D R Michael

B Ashcroft

Dr Pieter De Frenne

Mr Martin Kopecký

D R Robert

G Björk

Dr Martin Svatek

PROF Alistair Jump

Dr Michele Carbognani

D R Koenraad Van Meerbeek

Dr Jiri Dolezal

PROF Juergen Kreyling

Ms Andrea Lamprecht

Dr Sylvia Haider

D R Marijn Bauters

Dr Miguel Portillo-Estrada

D R Isla

H Myers-Smith

D R Jörg

G Stephan

D R Rebecca

Anne Senior

Dr Fatih Fazlioglu

Dr Fernando Moyano

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

Juha Aalto

Michael B Ashcroft

Pieter De Frenne

Martin Kopecký

Jonathan Lenoir

Miska Luoto

Ilya M D Maclean

Olivier Roupsard

Eduardo Fuentes-Lillo

Rafael A García

Loïc Pellissier

Camille Pitteloud

Juha M Alatalo

Stuart W Smith

Robert G Björk

Lena Muffler

Simone Cesarz

Felix Gottschall

Amanda Ratier Backes

Joseph Okello

Josef Urban

Roman Plichta

Martin Svátek

Shyam S Phartyal

Wipf

Nico Eisenhauer

Mihai Pușcaș

Pavel Dan Turtureanu

Andrej Varlagin

Romina D Dimarco

Alistair S Jump

Krystal Randall

Ellen Dorrepaal

Keith Larson

Josefine Walz

Luca Vitale

Miroslav Svoboda

Rebecca Finger Higgens

H Aud

Halbritter

Salvatore R Curasi

Ian Klupar

Austin Koontz

William D Pearse

Elizabeth Simpson

Michael Stemkovski

Jessen Bente

Graae

Mia Vedel Sørensen

Toke T Høye

M Rosa

Fernández Calzado

Juan Lorite

Michele Carbognani

Marcello Tomaselli

G W Forte

Alessandro Petraglia

Stef Haesen

Ben Somers

Koenraad Van Meerbeek

Mats P Björkman

Kristoffer Hylander

Sonia Merinero

Mana Gharun

Nina Buchmann

Jiri Dolezal

Radim Matula

Andrew D Thomas

Joseph J Bailey

Dany Ghosn

George Kazakis

Miguel Angel De Pablo

Julia Kemppinen

Pekka Niittynen

Lisa Rew

Tim Seipel

Christian Larson

James D M Speed

Jonas Ardö

Nicoletta Cannone

Mauro Guglielmin

Francesco Malfasi

Maaike Y Bader

Rafaella Canessa

Angela Stanisci

Jonas Schmeddes

Laurenz Teuber

Valeria Aschero

Marek Čiliak

František Máliš

Pallieter De Smedt

Sanne Govaert

Camille Meeussen

Pieter Vangansbeke

Khatuna Gigauri

Andrea Lamprecht

Harald Pauli

Klaus Steinbauer

Manuela Winkler

Masahito Ueyama

Martin A Nuñez

Tudor-Mihai Ursu

Sylvia Haider

Ronja E M Wedegärtner

Marko Smiljanic

Mario Trouillier

Martin Wilmking

Jan Altman

Josef Brůna

Lucia Hederová

Martin Macek

Matěj Man

Jan Wild

Pascal Vittoz

Meelis Pärtel

Peter Barančok

Róbert Kanka

Jozef Kollár

Andrej Palaj

Agustina Barros

Ana Clara Mazzolari

Marijn Bauters

Pascal Boeckx

José Luis

Benito Alonso

Shengwei Zong

Valter Di Cecco

Zuzana Sitková

Katja Tielbörger

Liesbeth Van Den Brink

Robert Weigel

Jürgen Homeier

C Johan Dahlberg

Sergiy Medinets

Volodymyr Medinets

Hans J De Boeck

Miguel Portillo-Estrada

Lore T Verryckt

Ann Milbau

Gergana N Daskalova

Haydn J D Thomas

Isla H Myers-Smith

Benjamin Blonder

Jörg G Stephan

Patrice Descombes

Florian Zellweger

Esther R Frei

Bernard Heinesch

Christopher Andrews

Jan Dick

Lukas Siebicke

Adrian Rocha

Rebecca A Senior

Christian Rixen

Juan J Jimenez

Julia Boike

Aníbal Pauchard

Thomas Scholten

Brett Scheffers

David Klinges

Edmund W Basham

Jian Zhang

Zhaochen Zhang

Charly Géron

Fatih Fazlioglu

Onur Candan

Jhonatan Sallo Bravo

Filip Hrbacek

Kamil Laska

Edoardo Cremonese

Peter Haase

Fernando E Moyano

Christian Rossi

Ivan Nijs

Johan Dahlberg

Miguel Ángel

Pablo Hernández

Juan J Jiménez

Jhonatan Sallo

SoilTemp: a global database of near-surface temperature Running title -SoilTemp: call for data

Introduction

Current ecological research increasingly deals with large-scale patterns and processes, with global databases of species distributions and traits becoming increasingly available [START_REF] Bramer | Global trait-environment relationships of plant communities[END_REF][START_REF] Kissling | Towards global data products of Essential Biodiversity Variables on species traits[END_REF][START_REF] Kattge | TRY plant trait database-enhanced coverage and open access[END_REF]. Analyses of these patterns and processes -and their predictions under anthropogenic climate change -often rely on global climatic grids at coarse spatial resolutions interpolated from standardized weather stations that represent long-term average atmospheric conditions [START_REF] Lembrechts | Incorporating microclimate into species distribution models[END_REF]. Moreover, sensors in these weather stations are shielded from direct solar radiation and located at ~2 meters above a frequently mown lawn (free-air temperature or 'macroclimate ', Jarraud, 2008). These climatic grids thus ignore many climate-forcing processes that operate near the ground surface, at fine spatiotemporal resolutions, and in environments that vary in their exposure to winds, radiation and moisture ('microclimate', [START_REF] Daly | Guidelines for assessing the suitability of spatial climate data sets[END_REF][START_REF] Bramer | Global trait-environment relationships of plant communities[END_REF][START_REF] Körner | The 90 ways to describe plant temperature[END_REF]. Importantly, while these microclimatic processes often operate at fine spatiotemporal resolutions, they can affect ecological relations both at the local and the global scale [START_REF] De Frenne | Microclimate moderates plant responses to macroclimate warming[END_REF], Ashcroft et al., 2014, Lembrechts et al., 2019). For example, they can potentially protect grounddwelling biota against long-term climate variability, providing microrefugia for these species to survive in locations deemed unsuitable in models using climate data at coarse spatial resolutions, or buffer organisms against short-term extreme events [START_REF] De Frenne | Microclimate moderates plant responses to macroclimate warming[END_REF][START_REF] Lembrechts | Climatic microrefugia under anthropogenic climate change: implications for species redistribution[END_REF][START_REF] Bramer | Global trait-environment relationships of plant communities[END_REF], Suggitt et al., 2018). Microclimates can however also expose organisms to more extreme temperatures, in which case distribution models that ignore such microclimates may erroneously predict species survival instead of extinction [START_REF] Pincebourde | Narrow safety margin in the phyllosphere during thermal extremes[END_REF]. In order to provide realistic forecasts of species distributions and performance, as well as of the functioning of the ecosystems they operate in, climate data that incorporates microclimatic processes, ideally measured in-situ, are thus urgently needed [START_REF] Körner | The 90 ways to describe plant temperature[END_REF].

Horizontal and vertical features driving microclimate

The offset between micro-and macroclimate is particularly pronounced around the soil surface, as temperatures measured at 2 m above the ground can differ substantially from those at ground level, or in the layers just above and below it [START_REF] Geiger | The climate near the ground[END_REF], Lembrechts et al., 2019). This offset can result from both 'horizontal' and 'vertical' features (Fig. 1), and can exceed several degrees centigrade in annual averages. For example, [START_REF] Kearney | MicroclimOz-A microclimate data set for Australia, with example applications[END_REF] modelled coarse-scale soil temperatures at various depths considering the vertical features affecting the radiation balance. These vertical features include the effects of vegetation characteristics (e.g. structure and cover), snow cover and soil characteristics (e.g. moisture content,

Accepted Article

This article is protected by copyright. All rights reserved geological types, texture and bulk density) [START_REF] Li | Soil temperature as influenced by forest cover[END_REF][START_REF] Zhang | Impact of snow cover on soil temperature and its simulation in a 458 boreal aspen forest[END_REF], Lembrechts et al., 2019). The result of these vertical features is not only an instantaneous temperature offset between air and soil temperatures, but also a buffering effect, i.e. the temporal variability in temperature changes is lower in the soil than in the air [START_REF] Geiger | The climate near the ground[END_REF][START_REF] Ashcroft | Moisture, thermal inertia, and the spatial distributions of near-surface soil and air temperatures: Understanding factors that promote microrefugia[END_REF]. Horizontal processes on the other hand relate more to the spatial resolution of the climatic data. They can be broken up into those that require only fine-resolution environmental information for specific sites (e.g. effects of slope and aspect on radiation balances; [START_REF] Bennie | Slope, aspect and climate: Spatially explicit and implicit models of topographic microclimate in chalk grassland[END_REF], and those where temperatures are also affected by neighboring locations (e.g. topographic shading, cold-air drainage and atmospheric temperature inversions, which are landscape context dependent; [START_REF] Whiteman | Breakup of temperature inversions in deep mountain valleys: Part I. Observations[END_REF][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]. How horizontal and vertical features interact to define differences between soil and air temperature may differ with the biome, season and day time. For example, in grasslands during summer, incoming shortwave solar radiation is usually the dominant factor determining daytime soil surface temperatures, which in turn result in higher air temperatures through convective heating [START_REF] Geiger | The climate near the ground[END_REF]. However, during winter, horizontal processes such as cold-air drainage and coastal buffering can have larger effects, especially on overnight air temperatures, when air temperatures may be driving soil temperatures rather than vice-versa [START_REF] Vitasse | Intensity, frequency and spatial configuration of winter temperature inversions in the closed La Brevine valley, Switzerland[END_REF]. In dense forests, the situation is even more complex: upper canopies block the bulk of short wave solar radiation, such that sub-canopy temperatures are determined by convective heat transfer between the air surrounding the canopy and direct conductance through physical contact of different parts of the canopy layer, in addition to the limited radiation that does permeate the canopy [START_REF] Körner | A world-wide study of high altitude treeline temperatures[END_REF][START_REF] Lembrechts | Climatic microrefugia under anthropogenic climate change: implications for species redistribution[END_REF][START_REF] Zellweger | Advances in microclimate ecology arising 456 from remote sensing[END_REF]. As a result, horizontal processes such as passing fronts, and winds blowing in hotter or colder air from outside the forest, will in large part define the -dampened -temperature patterns under forest canopies [START_REF] Ashcroft | The effect of exposure on landscape scale soil surface temperatures and species distribution models[END_REF].

