time.

One solution to this problem is to utilize more direct and causal (i.e. proximal) predictors or resource variables [START_REF] Austin | Spatial prediction of species distribution: an interface between ecological theory and statistical modelling[END_REF]; sensu [START_REF] Guisan | Predictive habitat distribution models in ecology[END_REF]. For example, SDMs can be calibrated with soil properties including nutrient status or soil acidity [START_REF] Bertrand | Disregarding the edaphic dimension in species distribution models leads to the omission of crucial spatial information under climate change: the case of Quercus pubescens in France[END_REF][START_REF] Buri | Soil factors improve predictions of plant species distribution in a mountain environment[END_REF][START_REF] Coudun | Soil nutritional factors improve models of plant species distribution: an illustration with Acer campestre (L.) in France[END_REF][START_REF] Dubuis | Improving the prediction of plant species distribution and community composition by adding edaphic to topo-climatic variables[END_REF][START_REF] Vries | Use of dynamic soilvegetation models to assess impacts of nitrogen deposition on plant species composition: an overview[END_REF], as well as fine resolution climatic predictors based on topography and RS derived estimates of vegetation cover [START_REF] Ashcroft | Fine-resolution (25 m) topoclimatic grids of near-surface (5 cm) extreme temperatures and humidities across various habitats in a large (200 × 300 km) and diverse region[END_REF]. Digital elevation models (DEMs), in turn, can be used to directly estimate cold-air drainage, which can lead to improved predictions of species distributions over indirect estimates of topography [START_REF] Ashcroft | Testing the ability of topoclimatic grids of extreme temperatures to explain the distribution of the endangered brush-tailed rock-wallaby (Petrogale penicillata)[END_REF][START_REF] Patsiou | The contribution of cold air pooling to the distribution of a rare and endemic plant of the Alps[END_REF].

RS offers another solution. Relevant accurate high-resolution terrain data [START_REF] Jaboyedoff | Use of LIDAR in landslide investigations: a review[END_REF][START_REF] Leempoel | Very high-resolution digital elevation models: are multi-scale derived variables ecologically relevant?[END_REF] are increasingly obtained using Light Detection and Ranging (LiDAR) technology (e.g. [START_REF] Mathys | Spatial pattern of forest resources in a multifunctional landscape[END_REF][START_REF] Sørensen | Effects of DEM resolution on the calculation of topographical indices: TWI and its components[END_REF][START_REF] Vierling | Lidar: shedding new light on habitat characterization and modeling[END_REF].

The benefits of LiDAR are specifically related to its capacity to detect minor terrain features, such as hill tops, ridges, small depressions, and minor hydrological [START_REF] Engstrom | Spatial distribution of near surface soil moisture and its relationship to microtopography in the Alaskan Arctic coastal plain[END_REF][START_REF] Kammer | The relationship between soil water storage capacity and plant species diversity in high alpine vegetation[END_REF][START_REF] Kemppinen | Modelling soil moisture in a highlatitude landscape using LiDAR and soil data[END_REF], which are expected to play an important role in determining species distribution [START_REF] Graf | Habitat assessment for forest dwelling species using LiDAR remote sensing: Capercaillie in the Alps[END_REF][START_REF] Pradervand | Very high resolution environmental predictors in species distribution models[END_REF]. Moreover, high point return densities (1-10 points/m) and relative ease of data collection across large areas makes it a popular option for measuring bare earth elevation and vegetation height [START_REF] Hancock | Measurement of fine-spatialresolution 3D vegetation structure with airborne waveform lidar: calibration and validation with voxelised terrestrial lidar[END_REF]. However, the accuracy of LiDAR-derived digital elevation models can vary significantly across topographic and land cover gradients [START_REF] Leitold | Airborne lidar-based estimates of tropical forest structure in complex terrain: opportunities and trade-offs for REDD+[END_REF]. For instance, it is common to achieve high elevation accuracies (<0.15 m root mean square error) in areas with low vegetation cover and relatively flat terrain, [START_REF] Montané | Accuracy assessment of Lidar saltmarsh topographic data using RTK GPS[END_REF][START_REF] Spaete | Vegetation and slope effects on accuracy of a LiDAR-derived DEM in the sagebrush steppe[END_REF], but DEM elevation errors tend to increase in areas covered by dense vegetation. Further work is required to determine how these errors in elevation are propagated to the direct predictors that are desirable in SDMs (cold air drainage, vegetation structure, exposure to winds and radiation, microclimate), and to the SDM itself.

Unlike high-resolution topographic information, the availability of spatial layers of soil conditions is still limited [START_REF] Fang | An inter-comparison of soil moisture data products from satellite remote sensing and a land surface model[END_REF]). Yet multispectral and hyperspectral satellites have a high potential for mapping topsoil carbon [START_REF] Peón | Prediction of topsoil organic carbon using airborne and satellite hyperspectral imagery[END_REF] and organic matter content as well as soil physical properties [START_REF] Rosero-Vlasova | Modeling soil organic matter and texture from satellite data in areas affected by wildfires and cropland abandonment in Aragón, Northern Spain[END_REF]. These novel possibilities should be tested in SDMs in the future.

