# Proximal microclimate: Moving beyond spatiotemporal resolution improves ecological predictions

David Klinges, J. Alex Baecher, Jonas Lembrechts, Ilya Maclean, Jonathan Lenoir, Caroline Greiser, Michael Ashcroft, Luke Evans, Michael Kearney, Juha Aalto, et al.

► To cite this version:

David Klinges, J. Alex Baecher, Jonas Lembrechts, Ilya Maclean, Jonathan Lenoir, et al. Proximal microclimate: Moving beyond spatiotemporal resolution improves ecological predictions. Global Ecology and Biogeography, 2024, 33, pp.e13884. 10.1111/geb.13884. hal-04630607

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Proximal microclimate: moving beyond spatiotemporal resolution improves ecological predictions

David H. Klinges¹, J. Alex Baecher², Jonas J. Lembrechts³, Ilya M. D. Maclean⁴, Jonathan Lenoir⁵, Caroline Greiser⁶,⁷, Michael Ashcroft⁸, Luke J. Evans⁸,¹⁹, Michael R. Kearney¹⁰, Juha Aalto¹¹, Isabel Barrio¹², Pieter De Frenne¹³, Joannès Guillemot¹⁴, Kristoffer Hylander¹⁵, Tommaso Jucker¹⁶, Martin Kopecký¹⁷,¹⁸, Miska Luoto¹⁹, Martin Macek²⁰, Ivan Nijs²¹, Josef Urban²², Liesbeth van den Brink²³,²⁴, Pieter Vangansbeke²⁵, Jonathan Von Oppen²⁶, Jan Wild²⁷, Julia Boike²⁸, Rafaella Canessa²⁹, Marcelo Nosetto³⁰,³¹, Alexey Rubtsov³², Jhonatan Sallo-Bravo³³, Brett R. Scheffers³⁴

*Corresponding author:

David H. Klinges, dklinges9gmail.com

110 Newins Ziegler Hall PO Box 110430

Gainesville, FL 32601

¹University of Florida, School of Natural Resources and Environment (dklinges9@gmail.com)

²University of Florida, School of Natural Resources and Environment (jbaecher@gmail.com)

³University of Antwerp, Department of Biology (lembrechtsjonas@gmail.com)

⁴University of Exeter Penryn Campus, Environment and Sustainability Institute
(i.m.d.maclean@exeter.ac.uk)

⁵UMR CNRS 7058, Ecologie et Dynamique des Systèmes Anthropisés (EDYSAN), Université de Picardie Jules Verne (Jonathan.lenoir@u-picardie.fr)

⁶Stockholm University, Department of Physical Geography (caroline.greiser@su.se)

⁷Swedish University of Agricultural Sciences, Department of Forest Ecology and Management

⁸University of Wollongong, Centre for Sustainable Ecosystem Solutions (mickelfi@iprimus.com.au)

⁹University of Florida, Wildlife Ecology and Conservation (lukeevans603@gmail.com)

¹⁰The University of Melbourne, School of BioSciences (m.kearney@unimelb.edu.au)

¹¹Finnish Meteorological Institute, Weather and Climate Change Impact Research (juha.aalto@fmi.fi)

¹²Agricultural University of Iceland, Faculty of Environmental and Forest Sciences (isabel@lbhi.is)

¹³Ghent University, Forest & Nature Lab (pieter.defrenne@ugent.be)

¹⁴CIRAD, UMR Eco&Sols (joannes.guillemot@cirad.fr)

¹⁵Stockholm University, Department of Ecology, Environment and Plant Science
(kristoffer.hylander@su.se)

¹⁶University of Bristol, School of Biological Sciences (t.jucker@bristol.ac.uk)

¹⁷Institute of Botany of the Czech Academy of Sciences, Department of Geoecology
(ma.kopecky@gmail.com)

¹⁸Czech University of Life Sciences Prague, Faculty of Forestry and Wood Sciences

¹⁹University of Helsinki, Department of Geosciences and Geography (miska.luoto@helsinki.fi)

²⁰Institute of Botany of the Czech Academy of Sciences, Department of Geoecology
(martin.macek@ibot.cas.cz)

²¹University of Antwerp, Department of Biology, University of Antwerp (ivan.nijs@uantwerpen.be)

²²Mendel University in Brno, Faculty of Forestry and Wood Technology, Department of Forest Botany, Dendrology and Geobiogenology (josef.urban@mendelu.cz)

²³University of Tübingen, Plant Ecology Group (liesbethvandenbrink78@gmail.com)

²⁴University of Concepcion, ECOBIOSIS

²⁵Ghent University, Forest & Nature Lab (pieter.vangansbeke@ugent.be)

²⁶Aarhus University, Department of Biology (jonathan.vonoppen@bio.au.dk)

²⁷Institute of Botany of the Czech Academy of Sciences, Department of Geoecology
(jan.wild@ibot.cas.cz)
## Acknowledgements