The need for microclimate data across the field of ecology

Many organisms living in the soil and close to the soil surface (e.g. soil micro-organisms like fungi, ground arthropods, herbs, mosses, tree seedlings and small vertebrates) only experience fine-scale soil and/or near-surface temperatures, and thus likely relate less strongly to free-air temperatures [START_REF] Randin | Introduction of snow and geomorphic disturbance variables into predictive models of alpine plant distribution in the Western Swiss Alps[END_REF][START_REF] Niittynen | The importance of snow in species distribution models of arctic vegetation[END_REF], Lembrechts et al., 2019). This may be reflected in a species' distribution, but also their morphology, physiology and behavior [START_REF] Körner | A world-wide study of high altitude treeline temperatures[END_REF][START_REF] Kearney | The potential for behavioral thermoregulation to buffer "coldblooded" animals against climate warming[END_REF][START_REF] Opedal | Linking small-scale topography with microclimate, plant species diversity and intra-specific trait variation in an alpine landscape[END_REF][START_REF] Boeck | Ideas and perspectives: Heat stress: more than hot air[END_REF]. Many species indeed survive, live and reproduce where average background climate appears unsuitable, and equally may be gone from sites within apparently suitable

Accepted Article

This article is protected by copyright. All rights reserved areas where microclimatic extremes exceed their limits [START_REF] Suggitt | Habitat microclimates drive fine-scale variation in extreme temperatures[END_REF]. Without microclimate data, we not only lack information on the potential thermal heterogeneity that is available for species to thermoregulate in situ, but also on the true magnitude of climate change that species will be exposed to [START_REF] Pincebourde | Fine-scale microclimatic variation can shape the responses of organisms to global change in both natural and urban environments[END_REF][START_REF] Maclean | Fine-scale climate change: modelling spatial variation in biologically meaningful rates of warming[END_REF]. Accurately predicting how species' ranges will shift under climate change requires a good understanding of the variety of climate niches truly available to them [START_REF] Maclean | Microclimates buffer the responses of plant communities to climate change[END_REF][START_REF] Lembrechts | Climatic microrefugia under anthropogenic climate change: implications for species redistribution[END_REF]. The latter requires both a good understanding of what defines current microclimates, as well of how climate change will interact with the drivers of microclimatic conditions [START_REF] Maclean | Predicting future climate at high spatial and temporal resolution[END_REF]. Additionally, it is the soil temperature rather than the air temperature that defines many ecosystem functions in and close to the soil, like evapotranspiration, decomposition, root growth, biogeochemical cycling and soil respiration [START_REF] Pleim | An indirect data assimilation scheme for deep soil temperature in the Pleim-Xiu land surface model[END_REF], Portillo-Estrada et al., 2016[START_REF] Hursh | The sensitivity of soil respiration to soil temperature, moisture, and carbon supply at the global scale[END_REF][START_REF] Gottschall | Tree species identity determines wood decomposition via microclimatic effects[END_REF][START_REF] Medinets | Seasonal dynamics and profiles of soil NO concentrations in a temperate forest[END_REF]. Given the repeatedly proven sensitivity of many of these processes to temperatures [START_REF] Rosenberg | From climate and CO2 enrichment to evapotranspiration[END_REF][START_REF] Coûteaux | Litter decomposition, climate and litter quality[END_REF][START_REF] Schimel | Climate and nitrogen controls on the geography and timescales of terrestrial biogeochemical cycling[END_REF], here again having accurate measurements will be of utmost importance. The carbon balance in boreal forests, for example, is largely dependent on soil thaw and thus soil rather than air temperatures [START_REF] Goulden | Sensitivity of boreal forest carbon balance to soil thaw[END_REF]. These realizations highlight the urgency to start using soil and near-surface microclimate data when modelling the ecology and biogeography of surface and soil-dwelling organisms, as well as the functioning of soil ecosystems, instead of readily available coarse-scaled free-air climate data (from e.g. CHELSA [START_REF] Karger | Climatologies at high resolution for the earth's land surface areas[END_REF], TerraClimate [START_REF] Abatzoglou | TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958-2015[END_REF] or WorldClim [START_REF] Fick | WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas[END_REF]). While a suit of models now exist that produce fine-scale climate data [START_REF] Bramer | Global trait-environment relationships of plant communities[END_REF][START_REF] Lembrechts | Incorporating microclimate into species distribution models[END_REF], we do not yet fully understand whether models using data that represent average conditions over large areas provide adequate "mean field approximations" of (i.e. are representative for) more complex spatiotemporal effects driven by the climatic conditions that organisms experience [START_REF] Bennie | Seeing the woods for the trees -when is microclimate important in species distribution models?[END_REF]. To accomplish the latter, global in-situ data is needed for large-scale fine-resolution calibration and validation of these models. However, while the quality and resolution of free-air temperature data and models at the global scale is rapidly improving [START_REF] Bramer | Global trait-environment relationships of plant communities[END_REF], soil temperature datasets used in biogeography and biogeochemistry are still largely restricted to the landscape or regional scale, at best, and from intensively studied regions only [START_REF] Ashcroft | The effect of exposure on landscape scale soil surface temperatures and species distribution models[END_REF][START_REF] Ashcroft | Climate change at the landscape scale: predicting finegrained spatial heterogeneity in warming and potential refugia for vegetation[END_REF][START_REF] Carter | Modelling the soil microclimate: does the spatial or temporal resolution of input parameters matter?[END_REF][START_REF] Aalto | Biogeophysical controls on soil-atmosphere thermal differences: implications on warming Arctic ecosystems[END_REF], or they are derived from models lacking fine-grained ground-truthing data (e.g. Copernicus Climate Change Service (C3S), 2019). Land surface temperatures as obtained from satellite data, on the other hand, are hampered by their inability to measure below the vegetation cover [START_REF] Bramer | Global trait-environment relationships of plant communities[END_REF].

Accepted Article

This article is protected by copyright. All rights reserved In order to accurately describe and predict the (future) distribution and/or traits of surface and soildwelling species at larger scales, we need to improve our general knowledge of the offsets and spatiotemporal changes in variability between soil-level and free-air temperatures [START_REF] Aalto | Biogeophysical controls on soil-atmosphere thermal differences: implications on warming Arctic ecosystems[END_REF], Lembrechts et al., 2019). There is an urgent need to work towards globally available soil and near-surface temperature data based on in-situ measurements and at relevant spatiotemporal resolutions [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][START_REF] Pradervand | Very high resolution environmental predictors in species distribution models: Moving beyond topography?[END_REF][START_REF] Slavich | Topoclimate versus macroclimate: how does climate mapping methodology affect species distribution models and climate change projections[END_REF][START_REF] Opedal | Linking small-scale topography with microclimate, plant species diversity and intra-specific trait variation in an alpine landscape[END_REF], Meineri & Hylander, 2017).

Launch of the SoilTemp database

To tackle these issues, we launch an ambitious database initiative, compiling soil and near-surface temperature data from all over the world into a global geospatial database: SoilTemp. At the time of writing, we brought together temperature data from 7538 sensors placed both below, at and above (up to 2 m) the soil surface (Fig. 2a), which is an accumulation of over 180.000 months of temperature data with measurement intervals between 1 and 240 minutes (>30% every 60 minutes). The database hosts loggers from 51 different countries spread across all continents, with a broad distribution across the world's climatic space (Fig. 2b). There is a dominance of time series from Europe and areas below 1500 m a.s.l. (Fig. 2c,d). More than 75% of sensor measurements occurred within the last decade, but the database does contain several time series covering longer time periods as well, with a maximum of 42 years (Fig. 2d). When the remaining critical gaps in our spatial coverage will be filled (see below), this database will allow global assessments of the long-established theories on boundary layer climatology in heterogeneous environments [START_REF] Geiger | The climate near the ground[END_REF], which has so far been lacking. The growing database provides a unique opportunity to disentangle the role of the different horizontal and vertical features influencing soil and near-surface temperature across all biomes of the world, with high spatial and temporal resolutions. It will allow relating patterns in soil temperature to processes in the lower air layers and calibrate and validate global models of soil temperature and (micro)climate (Kearney et al., 2014a, Kearney et al., 2014b[START_REF] Carter | Modelling the soil microclimate: does the spatial or temporal resolution of input parameters matter?[END_REF][START_REF] Maclean | Fine-scale climate change: modelling spatial variation in biologically meaningful rates of warming[END_REF]. It will also allow us to create global maps of a wide array of general and microclimate-specific bioclimatic variables (e.g. growing degree days, growing season length) at relevant spatiotemporal resolutions [START_REF] Körner | The 90 ways to describe plant temperature[END_REF]. Ultimately, this joint global effort and the resulting global microclimatic products will enable us to improve analyses of the relationships between species' macroecology and the microclimate they experience, identify microrefugia and stepping stones and improve global models of ecosystem functioning and element cycling. Indeed, replacing the coarse-scaled free-air temperature averages used

Accepted Article

This article is protected by copyright. All rights reserved traditionally in models in all fields of ecology with these more relevant soil-specific data products is likely to increase their descriptive and predictive power, as the countless above-mentioned regional studies exemplify (Lembrechts et al., 2019). Additionally, this first global effort to combine and collect in-situ measurements will help solve long-standing issues regarding sensor comparability and data collection variability [START_REF] Bramer | Global trait-environment relationships of plant communities[END_REF], as well as address the question at what spatial scale microclimate data can prove most informative for ecological modelling [START_REF] Jucker | A research agenda for microclimate ecology in human-modified tropical forests[END_REF]. The temperature time series in the database, many of which are covering increasingly long time periods of up to a decade or more, will also allow fine-tuning forecasts of microclimate data into the future by deepening our understanding of the link between microclimatic dynamics in the soil and the air [START_REF] Lembrechts | Climatic microrefugia under anthropogenic climate change: implications for species redistribution[END_REF][START_REF] Wason | Climates on the move: Implications of climate warming for species distributions in mountains of the northeastern United States[END_REF][START_REF] Bramer | Global trait-environment relationships of plant communities[END_REF][START_REF] Maclean | Predicting future climate at high spatial and temporal resolution[END_REF], improving our predictions of biodiversity and ecosystem functioning under climate change.

