Charly Géron
email: cgeron@uliege.be

Jonas Lembrechts
email: jonas.lembrechts@uantwerpen.be

Jan Borgelt
email: jan.borgelt@ntnu.nolenoir

Jonathan Roger Michel

Henri Lenoir

Rafiq Hamdi
email: rafiq.hamdi@meteo.be

Grégory Mahy
email: g.mahy@uliege.be

Ivan Nijs
email: ivan.nijs@uantwerpen.be

Arnaud Monty
email: arnaud.monty@uliege.be

Géron

Charly

Johan

Hamdi, Rafiq e,f , Mahy, Grégory a , Nijs, Ivan Jonathan D Lenoir
email: jonathan.lenoir@u-picardie.fr

Monty Arnaud

Urban alien plants in temperate oceanic regions of Europe originate from warmer native ranges

Keywords: Western Europe alien plant species, biological invasions, urbanization, native climate, global changes

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

Introduction

The second half of the twentieth century has been characterized by a steep rise in international trade, travel, and transport, resulting in the biotic homogenization of the world through increased alien species introductions [START_REF] Olden | On defining and quantifying biotic homogenization[END_REF][START_REF] Pyšek | Disentangling the role of environmental and human pressures on biological invasions across Europe[END_REF][START_REF] Van Kleunen | The changing role of ornamental horticulture in alien plant invasions[END_REF]. Data from Europe shows that most introductions happened after 1990, during the so-called "globalization era" [START_REF] Hulme | Trade, transport and trouble: managing invasive species pathways in an era of globalization[END_REF]. The success and speed of alien species invasion depend on the propagule pressure of alien species and the lifting of certain barriers, such as lower abiotic or biotic constraints in the introduced range [START_REF] Dullinger | Climate change will increase the naturalization risk from garden plants in Europe[END_REF]. Only a small portion of introduced alien species passes all of these barriers [START_REF] Walther | Alien species in a warmer world: risks and opportunities[END_REF] to become invasive, and significantly impacts native biota [START_REF] Dullinger | Climate change will increase the naturalization risk from garden plants in Europe[END_REF]. Alien plant invasions occur in all habitats, extending from rural to urban ones; however, the susceptibility to invasion (i.e., invasibility) of these habitats varies. Some land use categories, especially the most anthropogenic ones (e.g., urban areas), display higher alien plant invasions and often concentrate newly arrived alien plant species, due to high propagule pressure, concentration of transport infrastructure, and recurring anthropogenic disturbance [START_REF] Botham | Do urban areas act as foci for the spread of alien plant species? An assessment of temporal trends in the UK[END_REF][START_REF] Catford | Hotspots of plant invasion predicted by propagule pressure and ecosystem characteristics[END_REF][START_REF] Godefroid | Alien plant species do have a clear preference for different land uses within urban environments[END_REF]. In particular, climatic conditions are recognized as the first filter that either holds back alien plant species or allows successful establishment and spread in novel regions [START_REF] Richardson | Naturalization and invasion of alien plants: concepts and definitions[END_REF]. In recent decades, an increasing number of thermophilous (e.g., warm-loving) and drought-resistant alien plant species has been introduced to temperate regions, with some surviving up to 1000 km further north than their natural range limits, at colder latitudes and altitudes, where they are not naturally present [START_REF] Mckinney | Urbanization as a major cause of biotic homogenization[END_REF][START_REF] Walther | Alien species in a warmer world: risks and opportunities[END_REF]. The establishment, survival, and spread of such plant species from warmer origins might depend on "heat islands," such as urban areas; yet, this hypothesis has not been formally assessed [START_REF] Walther | Alien species in a warmer world: risks and opportunities[END_REF]. Therefore, urban environments represent useful systems to study climatic constraints on the invasion success of alien plant species [START_REF] Brans | Ecoevolutionary dynamics in urbanized landscapes: Evolution, species sorting and the change in zooplankton body size along urbanization gradients[END_REF][START_REF] Van Kleunen | The changing role of ornamental horticulture in alien plant invasions[END_REF].

Urban environments provide unique growing conditions for plants. However, the intensity to which environmental conditions are modified by human activities sharply decreases from city centers towards rural peripheries [START_REF] Alberti | Integrating Humans into Ecology: Opportunities and Challenges for Studying Urban Ecosystems[END_REF][START_REF] Ortega | Weak vs. strong invaders of natural plant communities: assessing invasibility and impact[END_REF][START_REF] Schmidt | Effects of urban structure on plant species richness in a large European city[END_REF]. One common and wellknown aspect of urban environments is their modified microclimate. Indeed, city areas are warmer than the rural surroundings, which is a phenomenon called the urban heat island (UHI) effect [START_REF] Oke | Urban Climates[END_REF][START_REF] Bader | Urban Climate Science[END_REF]. Three main factors contribute to the establishment of urban heat islands: 3-D urban geometry, thermal characteristics of impervious surfaces, and anthropogenic heat release. Local background climate also strongly contributes to the magnitude of the urban heat island [START_REF] Zhao | Strong contributions of local background climate to urban heat islands[END_REF][START_REF] Ward | Heat waves and urban heat islands in Europe: A review of relevant drivers[END_REF]. The proportion of built up area (i.e., imperviousness) is a commonly used proxy to characterize urbanization. Areas with 10% built up surfaces are considered to be urban in western Europe [START_REF] Kaiser | The Urban Heat Island and its spatial scale dependent impact on survival and development in butterflies of different thermal sensitivity[END_REF][START_REF] Brans | Ecoevolutionary dynamics in urbanized landscapes: Evolution, species sorting and the change in zooplankton body size along urbanization gradients[END_REF]. Plant urbanity (i.e., the proportion of built up surface in the plant's vicinity) is used to characterize its growing environment in relation to urbanization [START_REF] Hill | Hemeroby , urbanity and ruderality : bioindicators of disturbance and human impact[END_REF]. The high percentage of impervious surfaces associated with intense anthropogenic activity modify soil characteristics and disrupt the water cycle, resulting in highly fluctuating amounts of surface runoff water [START_REF] Hamdi | Effects of urbanization and climate change on surface runoff of the Brussels Capital Region : a case study using an urban soil -vegetation -atmosphere-transfer model[END_REF]. Consequently, urban habitats induce warmer and drier microclimates, broadening regional climatic variation, whereas rural habitats present naturally occurring climatic conditions of a given region. Native niche conditions of alien plants that are newly introduced to foreign environments strongly influence their establishment, and determine their population growth and capacity to spread [START_REF] Alexander | Limits to the niche and range margins of alien species[END_REF]. Native niche optimum is often used to characterize climatic suitability for alien plant species [START_REF] Van Kleunen | Characteristics of successful alien plants[END_REF]. Urban-to-rural gradients show variability in their microclimatic conditions, and might filter alien plant species based on their native niche optimum. For example, the preference of native plants for urban habitats is often related to the level of thermophily; in other words, plants mostly found in cities would be pre-adapted to the warmer and drier urban conditions [START_REF] Mckinney | Urbanization as a major cause of biotic homogenization[END_REF][START_REF] Schmidt | Effects of urban structure on plant species richness in a large European city[END_REF][START_REF] Szymura | Effect of anthropogenic factors, landscape structure, land relief, soil and climate on risk of alien plant invasion at regional scale[END_REF].