We thank the following funding sources: NSF-GRFP N°DGE-1842473 (DK); FWO-G018919N, FWO-12P1819N, FWO-1512720N and FWO-W001919N, and BiodivERsA+ projects ASICS and Forest-Web-3.0 (JJL, IN); ANR JCJC-N°ANR-19-CE32-0005-01 IMPRINT (JL); ANR PRC-N°ANR-21-CE32-0012-03 MaCCMic (JL); FORMAS-2021-01993 (CG); NERC-NE/S01537X/1 (TJ); Czech Science Foundation project 23-06614S and Academy of Sciences of the Czech Republic project RVO 67985939 (MK, MM, JW); Academy of Finland 342890 (ML); Academy of Finland 337552 (JA); GACR-21-11487S (JU); DFG TI 338/14-1 and -2, ANID PIA/ACT 210038 (LvdB); European Research Council ERC Starting Grant FORMICA-757833 (PDF and PV); Independent Research Fund of Denmark 7027-00133B (JvO); and PIP-363/2020 and PICT-504/2020 (MN). We thank T. Michael Keesey, Sean McCann, Benjamin Monod-Droca, and PhyloPic for providing animal silhouettes for free modification under CC licenses.

## Biosketch

We are a collective of researchers focusing on the impact of global change on animal and plant communities and ecological functions, with a shared interest in exploring the importance of climate scale and microclimate.

## Author contributions

DHK initiated the project and conceived the core concepts, with early input from BRS, IMDM, MRK, and JL. JJL collated co-author data for the climate data comparison. DHK performed all microclimate modeling, and analyses for the temperature product comparison and case studies 1 and 2. JAB collated data and performed initial model fitting for case study 3, with subsequent analyses by DHK. DHK wrote the first draft of the manuscript, with early contributions by JAB, JJL, IMDM, LJE, MRK, and BRS. BRS supervised DHK. IB, PDF, JG, MK, MM, JU, LvdB, JvO, JW, JB, RC, MN, AR, and JSB all contributed data for the temperature data comparison. All authors contributed substantially to manuscript editing.

<footnote n="28">²⁸Permafrost Research, Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, (julia.boike@awi.de)</footnote>

<footnote n="29">²⁹University of Tübingen, Plant Ecology Group & Department of Geosciences (rcaness1@uc.cl)</footnote>

<footnote n="30">³⁰CONICET, Grupo de Estudios Ambientales (marcelo.nosetto@gmail.com)</footnote>

<footnote n="31">³¹Cátedra de Climatología Agrícola (FCA-UNER)</footnote>

<footnote n="32">³²Siberian Federal University, Institute of Ecology and Geography (alexeyruss@gmail.com)</footnote>

<footnote n="33">³³Universidad Nacional de San Antonio Abad del Cusco, Facultad de Ciencias Biológicas (sallobravo@gmail.com)</footnote>

<footnote n="34">³⁴University of Florida, Wildlife Ecology and Conservation (brett.scheffers@ufl.edu)</footnote>
1 **Proximal microclimate: moving beyond spatiotemporal resolution improves ecological predictions**
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3

Running title: climate scaling and proximity

4 Abstract

**Aim:** The scale of environmental data is often defined by their extent (spatial area, temporal duration) and resolution (grain size, temporal interval). Although describing climate data scale via these terms is appropriate for most meteorological applications, for ecology and biogeography, climate data of the same spatiotemporal resolution and extent may differ in their relevance to an organism. Here, we propose that climate proximity, or how well climate data represent the actual conditions that an organism is exposed to, is more important for ecological realism than the spatiotemporal resolution of the climate data.

**Location:** Temperature comparison in nine countries across four continents; ecological case studies in Alberta (Canada), Sabah (Malaysia), and North Carolina/Tennessee (USA).

**Time period:** 1960-2018.

**Major taxa studied:** Case studies with flies, mosquitoes, and salamanders, but concepts relevant to all life on earth.

**Methods:** We compare the accuracy of two macroclimate data sources (ERA5, WorldClim) and a novel microclimate model (*microclimf*) at predicting soil temperatures. We then use ERA5, WorldClim, and *microclimf* to drive ecological models in three case studies: temporal (fly phenology), spatial (mosquito thermal suitability), and spatiotemporal (salamander range shifts) ecological responses.

**Results:** For predicting soil temperatures, *microclimf* had 24.9% and 16.4% lower absolute bias than ERA5 and WorldClim, respectively. Across the case studies, we find that increasing proximity (from macroclimate to microclimate) yields a 247% improvement in performance of ecological models on average, compared to 18% and 9% improvements from increasing spatial resolution 20-fold, and temporal resolution 30-fold, respectively.
33 Main conclusions: We propose that increasing climate proximity, even if at the sacrifice of finer climate spatiotemporal resolution, may improve ecological predictions. We emphasize biophysically-informed approaches, rather than generic formulations, when quantifying ecoclimatic relationships. Redefining the scale of climate through the lens of the organism itself helps reveal mechanisms underlying how climate shapes ecological systems.