Dig out your loggers! A call for contributions

To reach these goals, we encourage scientists owning in-situ measured temperature data to submit these to the growing SoilTemp database. All time series spanning one month or more, with temperature measurements a maximum of 4 hours apart, all soil depths, all heights above the ground up till two meters, all biomes, and all sensor types and brands will be accepted. Note that both spatially dense and sparse logger networks, as well as single loggers are accepted. The achieved spatial resolution is dependent on the provision of spatially precise coordinates to achieve a good relationship with potential explanatory variables (e.g. high resolution remotely sensed environmental data). If we have these coordinates and thus the location and distance between loggers, we can effectively obtain the extent and spacing for each logger network [START_REF] Western | Scaling of soil moisture: A hydrologic perspective[END_REF]. We include data from both observational and experimental plots, yet sensors have to be measuring in-situ and not in pots, and experiments manipulating the local climate (e.g. open-top chambers, rain-out shelters or vegetation-removal experiments) are excluded (Table 1). Given currently less well-represented climate regions, we especially encourage submissions from extreme cold and hot environments to fill the remaining gaps in our global coverage. More specifically, hot tropical climates (both tropical rainforests and tropical seasonal forests and savannas) and cold and hot deserts are currently still largely underrepresented (Fig. 2b), in particular from Africa, Asia, Antarctica and the Americas (Fig. 2a). Data contributors will be invited as co-authors on the main global papers resulting from this database (see Supplementary Materials for details on terms of use and data ownership).

Accepted Article

This article is protected by copyright. All rights reserved By encouraging sampling and submissions from remote areas, we aim to help solve the global sampling bias in soil ecological data [START_REF] Cameron | Global gaps in soil biodiversity data[END_REF][START_REF] Guerra | Blind spots in global soil biodiversity and ecosystem function research[END_REF], and we hope to build a truly global network representing -and actively engaging -scientists from a wide diversity of cultural backgrounds [START_REF] Maestre | Recommendations for establishing global collaborative networks in soil ecology[END_REF]. More information is available on the SoilTemp website, accessible via Figshare (DOI 10.6084/m9.figshare.12126516). When fully established, the SoilTemp database and its derivative products (e.g. bioclimatic variables) will be made freely available to facilitate the analysis of global patterns in microclimates, increase the comparability between regional studies and simplify the use of accurate microclimatic data in ecology [START_REF] Bramer | Global trait-environment relationships of plant communities[END_REF]. At the moment, critical metadata is already freely accessible via Figshare (DOI 10.6084/m9.figshare.12126516). Given the absence of and the need for globally available soil microclimate data products at relevant spatial resolutions for use in ecological analyses, we believe that SoilTemp has the potential to become a highly important resource that will enable a step change in ecological modelling.

Accepted Article

This article is protected by copyright. All rights reserved

Accepted Article Figures Figure 1: The horizontal and vertical drivers of the offset between in-situ soil and free-air temperatures.

Conceptually, there are two different sets of features responsible for the offset between coarse-scale free air temperatures (top left, e.g. WorldClim, Fick & Hijmans, 2017) and fine-scale soil temperatures (bottom right, e.g. [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], Lembrechts et al., 2019),. Firstly, one can incorporate fine-scale horizontal climate-forcing factors like topography and terrain-related features, land cover types and distance to water bodies to go from coarse-scaled to finer resolutions (top right, e.g. [START_REF] Aalto | Revealing topoclimatic heterogeneity using meteorological station data[END_REF][START_REF] Macek | Maximum air temperature controlled by landscape topography affects plant species composition in temperate forests[END_REF]. Secondly, one can consider observation height, and the effects of vegetation characteristics (like structure and cover), snow cover and soil characteristics (like moisture, geological types, texture and bulk density) on the radiation balance to convert from free-air to soil temperatures (e.g. [START_REF] Kearney | MicroclimOz-A microclimate data set for Australia, with example applications[END_REF]. Both horizontal and vertical features can introduce positive or negative differences (offset values) between soil and air temperatures through their effects on processes related to the radiation balance, like wind, convective heat transfer and surface albedo. The complexities of these horizontal and vertical processes can vary with biome, season and time of day. Temperatures are represented here using an unspecified temperature range from cold (blue) to warm (red).

Accepted Article

Figure 2 :

 2 Figure 2: Overview of the status of the SoilTemp-database as of March 2020. Spatial (a), climatic (b), elevational (c) and temporal (d) distribution of sensors in the SoilTemp-database as of March 2020. (a) Background world map in WGS1984, hexagons with a resolution of approximately 70.000 km² using the dggridR-package in R. (b) Colors of hexagons indicate the number of sensors at each climatic location, with a 40 × 40 bin resolution. Small dots in the background represent the global variation in climatic space (obtained by sampling 1.000.000 random locations from the CHELSA world maps at a spatial resolution of

Table 190 Table 1 :

 1901 Minimal data requirements and obligatory metadata for submission to the database. For more

	189
	191	details, see Supplementary Material.
	192
		Minimum data requirements	Obligatory metadata
		Minimum one consecutive month of in-situ	Accurate (handheld GPS or finer) spatial
		measured temperature time series	coordinates of the loggers (+ estimated
			accuracy)
		Maximum time interval between measurements: 4	Height/depth of the sensor relative to the
		hours	soil surface
		No climate manipulation experiments (only control	Type or brand of temperature sensor used,
		plots of those experiments, or observational	and type of shelter (e.g. no shelter, home-
		studies)	made shelter, Stevenson screen…)
		No modelling studies (only empirical data)	Temporal resolution of the sensor
			Habitat classification
	193
	194

  000.000 random locations from the CHELSA world maps at a spatial resolution of 2.5 arc minutes. Overlay with dotted lines and numbers (from 1 to 9) depict a delineation of Whittaker biomes (adapted fromWhittaker, 1970): (1) tundra and ice, (2) boreal forest, (3) temperate seasonal forest, (4) temperate rainforest, (5) tropical rainforest, (6) tropical seasonal forest/savanna, (7) subtropical desert, (8) temperate grassland/desert, (9) woodland/shrubland. (c) Number of sensors in each elevation class. (d) Time span covered by each sensor in the database, ranked by starting date. Data showed from 1992 onwards, note that the time period covered by 4 loggers with starting dates in 1976 is truncated.

Acknowledgments

We thank Sylvain Pincebourde and an anonymous reviewer for their critical evaluation of our manuscript. This work was supported by the Research Foundation Flanders (FWO) through a postdoctoral fellowship to Jonas J. Lembrechts (12P1819N) and a Research Network Grant (WOG W001919N). We gratefully acknowledge all data contributors, all staff of the author institutions engaged in field measurements and equipment maintenance (namely Erik Herberg, Iris Hamersveld, Ida Westman, Fredrik Brounes, Pernille Eidsen, Eleanor Walker and the teachers participating in the Tepåseförsöket 2015) and the ILTER-network, and thank local peoples for permission to collect data on their lands. Temperature data collection on European GLORIA summits was funded by European Union