Non-anthropogenic physical disturbances

Indirect topographic and soil variables have been used successfully as surrogates for Earth surface processes (ESPs) in plant SDMs [START_REF] Dirnböck | A regional impact assessment of climate and land-use change on alpine vegetation[END_REF][START_REF] Mellert | Hypothesis-driven species distribution models for tree species in the Bavarian Alps[END_REF]Randin et al., 2009a). Recent studies demonstrate that the incorporation of direct ESP variables improve explanatory and predictive power of SDMs (le Roux et al., 2013b;[START_REF] Le Roux | Earth surface processes drive the richness, composition and occurrence of plant species in an arctic-alpine environment[END_REF][START_REF] Niittynen | The importance of snow in species distribution models of arctic vegetation[END_REF]. Based on the relative importance of ESP variables, these processes are often as important as topography and soil variables in explaining plant species distributions (le Roux and Luoto, 2014), highlighting the significance of geomorphic data in a SDM framework [START_REF] Mod | What we use is not what we know: environmental predictors in plant distribution models[END_REF]. ESPs create a wide range of disturbance regimes across landscapes [START_REF] Aalto | Statistical modelling predicts almost complete loss of major periglacial processes in Northern Europe by 2100[END_REF][START_REF] Gooseff | Snow-patch influence on soil biogeochemical processes and invertebrate distribution in the McMurdo Dry Valleys, Antarctica[END_REF][START_REF] Niittynen | The importance of snow in species distribution models of arctic vegetation[END_REF]. These dynamic processes may significantly alter local soil stability, moisture conditions, and nutrient availability [START_REF] Kozłowska | Vegetation as a tool in the characterisation of geomorphological forms and processes: an example from the abisko mountains[END_REF], making ESPs potentially key drivers of local habitat heterogeneity [START_REF] Cannone | Vascular plant changes in extreme environments: effects of multiple drivers[END_REF], variation in ecosystem functioning [START_REF] Frost | Patterned-ground facilitates shrub expansion in Low Arctic tundra[END_REF], and species assemblages (le Roux et al., 2013b;[START_REF] Malanson | Geomorphic determinants of species composition of alpine tundra, Glacier National Park[END_REF]. Due to ongoing land use and climate change, physical disturbance regimes are predicted to change rapidly as many geomorphic processes have a significant climate response [START_REF] Knight | The impacts of climate change on terrestrial Earth surface systems[END_REF], with small changes in climate forcing triggering large changes in ESPs [START_REF] Aalto | Statistical modelling predicts almost complete loss of major periglacial processes in Northern Europe by 2100[END_REF].

The type and necessity of including disturbance variables in models are highly environmentspecific. For decades, RS data have been used for the mapping of geomorphological landforms and processes [START_REF] Walsh | An overview of scale, pattern, process relationships in geomorphology: a remote sensing and GIS perspective[END_REF]. Regarding extensive studies like the detection of landforms, visual interpretation of aerial photos has proved to be very effective, and its advantages for fieldsurveying are well documented (Hjort andLuoto, 2012, 2006;Siart et al., 2009 and references therein). The high spatial resolution of airborne photographs provides a valuable data source, particularly for detecting smaller landforms (e.g. 1-10m). Yet, the precision (1.24 -10 m) and increasing temporal resolution (revisit time of 1-5 days) of satellite data, such as WorldView 3 (http://worldview3.digitalglobe.com) and 4, the Planetscope satellite constellation (https://www.planet.com), or open access ESA Sentinel-2 (https://sentinel.esa.int), can now compete with that of aerial photography. High-resolution satellite imagery is thereby becoming a valuable data source for the modeling of dynamic processes. To our knowledge, no attempts have been made to include RS-based geomorphological variables into SDMs and very few examples are found for other non-anthropogenic physical disturbances used in SDMs (e.g. [START_REF] Madani | Remote sensing derived fire frequency, soil moisture and ecosystem productivity rxplain regional movements in emu over Australia[END_REF].

Land use and land cover

The availability of spatially and temporally highly resolved land cover information is central to many monitoring programs and land cover mapping is probably the oldest application of EO, starting with aerial photographs in the 1930's [START_REF] Fuller | The Land Cover Map of Great Britain: An Automated Classification of Landsat Thematic Mapper Data[END_REF]. Land system change assessments range from local to regional and global [START_REF] Stürck | Multifunctionality at what scale? A landscape multifunctionality assessment for the European Union under conditions of land use change[END_REF][START_REF] Van Asselen | Land cover change or land-use intensification: simulating land system change with a global-scale land change model[END_REF] and from historical [START_REF] Bolliger | Reconstructing forestcover change in the Swiss Alps between 1880 and 2010 using ensemble modelling[END_REF][START_REF] Kaim | Broad scale forest cover reconstruction from historical topographic maps[END_REF][START_REF] Loran | Long-term change in drivers of forest cover expansion: an analysis for Switzerland (1850-2000)[END_REF] to scenario-based future assessments on potential changes of land use [START_REF] Martinuzzi | Scenarios of future land use change around United States' protected areas[END_REF][START_REF] Pazúr | Land changes in Slovakia: Past processes and future directions[END_REF][START_REF] Price | Future landscapes of Switzerland: risk areas for urbanisation and land abandonment[END_REF].

Changes in land cover and land use affect biodiversity in different ways. In the case of urbanization, there is mostly a complete replacement of (semi-)natural open land with buildings or other impervious infrastructure, such as roads, that profoundly affect species distributions (Lembrechts et al., 2017). However, impacts on species distributions or abundances can also be triggered by more subtle modifications of land management, such as changes in forest management practices or in the intensity of agricultural management (Randin et al., 2009b).