Existing studies have demonstrated that a high number of urbanophile plant species are thermophilous, with the high diversity and proportion of alien plant species in cities being positively coupled to city size and percentage of built-up surface [START_REF] Schmidt | Effects of urban structure on plant species richness in a large European city[END_REF][START_REF] Čeplová | Effects of settlement size, urban heat island and habitat type on urban plant biodiversity[END_REF]; however, a comprehensive assessment of the supposed link between climatic conditions in the native range and the level of urbanity in the invaded range of alien plant species remains unproven, to the best of our knowledge. This is a crucial step towards identifying the characteristics of more urban alien species for conserving native species more effectively, as their high number and diversity has already transformed cities into invasion hotspots. This phenomenon will likely be amplified in the future by global changes if urban invasion is, indeed, strongly driven by the microclimate. Since the surrounding landscape will become warmer in parallel, these urban alien plants might subsequently spill over more abundantly to the rural surroundings, using cities as stepping stone for further spread.

This study evaluated 24 emerging alien plant species originating from outside western Europe, using oceanic Europe (area of western Europe with a temperate climate, with warm summers but no dry season) as the study zone. The study aimed to: (a) disentangle the relationships between the climatic conditions in the native range (i.e., niche optimum) and the level of urbanity in the invaded range, while taking the first date of observation in the wild into account, and (b) test whether more urban alien plant species originate from native ranges with a warmer climate than that of the invaded range.

Methods

Study area, species selection, and native ranges

To delineate our study area, we used the Köppen-Geiger climate classification and focused on the temperate oceanic climate of Europe (Fig. 1): Cfb (i.e., temperate climatic conditions with warm summers and no dry season) [START_REF] Beck | Data Descriptor: Present and future Köppen-Geiger climate classification maps at 1-km[END_REF]. This geographic entity was considered to represent the invaded range in this study, and is referred to as "oceanic Europe" in this study (Fig. 1). This delineation was chosen to keep the background macroclimatic conditions as homogeneous as possible, and to better contrast with the urban heat island (UHI) effect of the main cities occurring in the study area. We then selected a list of alien plant species growing in terrestrial environments, which stages of invasion are still considered as incipient with distributions that remain restricted in oceanic Europe, but which are also having moderate to high impacts on biodiversity. This selection was based on the Harmonia database ("Harmonia database" 2019) and European Union (EU) Regulation no. 1143/2014. Species with unclear native ranges (e.g., hybrids) were removed. This led to an initial total of 33 species eligible for inclusion (Fig. 2).

Alien species distribution data

Modelled native ranges

Native countries and native regions for each of the 33 species were retrieved from Plants of the World Online (POWO 2020) and occurrences were acquired from the Global Biodiversity Information Facility (GBIF 2020) using the R packages rgbif and taxize [START_REF] Chamberlain | taxize: Taxonomic information from around the web[END_REF][START_REF] Chamberlain | rgbif: Interface to the Global Biodiversity Information Facility API[END_REF]. Native regions were reported following the World Geographical Scheme for Recording Plant Distributions (WGSRPD) and range from continental to regional scale [START_REF] Brummitt | World Geographical Scheme for Recording Plant Distributions[END_REF]. In order to minimize variability in number of occurrence points across countries, a maximum of 300 occurrence points was retrieved from GBIF (GBIF Secretariat 2019) for each species per native country. By default, this included only the most recent occurrence points. Then, only the points falling inside reported native WGSRPD regions were kept.

Duplicates were removed, as well as outliers that were detected based on Mahalanobis distance (MD) and defined as any points having an MD four times larger than the 0.95 percentile of the entirety [START_REF] Mahalanobis | On the generalized distance in statistics[END_REF]. MD was calculated for each occurrence based on its location and bioclimatic conditions. Since outliers already influence the calculations of MD, we applied this approach again after removing the first set of outliers. Since species distributions hardly follow any administrative boundaries, we define the modelling extent for each studied alien plant species as all ecoregions [START_REF] Olson | Terrestrial Ecoregions of the World: A New Map of Life on Earth[END_REF]) containing at least one of its remaining occurrence points. Furthermore, to acknowledge the fact that defining native countries and WGSRPD regions as well as data availability within ecoregions may be suspect to errors and biases, we included all neighboring ecoregions as well. WorldClim bioclimatic predictors and land cover types were used to predict the possible native range of each species at 30 arcmin resolution across the defined modelling extent using maximum entropy modelling (Maxent) [START_REF] Phillips | A maximum entropy approach to species distribution modeling[END_REF][START_REF] Phillips | Maximum entropy modeling of species geographic distributions[END_REF][START_REF] Phillips | Opening the black box: an open-source release of Maxent[END_REF].

WorldClim bioclimatic predictors (n = 19, see https://www.worldclim.com/bioclim for full list) [START_REF] Fick | Worldclim 2: New 1-km spatial resolution climate surfaces for global land areas[END_REF] were downloaded in 10 arcmin resolution and aggregated to modelling resolution (30 arcmin, approximately 56 km at the equator) by averaging. Land cover classes (n = 22, including different types and mosaics of vegetation, see http://maps.elie.ucl.ac.be/CCI/viewer/download/CCI-LC_Maps_Legend.pdf for full list) were calculated based on the European Space Agency's land cover product (original scale 300 m X 300 m, ESA 2017). Each class was transformed into a binary raster depicting presence (= 1) or absence (= 0) of the land cover type, and then aggregated to modelling resolution by averaging, resulting in one raster for each land cover class. Handling of spatial data was conducted using the R packages raster, rgdal, maptools, rgeos and sp [START_REF] Bivand | Applied spatial data analysis with R, Second edition[END_REF](Bivand et al. , 2019;;[START_REF] Bivand | maptools: Tools for Handling Spatial Objects[END_REF][START_REF] Bivand | rgeos: Interface to Geometry Engine -Open Source[END_REF][START_REF] Hijmans | raster: Geographic Data Analysis and Modeling[END_REF]. Maxent is a probability density estimation approach suitedfor predicting species distributions based on presence-only data [START_REF] Elith | Novel methods improve prediction of species' distributions from occurrence data[END_REF]. In Maxent, environmental variables and transformations thereof are used to predict species distributions. For our spatial predictions, we kept the Maxent default settings for selecting appropriate variables, transformations (out of linear, quadratic, product, hinge and threshold) and regularization values used to reduce overfitting [START_REF] Elith | A statistical explanation of MaxEnt for ecologists[END_REF][START_REF] Merow | A practical guide to MaxEnt for modeling species' distributions: what it does, and why inputs and settings matter[END_REF]). In addition, Maxent requires background information to contrast the environmental background against presence locations, and ultimately, fit response curves [START_REF] Elith | A statistical explanation of MaxEnt for ecologists[END_REF]). We collected background information from each cell across the modelling extent to avoid fitting the model with randomly generated points. Model fitting, prediction and range generation were executed with the R package dismo [START_REF] Hijmans | dismo: Species Distribution Modeling[END_REF]. The Maxent prediction was transformed into presence cells using the cut-off threshold that maximized model sensitivity for each species. This step results in a patchy landscape consisting of suitable and unsuitable habitat patches. We temporarily increased the extent of each patch by 10 % using buffering, to remove patches that would not contain any occurrence points, hence, removing patches that are unlikely being colonized by the species. This gave us a native range area estimation for each of the studied alien plant species, and the 19 WorldClim bioclimatic predictors [START_REF] Fick | Worldclim 2: New 1-km spatial resolution climate surfaces for global land areas[END_REF] were further sampled with a 5 km X 5 km grid over each of them. These bioclimatic predictors data were used to depict climatic conditions in the modelled native ranges of species (Fig. 2). Finally, for each of the 24 species, we defined the main Köppen-Geiger climate class [START_REF] Beck | Data Descriptor: Present and future Köppen-Geiger climate classification maps at 1-km[END_REF] for its modelled native range as the class in which most of native range area 5 km X 5 km fishnet points fell.