Accepted Article

This article is protected by copyright. All rights reserved 1 Research Group PLECO (Plants and Ecosystems), University of Antwerp, 2610 Wilrijk, Belgium, 2 Finnish Meteorological Inst., P.O. Box 503, FI-00101 Helsinki, Finland, 3 Dept of Geosciences and Geography, Gustaf Hällströmin katu 2a, Finland, 4 Centre for Sustainable Ecosystem Solutions, School of Biological Sciences, University of Wollongong, Wollongong, Australia, 5 Australian Museum, Sydney, Australia, 6 Forest & Nature Lab, Department of Environment, Ghent University, Geraardsbergsesteenweg 267, Belgium, 7 Institute of Botany of the Czech Academy of Sciences, Zámek 1, CZ-25243, Průhonice, Czech Republic, 8 Faculty of Forestry and Wood Sciences, Czech University of Life Sciences Prague, Kamýcká 129, Czech Republic, 9 UR 'Ecologie et Dynamique des Systèmes Anthropisées' (EDYSAN, UMR 7058 CNRS-UPJV), Univ. de Picardie Jules Verne, Amiens, France, 10 Environment and Sustainability Institute, University of Exeter, Penryn Campus, Penryn, UK, TR10 9FE, 11 CIRAD, UMR Eco&Sols, B.P. 1386, CP 18524, Dakar, Senegal, 12 Eco&Sols, Univ Montpellier, CIRAD, INRAE, IRD, Institut Agro, Montpellier, France, 13 Laboratorio de Invasiones Biológicas (LIB), Facultad de Ciencias Forestales, Universidad de Concepción, Concepción, Chile, 14 Instituto de Ecología y Biodiversidad (IEB), Santiago, Chile, 15 School of Education and Social Sciences, Adventist University of Chile, Chile, 16 Landscape Ecology, Institute of Terrestrial Ecosystems, Department of Environmental Systems Science, ETH Zürich, 8092 Zürich, Switzerland , 17 Unit of Land Change Science, Swiss Federal Research Institute WSL, 8903 Birmensdorf, Switzerland, 18 Department of Biological and Environmental Sciences, Qatar University, Doha, Qatar, 19 Environmental Science Center, Qatar University, Doha, Qatar, 20 Department of Biology, Norwegian University of Science and Technology, 7491 Trondheim, Norway, 21 Asian School of Environment, Nanyang Technological University, 42 Nanyang Ave, Singapore 639815, Singapore, 22 Department of Earth Sciences, University of Gothenburg, P.O. Box 460, SE-40530 Gothenburg, Sweden, 23 Gothenburg Global Biodiversity Centre, P.O. Box 461, SE-405 30 Gothenburg, Sweden, 24 Experimental Plant Ecology, Institute of Botany and Landscape Ecology, University of Greifswald, D-17487 Greifswald, Germany, 25 Plant Ecology, Albrecht-von-Haller-Institute for Plant Sciences, University of Goettingen, 37073 Goettingen, Germany, 26 German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany, 27 Institute of Biology, Leipzig University, Leipzig, Germany, 28 Institute of Biology / Geobotany and Botanical Garden, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany, 29 Isotope Bioscience Laboratory -ISOFYS, Ghent University, Coupure Links 653, 9000 Gent, Belgium, 30 Mountains of the Moon University, P.O Box 837, Fort Portal, Uganda, 31 Department of Forest Botany, Dendrology and Geobiocoenology, Mendel University in Brno, Czech Republic, 32 Siberian Federal University, Krasnoyarsk, Russia, 33 School of Ecology and Environment Studies, Nalanda University, Rajgir, India, 34 Department of Forestry and NR, H.N.B. Garhwal University, Srinagar-Garhwal, India, 35 WSL Institute for Snow and Avalanche Research SLF, 7260 Davos, Switzerland, 36 Swiss National Park, Chastè Planta-Wildenberg, 7530 Zernez, Switzerland, 37 A. Borza Botanical Garden and Department of Taxonomy and Ecology, Faculty of Biology and Geology, Babeș-Bolyai University, Cluj-Napoca, Romania, 38 A. Borza Botanical Garden, Babes-Bolyai University, Cluj-Napoca, Romania, 39 A.N. Severtsov Institute of Ecology and Evolution, Russian Academy of Sciences, 119071, Leninsky pr.33, Moscow, Russia, 40 Grupo de Ecología de Poblaciones de Insectos, IFAB (INTA -CONICET), Isla Victoria 4450, Bariloche, Argentina, 41 Biological and Environmental Sciences, Faculty of Natural Sciences, University of Stirling, Scotland, FK9 4LA, 42 Centre for Sustainable Ecosystem Solutions, School of Earth, Atmospheric and Life Sciences, University of Wollongong, Wollongong, New South Wales, 2522, Australia, 43 Climate Impacts Research Centre, Department of Ecology and Environmental Sciences, Umeå University, Abisko, Sweden, 44 CNR -Institute for mediterranean Agricultural and Forest Systems, Via Patacca 85, ercolano (napoli), Italy, 45 Dartmouth College, Hanover, NH, USA, 46 Department of Biological Sciences and Bjerknes Centre for Climate Research, University of Bergen, N-5020 Bergen, Norway, 47 Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556, USA, 48 Department of Biology and Ecology Center, Utah State University, 5305 Old Main Hill, Logan, UT 84322, USA, 49 Department of Life Sciences, Imperial College, Silwood Park Campus, Ascot, Berkshire SL5 7PY, UK, 50 Department of Bioscience and Arctic Research Centre, Grenåvej 14, 8410 Rønde, Denmark, 51 Department of Botany, University of Granada, Accepted Article This article is protected by copyright. All rights reserved 18071, Granada, Spain, 52 Department of Chemistry, Life Sciences and Environmental Sustainability, University of Parma, Parco Area delle Scienze 11/A, 43124 Parma, Italy, 53 Department of Earth and Environmental Sciences, Celestijnenlaan 200E, 3001 Leuven, Belgium, 54 Department of Ecology, Environment and Plant Sciences and Bolin Centre for Climate Research, Stockholm University, 106 91 Stockholm, Sweden, 55 Department of Ecology, Environment and Plant Sciences, Stockholm University, SE-106 91 Stockholm, Sweden, 56 Department of Environmental Systems Science, ETH Zurich, Universitaetstrasse 2, 8092 Zurich, Switzerland, 57 Faculty of Science, Department of Botany, University of South Bohemia, Na Zlaté Stoce 1, 37005 České Budějovice, Czech Republic, 58 Department of Geography and Earth Sciences, Aberystwyth University, Wales, UK, 59 Department of Geography, York St John University, Lord Mayor's Walk, York, YO31 7EX, United Kingdom, 60 Department of Geo-information in Environmental Management, Mediterranean Agronomic Institute of Chania, PO Box 85, 73100 Chania, Greece, 61 Department of Geology, Geography and Environment. University of Alcalá. 28805 Alcalá de Henares, Madrid, Spain., 62 Department of Land Resources and Environmental Sciences, Montana State University, Bozeman MT, USA, 59717, 63 Department of Natural History, NTNU University Museum, Norwegian University of Science and Technology, NO-7491 Trondheim Norway, 64 Department of Physical Geography and Ecosystem Science, Lund University, Sölvegatan 12 223 62 Lund Sweden, 65 Department of Science and High Technology, Insubria University, Via Valleggio 11, 22100 Como, Italy, 66 Department of Theoretical and Applied Sciences, Insubria University, Via Dunant 3, 21100 Varese, Italy, 67 Ecological Plant Geography, Faculty of Geography, University of Marburg, Deutschhausstr. 10, 35032, Marburg, Germany, 68 EnvixLab, Dipartimento di Bioscienze e Territorio, Università degli Studi del Molise, Via Duca degli Abruzzi s.n.c., 86039 Termoli, Italy, 69 Facultad de Ciencias Exactas y Naturales, Universidad Nacional de Cuyo, 70 Instituto Argentino de Nivologiá, Glaciologiá y Ciencias Ambientales (IANIGLA), CONICET, CCT-Mendoza, 71 Faculty of Ecology and Environmental Sciences, Technical University in Zvolen, T.G.Masaryka 24, 960 01 Zvolen, Slovakia, 72 Faculty of Forestry, Technical University in Zvolen, T.G.Masaryka 24, 960 01 Zvolen, Slovakia, 73 Georgian Institute of Public Affairs, Tbilisi, Georgia, 74 GLORIA Coordination, Institute for Interdisciplinary Mountain Research, Austrian Academy of Sciences (ÖAW) & Department of Integrative Biology and Biodiversity Research, University of Natural Resources and Life Sciences Vienna (BOKU), Silbergasse 30/3, 1190 Vienna, Austria, 75 Graduate School of Life and Environmental Sciences, Osaka Prefecture University, 599-8531, Japan, 76 Grupo de Ecología de Invasiones, INIBIOMA, CONICET/ Universidad Nacional del Comahue, Av. de los Pioneros 2350, Bariloche 8400, Argentina, 77 Institute of Biological Research Cluj-Napoca, National Institute of Research and Development for Biological Sciences, Bucharest, Romania, 78 Institute of Botany and Landscape Ecology, University Greifswald, D-17487 Greifswald, Germany, 79 Institute of Earth Surface Dynamics, Faculty of Geosciences and Environment, University of Lausanne, Géopolis, 1015 Lausanne, Switzerland, 80 Institute of Ecology and Earth Sciences, University of Tartu, Lai 40, Tartu 51005, Estonia, 81 Institute of Landscape Ecology Slovak Academy of Sciences, Štefánikova 3, 81499 Bratislava, Slovakia, 82 Jolube Consultor Botánico. C/Mariano R de Ledesma, 4. E-22700 Jaca, Huesca, SPAIN, 83 Key Laboratory of Geographical Processes and Ecological Security in Changbai Mountains, Ministry of Education, School of Geographical Sciences, Northeast Normal University, Changchun 130024, China, 84 Majella Seed Bank, Majella National Park, Colle Madonna, 66010 Lama dei Peligni, Italy, 85 National Forest Centre, Forest Research Institute Zvolen, T. G. Masaryka 22, 96001 Zvolen, Slovakia, 86 Plant Ecology Group, Department of Evolution and Ecology, University of Tübingen, Tübingen, Germany, 87 the County Administrative Board of Västra Götaland, SE-403 40 Gothenburg, Sweden, 88 Regional Centre for Integrated Environmental Monitoring, Odesa National I.I. Mechnikov University, 7 Mayakovskogo lane, 65082 Odesa, Ukraine, 89 Research Institute for Nature and Forest (INBO), Havenlaan 88, bus 73, 1000 Brussel, Belgium, 90 School of GeoSciences, University of Edinburgh, King's Buildings, Edinburgh, EH9 3FF, United Kingdom, 91 School of Life Sciences, Arizona State University, Tempe, AZ, USA, 92 Department of Environmental Science, Policy, and Management, University of California, Berkeley, CA 94720 USA, 93 Swedish University of Agricultural Sciences, Swedish Species Information Centre, Almas allé 8 E, 75651 Uppsala, Sweden, 94 Swiss Federal Research Institute WSL, 8903 Birmensdorf, Accepted Article This article is protected by copyright. All rights reserved Switzerland, 95 TERRA Teaching and Research Center, Faculty of Gembloux Agro-Bio Tech, University of Liege, Passage des déportés, 2, 5030 Gembloux, Belgium, 96 UK Centre for Ecology & Hydrology, Bush Estate, Penicuik, Midlothian, EH26 0QB, United Kingdom, 97 University of Goettingen, Bioclimatology, Büsgenweg 2, 37077 Göttingen, Germany., 98 University of Notre Dame, Department of Biological Sciences and the Environmental Change Initiative, 99 Woodrow Wilson School of Public and International Affairs, Princeton University, Princeton, NJ 08540, USA, 100 ARAID Research and Development, Zaragoza, Spain, 101 Alfred Wegener Institute Helmholtz Center for Polar and Marine Research, Telegrafenberg A45, 14473 Potsdam, Germany, 102 Geography Department, Humboldt-Universität zu Berlin, Germany, 103 Chair of Soil Science and Geomorphology, Department of Geosciences, University of Tuebingen, 72070 Tuebingen, Germany, 104 Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, FL 32611, USA, 105 School of Natural Resources and Environment, University of Florida, Gainesville, FL 32611, USA, 106 Zhejiang Tiantong Forest Ecosystem National Observation and Research Station, School of Ecological and Environmental Sciences, East China Normal University, Shanghai 200241, China, 107 Biodiversity and Landscape, TERRA research centre, Gembloux Agro-Bio Tech, University of Liège, Gembloux, 5032, Belgium ; Research Group PLECO (Plants and Ecosystems), University of Antwerp, 2610 Wilrijk, Belgium, 108 Faculty of Arts and Sciences, Department of Molecular Biology and Genetics, Ordu University, 52200, Ordu, Turkey, 109 Universidad Nacional de San Antonio Abad del Cusco, Cusco, Peru, 110 Department of Geography, Masaryk University, Brno, Czech Republic, 111 Climate Change Unit, Environmental Protection Agency of Aosta Valley, Sain Christophe, Aosta, Italy, 112 Senckenberg Research Institute and Natural History Museum Frankfurt, 63571 Gelnhausen, Germany, 113 Faculty of Biology,

FP-5 project GLORIA-Europe (EVK2-CT-2000-0006) and the Swiss MAVA Foundation project 'Climate change impacts in protected areas of the Alps and high mountains of Eastern Europe and the Mediterranean region', on the Eastern Swiss GLORIA summits by the Swiss Federal Office for the Environment (FOEn), the Research Commission and staff of the Swiss National Park, and the Foundation Dr. Joachim de Giacomi, on Tenerife in the framework of the Flexible Pool project (W47014118) of Sylvia Haider funded by the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, on Livingston Island, Antarctica by different research projects of the Gobern of Spain (PERMAPLANET CTM2009-10165-E; ANTARPERMA CTM2011-15565-E; PERMASNOW CTM2014-52021-R), and the PERMATHERMAL arrangement between the University of Alcalá and the Spanish Polar Committee and on the Western Swiss GLORIA summits by Département de la culture et des sports du Valais, Fondation Mariétan, Société académique de Genève, Swiss Federal Office of Education and Science and Swiss Federal Office for the Environment. Jan Wild, Martin Macek, Martin Kopecký, Lucia Hederová, Matěj Man and Josef Brůna were supported by the Czech Science Foundation (project 17-13998S) and the Czech Academy of Sciences (project RVO 67985939), Meelis Pärtel by an Estonian Research Council grant (PRG609) and by the European Regional Development Fund (Centre of Excellence EcolChange), Lena Muffler, Juergen Kreyling, Robert Weigel, Mario Trouillier, Martin Wilmking and Jonas Schmeddes by DFG GraKo 2010 Response, Juha M. Alatalo by Qatar Petroleum (QUEX-CAS-QP-RD-18/19), the authors from Odesa National I. I. Mechnikov University (Sergiy Medinets and Volodymyr Medinets) by EU FP6 The nitrogen cycle and its influence on the European greenhouse gas balance (NitroEurope), EU FP7 Effects of Climate Change on Air Pollution Impacts and Response Strategies for European Ecosystems (ÉCLAIRE), Ukrainian national research projects (No. 505, 550, 574) funded by Ministry of Education and Science of Ukraine and GEF-UNEP funded 'Towards INMS' project, see www.inms.international for more details. Florian Zellweger was supported by the Swiss National Science Foundation (grant no. 172198), Peter Barančok, Róbert Kanka, Jozef Kollár and Andrej Palaj by the Slovak Scientific Grant Agency (project VEGA 2/0132/18), Jonas Ardö by a infrastructure grant from faculty of Science, Lund University, Julia Kempinen by the Doctoral Programme in Geosciences at the University of Helsinki, Jan Altman by the Czech Science Foundation (projects 17-07378S