Whereas land cover changes are relatively easily interpreted from RS data, the use of RS data to characterize more subtle changes in land management is more challenging. Yet progress has been made over recent years using spatio-temporal patterns of change in the earth system reflections as indicators of changes in management and land use intensity [START_REF] Eckert | Agriculturaliexpansion and intensification in the foothills of Mount Kenya: a landscape perspective[END_REF][START_REF] Franke | Assessment of grassland use intensity by remote sensing to support conservation schemes[END_REF][START_REF] Gómez Giménez | Determination of grassland use intensity based on multi-temporal remote sensing data and ecological indicators[END_REF][START_REF] Jakimow | Mapping pasture management in the Brazilian Amazon from dense Landsat time series[END_REF][START_REF] Rufin | Land use intensity trajectories on Amazonian pastures derived from Landsat time series[END_REF].

Examples include the identification of grassland mowing frequencies through the identification of typical variations in greenness during the growing season [START_REF] Kolecka | Regional scale mapping of grassland mowing frequency with Sentinel-2 time series[END_REF], observed agricultural intensification in Kenya through the successful long-term monitoring of rainfed and irrigated agriculture using monthly satellite data composites [START_REF] Eckert | Agriculturaliexpansion and intensification in the foothills of Mount Kenya: a landscape perspective[END_REF], or the occurrence of plantation forests based on high resolution spatial patterns [START_REF] Fagan | Mapping pine plantations in the southeastern U.S. using structural, spectral, and temporal remote sensing data[END_REF].

Until recently, small or heterogeneous areas important to landscape structure and land use management were not distinguishable due to low spatial and temporal resolution. These Land use descriptions have traditionally relied on two-dimensional (2D) representations of the environment. Yet, evidence that 3D vegetation structure not only allows for more continuous landscape representations, but is also a crucial determinant of species habitat [START_REF] Fawcett | Advancing retrievals of surface reflectance and vegetation indices over forest ecosystems by combining imaging spectroscopy, digital object models, and 3D canopy modelling[END_REF][START_REF] Gastón | Species' habitat use inferred from environmental variables at multiple scales: How much we gain from high-resolution vegetation data?[END_REF][START_REF] Huber | Using remote-sensing data to assess habitat selection of a declining passerine at two spatial scales[END_REF][START_REF] Milanesi | Three-dimensional habitat structure and landscape genetics: a step forward in estimating functional connectivity[END_REF][START_REF] Torabzadeh | Fusion of imaging spectroscopy and airborne laser scanning data for characterization of forest ecosystems -A review[END_REF][START_REF] Zellweger | Environmental predictors of species richness in forest landscapes: abiotic factors versus vegetation structure[END_REF] and functional connectivity [START_REF] Marrotte | Multi-species genetic connectivity in a terrestrial habitat network[END_REF][START_REF] Milanesi | Three-dimensional habitat structure and landscape genetics: a step forward in estimating functional connectivity[END_REF] stresses the need for more detailed landscape-content information, and for 3D land use structure to supplement habitat assessments. Accordingly, the past decade has seen developments towards 3D land use structure. These structures are captured using digital aerial stereo-photographs [START_REF] Ginzler | Countrywide stereo-image matching for updating digital surface models in the framework of the Swiss national forest inventory[END_REF] or active remote sensors e.g., LiDAR [START_REF] Bergen | Remote sensing of vegetation 3-D structure for biodiversity and habitat: review and implications for lidar and radar spaceborne missions[END_REF][START_REF] Merrick | Circuit theory to estimate natal dispersal routes and functional landscape connectivity for an endangered small mammal[END_REF].

While moving from simple land cover representations to more species-relevant representations of land use requires advances in RS and the integration with other data, there is also a need to adapt SDMs to properly account for such novel landscape representations. In many cases, such scenarios still only account for climate change impacts, ignoring scenarios of land use changes [START_REF] Titeux | Biodiversity scenarios neglect future land-use changes[END_REF]. Increasing the detail in landscape characterization not only requires SDMs to be capable of addressing the represented diversity, it also requires understanding of the temporal dynamics and climate responses of land use at a higher level of detail. To avoid overwhelming and, sometimes unnecessary, complexity, the sensitivity of the SDMs to the refined detail should be continuously tested and simplifications made as part of the modeling process.

Besides land use composition, land use heterogeneity can in some cases represent a good proxy for those species requiring heterogeneous environments, with e.g. corridors and landscape borders, to survive. In this view, heterogeneity of land use or of satellite reflectance data has been widely assessed in the past, relying on several algorithms like: (1) multivariate statistical analysis [START_REF] Feilhauer | Mapping continuous fields of forest alpha and beta diversity[END_REF], ( 2) the spectral species concept [START_REF] Féret | Mapping tropical forest canopy diversity using high-fidelity imaging spectroscopy[END_REF], (3) self-organizing feature maps [START_REF] Foody | Applications of the self-organising feature map neural network in community data analysis[END_REF], (4) multidimensional distance metrics [START_REF] Rocchini | Satellite remote sensing to monitor species diversity: potential and pitfalls[END_REF], and (5) Rao's Q diversity [START_REF] Rocchini | Measuring Rao's Q diversity index from remote sensing: a n open source solution[END_REF], with each of these measures addressing one or several issues related to heterogeneity measurements.

Modeling species distribution using RS data: closing gaps and moving forward

We are coming to an era of cost-efficient mass processing of high-resolution RS data products over extensive geographical areas and long period of time [START_REF] Hansen | High-resolution global maps of 21st-century forest cover change[END_REF]. Here we propose joint ventures between the ecological modeling and RS community to fill remaining data and conceptual gaps and allow ecologists to benefit from the full potential of EO data.