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39 Keywords: biophysical ecology, climate change, ecophysiology, macroclimate, microclimate, nonlinearity, resolution, species distribution model

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42 Introduction

43 Explorations of scale are fundamental to understanding patterns and processes in the natural world (Wiens, 1989). Given that most urgent issues of global change involve phenomena that occur across spatial and temporal scales, it is crucial to understand how drivers of ecological responses shift across scales, and how a process that is shaped at one scale plays a role at another scale (Levin, 1992).

48 Climate is an omnipresent environmental component that is notably scale-dependent. For instance, at the synoptic (or continental) spatial scale, across thousands of kilometers, climate is principally determined by latitude, elevation, distance to large bodies of water, and ocean dynamics. In ecology and biogeography, climate at this scale is often referred to as macroclimate (Geiger et al., 2009). Locally (one hundred meters or less), however, climate varies substantially due to variation in vegetation, terrain, and soil conditions – climate at this scale has been referred to as microclimate (Geiger et al., 2009). Climate is also scale-dependent in the temporal domain, as the processes that shape climate differ between seasonal, decadal, and geological periods (Clark, 1985). Unsurprisingly, how climate drives ecological responses also differs across scales. For instance, thermal variation over the course of a day may determine periods of an organism's foraging activity or its exposure to stressful extremes, while annual fluctuations influence phenology or development (Kefford et al., 2022).

60 In classic macroecological studies, climate has been considered to be more important for distributions of species and communities at broad, rather than local, spatiotemporal scales (Woodward, 1987). Yet both time-honored and recent work has revealed the many roles of microclimate in ecology and evolution (Huey, 1991; Helmuth, 1998; Kearney & Porter, 2009; Potter et al., 2013; Lembrechts et al., 2019; Maclean & Early, 2023). Given that climate and its ecological effects are both scale-dependent, the source of climate data, and the structure of
66 such data in a modeling workflow, may critically impact detection of ecological signals (Buckley et al., 2023a).

For much work in spatial ecology – especially related to species’ distributions and diversity – researchers use macroclimate maps generated from mechanistic or statistical models (e.g. Fick & Hijmans, 2017; Hersbach et al., 2020). As with all model predictions, these climate maps are imperfect. In an effort to improve climate data for ecological applications, many studies have explored the impact of increasing either spatial or temporal resolution of climate data, and occasionally both in tandem, on the accuracy of ecological predictions (see review by Lembrechts et al., 2019). Yet increasing spatiotemporal resolution of climate data alone does not necessarily increase the likelihood of capturing the relevant microclimates for a given organism or process, and may indeed result in lower prediction accuracy of ecological responses (Abdulwahab et al., 2022). Macroecological studies rarely measure how well climate data represent the actual exposure of an organism or system, independent of the data’s spatiotemporal resolution. We call this the “proximity” of climate data, which we define in further detail below (Box 1). By focusing on climate data resolution rather than proximity, many large-scale studies tend to overlook an established legacy in ecophysiology of understanding climate as experienced by organisms (Huey, 1991; Kearney & Porter, 2009). Unfortunately, this concept of climate proximity is rarely incorporated into macroecology or biogeography, and thus its importance for spatiotemporal ecological modeling has not yet been systematically explored and quantified.

Here, we investigate how climate spatial resolution, temporal resolution, and proximity each influence the accuracy of temperature estimates and performance of climate-driven ecological models (Figure 1). We employ a set of temperature data sources that differ in these dimensions of scale, and perform spatial and temporal aggregation/disaggregation, to evaluate the importance of spatial resolution, temporal resolution, and proximity of temperature data in each of three case studies: crop pest emergence, mosquito thermal suitability, and salamander distribution changes. These case studies correspond to a spatial, temporal, and spatiotemporal ecological response, respectively, and exemplify common applications of climate data via disparate models, ecosystems, and organisms. We expect to find that climate data of high proximity will predict ecological responses better than climate data that are of higher spatial or temporal resolution yet low proximity (“distal”). Such findings would improve our understanding of the role of scale in bioclimatic relationships, and would call for revision to our methods for quantifying such processes.

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100 Methods

101 Temperature data

102 We used three sources of global climate data that each differ for two of the three studied dimensions (spatial resolution, temporal resolution, and/or proximity; Figure 2). The proximity of these climate data is gauged according to the microhabitat requirements and life-cycle processes of three case study organisms below. For these organisms (fly larvae, mosquitoes, and salamanders), proximal climate is that near and below the ground as well as underneath vegetation, which we represent using a microclimate model (see below).

108 The first climate product, ERA5, is an assimilation of numerical weather model predictions, satellite imagery, and free-air weather station measurements (Hersbach et al., 2020). ERA5 data are available at several spatiotemporal resolutions and heights; here we used hourly measurements (i.e. fine temporal resolution) at 0.25° resolution (c. 27.75 x 27.75-km at the equator or 770-km²; i.e. coarse spatial resolution) at 2-m height above ground.