Accepted Article

This article is protected by copyright. All rights reserved and 20-05840Y), the Czech Academy of Sciences (project RVO 67985939) and Ministry of Education, Youth and Sport of the Czech Republic, program Inter-Excellence, subprogram Inter-Action (project LTAUSA19137), Toke Thomas Høye by the Carlsberg Foundation (grant no. CF16-0896) and the Villum Foundation (grant no. 17523), Jiri Dolezal by the Czech Science Foundation (projects 17-19376S), and Ministry of Education, Youth and Sport of the Czech Republic, program Inter-Excellence, subprogram Inter-Action (project LTAUSA18007), Nico Eisenhauer, Felix Gottschall and Simone Cesarz by the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, funded by the German Research Foundation (FZT 118), Stuart W. Smith by AfricanBioServices project funded by the EU Horizon 2020 grant number 641918, Haydn Thomas by a K Natural Environmental Research Council doctoral training partnership grant NE/L002558/1, Isla H. Myers-Smith by the UK Natural Environmental Research Council ShrubTundra Project NE/M016323/1, Anibal Pauchard, Rafael Garcia and Eduardo Fuentes-Lillo by the projects Fondecyt 1180205, Fondecyt 11170516 and CONICYT PIA APOYO CCTE AFB170008, Rafaella Canessa, Maaike Y. Bader, Liesbeth van den Brink, and Katja Tielbörger by the DFG Priority Programme 1803 EarthShape (projects 1 (BA 3843/6-1) and 11 (TI 338/14-1&2)), Martin Svátek by a grant from the Ministry of Education, Youth and Sports of the Czech Republic (grant number: INTER-TRANSFER LTT17017), Mihai Pușcaș by ODYSSEE project (ANR-13-ISV7-0004 France, PN-II-ID-JRP-RO-FR-2012 UEFISCDI Romania), Pavel Dan Turtureanu by UEFISCDI in Romania, MEMOIRE grant no. PN-III-P1-1.1-PD2016-0925, Jonathan Lenoir by the Agence Nationale de la Recherche (ANR) within the framework of the IMPRINT project "IMpacts des PRocessus mIcroclimatiques sur la redistributioN de la biodiversiTé forestière en contexte de réchauffement du macroclimat" (grant number: ANR-19-CE32-0005-01), Radim Matula and Roman Plichta by a grant Inter-Excellence (project: INTER-TRANSFER LTT17033) from the Ministry of Education, Youth and Sports of the Czech Republic, Lisa Rew by the National Institute of Food and Agriculture, U.S. Department of Agriculture Hatch MONB00363, Tim Seipel and Christian Larson by a grant from the United States National Institute of Food and Agriculture grant 2017-70006-27272, Nina Buchmann by the SNF (projects M4P 40FA40_154245, ICOS-CH 20FI21_148992, 20FI20_173691, InnoFarm 407340_172433) and the EU (SUPER-G contract no. 774124) for the Swiss FluxNet, Mana Gharun by the SNF project ICOS-CH Phase 2 20Fl20_173691, Sanne Govaert by the Research Foundation Flanders (FWO) (project G0H1517N Pieter De Frenne, Camille Meeussen. and Pieter Van Gansbeke by the European Research Council (ERC) under the European Union's Horizon

2020 research and innovation programme (ERC Starting Grant FORMICA 757833), Olivier Roupsard by EU-LEAP-Agri (RAMSES II), Agropolis and Total Foundation (DSCATT), CGIAR (GLDC) and EU-DESIRA (CASSECS), Zuzana Sitková by the Slovak Research and Development Agency under the project No. APVV-16-0325 and project ITMS 26220220066 co-funded by the ERDF, Brett Ryan Scheffers by National Geographic Society (grant no. 9480-14 and WW-240R-17), James D. M. Speed by the Research Council of Norway (262064), William D. Pearse and the Pearse Lab by National Science Foundation ABI-1759965, NSF EF-1802605 and United States Department of Agriculture Forest Service agreement 18-CS-11046000-041, Isla H. Myers-Smith by the UK Natural Environmental Research Council ShrubTundra Project NE/M016323/1, Andrew D Thomas by a Leverhulme Trust Research Fellowship under Government of Botswana permit EWT8/ 36/4 VIII(4), Shengwei Zong by National Natural Science Foundation of China (No. 41971124), Roman Plichta by the post-doc project 7.3 of Institutional plan of Mendel University in Brno 2019-2020, František Máliš by the Slovak Research and Development Agency project APVV-15-0270, Filip Hrbacek and Kamil Laska by the projects LM2015078 and CZ.02.01/0.0/0.0/16_013/0001708 of Ministry of Youth and Sports of the Czech Republic, T-M. Ursu was supported by the Ministry of Research and Innovation through Projects for Excellence Financing in RDI: Contract no. 22 PFE/2018 and PN2019-2022/19270201 -Ctr. 25N BIODIVERS 3-BIOSERV and Andrej Varlagin by RFBR project number 19-04-01234-a. Lore T. Verryckt is funded by a PhD fellowship from the Research Foundation Flanders (FWO) and acknowledges support from the European Research Council Synergy Grant; ERC-2562013-SyG-610028 IMBALANCE-P and Pallieter De Smedt holds a postdoctoral fellowship of the Research Foundation-Flanders (FWO) and The Kreinitz Experiment is a cooperative research project initiated by the Helmholtz Centre for Environmental Research -UFZ. We also acknowledge project 18-74-10048 from the Russian Science

Foundation, the Dirección General de Cambio Climático del Gobierno de Aragón, the Ordesa y Monte Perdido National Park and the Servicio de Medio Ambiente de Soria de la Junta de Castilla y León, the National Swiss Fund for research (SNSF, project "Lif3web", n°162604).

Conflict of Interest:

The authors declare that they have no conflict of interest.

Accepted Article


Anna Weigand
email: anna.weigand@uzh.ch.

Stefan Abrahamczyk

Isabelle Aubin

Claudia Bita-Nicolae

Helge Bruelheide

Cesar I Carvajal-Hernández

Daniele Cicuzza

Lucas Erickson

Nascimento Da Costa

Janos Csiky

Jürgen Dengler

János Csiky

André Luís De Gasper

Greg R Guerin

Sylvia Haider

Adriana Rojas Hernandez

Ute Jandt

Johan Reyes-Chávez

Dirk N Karger

Phyo Kay Khine

Jürgen Kluge

Thorsten Krömer

Marcus Lehnert

Jonathan Lenoir

Gabriel M Moulatlet

Daniela Aros Mualin

Sarah Noben

Ingrid Olivares

Luis García Quintanilla

Peter B Reich

Laura Salazar

Libertad Silva-Mijangos

Hanna Tuomisto

Patrick Weigelt

Gabriela Zuquim

Holger Kreft

Michael Kessler

Dr Roberto Miguel Klein

Global fern and lycophyte richness explained: How regional and local factors shape plot richness

come

Introduction

One of the most fundamental questions in plant ecology and biogeography asks which processes determine species richness patterns at different spatial scales (i.e. extents and resolutions/grain sizes). Regional and local species richness are interlinked through a hierarchy of historical, environmental and biotic processes but their relative influences on both regional and local richness remain unknown [START_REF] Cornell | Species Interactions, Local and Regional Processes, and Limits to the Richness of Ecological Communities: A Theoretical Perspective[END_REF]. Even though the importance of scale in ecological analyses has been commonly acknowledged since the 1950s (e.g. [START_REF] Hutchinson | The concept of pattern in ecology[END_REF][START_REF] Whittaker | Evolution of species diversity in land communities[END_REF][START_REF] Ricklefs | Community diversity: relative roles of local and regional processes[END_REF][START_REF] Levin | The problem of pattern and scale in ecology[END_REF][START_REF] Rahbek | The role of spatial scale and the perception of large-scale species-richness patterns[END_REF][START_REF] Chase | Species richness change across spatial scales[END_REF], detailed global analyses of the processes influencing biodiversity patterns at different spatial scales have long been hindered by lack of data and appropriate statistical methods [START_REF] Beck | What's on the horizon for macroecology[END_REF]. Today, advanced statistical methods (e.g. [START_REF] Keil | Integrating global patterns and drivers of tree diversity across a continuum of spatial grains[END_REF] and comprehensive datasets (e.g. Bruelheide et al., 2018) at different spatial scales have become available, allowing the study of macroecological patterns across scales for many organism groups;.

Ferns and lycophytes (hereafter together referred to as "ferns") have a global distribution, and with around 10,500 and 1350 currently accepted species, respectively (PPG1, 2016), make up the second and third largest vascular plant lineages on Earth after angiosperms [START_REF] Smith | Comparison of fern and flowering plant distributions with some evolutionary interpretations for ferns[END_REF]. With an estimated age of about 400 Myr, ferns are an exceptionally old group of vascular plants that -together with gymnosperms -dominated terrestrial vegetation before the rise of the angiosperms [START_REF] Niklas | Patterns in vascular land plant diversification[END_REF]. Large portions of their extant diversity, however, are of recent origin and their diversification happened "in the shadow of angiosperms" [START_REF] Schneider | Ferns diversified in the shadow of angiosperms[END_REF], commonly as understorey or epiphytic species in the newly developing angiosperm forests [START_REF] Schuettpelz | Evidence for a Cenozoic radiation of ferns in an angiosperm-dominated canopy[END_REF].

Over the last three decades, several studies have focussed on the spatial distribution of fern diversity at different spatial resolutions and extents: At a global extent, [START_REF] Kreft | Contrasting environmental and regional effects on global pteridophyte and seed plant diversity[END_REF] found regional fern species richness (i.e. the number of species occurring in regions with an extent of hundreds to thousands of square kilometres) to be strongly and positively related to water-energy variables such as potential evapotranspiration and precipitation. They also found a strong positive relationship with geographical habitat heterogeneity, whereas the total surface area of the focal region only had moderate effects.