Time series and temporal stacking

Most SDM studies that have included RS products so far have used static and temporally aggregated RS-derived layers as predictors (e.g. surface temperature, water availability, topography, land cover and 3D structure, Fig. 1). Fewer attempts have been made to take advantage of the existing time series data and the dynamic information contained in RS products, despite the pivotal role such temporally explicit data play. For instance, long-term time series of RS data are key to test the temporal transferability of SDMs [START_REF] Yates | Outstanding challenges in the transferability of ecological models[END_REF]; Fig. 1b), a basic requirement to formally guide and inform monitoring strategies in changing environments and make sure that model projections follow the observed trajectories of species.

Likewise, long-term observations of response variables, such as occurrences or abundances of focal organisms, are essential to understand and project the impact of global change with SDMs. However to our knowledge, only a few SDMs studies have so far integrated occurrence data derived from RS technology (Fig. 1c; e.g. [START_REF] Andrew | Habitat suitability modelling of an invasive plant with advanced remote sensing data[END_REF][START_REF] Bradley | Characterizing the landscape dynamics of an invasive plant and risk of invasion using remote sensing[END_REF], even though cost-effective tools for collecting them are already available (e.g. [START_REF] Kellenberger | Detecting mammals in UAV images: Best practices to address a substantially imbalanced dataset with deep learning[END_REF]. Finally, long-term time series are also critical for estimating lag times.

The Anthropocene is an era of rapid environmental changes. Under such conditions, lag times in cause-effect chains may severely confound the identification of species-environment relations via correlated distribution patterns. Rapid climate change, for example, is expected to cause a severe disequilibrium between climate and species distribution due to both slow colonization of areas that become newly suitable and delayed extinction from those sites that are no longer suitable to the species (i.e., extinction debts; [START_REF] Dullinger | Extinction debt of high-mountain plants under twenty-first-century climate change[END_REF][START_REF] Svenning | Disequilibrium vegetation dynamics under future climate change[END_REF][START_REF] Talluto | Extinction debt and colonization credit delay range shifts of eastern North American trees[END_REF]. Land use changes may have similar effects and many studies have demonstrated that in landscapes undergoing changes in human usage, spatial biodiversity patterns often represent habitat configurations of decades back rather than current ones [START_REF] Auffret | Super-regional land-use change and effects on the grassland specialist flora[END_REF][START_REF] Krauss | Habitat fragmentation causes immediate and time-delayed biodiversity loss at different trophic levels[END_REF]. Matching current species distributions and environmental conditions in statistical models will hence result in flawed correlation and, as a corollary, inappropriate prediction of future development. RS products offer a way forward here, because time series of many of these products now cover two decades, and several of them up to five [START_REF] He | Will remote sensing shape the next generation of species distribution models ? Remote Sens[END_REF]. These time series have great potential in detecting and quantifying lag times, e.g. in the response of biological populations to land cover conversions [START_REF] Wearn | Extinction debt and windows of conservation opportunity in the Brazilian Amazon[END_REF].

Incorporating these lag times into models of species responses to past, current, and future environmental change has important ramifications for the management of biodiversity because it defines 'windows of opportunity' for mitigating the anticipated consequences [START_REF] Kuussaari | Extinction debt: a challenge for biodiversity conservation[END_REF][START_REF] Wiens | Herbivory increases diversification across insect clades[END_REF].

One reason for the limited transferability of purely correlative models is the generally coarse or inadequate spatial and temporal resolution of the data used to calibrate models [START_REF] Connor | Effects of grain size and niche breadth on species distribution modeling[END_REF][START_REF] Manzoor | Species distribution model transferability and model grain sizefiner may not always be better[END_REF][START_REF] Potter | Microclimatic challenges in global change biology[END_REF]. This spatial-resolution paradox [START_REF] Lenoir | Climatic microrefugia under anthropogenic climate change: implications for species redistribution[END_REF] is inherent to correlative models and stems from the spatial mismatch between the resolution at which the predictor variables (e.g. topoclimatic and biophysical variables) are available, the resolution that matches the response variables (e.g., species occurrence, presence-absence, abundance or trait data; [START_REF] Guisan | Predicting species distribution: offering more than simple habitat models[END_REF], and the size of the studied organism [START_REF] Potter | Microclimatic challenges in global change biology[END_REF].

Here, we argue that RS could be used to better calibrate SDMs, by integrating spatially and temporally (through multiple years) more proximal environmental data to derive more comprehensive quantifications of the species' realized responses (and corresponding niches).

An improved calibration processes may in turn increase the spatial and temporal transferability of both correlative and process-based models. This can be illustrated using species response curves along environmental gradients (see [START_REF] Austin | Current problems of environmental gradients and species response curves in relation to continuum theory[END_REF]. Species response curves such as temperature response curves (sensu [START_REF] Austin | Spatial prediction of species distribution: an interface between ecological theory and statistical modelling[END_REF]Fig. 2a) and thermal performance curves (sensu [START_REF] Schulte | Thermal performance curves, phenotypic plasticity, and the time scales of temperature exposure[END_REF]Fig. 2b) are at the foundation of both correlative [START_REF] Guisan | Predictive habitat distribution models in ecology[END_REF] and certain types of process-based [START_REF] Kearney | Mechanistic niche modelling: combining physiological and spatial data to predict species' ranges[END_REF]) models, respectively. Temperature response curves generated by SDMs are usually parameterized by relating field observations to spatial layers of temperature.