113 To complement ERA5's coarse spatial yet fine temporal resolution, we also included monthly (coarse temporal resolution) macroclimatic data from the WorldClim database (v2.1, Fick & Hijmans, 2017), which are global interpolations derived from weather station measurements (i.e. low proximity, or distal, for most taxa). Relative to other global gridded products, WorldClim has finer spatial resolution (30" or c. 1-km² at the equator) – although we note that 1-km² can still contain high microclimate variability that is relevant to physiology and ecology.

120 To represent proximal climate data, we employed microclimf (Maclean, 2023), a recently developed grid version of a previously published mechanistic microclimate model (Maclean & Klinges, 2021). In brief, microclimf estimates near-ground air and soil temperatures principally based upon the net energy flux density absorbed by surfaces (vegetation/soils) by emulating a Lagrangian localized near-field model (see Table S1 and Supporting Information for details). Using microclimf, we generated spatiotemporal predictions of below-canopy topsoil temperature (5-cm depth), which is proximal for the case study organisms as detailed below, at 1-km² and hourly resolution. While estimating microclimate can be better achieved at the meter scale (Briscoe et al., 2023), here we matched microclimf predictions to the 1-km² spatial resolution of WorldClim and hourly resolution of ERA5 to compare climate data of the same resolution yet different proximity.

131 We assessed how closely ERA5, WorldClim, and microclimf temperature predictions matched 371 time series of in-situ soil temperature measurements (proximal microclimate for
133 our case study organisms) from across four continents (Lembrechts et al., 2020; see Figure S1 and Supporting Information for details).

## Case study 1: Temporal crop pest emergence

Temporal models with climate as input are commonly employed for modeling population dynamics, tracking phenology, and ecological forecasting. To illustrate a biological process using a temporally-explicit model, we predicted the emergence rates of fossorial larvae for two fly species: the cabbage maggot (Delia radicum; Linnaeus, 1758) and the seedcorn maggot (Delia platura; Meigen, 1826). As two prominent pests that feed on crop roots, these flies cause massive damage, such as up to a $73 million annual loss in Canada (Broatch et al., 2006). Accurate predictions of fly phenology are useful for informing management practices, such as the timing of planting new crops or of pesticide treatments (Dent, 2000). Given that larval survival and growth of both species is suspected to be more sensitive to soil temperatures than air temperatures (Lepage et al., 2012), phenological predictions may diverge when calculated from temperature sources of different proximity (e.g. ERA5 or WorldClim macroclimate vs microclimf soil microclimate).

We derived rates of insect emergence from topsoil as predicted by ERA5, WorldClim, and microclimf, during 2002 averaged across five locations in Alberta, Canada, matching locations and days of observations of these two species by Broatch et al. (2006), who reported the proportion of a larval population that emerged as a winged adult per day. We extracted hourly temperature predictions from ERA5 and microclimf for the year 2002, while for WorldClim we extracted the average monthly means, minima, and maxima for the corresponding decade (2000-2009), as 1-km² WorldClim does not include hourly data, nor data for just 2002. We also temporally aggregated/disaggregated each climate data source (hourly to monthly) to isolate the effects of spatial resolution, temporal resolution, and proximity (see Supporting Information for details). We then converted temperature data into growing degree days (GDDs). GDDs are indices for the accumulation of heat units above a minimum (base) temperature for organismal development, and are frequently used for predicting insect and plant phenology (Arnold, 1960). Per Arnold (1960), we calculated GDDs as the cumulative sum of the deviations of the average of daily minima and maxima above species-specific base temperature thresholds: 4.0 °C for *D. radicum* (Collier & Finch, 1985), 3.9 °C for *D. platura* (Sanborn et al., 1982). We then used GDDs from ERA5, WorldClim, and microclimf to drive a simple mathematical model for emergence rates of each fly species. We used the model originally developed by Pearl and Reed (1920) and parameterized by Broatch et al. (2006), which estimates the proportion of
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3 166 insect emergence per day using a logistic function driven by GDDs (see Supporting Information
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5 167 for model specification). Given that this model was developed to inform the timing of pesticide
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7 168 application, it does not necessitate, nor provide, absolute values of adult abundance, only
8 169 proportions of adults emerging over time for identifying the timing of peak emergence. To
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10 170 validate predictions from each temperature-driven emergence model, we measured the
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12 171 absolute error of species-specific predictions from the observed emergence rates (error
13 172 measured as the number of days between predicted timing of an emergence threshold, e.g.
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15 173 50% emergence by day of year 180, and the observed day of year when that threshold was
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17 actually crossed).
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19 175 Case study 2: Spatial bioclimate and mosquito thermal performance
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22 176 To examine whether the spatial, temporal, and proximal dimensions of climate data may
differentially change estimates of bioclimate and organismal thermal performance, we
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24 177 generated maps of (1) mean temperature, (2) extreme temperature, and (3) temperature-
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26 179 dependent fecundity for the mosquito *Aedes aegypti* (Linnaeus, 1762) in Sabah within
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28 180 Malaysian Borneo (45,785 km²). *Aedes aegypti* is a prominent vector of several human
diseases, including dengue and yellow fever, which causes millions of infections and deaths per
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30 182 year (Bhatt *et al.*, 2013). We chose this tropical region given its widespread land use change
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32 183 from primary forest to plantation forest (e.g. oil palm), which may facilitate mosquito
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34 184 establishment (Saager *et al.*, 2023). *Aedes aegypti* rests under vegetation or in urban
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37 185 microhabitats, where it deposits eggs in water (Cheong *et al.*, 2014). Therefore microclimate
38 186 predictions that represent vegetative shading (e.g. *microclimf*) are more proximal than free-air
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40 187 macroclimate for adult mosquitoes. Across Sabah, we extracted hourly temperature predictions
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42 189 from ERA5 and *microclimf* for the year 2018, while for WorldClim we extracted the monthly
means, minima, and maxima estimated at 1-km² for the corresponding decade (2010-2019).
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44 190 From each temperature product we then calculated spatial layers of mean annual temperature
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46 191 (BIO1), maximum temperature of the warmest month (BIO5), and average fecundity of A.
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48 192 aegypti using a thermal reaction norm (see Supporting Information for details). This reaction
49 193 norm was developed by Mordecai et al. (2017) using air temperatures, rather than water
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51 194 temperatures, although the latter more proximally represents exposure of larval mosquitoes
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53 195 (Paaijmans *et al.*, 2013). To validate fecundity predictions, we averaged *A. aegypti* fecundity
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56 197 measurements across temperature from several laboratory studies (Day *et al.*, 1994; Braks *et
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60 198 area of integration (overlap) between lab-derived fecundity observations with ERA5, WorldClim,
199 and microclimf fecundity predictions (see Klinges & Scheffers, 2021 and Supporting Information for details).