Together, their results confirmed a strong latitudinal gradient in fern species richness, which peaked in tropical regions, especially in tropical mountains. Beyond effects of contemporary environment, significant differences between biogeographical realms (e.g. tropical Africa being less diverse than other tropical regions), pointed to a strong role of biogeographical and historical processes. This pattern is also known from other plant groups, such as palms [START_REF] Couvreur | Tropical rain forest evolution: palms as a model group[END_REF] or vascular plants in general [START_REF] Kreft | Global patterns and determinants of vascular plant diversity[END_REF]. Also for small grain sizes of typically a few hundred square metres, numerous studies have shown that fern diversity typically peaks in tropical regions and that especially the midelevations along elevational gradients are particularly species-rich (e.g. [START_REF] Salazar | Diversity patterns of ferns along elevation in Andean tropical forests[END_REF]Kessler, Kluge, Hemp & Ohlemüller, 2011, and references therein). For instance, in a comparison of 20 elevational gradients on different continents, [START_REF] Kessler | A global comparative analysis of elevational species richness patterns of ferns: Global analysis of fern transects[END_REF] found that the fern species richness correlated most strongly with climatic variables, mainly heat-related water deficiency at low elevations and predictors related to low temperatures that limit diversity at high elevations. The authors compare the elevational gradient to the latitudinal gradient in that temperatures decline towards higher elevations/latitudes. They also noted differences in local species richness among continents. Going one step further, Karger et al. (2011) compared five elevational transects within the tropics and found that after the effect of area of the representative elevational belt was accounted for, local and regional richness patterns were almost identical, suggesting that they are either driven by the same factors or that local richness equals a certain subset of regional richness and is directly driven by the size of the regional species pool. In contrast to these results, [START_REF] Karger | Island biogeography from regional to local scales: evidence for a spatially scaled echo pattern of fern diversity in the Southeast Asian archipelago[END_REF] found that on islands of different sizes in the Indo-Malayan archipelago, local species richness was driven by local environmental conditions and not by differences in regional (island-wide) species richness, suggesting that the two are independent. However, [START_REF] Tuomisto | Species richness and diversity along edaphic and climatic gradients in Amazonia[END_REF] found that local fern species richness in Amazonian rain forests increased with soil nutrient concentration, and that this was paralleled by an increase in the total number of species (substrate-specific species pool size).

In a broader context, it has been argued that the environment can strongly limit the number of locally co-occurring species numbers, thus leading to local saturation in that increases of regional species richness will not be reflected at the local scale [START_REF] Srivastava | Using local-regional richness plots to test for species saturation: pitfalls and potentials[END_REF][START_REF] Olivares | Assessing species saturation: conceptual and methodological challenges[END_REF]. Nevertheless, the cross-scale relationships between regional and local fern richness and their drivers remain puzzling. As mentioned above, environmental predictors at different resolutions have been discussed as underlying causes, but whether factors at larger grain size only act on regional richness or also on local/plot richness remains widely untested. Similarly, it has been shown that historical factors are correlated with both regional and local species richness. But it is unclear whether these factors, which typically act at large spatial extents and over long time periods, do not only influence regional richness but may also have more local effects. To understand the causal relationships, we analysed the hierarchical structure of environmental and biogeographical determinants of fern species richness at two resolutions, "regional" (at a large grain size of 7666 km 2 ) and "local" (i.e. at a fine grain size resolution of 4 km 2 ) on regional (7666 km 2 ) and plot fern richness (300-500 m 2 ). We combined a large dataset of almost 83,000 vegetation plots with a model of regional fern richness, which fills in gaps of unknown regional species richness in some areas, and makes regional richness comparable independently of age and completeness of the input species lists. Using structural equation modelling, we tried to answer the following main questions: Q1: How do plot and regional fern richness relate to each other on a global scale? Can we observe saturation? Q2: Do historical factors, summarised in biogeographical/botanical realms, act on regional and plot fern richness or is only regional richness impacted, which in turn shapes plot richness? Q3: Do regional environmental conditions (at large grain size) impact regional and plot fern richness or only regional, which in turn impacts plot richness?

Material and methods

Regional fern richness data

In order to bridge differences in completeness, age, and overall availability between regional species lists of protected areas, regions, states or countries, we built a model of regional fern species richness. For this, we extracted species presence information on 1243 geographic entities from the Global Inventory of Floras and Traits database (GIFT; Weigelt, König & Kreft, 2019). All originally available entities were filtered for those mentioning fern species numbers, including true absences. Entities with less than five occurring fern species were double checked to be certain that false underrepresentation is avoided. When an entity overlapped with another one by more than 10%, the larger one was excluded from analyses. Environmental predictor variables were extracted from 30-arc second resolution environmental raster layers and summarised across the extent of each geographic entity (as mean, median, 75% quantile or 95% quantile; the best fitting summary was chosen for each predictor). Botanical continent (continental scheme level 1 of the Taxonomic Database Working Group (Brummitt, 2001) was additionally included as a substitute variable of not directly measurable historical influences, which might influence vegetation patterns differently across biogeographical realms until today. Global coverage ranged from a minimum of 30 entities in the Antartic realm to a maximum of 315 in South America. In order to assess regional diversity independently from the covered geographical extent of the regional entity, we used generalised linear models (GLMs, with Poisson distribution) to ascertain environment-richness relationships and project regional fern richness on a hexagon equal area grid with a grid cell size of 7666 km 2 [START_REF] Barnes | dggridR: Discrete Global Grids for R. R package version 0[END_REF]. Based on what is known about determinants of global fern richness [START_REF] Kreft | Contrasting environmental and regional effects on global pteridophyte and seed plant diversity[END_REF], we tested environmental predictors (Supp. Table 1) variables from for the models and the best performing ones were included in the final model.

Plot richness data

To analyse plot fern richness, we collated a global dataset of almost 83,000 vegetation plots ranging in size between 300 m 2 and 500 m 2 from the sPlot consortium (Bruelheide et al., 2018), species lists from ground vegetation surveys carried out on Level II plots of the Europewide forest monitoring programme ICP Forests (http://www.icp-forests.net, [START_REF] Ferretti | Forest Monitoring: Methods for Terrestrial Investigations in Europe with an Overview of North America and Asia[END_REF][START_REF] Canullo | Part VII.1: Assessment of ground vegetation, Manual on methods and criteria for harmonized sampling, assessment, monitoring and analysis of the effects of air pollution on forests[END_REF], and published and unpublished data from fern inventories carried out by a research network on fern diversity (e.g. Kessler et al., 2011;[START_REF] Karger | Island biogeography from regional to local scales: evidence for a spatially scaled echo pattern of fern diversity in the Southeast Asian archipelago[END_REF][START_REF] Zuquim | Predicting environmental gradients with fern species composition in Brazilian Amazonia[END_REF][START_REF] Tuomisto | Species richness and diversity along edaphic and climatic gradients in Amazonia[END_REF][START_REF] Hernández-Rojas | Richness patterns of ferns along an elevational gradient in Sierra de Juarez, Oaxaca, Mexico: a comparison with Central and South America[END_REF] see Figure 1 for plot distribution and Supp. Table 2 for full plot list). For the sPlot data (sPlot version 1.2, 20 th April 2018), the following filters applied: at least one fern species occurring, explicit geographical coordinates with a location uncertainty below 5,000 m and an area size between 300 m 2 and 500 m 2 . ICP Forest data included Level II ground vegetation data between the years 1994 and 2012, accessed in October 2016. Plots that were sampled repeatedly throughout this time frame were kept, but never selected within the same run of the statistical analyses (see details below). For the Amazon plots that originally only included terrestrials and hemi-epiphytes up to 2 m height, species numbers were increased by 10% and rounded to the next full number in agreement with the responsible specialists. Although the data were collected in the scope of many different projects, they all included complete species inventories of fern and lycophyte species. Some of them applied thresholds to juvenile species, but as we are dealing with species presence numbers instead of abundance, we consider these differences to be negligible in comparison to the differences caused by environmental conditions.

Environmental data for each plot were extracted on a 2x2 km 2 scale due to potential GPS inaccuracies. Additionally, we extracted the number of predicted regional fern species from the regional model for the polygon in which each plot was located. Soil properties were not included due to the small-scale variation in soils in many regions of the Earth and the fact that soil samples from the plots were only available for a very small number of plots.

INSERT TABLE 1 AND FIGURE 1 AROUND HERE.

Statistical analyses

All spatial and statistical analyses were carried out in the programming environment R (version 3.5.1, R Core Team 2016). To analyse the hierarchical relationships between regional (coarse grain size: 7666 km 2 ) and local (fine grain size: 4 km 2 ) environment as well as fern richness at regional and plot scale, we used structural equation modelling (SEM; [START_REF] Shipley | Cause and Correlation in Biology: A User's Guide to Path Analysis, Structural Equations and Causal Inference[END_REF] of GLMs as implemented in the R-package "piecewiseSEM" [START_REF] Lefcheck | piecewiseSEM: Piecewise structural equation modeling in R for ecology, evolution, and systematics[END_REF].

We constructed multiple competing models to test the direction of environmental influences, allowing for coarse-grain size environment to only impact regional richness which in turn impacts plot richness (Figure 2A), or allowing coarse-grain size environment to additionally impact plot richness (Figure 2B).

INSERT FIGURE 2 AROUND HERE.

We did not link coarse-grain size and fine-grain size environment because we assume that they are being driven by the same external forces and are not causally connected. Finegrain size environment was tested as predictor of regional richness in model 6 (Figure 2B), but was defined as correlated error of regional richness in all other models to exclude it as a potential correlate. We also tested one model without a direct impact of regional fern richness on plot fern richness for completeness (model 7). To identify the most important environmental predictors for the models, we ran generalised linear models with a Poisson distribution for each response variable against all explanatory factors and evaluated the R-squared-values for each (Table 1). Afterwards, we used principal component analysis (PCA, R package ade4, [START_REF] Dray | The ade4 Package: Implementing the Duality Diagram for Ecologists[END_REF] to choose from the strongest predictors those that were not strongly correlated.

Even though it had a comparably low R 2 -value, we included habitat homogeneity on a regional, large grain size, as it was shown to be a strong predictor of regional richness in previous studies [START_REF] Kreft | Contrasting environmental and regional effects on global pteridophyte and seed plant diversity[END_REF], Table 1). Additionally, we tried to prevent the use of the same predictors at different grain sizes to prevent circularity (except for annual precipitation, which on both grain sizes was found to be extremely important). Since the structural equation models do not test whether the link between regional environment and regional richness exists but rather measures the strengths for the plot richness numbers, we believe that using the same predictors for the regional model as well as in the SEM is not problematic. However, the estimates of these relationships, which demonstrate how strong the predictors were for regional richness at the 83,000 plot localities, are less independent than for local environment and plot richness.