Temperature performance curves used in process-based models on the other hand are best parameterized from experimental data depicting metabolic requirements. Because they explicitly rely on a physiological basis, temperature performance curves are expected to better identify species thermal tolerance limits that set range boundaries and to be thus more robust when extrapolating species redistributions under future climate change [START_REF] Eckert | Agriculturaliexpansion and intensification in the foothills of Mount Kenya: a landscape perspective[END_REF]. However, physiologically-based species performance curves are not as time-and cost-efficient as statistically-based species response curves. For some species, these quantifications might get closer to their fundamental responses (response niche; [START_REF] Maiorano | Building the niche through time: using 13,000 years of data to predict the effects of climate change on three tree species in Europe[END_REF]e.g. for dominant late-successional species; [START_REF] Pearman | Prediction of plant species distributions across six millennia[END_REF] obtained from experiments (Fig. 2c), and thereby be potentially more transferable [START_REF] Maiorano | Building the niche through time: using 13,000 years of data to predict the effects of climate change on three tree species in Europe[END_REF]. Similarly, the combination of experimental and RS data to derive thermal performance curves could better capture the geographic variability caused by local adaptations (Fig. 2d). Temporal stacking of RS images allowed more observations of both response and predictor variables to be obtained. An example of such stacking is that done by [START_REF] George | Using LiDAR and remote microclimate loggers to downscale near-surface air temperatures for site-level studies[END_REF]. Temporal stacking of RS images (e.g. hyperspectral, thermal or radar images; Fig. 2) can be used to reduce the temporal mismatch between the collection of predictor and response variables. Hyperspectral images can be used to gather a large amount of occurrence, abundance, and trait data (e.g. [START_REF] Van Ewijk | Predicting fine-scale tree species abundance patterns using biotic variables derived from LiDAR and high spatial resolution imagery[END_REF].

Temporal stacking of RS data allows the production of large amounts of observation points for both response and predictor variables. This in turn allows the generation of more comprehensive representations of the realized response curves. Conversely, RS data can also be used to develop more accurate estimates of elevation, microclimate and other direct environmental predictors (see section "Topography"), which will improve estimates based on coarse-scale climate grids or indirect predictors alone. Indeed, the coarser the spatial resolution of the predicting variables, the broader the environmental tolerance of a given species' realized niche estimated from a correlative SDM [START_REF] Harwood | Microclimate is integral to the modeling of plant responses to macroclimate[END_REF].

Similarly, process-baed distribution models integrating e.g. phenology and frost resistance such as Phenofit [START_REF] Chuine | Phenology is a major determinant of tree species range[END_REF]) also strongly rely on experimental response curves (Fig. 2b). As a consequence, responses such as the completion of a phenological phase as a function of temperature are usually limited to a restricted set of plant species for which data are available. The same combination of temporally-stacked RS images could potentially allow to extend such models to more species and to take the variability due to e.g. local adaptation into account. Additionally, RS data may also help in considering data and processes that are not usually considered so far, even though they might be key drivers of species distributions (e.g. fine-scale drought or frost events for plant species), thereby improving the robustness of the predictions by better considering causal processes. We thus advocate for the use of RS products at a spatiotemporal resolution that matches the biology of the organisms under study as a means to better inform the shape of species response curves extracted from correlative models [START_REF] Lembrechts | Incorporating microclimate into species distribution models[END_REF] and get closer to the biological processes underlying biodiversity distribution and ecosystem functioning, even though one will never be able to estimate fundamental responses (i.e. niches) from such empirical data that can only inform on species realized niches [START_REF] Guisan | Habitat suitability and distribution models[END_REF]. However, it is important to note that the spatiotemporal accuracy of species' occurrence, presence-absence or abundance data collected from field observations need to be at least as high as the spatiotemporal resolution of the predictors used to fit the model to ensure robust model transferability [START_REF] Manzoor | Species distribution model transferability and model grain sizefiner may not always be better[END_REF]. Optimizing environmental and biological monitoring for better data availability is hence key for the usefulness of RS in SDMs [START_REF] Bush | Connecting Earth observation to high-throughput biodiversity data[END_REF]. A promising development is the European research infrastructure for Long-Term Ecological Research (eLTER http://www.lter-europe.net/elter-esfri), which is being rolled out during the coming years to provide the combined in situ data needed for future SDM improvements [START_REF] Haase | The next generation of site-based long-term ecological monitoring: linking essential biodiversity variables and ecosystem integrity[END_REF].

Direct detection and sampling of species

We are getting close to direct detection of plant species using full-range (400-2500 nm) spectroscopic data [START_REF] Féret | Mapping tropical forest canopy diversity using high-fidelity imaging spectroscopy[END_REF]. However, the spatial resolution of data collection remains critical. [START_REF] Ustin | Remote sensing of plant functional types[END_REF] have long articulated the capability of spectroscopy for capturing plant functional types. Functional traits [START_REF] Asner | Airborne spectranomics: mapping canopy chemical and taxonomic diversity in tropical forests[END_REF][START_REF] Singh | Imaging spectroscopy algorithms for mapping canopy foliar chemical and morphological traits and their uncertainties[END_REF] and changes in traits can now be mapped with high accuracy [START_REF] Jetz | Monitoring plant functional diversity from space[END_REF][START_REF] Schneider | Mapping functional diversity from remotely sensed morphological and physiological forest traits[END_REF]. Also direct species detection and linking spectra to the tree of life [START_REF] Cavender-Bares | Harnessing plant spectra to integrate the biodiversity sciences across biological and spatial scales[END_REF]) is imminently possible, requiring the combination of high spatial and high spectral resolution. Spectra from leaves [START_REF] Cavender-Bares | Associations of leaf spectra with genetic and phylogenetic variation in oaks: prospects for remote detection of biodiversity[END_REF][START_REF] Deacon | Genetic, morphological, and spectral characterization of relictual Niobrara River hybrid aspens ( Populus × smithii )[END_REF]) can be used with high accuracy to differentiate populations within a species and to separate hybrids from parental species. Partial Least Squares Regression methods applied to spectral profiles differentiate species with higher accuracy than genotypes and clades with higher accuracy than species [START_REF] Cavender-Bares | Associations of leaf spectra with genetic and phylogenetic variation in oaks: prospects for remote detection of biodiversity[END_REF]. In some cases with 1 m 2 spatial resolution RS allows differentiation of different genotypes of poplar clones [START_REF] Madritch | Imaging spectroscopy links aspen genotype with below-ground processes at landscape scales[END_REF]. Tree canopies are likely to be well distinguished if spectral libraries for the pool of species are available. In forests where species groups are well characterized and occur in clumps, species distributions can be fairly readily mapped using satellite derived data [START_REF] Chastain | Use of Landsat ETM and topographic data to characterize evergreen understory communities in appalachian deciduous forests[END_REF]. Many living resources exist that contain geolocated and botanically identified trees for developing spectral libraries for tree canopies.