## Case study 3: Spatiotemporal salamander prevalence shifts

Given the widespread interest in climate change-induced range shifts (Lenoir et al., 2020), we tested how the spatial resolution, temporal resolution, and proximity of climate data affect predictions of spatiotemporal changes in prevalence of Jordan's red-cheeked salamander (Plethodon jordani; Blatchley, 1901) across its range. Plethodon jordani is a fossorial salamander, endemic to the Great Smoky Mountains National Park (GSMNP, 2,090 km²) in the eastern United States, and relies upon cool, moist subsurface microhabitats (i.e. under rocks, logs, or in burrows). Given that such microclimates are better represented by soil temperatures than free-air temperatures, microclimf is more proximal than either ERA5 or WorldClim for *P. jordani*. Across GSMNP's wide elevation range (267–2025 masl), *P. jordani* is climatically constrained to above 600 meters (Gifford & Kozak, 2012). With climate change, this mountain-dwelling species may either shift towards higher elevations and/or experience abundance changes (Feeley et al., 2012).

To explore the impact of climate data resolution and proximity on spatiotemporal salamander prevalence change predictions, we conducted two steps, as detailed below. First, we fitted species distribution models (SDMs) separately for the 1960s and 1990s but without using any climate covariates (i.e. only using observer bias and topographic variables). Then, we compared how generalized additive models (GAMs) with climate covariates built from ERA5, WorldClim, or microclimf were able to predict changes in SDM prevalence estimates over time.

For SDMs, we collated georeferenced occurrence (presence-only) data from two primary survey periods: 1961–1970 (“1960s”, 975 surveys) and 1991–2000 (“1990s”, 704 surveys) (IRMA, 2021). Separately for the 1960s and 1990s, we fitted inhomogeneous Poisson point process models (IPPMs, Renner et al., 2015) to estimate relative prevalence of *P. jordani* on a 0–1 scale. The topographic predictors used in each of these models included elevation, slope, and aspect (measured in radians) at 1-km² resolution (obtained from the Amazon Web Services Terrain Tiles). An additional spatial predictor, distance from the closest trail on which salamander surveys were conducted, was also included in each model to control for observer bias. The quality of fit was determined through five-fold cross validation and model performance was assessed using CBI, AUC, and TSS criteria (Allouche et al., 2006). CBI results, which are most appropriate for presence-only data paired with pseudo-absences (Boyce et al., 2002), are presented in the main text. Salamander prevalence changes were then considered as the
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3 232 differences in SDM-derived prevalence from the 1960s to the 1990s (see Supporting
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5 233 Information for details on model calibration, tuning, pseudo-absence point selection, and
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7 234 validation).