Oceanic Europe

The occurrence of the selected alien plant species, for the invaded ranges, was downloaded from the Global Biodiversity Information Facility (GBIF, see Appendix S1 Table S1.2 in Supporting Information, GBIF.org, 2019), were acquired for 1950-2018, and were handled in ArcMap 10.5.1 (2017). This time period was set because it encompassed 90% of the observations available on GBIF.org across all species.

We only included species with at least 50 observations in the invaded range, which led to the removal of 9 out of 33 species, and a final list of 24 species (Fig. 2, see Appendix S1 Table S1.2 in Supporting Information). The invaded distribution data of species were processed to correct for variation in the resolution of data in GBIF.org, and to solve high local spatial autocorrelation for example bias towards areas with high occurrence densities. To do so, fishnets of 5 km X 5 km were placed over oceanic Europe.

In each cell, and for each species, if several occurrence records were present, only a randomly chosen one was kept. This resulted in a total of 20000 processed occurrence records for oceanic Europe (See Appendix S2 in Supporting Information for details on species occurrence and maps in modelled native ranges and oceanic Europe). The density of impervious areas, representing the percentage of built-up land surface in 2015 (land.copernicus.eu, 2018) was used to characterize the level of urbanization throughout the invaded range of each study species (Fig. 2). To do so, the imperviousness layer was aggregated, using the mean, at a spatial resolution of 5 km X 5 km (original scale: 20 m X 20 m), to match the resolution of the occurrence datasets. The proportion of impervious area was extracted for each occurrence cell in the invaded range, and the mean value was calculated for each species. This parameter was subsequently referred to as "mean urbanity" (value between 0 and 1) (Fig. 2). Moran's index was calculated for each species in ArcMap 10.5.1 (2017), checking for spatial autocorrelation in the processed occurrences of each of the alien plant species in oceanic Europe.

Statistical analyses

A principal component analysis (PCA) was conducted on the bioclimatic conditions within the modelled native ranges (R package factoextra, [START_REF] Kassambara | Package "factoextra" for R: Extract and Visualize the Results of Multivariate Data Analyses[END_REF] to simplify the large set (19) of explanatory variables to a 3-dimensional climatic niche space. The three first PCA axes were retained, with the three bioclimatic predictors that contributed the most to each of these three PCA axes. We extracted the values (i.e., coordinates) along the three first PCA axes for the sampled 5 km X 5 km points over the modelled native ranges of each studied alien species (Fig. 1). The first three PCA axes explained 39%, 22%, and 14% variance, respectively, of the WorldClim bioclimatic data (Table 1).

The minimum temperature of the coldest month (Bio 6, positively correlated), the mean temperature of the coldest quarter (i.e., the coldest four months of the year, Bio 11, positive), and the annual mean temperature range (Bio 1, positive) contributed the most to PCA axis 1 (Table 1). This first PCA axis, thus, represented the severity of the native winter climate, and the extent of temperature variations throughout the year. High values along this PCA axis indicate a native range climate with milder winter temperatures and little annual variation in temperature. Thus, we called this axis native "winter temperature" conditions.

The annual precipitation (Bio 12, positive), precipitation of the driest quarter (Bio 17, positive), and precipitation of the driest month (Bio 14, positive) contributed the most to PCA axis 2 (Table 1). The second PCA axis thus represented the precipitation amount of the native range climate, with high values indicating more precipitations. The second PCA axis thus reflected native "precipitation" conditions.

The mean temperature of the warmest quarter (Bio 10, positive), maximum temperature of the warmest month (Bio 5, positive), and mean temperature of the wettest quarter (Bio 8, positive) contributed the most to the third PCA axis (Table 1). High values along this PCA axis meant warmer high temperature values in the native range climate. The third PCA axis thus represented native "summer temperature" conditions.

The mean position along these three PCA axes was calculated for each focal species separately, to depict niche optimum. This parameter was referred to as "barycenter".

The date of first observation of the studied species in oceanic Europe was derived from [START_REF] Zieritz | Changes in pathways and vectors of biological invasions in Northwest Europe[END_REF] and the "Harmonia database" 2019, and compiled the first observation data in Belgium, France, Great Britain, and the Netherlands, which are the countries having the biggest area in oceanic Europe.

The analysis of the relationships for mean urbanity (response variable) with respect to the species barycenter and the date of first observation (explanatory variables) was performed using betaregressions (R package betareg, Cribari-Neto & Zeileis 2010), as we hypothesized that native climatic conditions explained the distribution of alien plant species in urban versus rural areas in oceanic Europe. The response variable, mean urbanity, was continuous, and was derived from proportions and restricted to the unit interval (0, 1), but no values were exactly equal to 0 or 1, thus justifying the use of betaregressions [START_REF] Douma | Analysing continuous proportions in ecology and evolution: A practical introduction to beta and Dirichlet regression[END_REF]. The full model included all one-way interactions of the explanatory variables. We used the dredge function (package MuMIn, Barton, 2019) to select the models to be averaged. In other words, the best model had the lowest Akaike Information Criterion (AIC) and highest Phi coefficient (model precision), and selected models had a delta AIC lower than 2 relative to the best model. Using this procedure, two candidate models were retained. We then performed model averaging on these selected models (R package MuMIn, Barton, 2019).

To characterize the origin of the studied alien plant species in term of native bioclimatic values, baseline values for each of the 19 WorldClim bioclimatic predictors were calculated as their mean value over the species respective native ranges. To compare those values with climatic conditions occurring in the invaded range, baseline values for each of the 19 WorldClim bioclimatic predictors were calculated as their mean value over the oceanic Europe study zone. To define the species as more urban or more rural, a cutoff value of mean urbanity of the studied species was obtained by calculating the mean of the mean urbanity of the 24 studied species (= 8.6%). This mean urbanity cut-off value was used to distinguish species displaying a mean urbanity above this threshold as more urban, and species displaying a mean urbanity below this threshold as more rural, in oceanic Europe. Confusion matrixes were produced for all of the bioclimatic predictors, and only the best is shown here. Following their position along the reference values, species considered as true positive (TP), true negative (TN), false positive (FP), and false negative (FN) were determined. The precision of species placement was calculated as

𝑇𝑃 (𝑇𝑃+𝐹𝑃)
. Finally, a generalised linear model was used to test the relationship of the mean urbanity of the studied species (response variable) with their main native climate, which was described as the main Köppen-Geiger climate class in which they occur in their native range (explanatory variable, treated as factor) (package car, John et al., 2019). A binomial family was used, with the response variable being treated as a vector accounting for the urbanity and non-urbanity percentages (i.e. c(%urbanity, %non-urbanity)) following the method of [START_REF] Crawley | The R Book[END_REF] All statistical analyses were performed in R, version 1.1.463 (RStudio Team 2016).