Unmanned Aerial Vehicles (UAVs) or drones are mainly used to capture RVB images, to acquire thermal data or to produce VHR DEMs by means of stereophotogrammetry. But another function is the collection of animal occurrences to calibrate SDMs with presence only or presence/absence data. [START_REF] Van Gemert | Nature conservation drones for automatic localization and counting of animals[END_REF] evaluated how animal detection and animal counting could be implemented on the basis of a combination of images acquired by drones and state-of-the-art object recognition methods. Most of the time, such images are used to carry out surveys and to count animals in a management or conservation projects [START_REF] Hodgson | Drones count wildlife more accurately and precisely than humans[END_REF][START_REF] Koh | Dawn of drone ecology: low-cost autonomous aerial vehicles for conservation[END_REF]. But as all UAVs are equipped with a GPS device, the exact location of investigated individuals can be retrieved from precisely georeferenced image data. The main issue is related to the detection and recognition of the correct species by means of machine learning algorithms [START_REF] Kellenberger | Detecting mammals in UAV images: Best practices to address a substantially imbalanced dataset with deep learning[END_REF][START_REF] Ofli | Combining human computing and machine learning to make sense of big (aerial) data for disaster response[END_REF][START_REF] Rey | Detecting animals in African Savanna with UAVs and the crowds[END_REF]. But beyond this step, the generation of presence/absence of a single taxon is straightforward. This is a component included in the concept of Next Generation Species Distribution Models (NG-SDMs) proposed by [START_REF] He | Will remote sensing shape the next generation of species distribution models ? Remote Sens[END_REF].

Improving integrated and dynamic range models

Demographic processes and demographic data are increasingly integrated in models of the spatio-temporal dynamic of species' ranges (DRMs). This results from the realization that considering dynamic aspects is important and potentially markedly improves the quantification of ecological niches, the process-based understanding of range dynamics, and the forecasting of species responses to environmental change [START_REF] Pagel | Forecasting species ranges by statistical estimation of ecological niches and spatial population dynamics[END_REF]. This is because commonly-used static SDMs ignore spatial population dynamics, which can cause mismatches between species niches and their distributions [START_REF] Holt | Bringing the Hutchinsonian niche into the 21st century: ecological and evolutionary perspectives[END_REF][START_REF] Pellissier | Suitability, success and sinks: how do predictions of nesting distributions relate to fitness parameters in high arctic waders?[END_REF]. The data needed to parameterize DRMs can be obtained from demographic field measurements and small-scale experiments. However, small-scale environmental responses are not necessarily transferable to the spatial and temporal scales of DRMs. In this context, time series of multispectral, hyperspectral, and LIDAR images (Fig. 3a) can help to quantify changes in the environment of the focal and modeled species such as changes of suitable vegetation patches at the species level [START_REF] Strecha | Developing species specific vegetation maps using multi-spectral hyperspatial imagery from unmanned aerial vehicles[END_REF]Fig. 3b) or 3D structures such as buildings or tree canopy height (e.g. [START_REF] Droz | Moderately urbanized areas as a conservation opportunity for an endangered songbird[END_REF]Fig. 3b). Knowledge of suitable areas for-and population size of animals in large wildlife reserves is key for park rangers and managers in their efforts to protect endangered species [START_REF] Guisan | Predicting species distributions for conservation decisions[END_REF]. For instance, as the amount of snow was shown to affect a wader's (bird) species reproduction success in the arctic, quantifying snow patterning through RS would greatly improve a model-based management of this type of species [START_REF] Pellissier | Suitability, success and sinks: how do predictions of nesting distributions relate to fitness parameters in high arctic waders?[END_REF]. However, correlative SDMs rely on the assumptions that species location data used for modeling are representative of a species' true distribution and that observed species distributions are in equilibrium with environmental factors that limit those distributions. To properly support conservation practice, conservation biogeography should thus favor DRMs and metapopulation dynamics rather than correlative SDMs. However, the more detailed information needed for DRMs (e.g. manual animal censuses) are expensive and sometimes potentially dangerous to collect. Hence, unmanned aerial vehicles (UAVs) with consumer level digital cameras are becoming a popular alternative tool to estimate mammal populations (Fig. 3a; [START_REF] Kellenberger | Detecting mammals in UAV images: Best practices to address a substantially imbalanced dataset with deep learning[END_REF]. Furthermore, such data allow metapopulation dynamics and species migration to be modelled in order to understand the ability of a species to occupy suitable habitat in new locations. At the same time, movements of species can be linked to landscape disturbance and succession also obtained by RS and models of habitat suitability (Fig. 3b;[START_REF] Franklin | Moving beyond static species distribution models in support of conservation biogeography[END_REF].