8 235 Next, we aimed to understand how prevalence shifts over time, as predicted by our
9 236 SDMs, were explained by climatic changes expressed by different temperature products. For
10 237 this, we fitted a set of GAMs using bioclimatic layers from one of either ERA5, WorldClim, or
11 238 microclimf as predictors, and changes in salamander prevalence as the response variable
12 239 assuming a Gaussian distribution. For bioclimatic variables, we used the only two variables with
13 240 historic decadal WorldClim data at 1-km² – annual thermal maximum (BIO5) and minimum
14 241 (BIO6) – which have been shown to correlate with distributions of salamanders in this region
15 242 (Baken et al., 2021). For each climate source, we calculated the per-pixel change in each
16 243 bioclimatic variable between the 1960s and 1990s, and used these climate change estimates as
17 244 GAM predictors. To further explore how the spatial resolution of climate data influences
18 245 predictions independent of the temperature product used, we spatially aggregated (averaged
19 246 across cells) and disaggregated (bilinear interpolation) temperatures from microclimf,
20 247 WorldClim, and ERA5 – each at 1-km, 3-km, 10-km, and 27.75-km resolution. We then fitted
21 248 GAMs for each temperature product at each of these spatial resolutions, corresponding to 12
22 249 models in total (see Supporting Information for more details on climate data processing, fitting
23 250 GAMs, and measures taken to control for survey effort and initial prevalence).

251 Software
252 All analyses were conducted in R v4.2 (R Core Team, 2022); all packages used for data access,
253 processing, and analysis are provided in the Supporting Information.

254
255 Results
256 Across 371 time series of in-situ soil temperature data globally, microclimf had 8.2% and 25.2%
257 lower root mean square error (RMSE) than ERA5 and WorldClim, respectively, and 24.9% and
258 16.4% lower absolute bias than ERA5 and WorldClim, respectively (Figure 3, Table S2, Figure
259 S2).

260 In case study 1, temperature predictions by ERA5, WorldClim, and microclimf in Alberta,
261 Canada were similar (Figure 4a). Yet, marginal differences among the three temperature
262 products yielded disparate calculations of growing degree days (Figure 4b), thereby resulting in
263 divergent estimates of insect emergence (Figure 4c, Table S3). Emergence models driven by microclimf (proximal climate) at hourly resolution were most accurate, with an average error of 264 6.57 days, followed by ERA5 at hourly resolution (17.0 days) and all other models descending 265 with temporal resolution except monthly microclimf (17.6 days), which performed better than 266 daily WorldClim (20.42 days; Figure S3). Average error reported here is the mean error across 267 all proportion emergence thresholds for both species. 268

269 In case study 2, microclimf predictions of A. aegypti mosquito fecundity were the most accurate, with 6.22-fold and 7.76-fold increases in overlap with the empirical fecundity 270 distribution as compared to those of ERA5 and WorldClim, respectively (Table S4). microclimf 271 fecundity predictions were also higher (x̄ = 6.96 eggs laid / day) than fecundity derived from 272 ERA5 or WorldClim (x̄ = 5.30 and x̄ = 4.92, respectively; Figure 5, Table S5). Contrasting with 273 fecundity predictions, BIO1 calculated from ERA5, WorldClim, and microclimf all had similar 274 distributions that overlapped considerably (mean overlap = 70.1%), while BIO5 distributions 275 diverged between temperature products (mean overlap = 16.9%, Table S4). 276

277 In case study 3, the three temperature products yielded different predictions of changes 278 in the prevalence of P. jordani salamanders between the 1960s and the 1990s (Figure 6b-c). 279 The microclimf-driven GAM performed best, explaining 58.1% of all deviance compared to only 280 39.1% and 17.2% for GAMs using WorldClim, and ERA5, respectively (Table S6, S7). 281 Furthermore, the predicted rate of historic warming differed considerably across climate data 282 products (Figure 6a). For instance, the increase in BIO5 computed from WorldClim was 0.597 283 °C per decade, which was 84% and 88% faster than the rate at which BIO5 was warming based 284 on ERA5 and microclimf, respectively. Such warming corroborates upslope shifts of 285 salamanders as predicted by species distribution models (SDMs): the elevations of pixels with 286 at least 80% salamander prevalence on average shifted up by 136 meters. SDMs had good fit 287 to hold-out validation data as indicated by their CBI (CBI: 0.963 for 1960s model, 0.988 for 288 1990s model; see Supporting Information for further validation). 289

Discussion

290 Measuring the role of scale in biological and environmental processes requires proper definition 291 of scale itself. In our study, we defined climate proximity (Box 1) and explored its importance as 292 an ecologically-relevant component of climate scale. We incorporated several temperature data 293 products into a suite of ecological models to evaluate the benefits of increasing spatial 294 resolution, temporal resolution, or proximity of climate data relative to the focal species. We 295
1