Results

Native climatic conditions and invaded urbanity of alien plant species

The 24 studied alien plant species showed high variability in native climatic conditions, with native ranges encompassing a total of 26 out of the 30 Köppen-Geiger climate classes, yet with 87% of occurrences in seven of the 30 climate classes. The urbanity of all occurrence records and species together in oceanic Europe ranged from 0 to 93%; mean species-level urbanity varied between 4 and 17%. 22 of the 24 studied alien plant species displayed a high spatial autocorrelation.

Alien plant species from warmer and drier origins are more likely to develop in higher urbanity areas in oceanic Europe

The averaged betaregression model explaining the mean urbanity in oceanic Europe of a given alien plant species included the position of the barycenter of the bioclimatic niche of the species along PCA axes 2 and 3, and the date of first observation in the wild (Table 2).

Native climate "precipitation" (PCA axis 2) had a negative effect and "summer temperature" (PCA axis 3) had a positive effect on species mean urbanity in oceanic Europe, both displaying the strongest support overall (Table 2; present in the 2 selected models). Thus, in oceanic Europe, alien plant species from warmer (PCA axis 3) and/or drier (PCA axis 2) native ranges were, on average, found in more urban environments (Fig. 3a andb).

The date of first observation in the wild had a weak positive but not significant effect on species mean urbanity, and was only present in one of the selected models (Fig. 3c). Alien plant species present in the wild for a more recent period of time were found in more urban environments.

The main native Köppen-Geiger climate class had a non-significant effect on species mean urbanity (p > 0.05, Df = 3, LR Chisq = 5.79). However, visualization of the main Köppen-Geiger climate classes (Fig. 3a) showed that more urban alien species primarily originated from subtropical or continental climates in the native range. In comparison, more rural alien species primarily originated from contrasted and/or cooler climates in the native range (e.g., alpine or continental) (Fig. 3,4).

The confusion matrix of mean annual temperature had the strongest correlation with the urbanity of alien species, with the highest estimation precision (0.58). This result highlights the importance of mean annual temperature as a driver of the urbanity of alien species in oceanic Europe. Seven out of the 12 more urban aliens originated from native ranges with warmer mean annual temperatures compared to the mean annual temperature in oceanic Europe (TP, Fig. 4). In addition, ten out of the 12 more rural alien species originated from native ranges with cooler mean temperature compared to oceanic Europe (TN, Fig. 4).

Discussion

The current study showed that the variation in the urbanity of alien plant species was largely explained by the climatic conditions in their modelled native ranges. Native niche optimum, accounted for the PCA axis barycenters in our study, had a preponderant importance. A predominant proportion of more urban and more rural alien species originated from warmer and cooler native ranges, respectively, compared to the studied area, in terms of mean annual temperature. Alien plants that grew in more urban environments primarily originated from two main climate types: subtropical and continental climates. In comparison, alien plants that grew in more rural environments appeared to mainly originate from alpine, continental, and Mediterranean climates. Yet, variation in the native climate was more important for these rural alien species compared to more urban alien species.

We acknowledge that the date of first observation in the wild had a slight but not significant effect, and that we could not robustly prove that alien plant species spreading more recently were usually found in more urban environments. Even though the propagule pressure of the studied alien species is not the same everywhere in oceanic Europe; the introduction of plant species tends to happen at higher rates in densely inhabited areas. Alien species also tend to escape from cultivated areas, such as gardens, present in urban and rural areas [START_REF] Botham | Do urban areas act as foci for the spread of alien plant species? An assessment of temporal trends in the UK[END_REF][START_REF] Dullinger | Climate change will increase the naturalization risk from garden plants in Europe[END_REF]. However, we noticed that the occurrence of the studied alien species exhibited broad variety of urbanity values, showing that they were present in all types of environments, from densely built metropolitan areas (London, Paris, etc.) in highly urbanized regions (Flanders, Netherlands) to extensive rural zones (central France, Wales, etc.). Western Europe has the highest road and rail density of any continent, and is considered to be the world's most urbanized region [START_REF] Hulme | Trade, transport and trouble: managing invasive species pathways in an era of globalization[END_REF][START_REF] Terama | Modelling population structure in the context of urban land use change in Europe[END_REF]. Consequently, the displacement of propagules is far easier in this region compared to less human-modified parts of the world. Thus, we argue that the preference of some alien species in our study for more urban or more rural environments, could be linked to climatic filtering of urban climate. Indeed, our study indicates that alien plants of various origins do not establish randomly along the imperviousness gradient; rather, alien plant species from warmer and/or drier native ranges tend to establish in more urban environments, visible by the predominant effect of the native climate barycenters.

Oceanic Europe has a temperate mild and humid climate; however, urban environments have elevated summer temperatures due to the UHI-effect, and a modified water cycle due to impervious surfaces. These phenomena result in warmer and drier local conditions compared to rural surroundings [START_REF] Ortega | Weak vs. strong invaders of natural plant communities: assessing invasibility and impact[END_REF][START_REF] Walther | Alien species in a warmer world: risks and opportunities[END_REF][START_REF] Kaiser | The Urban Heat Island and its spatial scale dependent impact on survival and development in butterflies of different thermal sensitivity[END_REF][START_REF] Szymura | Effect of anthropogenic factors, landscape structure, land relief, soil and climate on risk of alien plant invasion at regional scale[END_REF]. Therefore, we hypothesize that it is more likely that alien species from warmer and drier native ranges than those in our study area develop in more urban environments, induced by the presence of warmer and drier microclimates in oceanic European cities.

More urban aliens from subtropical climates likely take advantage of the longer growing season and reduced winter freezing events associated with the UHI [START_REF] Walther | Alien species in a warmer world: risks and opportunities[END_REF][START_REF] Schmidt | Effects of urban structure on plant species richness in a large European city[END_REF]. The first developmental stages of several alien plant species from subtropical climates are particularly sensitive to temperature. For instance, the germination of Baccharis halimifolia and Lonicera japonica are optimal at around 20-25 °C [START_REF] Schierenbeck | Japanese Honeysuckle (Lonicera japonica) as an Invasive Species; History, Ecology, and Context[END_REF][START_REF] Fried | Botany Letters Monographs on Invasive Plants in Europe: Baccharis halimifolia L[END_REF], representing a relatively high temperature range for oceanic Europe. Moreover, seedlings of such species also tend to be highly frost sensitive, as illustrated by the high mortality of Ailanthus altissima seedlings after freezing events (von der [START_REF] Von Der Lippe | Cities as drivers for biological invasions -The role of urban climate and traffic[END_REF]. The phenology of these species often involves late flowering and fruiting, which represent crucial stages sensitive to the earliness, duration, and intensity of freezing events (e.g., Baccharis halimifolia, [START_REF] Fried | Botany Letters Monographs on Invasive Plants in Europe: Baccharis halimifolia L[END_REF], Cyperus eragrostis [START_REF] Iefländer | Spontanvorkommen von Duchesnea indica : Ein Neophyt breitet sich in den letzten Jahren verstärkt aus. Berichte der Bayer[END_REF], and Potentilla indica [START_REF] Dakskobler | Cyperus eragrostis Lam. -A new adventitious species in the Flora of Slovenia[END_REF]).