To prevent spatial unbalance in the input plot data, we randomly selected 10 plots from each 10° x 10° grid cell (see Figure 1). When less than 10 plots fell into a cell, all were selected.

Plots with identical GPS-coordinates were considered duplicates and all but one were excluded. This resulted in a final number of roughly 890-1000 plots for each run. This selection as well as the subsequent model testing was repeated 1000 repetition in order to account for differences in species richness of plots in the same grid cell as well as model uncertainty. We corrected for spatial autocorrelation using Moran Eigenvector GLM filtering (MEs, R-package "spdep";

Bivand & Wong, 2018), selecting those Eigenvectors that limit residual autocorrelation below an alpha of 0.05. These MEs were then added as additional variables in the competing SEMs, with links on regional and plot richness. The AIC-values of each model as well as the p-value and number of degrees of freedom of the Fisher's C-test of directed separation, which validates whether all unlinked paths are indeed independent, were extracted for each run, and the best performing model per run was noted. The final best model was the one that performed best most often in AIC and Fischer's C, averaged across all repetitions. Since the strength of the predictors depends strongly on the total range of values, we separated the two paths of the SEM and standardised the predictors to evaluate the models and predictor strengths separately in a later step.

To find the best curve shape describing the relationship between regional and plot richness, we tested a number of different regressions: linear, polynomial (2 nd and 3 rd degree) as well as Michaelis-Menten (R-package "drc", [START_REF] Ritz | Dose-Response Analysis Using R[END_REF] on the same subsampled plot dataset repeated 1000 times. We compared the AIC of the curves to identify the best fit.

Results

The predictor testing for the regional fern richness model revealed area size of input entity, elevational range, potential evapotranspiration, mean annual cloud frequency [START_REF] Wilson | Remotely Sensed High-Resolution Global Cloud Dynamics for Predicting Ecosystem and Biodiversity Distributions[END_REF], and habitat homogeneity (second order, [START_REF] Tuanmu | A global, remote sensing-based characterization of terrestrial habitat heterogeneity for biodiversity and ecosystem modelling[END_REF] as well as aridity index, temperature annual range, annual precipitation, and precipitation of warmest quarter obtained from the Climatologies at high resolution for the earth's land surface areas database (CHELSA; [START_REF] Karger | Climatologies at high resolution for the earth's land surface areas[END_REF]; Table 1) as strongest predictors. The final generalized linear model (Poisson distribution) of regional fern richness on a global extent had an explanatory power of 74.5% (R 2 ) and predictions ranged in species numbers between 0 and 439 at a spatial grain size of 7666 km 2 .

INSERT FIGURE 3 AROUND HERE.

For the environmental predictors on the fine grain-size of 4 km 2 , the GLM tests and principal component analyses (PCAs) revealed fine-grain size mean annual temperature, temperature seasonality, temperature annual range as well as mean annual cloud frequency and mean annual precipitation as strong predictors of plot fern richness. The coarse-grain size median of habitat homogeneity, of precipitation of warmest quarter, of annual precipitation, and the elevational range of each 7666 km 2 grid cell were identified as strong predictors of the regional richness at the plot localities.

The comparison of the regression models used to answer Q1 showed the polynomial curve of 3 rd degree to have the best fit in 99.9% of all cases (deltaAIC to next model: 28; Figure 4). The best model showed a gradual increase of plot richness with increasing regional richness up to a regional richness of around 300 species and a mean of plot richness of 23 fern species, with plot richness then decreasing to around 12 species at a regional richness of 440 species.

INSERT FIGURE 4 AND TABLE 2 AROUND HERE.

In the structural equation model, the AIC test as well as the X 2 -distributed Fisher's C statistic indicating the goodness-of-fit after tests of directed separation revealed the following order from most to least likely model (Table 2): The model chosen most frequently as the most likely (55.7%) was model 5, in which the coarse-grain size environment directly affects regional as well as plot richness (Q3) and botanical continent affects regional richness (Q2). The second most likely model was model 4 (44.2% chosen, deltaAIC = 19), which also included coarsegrain size environment affecting both regional and plot richness, but did not include an effect of botanical continent. Both models were almost equally often chosen as the most likely. However, the low Fisher's C value for model 4 indicates that a significant path is being missed in the modelincluding this link makes model 4 equal to model 5, supporting model 5 as the most likely again. The third most likely model (model 7, deltaAIC = 38) included effects of coarse-grain size and fine-grain size environment on regional and plot richness, as well as of botanical continent on regional richness. The next most likely model (model 1, deltaAIC = 43) included botanical continent as well as coarse-grain size environment as predictors of regional richness, but without a direct connection between coarse-grain size environment and plot richness, followed by model 6 (deltaAIC = 56), model 2 (deltaAIC = 63), and model 3 (deltaAIC = 111).

INSERT FIGURE 5 AROUND HERE.

The most likely model (model 5, Figures 2 and5) revealed that botanical continent had the strongest impact on regional richness (Q2). Comparing continents (Table 3), temperate and tropical Asia were modelled to be more species rich (indicated by positive estimates) compared to Africa, which as the baseline had an estimate of 0. North America and South America had weaker positive estimates, whereas Europe and Australasia had negative estimates when referenced against Africa. The test of directed separation showed that botanical continent did not have a significant effect on plot richness.

Overall, the effect of the predictors on plot richness in the structural equation model was smaller than for regional richness (Figure 5A). However, this depended strongly on the range of the response variables; in the case of plot richness 0-81 species and for regional richness 0-439 species. So, in order to identify the predictor strength independent of the range of the response variable, we standardised each model-path individually. After standardisation (Figure 5b, Table 3), fine-grain size temperature seasonality and temperature annual range were revealed as having the strongest impact on plot fern richness, followed by temperature diurnal range, regional fern richness, annual precipitation, and coarse-grain size cloud frequency. The standardisation also relativised the differences between the strengths of coarse-grain size predictors on regional richness, revealing annual temperature range as the strongest factor.

INSERT TABLE 3 AROUND HERE.

Discussion

Using our extensive dataset of almost 83,000 vegetation plots with the regional fern richness model at a resolution of 7666 km 2 , we were able to analyse the determinants and relationships of plot and regional fern richness. In the following, we will focus on the three aspects outlined in our focal questions, namely the relationship of plot and regional richness, the impact of historical or biogeographical factors at different scales and the impact of regional environment on richness at regional and plot scale. Overall, the results of our regional model corresponded with those of [START_REF] Kreft | Contrasting environmental and regional effects on global pteridophyte and seed plant diversity[END_REF] who used a broadly similar modelling approach, although the higher quantity and quality of the input data and the finer spatial resolution of the model imply that our model has higher explanatory value, as also reflected by the high R 2 values obtained for the model. In essence the model confirms that regional fern richness is highest in wet tropical mountains, in regions of high habitat heterogeneity, and is much lower in Africa compared to other tropical continents.

Focussing on the relationship of regional and local/plot fern richness, surprisingly, plot richness did not show a linear relationship and neither reached a saturation asymptote (Q1), but rather decreased at high levels of regional richness. To our knowledge, such a pattern has not yet been observed or predicted for global patterns of biodiversity of any group of organisms [START_REF] Srivastava | Using local-regional richness plots to test for species saturation: pitfalls and potentials[END_REF][START_REF] Olivares | Assessing species saturation: conceptual and methodological challenges[END_REF]. By showing that this pattern remained stable through 1000 different plot subsets, we can exclude sampling effects as the underlying cause. Additionally, our dataset included 1860 plots from 43 regional polygons with over 250 regional species with most of these plots having been conducted within the scope of fern-oriented studies. It thus seems unlikely that locally super-rich plots were coincidentally missed in super-rich regions. A possible explanation for this unexpected pattern may be that very species-rich regions tend to have a high degree of habitat heterogeneity, which would limit the surface area of each habitat in this region and thus via the species-area relationship the number of locally supported species.

Additionally, regions with high habitat heterogeneity can include habitats that support only few species, such as alpine outcrops or regions with very poor soils. Indeed, the underlying soils are an important factor known to impact fern richness (e.g. [START_REF] Tuomisto | Distribution and diversity of pteridophytes and melastomataceae along edaphic gradients in Yasuní National Park, Ecuadorian Amazonia1[END_REF] especially at the local scale, but at regional grain sizes, these local differences are harder to detect, leading to high turnover of local species pools that are only small subsamples of the regional species pool, which might be even larger because of the diversity of different soils occurring in a region.

From another point of view, species tend to overlap in their ecological preferences to some degree so that with growing regional richness, locally co-occurring species increasingly compete with each other for the same resources, again leading to higher turnover of species between plots [START_REF] Karger | The importance of species pool size for community composition[END_REF] or locally dominant species prevent many species from coexisting. Thus, we propose that there may be a regional effect on plot fern community assemblage processes, such as high local species turnover in regionally rich assemblages [START_REF] Karger | The importance of species pool size for community composition[END_REF]. This hypothesis should be tested by targeted sampling. In any case, our data does not support a linear increase of plot richness with regional richness as would be expected if plot richness is simply a subsample of regional richness (e.g. [START_REF] Karger | The effect of area on local and regional elevational patterns of species richness: Local biodiversity, regional biodiversity and area[END_REF][START_REF] Bhatta | Scale sensitivity of the relationship between alpha and gamma diversity along an alpine elevation gradient in central Nepal[END_REF]. Rather, the curve shape suggests that ecological species interactions may limit the number of locally co-occurring fern species at high levels of regional species richness. This effect has indeed been documented for fern assemblages in the Indo-Malayan archipelago, where realized niche widths of species decreased on large islands with large regional species pools [START_REF] Karger | Island biogeography from regional to local scales: evidence for a spatially scaled echo pattern of fern diversity in the Southeast Asian archipelago[END_REF]. [START_REF] Kessler | Species richness-productivity relationships of tropical terrestrial ferns at regional and local scales[END_REF] suggest that competitive species exclusions at high levels of individual densities of ferns occur in the Ecuadorian Andes. Such effects may occur more generally, but potentially experimental approaches are needed to unravel the details of the underlying mechanisms.