Predicting belowground processes, disease and biotic interactions

Belowground processes, disease, and biotic interactions are best predicted with hyperspectral data. Carbon-based defense traits can be retrieved from spectral information [START_REF] Couture | Spectroscopic determination of ecologically relevant plant secondary metabolites[END_REF], facilitating integration of information on host-specific herbivores and pathogens with leaf chemical composition. Variation in biomass and leaf chemistry, including condensed tannins, lignin, and nitrogen should be linked to the chemistry of below-ground root exudation and to litter chemistry and litter abundance [START_REF] Cavender-Bares | Harnessing plant spectra to integrate the biodiversity sciences across biological and spatial scales[END_REF]. These inputs from aboveground vegetation to soil influence substrates available as food for soil organisms, the activity of enzymes secreted by soil microorganisms, and thus decomposition and nutrient cycling [START_REF] Madritch | Imaging spectroscopy links aspen genotype with below-ground processes at landscape scales[END_REF], which are all important for species distribution. Declines in hemlock (Tsuga canadensis) stands in the eastern United States due to invasion of the exotic woolly adelgid (Adelges tsugae) are detectable via airborne hyperspectral imagery [START_REF] Hanavan | A 10-year assessment of Hemlock decline in the Catskill mountain region of New York State using hyperspectral remote sensing techniques[END_REF]. Recent work on Xylella fastidiosa has shown that a combination of hyperspectral and thermal imagery can be used to diagnose plants that are visually asymptomatic [START_REF] Zarco-Tejada | Previsual symptoms of Xylella fastidiosa infection revealed in spectral plant-trait alterations[END_REF]. The spread of invasive submerged aquatic vegetation has been tracked at high spatial resolution along California coasts using airborne imaging spectroscopy [START_REF] Santos | Measuring landscapescale spread and persistence of an invaded submerged plant community from airborne remote sensing[END_REF]. As a consequence of the information content of hyperspectral data, there is high potential to leverage it for the description of biotic environments in SMDs.

Conclusions

In their reviews, [START_REF] He | Will remote sensing shape the next generation of species distribution models ? Remote Sens[END_REF] have already pointed out the importance of RS data for the development of new predictor variables and the next generation of SDMs, which will include spatially explicit values of uncertainty. Besides these key aspects, we argue here that the immediate value of RS data relies in their temporal coverage. Indeed, the spatial continuity of resource, regulator and (non-)anthropogenic predicting variables can be currently achieved (Fig. 1). However, the main weakness of these existing temporally-aggregated variables used to build SDMs relies on their inability to reflect the intensity or the frequency of biophysical processes.

Taking advantage of long-term time series of RS data to extract (absolute) extremes and frequencies to improve these variables and then models is a way to explore. However, formal tests have to be realized (e.g. as in [START_REF] Zimmermann | Climatic extremes improve predictions of spatial patterns of tree species[END_REF].

Temporal stacking of available time series can also be performed to increase the transferability of SDMs by better capturing the realized niche of species. In this context, it has already been demonstrated that building the niche as an ensemble trough time allows a better understanding and forecasting of species' range in changing environmental conditions [START_REF] Maiorano | Building the niche through time: using 13,000 years of data to predict the effects of climate change on three tree species in Europe[END_REF].

Importantly in this context, airborne or satellite sensors can deliver a large amount of observations pertaining to the response variable at a very high spatio-temporal resolution for both animal and plant organisms (e.g. drone multispectral images, LiDAR or Quickbird). As a consequence, temporal stacking further allows tracking populations dynamics and dispersal, which are both essential parameters to build hybrid and process-based models such as DRMs.

Such observations can then be easily transformed from occurrences to abundance since species occurrence and abundance do not frequently display similar patterns, and often are not even well correlated [START_REF] Mi | Combining occurrence and abundance distribution models for the conservation of the Great Bustard[END_REF]. Ultimately, gathering a large amount of data to build models should allow correlative SDMs to better estimate the true response curves along environmental gradients.

Over the last decade, several studies have questioned the ability of SDMs to predict the persistence of species when these models are projected into warming conditions. Indeed, some species may be able to escape the negative effects of climate warming by moving into or persisting in microrefugia with unusual and stable climates conditions [START_REF] Ashcroft | Moisture, thermal inertia, and the spatial distributions of nearsurface soil and air temperatures: understanding factors that promote microrefugia[END_REF]. In this context and when available, high spatial resolution RS images should thus be included into SDMs to better capture microclimatic conditions for soil humidity as well as surface and air temperature. However, important challenges remain in determining to which extent microclimate detected by RS can be scaled and coupled to climate change projections from broader scale earth system models. Indeed, models such as regional climate models (RCMs) provide values and anomalies of e.g. 2 m air temperature, precipitations and cloudiness and it is still unknown whether relationships between microclimate detected by RS and climate from e.g.

RCMs can be described statistically and later projected into a future changing climate.