In addition, urban environments display more drained and disturbed substrates [START_REF] Schmidt | Effects of urban structure on plant species richness in a large European city[END_REF]) that might represent conditions with favorable humidity for more urban aliens from drier native ranges that are adapted to either limited and/or seasonal precipitation regimes in an oceanic humid climatic region. Some of the alien species in our study prefer disturbed and/or drained soil conditions (e.g., Reynoutria sachalinensis [START_REF] Sukopp | Reynoutria sachalinensis in Europe and in the Far East: A comparison of the species ecology in its native and adventive distribution range[END_REF]), and germinate better under contrasted temperatures (e.g. Elaeagnus augustifolia, [START_REF] Guilbault | The influence of chilling requirement on the southern distribution limit of exotic Russian olive (Elaeagnus angustifolia) in western North America[END_REF]). The urban microclimate also promotes the survival of the seedlings of some of these species (e.g., Acer negundo, [START_REF] Von Der Lippe | Cities as drivers for biological invasions -The role of urban climate and traffic[END_REF]).

Conversely, alien plant species associated with lower urbanity in oceanic Europe tend to originate from cooler ranges, which might perform less well in warmer and drier urban environments, limiting their development in cities. Warmer temperatures inhibit the development abilities of some of these species, especially for young individuals (e.g., Lupinus polyphillus [START_REF] Elliott | Germination of Three Native Lupinus Species in Response to Temperature[END_REF] and Lysichiton americanus [START_REF] Schrader | Pest Risk Analysis for Lysichiton americanus Hultén & ST. John (Araceae)[END_REF])), whereas drier substrates negatively impact the development of species, such as Prunus laurocerasus [START_REF] Berger | Bioclimatic limits and range shifts of cold-hardy evergreen broad-leaved species at their northern distributional limit in Europe[END_REF] and Spiraea alba [START_REF] Stanton | Maintenance of water uptake and reduced water loss contribute to water stress tolerance of Spiraea alba Du Roi and Spiraea tomentosa[END_REF]).

The separation of more urban aliens from the whole alien species pool indicates that urban conditions could act as a strong filter for warmth and drought-adapted plant species. This phenomenon is of great conservation concern, because many ornamental species introduced to temperate regions originate from dry and warm areas [START_REF] Van Kleunen | The changing role of ornamental horticulture in alien plant invasions[END_REF]. Moreover, urban environments might enhance alien plant invasions in the future, due to higher environmental disequilibrium caused by enhanced urbanization, trade, and climate change [START_REF] Van Kleunen | The changing role of ornamental horticulture in alien plant invasions[END_REF]. Because urban areas are highly connected with rural surroundings, they actively export propagules [START_REF] Hulme | Trade, transport and trouble: managing invasive species pathways in an era of globalization[END_REF][START_REF] Catford | Hotspots of plant invasion predicted by propagule pressure and ecosystem characteristics[END_REF]. Under climate change, surrounding rural areas will become suitable for drought and warm adapted species, in addition to urban environments [START_REF] Kendal | A cultivated environment: Exploring the global distribution of plants in gardens, parks and streetscapes[END_REF]. Cities could act as veritable migration outpost for these climate limited alien plant species, facilitating their escape to newly suitable rural areas [START_REF] Hulme | Trade, transport and trouble: managing invasive species pathways in an era of globalization[END_REF][START_REF] Dullinger | Climate change will increase the naturalization risk from garden plants in Europe[END_REF][START_REF] Pyšek | Naturalized alien flora of the world: species diversity, taxonomic and phylogenetic patterns, geographic distribution and global hotspots of plant invasion[END_REF].

This study demonstrated that global databases (such as GIBF.org, WorldClim, Copernicus Imperviousness Density), if cautiously used, are relevant for analyses at a continental scale. The strong relationship between urbanity in the invaded range and climatic conditions in the modelled native range, at a continental scale, suggests that even stronger microclimatic effects might be at play at local scales, as suggested by previous studies [START_REF] Botham | Do urban areas act as foci for the spread of alien plant species? An assessment of temporal trends in the UK[END_REF][START_REF] Walther | Alien species in a warmer world: risks and opportunities[END_REF][START_REF] Lembrechts | Habitat properties and plant traits interact as drivers of non-native plant species' seed production at the local scale[END_REF]. Even if we acknowledge variability within species and limited number of studied species, the strong pattern found in the current study confirmed that this global relationship with climatic conditions of origin is consistent, and highlighted that multi-species studies are essential for comprehending invasion processes and urban invasion ecology [START_REF] Catford | Hotspots of plant invasion predicted by propagule pressure and ecosystem characteristics[END_REF].

In an era of biotic homogenization [START_REF] Pyšek | Disentangling the role of environmental and human pressures on biological invasions across Europe[END_REF][START_REF] Terama | Modelling population structure in the context of urban land use change in Europe[END_REF], urban environments could represent hotspots for invasion by new alien plant species. Therefore, emphasis should be placed on invasion processes and monitoring invasion in urban environments to advance our understanding on what drives the early stages of invasion, when it might still be controlled. Furthermore, this phenomenon might be compounded by the fact that warmer and drier climates might become more common across the whole urban-to-rural gradient under climate change [START_REF] Hamdi | Future climate of Brussels and Paris for the 2050s under the A1B scenario[END_REF][START_REF] Brans | Ecoevolutionary dynamics in urbanized landscapes: Evolution, species sorting and the change in zooplankton body size along urbanization gradients[END_REF]. Therefore, it is important to raise the awareness of public institutions and private property owners, especially since massive urban plantations, such as public and/or private gardens, which could help cities adapt to climate change, might otherwise represent potential sites for the "seeding" of future plant invasions [START_REF] Essl | From ornamental to detrimental? The incipient invasion of Central Europe by Paulownia tomentosa[END_REF][START_REF] Gaertner | Non-native species in urban environments: patterns, processes, impacts and challenges[END_REF]. annual temperature (°C). Each point corresponds to a different species. The full vertical line represents the mean annual temperature (°C), considered as the reference in oceanic Europe: 9.9 °C. The dashed horizontal line corresponds with the mean urbanity value for the set of studied species: 8.6; species with lower mean urbanity than this value were considered to be more rural aliens in oceanic Europe (i.e. 12 out of the 24 studied species); with higher urbanity than this value were considered as more urban aliens in oceanic Europe (i.e. 12 out of the 24 studied alien species). Species are colored following their native main Köppen-Geiger climate class. Species represented with squares correspond to either true negative or true positive in the confusion matrix, and species represented with circles are either false negatives or false positives.

Figures

Figure 1 :

 1 Figure 1: Map of the Cfb climate zones in Europe (i.e., temperate climate with warm summers but no dry season, Beck et al. 2018), displayed in green and hereafter called "oceanic Europe".

Figure 2 :Figure 3 :Figure 4 :

 234 Figure 2: Organizational chart of data acquisition, treatment, and analyses.

This work was supported by the Fonds de la Recherche Scientifique (FNRS).