Considering the influence of inter-continental differences, our analysis confirmed that continents differ in "base levels" of fern species richness, with Africa long known to be the "odd man out" with much lower richness than other tropical continents [START_REF] Kreft | Contrasting environmental and regional effects on global pteridophyte and seed plant diversity[END_REF]. Indeed, South America and tropical Asia were found to be more species rich when measured against Africa in our analyses. Due to its geological past and history of climate oscillations, tropical Africa is known to have a lower regional richness of ferns [START_REF] Kreft | Contrasting environmental and regional effects on global pteridophyte and seed plant diversity[END_REF] but also of many other plant groups [START_REF] Couvreur | Odd man out: why are there fewer plant species in African rain forests?[END_REF] than found on other tropical continents. However, it has also been established that especially eastern Africa can have high local species diversity as well as high levels of endemism [START_REF] Couvreur | Odd man out: why are there fewer plant species in African rain forests?[END_REF], which is in agreement with our results that local richness is not defined by the biogeographic past of the region (Q2): Local richness does not appear to be directly influenced by geographical position, but only indirectly via regional richness.

Finally, focussing on the impact of environmental factors, the comparison of different structural equation models revealed that models allowing a direct impact of regional environmental predictors on both regional and plot fern richness were the only ones capturing all paths and thus performing best (Q3): Even though regional environmental predictors shape the number of regionally occurring fern species, they also have a direct impact on the number of locally occurring species in each plot that cannot be substituted with the regional species richness. The most significant coarse-grain size predictor impacting local richness was annual precipitation, followed by precipitation of warmest quarter. Locally, mean annual cloud frequency was the strongest predictor, which especially in higher elevations is related not only to rainfall but also to water input via fog as well as reduced solar irradiance and therefore also actual and potential evapotranspiration. Previous studies found that rainfall is the strongest predictor of regional fern richness [START_REF] Kreft | Contrasting environmental and regional effects on global pteridophyte and seed plant diversity[END_REF] and of local fern richness along elevational gradients [START_REF] Kessler | A global comparative analysis of elevational species richness patterns of ferns: Global analysis of fern transects[END_REF]. However, fog can also be of major importance, as shown by the correlation of fern diversity with epiphytic bryophyte cover [START_REF] Kessler | A global comparative analysis of elevational species richness patterns of ferns: Global analysis of fern transects[END_REF], which is a proxy of air humidity [START_REF] Karger | Bryophyte cover of trees as proxy for air humidity in the tropics[END_REF]. The influence of fog on water relations of ferns is presumably two-fold, on the one side via direct water input and on the other via reduced transpiration. Habitat homogeneity and the elevational range within one coarse-grain grid cell only have significant influence on regional richness. Both variables measure differences between habitats within the same grid cell, so that their influence on plot richness is mechanistically limited. Another aspect we could not include in the analyses, but which is likely to act more strongly on plot richness than on regional richness, are the local soil properties. As mentioned above, soils strongly act on local fern richness [START_REF] Tuomisto | Distribution and diversity of pteridophytes and melastomataceae along edaphic gradients in Yasuní National Park, Ecuadorian Amazonia1[END_REF], so that differences in soil could lead to different numbers of local richness at otherwise similar regional climatic conditions.

Putting the above considerations into context, our study shows that among the factors considered by us, regional fern richness is primarily driven by climatic factors in combination with strong inter-continental differences that reflect historical influences. Local richness is most strongly impacted by fine-grain size climatic factors, but regional fern richness as well as coarse-grain size environmental factors were also identified through structural equation modelling. Interestingly, we did not find evidence of historical influences acting directly on local richness. Such grain size-dependent differences in factors are commonly invoked in general terms [START_REF] Hutchinson | The concept of pattern in ecology[END_REF][START_REF] Whittaker | Evolution of species diversity in land communities[END_REF][START_REF] Ricklefs | Community diversity: relative roles of local and regional processes[END_REF][START_REF] Levin | The problem of pattern and scale in ecology[END_REF]Field et al., 2004;[START_REF] Rahbek | The role of spatial scale and the perception of large-scale species-richness patterns[END_REF], but are still poorly understood for different groups of organisms.

Structural equation modelling as applied here has proven a suitable tool to differentiate between direct and indirect influences of various factors at different spatial resolutions.

Figure 1 :

 1 Figure 1: Location and number of plots per 10°x10° grid cell.

Figure 2 :

 2 Figure 2: A priori conceptual structural equation models depicting environmental influences on fern regional richness. A. Model 1-3: without allowing direct impact of coarse-grain size environment on plot richness, B. Model 4-7: allowing direct impact of coarse-grain size environment on plot richness as well as other variations.

Figure 3 :

 3 Figure 3: Global prediction of regional fern richness at a grid cell size of 7666 km 2 , Robinson equal area projection, R 2 = 0.745.

Figure 4 :

 4 Figure 4: Relationship between regional and plot fern richness, and curves describing the relationship. DeltaAIC averaged across all repetitions.

Figure 5 :

 5 Figure 5: Structural equation models describing the influence of environmental variables on fern regional and plot richness based on the model 5, which was the best performing model identified by SEM. A. Unstandardised predictor strength. B. Standardised predictor strength and scaling performed for both paths (response: plot and response: regional) separately. Blue indicates positive and red negative correlations. The width to the arrows indicate the estimates of each predictor in the GLM. Q75 indicates predictors that were aggregated based on the 75% quantile across each coarse grain size grid cell. log indicates log-transformed predictors.

Table 1 :

 1 Predictors chosen for structural equation modelling.

	Predictor	Resolution (km 2 )	Data source
	Annual mean temperature [°C]	4	Karger et al., 2017
	Mean annual cloud frequency	4	Wilson & Jetz, 2016
	Temperature seasonality [°C]	4	Karger et al., 2017
	Temperature annual range [°C]	4	Karger et al., 2017
	Annual precipitation [mm]	4	Karger et al., 2017
	Regional fern richness	7666	This study, Fig. 3
	Precipitation of warmest quarter [mm]	7666	Karger et al., 2017
	Median habitat homogeneity	7666	Tuanmu & Jetz, 2015
	Annual precipitation [mm]	7666	Karger et al., 2017
	Elevational range (derived from GMTED2010)	7666	Danielson & Gesch, 2010
	Botanical continent	-	Brummitt, 2001

Table 2 :

 2 Mean AIC scores, deltaAIC values between each model and the likeliest, and p-values of X 2 distributed Fisher's C for all models after 1000 repetitions, ordered from most to least likely.

		AIC deltaAIC p-value Fisher's C
	Model 5	94	0	0.110803
	Model 4 113	19	0.028600
	Model 7 132	38	0.000338
	Model 1 137	43	<0.00001
	Model 6 150	56	<0.00001
	Model 2 157	63	<0.00001
	Model 3 205	111	<0 .00001

Table 3 :

 3 Standardised estimates for each response variable. Estimates equal predictor strength. Predictors in italics indicate coarse-grain size variables. Q75 indicates predictors that were aggregated based on the 75% quantile across each coarse grain size grid cell. log indicates log-transformed predictors. 0.1-0.05 (.), 0.049-0.01 (*), 0.009-0.001 (**), <0.0009 (***).

		Predictor	Estimate	Std..Error	z.value	Pr...z..
		Temperature seasonality	-0.426	0.084	-5.178 0.057 .
		Annual Precipitation [mm]	-0.109	0.062	-1.720 0.304
	Pl	Median habitat homogeneity	0.082	0.058	2.862 0.119
	ot	Annual temperature range [°C]	0.095	0.084	1.209 0.164
	ric	Annual mean temperature [°C]	0.113	0.031	3.726 0.152
	hn	Q75 Precipitation of warmest quarter [mm] (log)	0.175	0.042	4.187 0.015 *
	es	Elevational range [m]	0.183	0.053	7.021 0.528
	s	Regional fern richness	0.319	0.061	10.703 0.019 *
		Mean cloud frequency	0.578	0.035	16.673 0.000 ***
		Q75 annual precipitation [mm] (log)	0.594	0.121	9.839 0.040 *
		Median habitat homogeneity	0.090	0.007	12.926 0.002 **
	Re gi	Elevational range [m] Q75 Precipitation of warmest quarter [mm] (log)	0.320 0.400	0.005 0.009	68.208 0.000 *** 45.358 0.000 ***
	on	Q75 annual precipitation [mm] (log)	0.419	0.010	43.687 0.000 ***
	al	EUROPE	-0.364	0.024	-15.313 0.000 ***
	ric	AUSTRALASIA	-0.289	0.028	-10.451 0.000 ***
	hn	SOUTHERN AMERICA	0.110	0.017	6.125 0.053 .
	es	NORTHERN AMERICA	0.196	0.019	10.396 0.000 ***
	s	ASIA TROPICAL	0.341	0.017	20.335 0.032 *
		ASIA TEMPERATE	0.396	0.017	23.866 0.000 ***

Acknowledgements

We are grateful to the numerous people who have contributed to the data used in this paper by participating in field work, helping with practical arrangements for the field expeditions, data management or sharing their expertise for species identification. We thank the national authorities in each country for granting the permits to carry out field work and collect voucher specimens. Funding and help that have made this work possible have been provided by numerous agencies over the years, including the Academy of Finland, FAPESP/FAPEAM, CNPq, the Brazilian Program in Biodiversity Research (PPBio), Canadian Forest Service (Natural Resources Canada), Ontario Ministry of Natural Resources and Forestry, SERG International members, Natural Sciences and Engineering Research Council of Canada, and the Institute of Biology Bucharest of Romanian Academy (RO1567-IBB01/2019). The sPlot project was initiated by sDiv, the Synthesis Centre of the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, funded by the German Research Foundation (DFG FZT 118) and is now a platform of iDiv. We gratefully acknowledge the Programme Coordinating Centre of ICP Forests and all botanists who performed the vegetation surveys. The evaluation included data from the UNECE ICP Forests PCC Collaborative Database (see http://icp-forests.net). Data from the following participating countries (number of plots in parentheses): Austria (177), Belgium (351), Bulgaria (18), Cyprus (16), Czech Republic (47), Denmark (69), Estonia (110), Finland (121), France (6768), Germany (1329), the United Kingdom (390), Greece (15), Hungary (279), Ireland (x12), Italy (766), Latvia (11), Lithuania (18), Luxemburg (112), Netherlands (47), Norway (21), Poland (160), Portugal (58), Romania (37), Russia (147), Serbia (238), Slovenia (64), Slovakia (35), Spain (83), Sweden (166), and Switzerland (755) were included in this study. The collection of ICP Forests data was to a large extent funded by national research institutions and ministries, with support from governmental bodies, services and landowners and the European Commission. Biosketch Anna Weigand is a PhD student in the working group of Michael Kessler. Her research focuses on historical and current processes shaping distribution patterns of ferns.

Author contributions AW, MK, and HK designed the study, AW compiled the database and performed the analysis with help from SN and JL, PW helped with the regional model, PW and HK provided regional data. All other authors provided plot data. AW led the writing; all authors provided critical comments to the manuscript.

Supplementary material