Land cover has been identified as one of the thirteen terrestrial ECVs because of its influences on climate through the modification of water and energy exchanges with the atmosphere. Land use and land-use change, being assessed from the local to the global scale, are typically more difficult to map and in many cases cannot be remotely sensed. As a consequence, spatiallyexplicit data of land use are less available. For these reasons, land use or its changes and variability have too often been neglected when building SDMs, despite their potentially critical importance for species distribution. Despite recent progress to develop indicators of changes in management and land use intensity obtained from RS, online platforms with spatially explicit data of land use are needed. This is particularly critical to identify the contribution of land use in SDMs that are applied as explanatory tools or to improve the accuracy of projections in SDMs that are applied as predictive tools when modeling approaches are integrated in monitoring programs.

Global change impacts, whether driven by climate, land cover and land use changes or invasions, are and will change the strengths of biotic interactions within ecosystems. Assessing such changes are key to better project the distribution of individual species and to better predict the composition of novel communities and ecosystems.

Monitoring, modeling and RS: an inseparable triptych?

We stress that models should serve the same fundamental role in ecological monitoring as in any other scientific activity; that is, both for a priori guiding monitoring designs, and a posteriori guiding data analyses. Essential elements of the monitoring design are management actions, replicated spatial climatic gradients, as well as temporal resolution and extents that capture both fast and slow processes. Ecosystem-based monitoring should be dynamic and adaptive in the sense that models and monitoring designs are iteratively improved by new empirical results, new technologies and the evolving needs of stakeholders [START_REF] Ims | Ecosystem-based monitoring in the age of rapid climate change and new technologies[END_REF]Fig. 4). Once conceptual models (Fig. 4a) and appropriate monitoring designs (Fig. 4b) have been built, field data can be collected (Fig. 4c) for tracking the trajectories of individual species or the entire ecosystems. In this context, SDMs can serve as tools to identify the main drivers of changes or to project the fate of species or ecosystems (by e.g. stacked SDMs; [START_REF] Calabrese | Stacking species distribution models and adjusting bias by linking them to macroecological models[END_REF][START_REF] Guisan | SESAM -a new framework integrating macroecological and species distribution models for predicting spatio-temporal patterns of species assemblages[END_REF]Fig. 4d). Finally, new field monitoring can later validate projections of SDMs and the robustness of conceptual models (Fig. 4e). Here, RS data can strongly contribute to improve the strength of the loop of adaptive monitoring programs by providing simultaneously additional data that are complementing field monitoring and observations for the validation of SDM projections in-between two field campaigns that are often time and cost expensive.

The SDGs are one of the key global frameworks for addressing the environmental challenges of the Anthropocene. From a biodiversity perspective, to meet these goals it is crucial to understand current species distributions and how these may change under future land use and climate scenarios. SDMs make an essential contribution to providing this information but have several important limitations that can compromise their accuracy and hence the effectiveness of resulting conservation interventions and environmental policy. We suggest that currently available RS data can alleviate or resolve many of the data gaps that constrain SDMs and that the increasing availability of RS data should aid this. Techniques such as temporal image stacking can also improve confidence in SDMs by allowing better model calibration. Equally however, there are risks that non-specialists may unintentionally misinterpret RS data, and that key data requirements for SDMs are not fully appreciated and so go undeveloped or unpublicized by RS. As such, we argue that greater collaboration between the two communities by developing jointly data platforms with standardized metadata and documentation will be a key step in achieving the full potential of RS data and products for SDMs, thereby supporting more effective conservation monitoring, management, and policy decisions for a sustainable future.

Acknowledgements

We thank the European Space Agency, Future Earth, the Swiss National Science Foundation, and the University of Zurich for financially supporting the workshop that led to this paper.

Fig. 1 .

 1 Fig. 1. Characteristics and properties (i.e. continuity, intensity, and structure) of the four main

  limitations are now overcome by spatially and temporally highly resolved satellite images such as Sentinel-2 earth monitoring constellation(ESA, 2018). Every five days, these sensors provide global coverage of the land surface at spatial resolutions of 10 m, 20 m, and 60 m.Sentinel-2 offers potential for mapping land cover and land cover change over large areas(ESA, 2018).However, in spite of the novel developments and achievements of RS, limitations will persist in observing land management practices relevant to biodiversity. RS will remain limited to observing vegetation changes towards such management, while some management practices have indirect, lagged or unobserved impacts on biodiversity. Therefore, a proper characterization of land use intensities will also rely on other data sources that can be combined with RS. Additional proxies of land use intensities include socio-economic variables, but also crowd-sourced data. Examples of fusion of such datasets are provided by Van Asselen and Verburg (2013),[START_REF] Price | Future landscapes of Switzerland: risk areas for urbanisation and land abandonment[END_REF],[START_REF] See | Building a hybrid land cover map with crowdsourcing and geographically weighted regression[END_REF], and[START_REF] Estel | Combining satellite data and agricultural statistics to map grassland management intensity in Europe[END_REF].

Fig. 2 .

 2 Fig. 2. Temporal stacking of hyperspectral, thermal or radar images for improving response curves of statistical (a and c) and process-based (b and d) models. Temporal stacking of RS images potentially allows to increase the number of observations for statistical models (c) and the number of modeled species as well as the geographic variability for process-based models (d).

Fig. 3 .

 3 Fig. 3. Acquisition of demographic parameters for dynamic range models (DRMs) with time series of multispectral and / or hyperspectral images and airborne images and LiDar (a). The combination of such data allows to track movements of animals in suitable habitats.

Fig. 4 .

 4 Fig. 4. An ideal loop of adaptive monitoring in which remote sensing data and SDMs are combined (adapted from Ims and Yoccoz (2017)).

This publication is the result of a workshop supported by the European Space Agency and Future Earth that brought together participants from the ecological modeling, biodiversity, land systems science, and remote sensing communities.