Tables

 Table 1:


Jonas J Lembrechts
email: jonas.lembrechts@uantwerpen.be

Jonathan Roger Michel

Henri Lenoir

Brett Scheffers

Jonathan Lenoir

Pieter De Frenne
email: pieter.defrenne@ugent.be

Designing countrywide and regional microclimate networks

Keywords: biogeography, climate change, data loggers, location selection, microclimate, microweather, sensors, temperature

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

Introduction

Our knowledge of the ongoing changes in the climate system predominantly relies on a global network of countrywide weather-station networks. For their establishment, meteorologists generally follow the World Meteorological Organisation's guidelines that sensors should be installed in open landscapes where the wind mixes the air, above short grass, well away from trees, buildings or topographic features (World Meteorological Organization 2008, De Frenne and[START_REF] De Frenne | Weather stations lack forest data[END_REF]. Representativeness of a meteorological observation (the degree to which it accurately describes the value of the variable needed for a specific purpose) is thus key: synoptic observations have to represent an area up to 10-100 km around the weather station (referred to as 'free-air temperature', 'atmospheric temperature' or 'macroclimate') (World Meteorological Organization 2008). While meteorologists did their best to remove what they consider as local sources of "noise" (trees, buildings and topographic features) in the data, these sources of "noise" are home to many organisms and thus are highly relevant for ecologists. Indeed, it is the local temperatures near the ground or below the vegetation that set the bounds of organisms' range limits and dictate ecosystem processes such as primary production and hydrological, nutrient, and carbon cycles [START_REF] Lenoir | Climatic microrefugia under anthropogenic climate change: implications for species redistribution[END_REF][START_REF] Suggitt | Extinction risk from climate change is reduced by microclimatic buffering[END_REF][START_REF] Burrows | Ocean community warming responses explained by thermal affinities and temperature gradients[END_REF][START_REF] Laskin | Advances in phenology are conserved across scale in present and future climates[END_REF][START_REF] Pinsky | Greater vulnerability to warming of marine versus terrestrial ectotherms[END_REF]. For instance, temperatures below the canopies of forests are buffered by up to 10-20°C across the globe and depend on forest structure, season, tree species, latitude and management practices [START_REF] Alkama | Biophysical climate impacts of recent changes in global forest cover[END_REF][START_REF] De Frenne | Global buffering of temperatures under forest canopies[END_REF][START_REF] Zellweger | Advances in microclimate ecology arising from remote sensing[END_REF]. Likewise, surface temperatures on mountain slopes can also deviate by several degrees from the macroclimate due to vegetation and topography [START_REF] Scherrer | Infra-red thermometry of alpine landscapes challenges climatic warming projections[END_REF]Körner 2010, Lenoir et al. 2013), while urban structures in cities can create regional-scale heat islands, and meter-scale heterogeneity in climate conditions [START_REF] Dimoudi | Investigation of urban microclimate parameters in an urban center[END_REF]. In conclusion, the vertical, horizontal and temporal complexity of the surrounding landscape, all of which directly or interactively influence microclimates, makes microclimate prediction from synoptic weather stations very complex [START_REF] Lembrechts | Comparing temperature data sources for use in species distribution models: From in-situ logging to remote sensing[END_REF].

Recent progress in the field of microclimate ecology and biogeography has improved predictions of microclimate based on statistical downscaling of existing weather station data [START_REF] Meineri | Fine-grain, large-domain climate models based on climate station and comprehensive topographic information improve microrefugia detection[END_REF] as well as on mechanistic models [START_REF] Kearney | NicheMapR-an R package for biophysical modelling: the microclimate model[END_REF]. The problem with both approaches is, however, that actual measurements in non-standardized environments are needed to validate predictions [START_REF] Lembrechts | Microclimate shifts in a dynamic world[END_REF]. We thus argue that our hands-on training to design microclimate networks at the country or regional level -the level where existing weather station networks are currently established and maintained -is critical and necessary if we are to provide reliable future scenarios for biodiversity and ecosystem services in the context of the growing demands for resources on ecosystems. Indeed, accurate microclimate data should provide the foundation of any effort to implement key transnational policies such as the Intergovernmental Panel on Climate Change (IPCC), the Intergovernmental Panel on Biodiversity and Ecosystem Services (IPBES), and in light of the on-going discussions around drafting the post-2020 Global Biodiversity Framework under the Convention on Biological Diversity (CBD). We will indeed struggle to achieve the ambitious goals of the 2050 Vision of "Living in harmony with nature" under the CBD as long as we do not have the necessary and basic information to understand microclimate dynamics within habitats occupied by diverse life forms.

Microclimate networks

Here we propose an integrative framework to design countrywide or regional microclimate networks to monitor microclimate complementary to the existing weather station networks. Note, however, as implementation and associated costs of acquisition and maintenance can vary widely from country to country, we focus our framework on planning and design rather than addressing the actual implementation (however, see Supplementary Table S1 for some details on implementation costs).

Measuring microclimate at the country level using standardized protocols across the globe would result in a dramatic step-change for the nascent field of microclimate ecology and biogeography, which aims at better understanding biological and ecosystem responses to climate change. Such networks of microclimate sensors could serve to validate microclimate modelling approaches [START_REF] Maclean | Microclima: An r package for modelling meso-and microclimate[END_REF], as well as create micro-and mesoclimatic grids (e.g. 25-250 m, [START_REF] Bramer | Advances in monitoring and modelling climate at ecologically relevant scales[END_REF]. The proposed framework is also applicable at smaller spatial extents, for example to design microclimate networks at the landscape level, where one can then aim for much finer spatial resolutions. Noteworthy, our framework is not limited to applications in ecology and can span environmental and agricultural sectors to serve a broad range of societal needs. For example, high-resolution microclimate data, when extended to agricultural land-cover types, can inform a next generation of soil-crop simulation models to help decision-making in agriculture, such as conservation tillage practices or precision agriculture, or provide real-time growth metrics for crops, all of which would maximize farmer returns.

To help governments and scientists establish efficient countrywide and regional microclimate networks, we propose a multidimensional sampling design following gradients in macroclimate, as well as in the main drivers of microclimatic variability, topography and vegetation structure [START_REF] Naudts | Europe's forest management did not mitigate climate warming[END_REF][START_REF] Bramer | Advances in monitoring and modelling climate at ecologically relevant scales[END_REF][START_REF] Graae | Stay or go-how topographic complexity influences alpine plant population and community responses to climate change[END_REF][START_REF] Senior | Global loss of climate connectivity in tropical forests[END_REF][START_REF] Zellweger | Advances in microclimate ecology arising from remote sensing[END_REF], Lembrechts et al. 2020).

Hereafter, for illustrative purposes, we focus on the country level, yet the same approach can be implemented at any spatial level. In our sampling design, the goal is to capture the greatest variability of microclimates with the fewest number of country-level monitoring locations possible, thus optimising the costs. To do this, we implement a systematic sampling design within the environmental space [START_REF] Hattab | A unified framework to model the potential and realized distributions of invasive species within the invaded range[END_REF]. More specifically, we generate a principal component analysis (PCA) of the environmental space using available gridded products of the most important drivers of microclimate heterogeneity (i.e. variables reflecting the variation in macroclimatic conditions as well as in topography and vegetation structure, see Supplementary Information Table S2), and distribute our selected locations equally along the first three principal components from the PCA. This way, we ensure maximal spread of sampling locations throughout the environmental space, and selection of preferential locations that are representative of microclimatic conditions available within the focal country.

The number of locations needed within one country can be decided based on the existing macroclimatic gradient within the country (e.g. 25 locations per degree of variation in mean annual temperature at a 1 km²-resolution, see Supplementary Information 1 for full methodological details).