2

3 296 found via our case studies that the predictive power of climate-driven ecological models is
4 297 maximized when temporal resolution, spatial resolution, and proximity are all high. Yet,
5 298 increasing climate proximity (i.e. from macroclimate to microclimate while the spatiotemporal
6 299 resolution remained constant) resulted in a 247% mean improvement in ecological models
7 300 across case studies, relative to only an 18% improvement from a mean 20-fold increase in
8 301 spatial resolution, and a 9% improvement from a mean 30-fold increase in temporal resolution.
9 302 This entails that when selecting or processing climate data, prioritizing higher proximity, yet
10 303 coarser spatiotemporal resolution, may increase data quality (for ecological prediction) and also
11 304 decrease quantity (i.e. data density in space and time), and therefore possibly entail less
12 305 computational demand or storage space. Here we discuss how treating proximity separately
13 306 from spatiotemporal scale is not just a change of semantics, but it advances theory by helping to
14 307 identify the mechanisms of organismal responses to climate. By considering climate proximity,
15 308 one emphasizes biophysically-informed approaches rather than generic formulations when
16 309 matching climate data to an ecological system.
17 310 Modeling temporal ecological processes
18 311 In our first case study we found that temperature data with both fine temporal resolution and
19 312 high climate proximity yielded the most accurate predictions of the emergence rates of two
20 313 fossorial insect species, *D. platura* and *D. radicum* (Figure 4). Prediction errors varied across
21 314 temperature products from 6.57 days (hourly topsoil temperatures from *microclimf*) to 33.25
22 315 days (ERA5 macroclimate aggregated to monthly, Table S3). Given that 75% of all individual *D.*
23 316 *platura* and *D. radicum* emerged within windows of just 22.8 and 15.3 days, respectively,
24 317 predictions from temperature data with low proximity and coarse temporal resolution (e.g.
25 318 monthly WorldClim and monthly ERA5) almost entirely missed the emergence event.
26 319 Noteworthy is that even when *microclimf* predictions were aggregated to monthly resolution,
27 320 they yielded more accurate emergence predictions than WorldClim disaggregated to daily, and
28 321 were comparable in performance to hourly ERA5. These findings suggest that, for this system,
29 322 the proximity of climate data may be more important than their temporal resolution.
30 323 Several mechanisms might explain why climate data of both high temporal resolution
31 324 and high proximity accurately predict ecological time series. First, given that many ecological
32 325 responses to temperature are nonlinear, the *mean ecological response* to thermal variation over
33 326 time (what is typically of interest) does not necessarily equal the ecological response to the
34 327 *mean temperature* (Martin & Huey, 2008). This mathematical principle, known as Jensen's
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30 311 In our first case study we found that temperature data with both fine temporal resolution and
31 312 high climate proximity yielded the most accurate predictions of the emergence rates of two
32 313 fossorial insect species, *D. platura* and *D. radicum* (Figure 4). Prediction errors varied across
33 314 temperature products from 6.57 days (hourly topsoil temperatures from *microclimf*) to 33.25
34 315 days (ERA5 macroclimate aggregated to monthly, Table S3). Given that 75% of all individual *D.*
35 316 *platura* and *D. radicum* emerged within windows of just 22.8 and 15.3 days, respectively,
36 317 predictions from temperature data with low proximity and coarse temporal resolution (e.g.
37 318 monthly WorldClim and monthly ERA5) almost entirely missed the emergence event.
38 319 Noteworthy is that even when *microclimf* predictions were aggregated to monthly resolution,
39 320 they yielded more accurate emergence predictions than WorldClim disaggregated to daily, and
40 321 were comparable in performance to hourly ERA5. These findings suggest that, for this system,
41 322 the proximity of climate data may be more important than their temporal resolution.
42 323 Several mechanisms might explain why climate data of both high temporal resolution
43 324 and high proximity accurately predict ecological time series. First, given that many ecological
44 325 responses to temperature are nonlinear, the *mean ecological response* to thermal variation over
45 326 time (what is typically of interest) does not necessarily equal the ecological response to the
46 327 *mean temperature* (Martin & Huey, 2008). This mathematical principle, known as Jensen's
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32 313 fossorial insect species, *D. platura* and *D. radicum* (Figure 4). Prediction errors varied across temperature products from 6.57 days (hourly topsoil temperatures from *microclimf*) to 33.25 days (ERA5 macroclimate aggregated to monthly, Table S3). Given that 75% of all individual *D.* *platura* and *D. radicum* emerged within windows of just 22.8 and 15.3 days, respectively, predictions from temperature data with low proximity and coarse temporal resolution (e.g. monthly WorldClim and monthly ERA5) almost entirely missed the emergence event.

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49 324 and high proximity accurately predict ecological time series. First, given that many ecological responses to temperature are nonlinear, the *mean ecological response* to thermal variation over time (what is typically of interest) does not necessarily equal the ecological response to the *mean temperature* (Martin & Huey, 2008). This mathematical principle, known as Jensen's inequality, explains why the predicted emergence rates in response to coarse temporal data

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329 (e.g. monthly WorldClim and ERA5) differed considerably from average true emergence rates, which are likely sensitive to short-term fluctuations in temperature (e.g. across hours; Denny, 2019).