Countrywide microclimate networks could be plugged in the existing national networks of weather stations in order to assess the spatiotemporal dynamics of the offset between macroclimate and microclimate [START_REF] Lembrechts | Incorporating microclimate into species distribution models[END_REF][START_REF] De Frenne | Global buffering of temperatures under forest canopies[END_REF]) and to limit the need for sampling in areas with little microclimatic heterogeneity that are already well-covered by the existing weather station networks. Finally, each local 'microweather' station can incorporate a vertical gradient of sensors at different soil depths and heights above the ground. For example, some microclimate loggers are readily equipped with several builtin sensors that measure air, surface and soil temperature simultaneously [START_REF] Wild | Climate at ecologically relevant scales: A new temperature and soil moisture logger for long-term microclimate measurement[END_REF], while a new generation of wireless and autonomous sensors can integrate several sensors across multiple depths and heights (nkeWatteco 2019).

Proof-of-concept

To show proof-of-concept, we applied the above-mentioned concept of a multidimensional sampling design (see Table S2 for data sources) to twelve example countries, in four different and contrasting regions of the world: Europe (the Netherlands, Belgium and France); South America (Chile, Argentina and Uruguay); Africa (Morocco, Algeria and Tunisia) and Asia (India, Nepal and Bangladesh) (Fig. 1). Altogether these countries make exemplar case-studies in that they: (i) represent a broad range of political, social, economical and geographical entities, (ii) have contrasting topographic scenarios from topographically simple and flat to topographically complex and mountainous and (iii) they host contrasting conditions in terms of land use and vegetation structure. To populate our framework, our approach used as an example (Supplementary Information Table S2): (i) macroclimatic data in the shape of the mean annual temperature (MAT) and precipitation (MAP) from CHELSA [START_REF] Karger | Climatologies at high resolution for the earth's land surface areas[END_REF] -5, see Supplementary Information 1 for full methodological details).

These microweather stations would complement existing weather station infrastructure maintained by their national meteorological services.

Importantly, our framework does not aim to measure microclimate at the finest possible gridded resolution (which can range from loggers placed every 100 m all the way down to 0.01 m, [START_REF] Geiger | The climate near the ground[END_REF][START_REF] Bramer | Advances in monitoring and modelling climate at ecologically relevant scales[END_REF]. As has been argued before [START_REF] Bennie | Seeing the woods for the trees -when is microclimate important in species distribution models?[END_REF][START_REF] Lembrechts | Incorporating microclimate into species distribution models[END_REF], we emphasize that such a run for increasingly fine resolutions is not necessary: microclimatic precision or resolution is only valuable down to the level at which an increased resolution does not affect the actual pattern or process anymore. Rather, our framework takes on the much more critical task of accounting for the most important drivers of micro-and mesoclimate and measuring within environments that are representative of organism's habitats [START_REF] Lembrechts | Microclimate shifts in a dynamic world[END_REF], achieved here using in-situ data with data loggers placed in the most characteristic environments, up to several kilometres apart. Our approach thus allows for the creation of trustworthy gridded micro-and mesoclimate maps at the regional and global scale [START_REF] Lembrechts | Comparing temperature data sources for use in species distribution models: From in-situ logging to remote sensing[END_REF]. While the resolution of these final gridded microclimate maps at the country-level can be as high as the available remotely sensed products at the country-level (e.g. digital elevation models at 30 m resolution), it will not necessarily increase the resolution of gridded climate products compared to the literature, as high-resolution datasets based on macroclimatic measurements are now readily available [START_REF] Bramer | Advances in monitoring and modelling climate at ecologically relevant scales[END_REF][START_REF] Lembrechts | Incorporating microclimate into species distribution models[END_REF]). However, the resulting products will have the direct links to the ecology of organisms and ecosystems, as well as reduced error at the local scale, that are currently lacking. Also, if required, our approach could be reproduced at finer spatial resolutions (e.g. 1 m or finer) over smaller spatial extents when such environmental data is available (e.g. through LiDAR, [START_REF] George | Using LiDAR and remote microclimate loggers to downscale near-surface air temperatures for site-level studies[END_REF][START_REF] Randin | Monitoring biodiversity in the Anthropocene using remote sensing data products in species distribution models[END_REF].

Importantly, the goal of our approach is to create a network that captures the most representative microclimates for a given country or region. When the main research goal is to disentangle the interactive role of a specific set of environmental drivers on microclimate variability and thus minimize correlation across these variables [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 x 300 km) and diverse region[END_REF], this can be achieved by adding additional PCA axes to the selection procedure (and preferably also increase the number of sensors to reach more unique and peculiar microclimates), or by limiting the number of variables to those of principal interest and build a specific and orthogonal sampling design around these variables.

The resulting microclimate networks will also allow calibration of existing and developing mechanistic microclimate models [START_REF] Kearney | A method for computing hourly, historical, terrain-corrected microclimate anywhere on Earth[END_REF][START_REF] Maclean | Microclima: An r package for modelling meso-and microclimate[END_REF]). Indeed, one can predict the microclimatic conditions at all sensor locations across the environmental gradient using these models, and use the in-situ measured data from the established network for validation. In general, the output from all the above-mentioned modelling approaches will allow better calibration of species distribution and ecosystem modelling and increase our understanding of reproductive ecology or energetics of ectotherms [START_REF] Lembrechts | Microclimate shifts in a dynamic world[END_REF]. Finally, it also provides a step forward towards accurate predictions of future microclimate [START_REF] Lembrechts | Microclimate shifts in a dynamic world[END_REF]. Note however that the proposed approach is suitable for those organisms living on, in and around the soil surface (e.g. soil micro-organisms like fungi, ground arthropods, herbs, mosses, tree seedlings and small vertebrates), yet does not cover more peculiar habitats like those of invertebrates living under the bark of a tree, or parasites living in the fur of a mammal (Pincebourde and

Salle 2020).

Challenges

There are of course geographical, technical, as well as financial challenges that may hamper the rollout of such countrywide, regional or landscape-level microclimate networks. Geographically, study sites can be located both on private and/or public lands, equally accessible or remote, just as with existing weather stations. Technically, several approaches exist with regard to sensor choice and station management for recording soil and near-surface temperatures and soil moisture (e.g. [START_REF] Wild | Climate at ecologically relevant scales: A new temperature and soil moisture logger for long-term microclimate measurement[END_REF] ranging from: (i) fully-automated stations powered by solar cells, that remotely record and send data online; (ii) miniature battery-powered loggers; to, finally, (iii) manual 24h-readings of maximum and minimum temperatures by dedicated volunteers (as is currently done in many meteorological stations) (Supplementary Information Table S1). Of course, specific advantages and drawbacks are associated with each of these three approaches, and solutions might depend on local situations. For example, countries with substantial wilderness and willing to invest more money in the long-term monitoring of microclimate might opt for robust automated stations with a remote connection, while countries with limited economic resources could opt for cheaper battery-powered loggers or dedicated volunteers.

To ensure commitment and implementation by as many governments as possible, a promising solution would be to make the establishment of a microclimate network part of governments' obligations under international treaties such as the Paris Agreement. Ideally, intergovernmental organisations such as the European Union (EU) or the Association of Southeast Asian Nations (ASEAN) could coordinate the international roll-out of such networks. But who will pay the bill? The proposed 43 micro-weather stations for the Netherlands, 76 for Belgium and 457 for France in our European set of countries, for example, can be achieved without too much extra costs, given that miniature sensors to log in-situ microclimate are readily available at low prices per unit (e.g., < $100 USD), and final station numbers are in line with the number of existing weather stations in these countries (c. 46, 200 and 554 official weather stations for the Netherlands (KNMI), Belgium (RMI) and France (Météo France), respectively). The establishment, maintenance and data management of microclimate networks aligns seamlessly with synoptic weather stations and thus could be part of the tasks already ensured by national meteorological institutes. Besides, national meteorological institutes will directly benefit from data that incorporate microclimate to better inform the general public about microclimatic conditions in, for example, urban parks, gardens and residential areas. Installation in large and less wealthy countries might be harder to achieve, yet here again integration with existing meteorological networks could provide opportunities. As argued above, such microclimate networks do not necessarily need excessive amounts of loggers, nor focus on the most inaccessible locations, so long as climate is measured in-situ, i.e. at locations and scales relevant to the organisms of interest, and not in standardized environments that artificially filter away local climatic variability. Importantly, our approach allows flexibility in the final number of loggers used, making it feasible to match the optimal measurement strategy to any level of financial investment and research question (Supplementary Figure S4).

Finally, it remains important to find a representative location within a selected grid cell to install the microclimate sensors. Indeed, one should aim to achieve a location with average conditions for that grid cell (e.g. avoiding forest clearings in a pixel dedicated as forested habitat). For this, all assessed environmental variables in that pixel should be taken into consideration. Finding such a suitable location is far from trivial, especially in large regions where coarse-resolution environmental layers have been used. In such a case, selection could be facilitated through the use of finer-grained local or regional layers.

Conclusions

In sum, we propose a framework to generate an optimal sampling design for countrywide, regional or landscape-scale microclimate networks to lay an important foundation necessary for future implementation. Even with limited implementation by governments, organizations and/or ecologists, it will establish a necessary benchmark for climate monitoring at scales appropriate for understanding impacts to organisms [START_REF] Suggitt | Extinction risk from climate change is reduced by microclimatic buffering[END_REF]. If implemented widely, our proposed approach would provide landscape, regional and even global information on local climatic conditions in forests, mountains, cities and all other habitats where such in-situ climate data is currently critically needed. Indeed, if we are to provide reliable future scenarios for climate change, biodiversity and ecosystem services as aimed for by IPCC and IPBES, we urgently need to better quantify how temperatures are changing in mountains, cities, croplands, shrublands, forests and other habitats or microhabitats that are not well captured by standard terrestrial weather stations. Only then will we be able to detect and attribute climate change impacts with confidence and certainty.

Figures

Figure 1. Location of the four triptychs (in South America, Africa, Europe and Asia) used to exemplify our framework. Background map of mean annual temperatures (MAT, °C) from CHELSA [START_REF] Karger | Climatologies at high resolution for the earth's land surface areas[END_REF].

Numbers behind each country's name indicate the number of data loggers for each country following our approach, which aims for 25 locations per degree Celsius of mean annual temperature range at a 1 km²resolution within the focal country (see Fig. 2 for details). S2), for a-d) the Netherlands, e-h) Belgium and i-l)

France on a background map of elevation (m a.s.l.). The PCA was created using environmental conditions derived from gridded products at all points from a 1 km-resolution grid over the whole country (see Supplementary Information 1 for detailed methodology). Final selected locations are plotted with a red dot on the map. Scatterplots show for each country two example environmental drivers (Topographic Roughness Index and Fraction of Absorbed Photosynthetic Active Radiation (FAPAR)) as a function of elevation within the country, and the two main axes of the PCA. Small black dots in the scatterplots give the existing variability within the country -each dot represents one location of the grid -red crosses mark the selected locations. For results of the other triptychs, see Fig. 3. Morocco, f) Tunisia and Asia: g) India, h) Nepal and i) Bangladesh. Maps portray the potential locations of microweather stations, stratified according to the three main axes of a PCA of environmental variables on a background of elevation (m a.s.l.). For methodological details, see Supplementary Information 1, for exemplary scatterplots, see Supplementary Figures S1-3.

  (at a spatial resolution of 1 km² for the period 1979-2013), (ii) elevation, northness (the orientation of the slope relative to a northern bearing), the topographic position index (TPI), and the topographic roughness index (TRI) from Amatulli et al. (2018) (derived at 1 km² resolution from a global digital elevation model (DEM) at 250 m² resolution (GMTED2010)); and (iii) the annual Fraction of Absorbed Photosynthetic Radiation (FAPAR, COPERNICUS) to capture vegetation structure (Hengl 2018) (at a spatial resolution of 250 m² for the period 2014-2019). This sampling strategy resulted in between 42 (the Netherlands) and 520 (Chile) sampling locations per country (Figs. 1-3, Supplementary Figures 1

Figure 2 .

 2 Figure 2. A framework to establish country-wide microclimate networks. Maps portray the potential

Figure 3 .

 3 Figure 3. Examples of location selection following our framework to establish country-wide microclimate

JJL received a postdoctoral fellowship from the Research Foundation Flanders (FWO, 12P1819N). JL received funding from the Agence Nationale de la Recherche (ANR) under the framework of the young investigators (JCJC) funding instrument (ANR JCJC Grant project N°ANR-19-CE32-0005-01: IMPRINT). PDF received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (ERC Starting Grant FORMICA 757833). We thank Stijn Van de Vondel for trying out the R code.

Example code is available at https://doi.org/10.6084/m9.figshare.14130116.v1. This code

Supplementary Figures

Fig. S1. Scatterplots for a selection of environmental drivers (Topographic Position Index, Topographic Roughness Index and FAPAR) as a function of elevation within the country for Argentina (a-c), Chile (d-f) and Uruguay (g-i), with selected locations marked with a red dot. Small black dots in the scatterplots give the existing variability within the country -each dot represents one location of the 1 km grid -red dots mark the selected locations. Roughness Index and FAPAR) as a function of elevation within the country for Algeria (a-c), Morocco (d-f) and Tunisia (g-i), with selected locations marked with a red dot. Small black dots in the scatterplots give the existing variability within the country -each dot represents one location of the 1 km grid -red dots mark the selected locations.

Fig. S3. Scatterplots for a selection of environmental drivers (Topographic Position Index, Topographic

Roughness Index and FAPAR) as a function of elevation within the country for India (a-c), Nepal (d-f) and Bangladesh (g-i), with selected locations marked with a red dot. Small black dots in the scatterplots give the existing variability within the country -each dot represents one location of the 1 km grid -red dots mark the selected locations. 2, yet without the square-root correction of the principal components. Maps portray the potential locations of microclimate stations, stratified according to the three main axes of a PCA of environmental variables on a background of elevation (m a.s.l.). Scatterplots for a selection of environmental drivers (Topographic Position Index, Topographic Roughness Index and FAPAR) as a function of elevation within the country for Argentina (a-c), Chile (d-f) and Uruguay (g-i), with selected locations marked with crosses with a red dot. Small black dots in the scatterplots give the existing variability within the country -each dot represents one location of the grid -red dots mark the selected locations. Note that in this approach, more weight is given to outliers in the environmental space, with thus more investment in the topographically more complex regions of the countries.