332 Second, individual organisms are exposed not only to instantaneous temperature, but also to cumulative thermal variation at the scale of hours to months and beyond (Kefford et al., 2022). Even if differences between climate products are minimal at any given timestep (Figure 4a), small thermal deviations accumulate over time (e.g. divergence in GDDs, Figure 4b), rendering distal macroclimate unsuitable for capturing the collective climate exposure of organisms and their phenological responses (Figure 4c). Only proximal climate data of fine temporal resolution will adequately capture the cumulative effect of such intraseasonal variability. Yet temporal variation can be irrelevant when the climate data are not proximal to the ecological responses of interest. Therefore distal, yet fine temporal resolution, climate data may have a low “signal-to-noise” ratio. Increasing proximity entails representing ecologically-relevant climate variation (i.e. signal), while increasing resolution of distal climate data can introduce irrelevant climate variation (i.e. noise; for instance, the hot extreme air temperature of hourly free-air macroclimate that is not relevant to a fossorial organism; see also Dillon et al., 2016). This may explain why WorldClim disaggregated to daily intervals performed worse than monthly microclimf predictions.

347 Modeling spatial ecological processes

348 In our second case study, we illustrated that the method by which climate data are summarized from a time series and mapped into a single spatial index may either enhance or mitigate the variability, and utility, of fine-resolution proximal data. For the tropical region of Sabah in Malaysian Borneo, microclimate and macroclimate products yielded similar spatial distributions of mean annual temperatures (BIO1, Figure 5a), which may lead the modeler to conclude that microclimates reflect macroclimate in this region. However, when we calculated the maximum temperature of the warmest month (BIO5, Figure 5b) from each temperature product, there were larger differences in spatial profiles than for BIO1. Furthermore, when we calculated temperature-sensitive fecundity rates of A. aegypti, microclimate-derived estimates diverged considerably from macroclimate-derived estimates, and were more accurate (Figure 5c). Of note is that even microclimate still underpredicted mosquito fecundity, perhaps because microclimf predictions were imperfect at representing conditions of both airborne adult mosquitoes and waterborne larvae. Given that Aedes mosquitoes are sensitive to microclimatic gradients across land uses (Saager et al., 2023) and respond to temperature in a nonlinear
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8 365 carefully selecting bioclimatic variables deemed most relevant to the ecological response in
9 366 question.

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Most processes in nature vary in space and time, and are best modeled as such (Levin, 1992).
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In our spatiotemporal case study, using the highly-proximal microclimf microclimate predictions
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prevalence shifts of a fossorial salamander species, P. jordani (Figure 6). The microclimf-driven
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3 395 statistical model of prevalence shifts explained 1.5x more variation than the WorldClim-driven model, and 3.5x more variation than the ERA5-driven model. Furthermore, microclimf still outperformed WorldClim and ERA5 even when at coarser spatial resolution than them (Figure 6c, Table S6). Warming rates predicted by the highly-proximal microclimf model were also slower than those of either ERA5 or WorldClim. These results, in tandem, suggest that climate proximity may influence findings of climate change velocity or its ecological implications: coarse-resolution, distal climate data commonly used in macroecology may exaggerate predictions of climate change-induced species' extinctions and range shifts (Colwell, 2021; Maclean & Early, 2023).

17 404 Microclimate data provide several benefits for spatiotemporal analyses, with one benefit concerning drivers of climate, and a second concerning how organisms respond to climate. First, the relative importance of microclimate-forcing variables changes across the same spatial and temporal scales at which most organisms live. For instance across space, atmospheric turbulence and wind speeds drive near-surface temperature in barren environments but can have a negligible effect on understory temperatures within nearby forests. Across time, soil surface temperatures are largely a function of solar radiation during the day, yet are more dependent on cold air drainage and soil composition at night (Geiger et al., 2009). Such heterogeneous microclimate-forcing makes it difficult to compress a microclimatic dataset – of sufficient extent and duration to be ecologically relevant – into static spatial layers or single time series that adequately represent the climate history across an area. Conversely, the forcing of macroclimate, although complex, primarily operates at scales broader than any single organism is normally exposed to. This results in spatiotemporally smoother distributions of macroclimate that can be summarized to two- or one-dimensional data across kilometers and months. Second, organisms directly respond to proximal conditions (microclimates) rather than distal conditions, but realized exposure to microclimates can change in both space and time. For instance, mobile species can move between microclimates to behaviorally thermoregulate (Kearney & Porter, 2009). In concreto, when the structures of both climate data and ecological models are such to allow variability in both space and time, then climate data of high proximity will become dramatically more useful for accurate ecological predictions.

39 418 Second, organisms directly respond to proximal conditions (microclimates) rather than distal conditions, but realized exposure to microclimates can change in both space and time. For instance, mobile species can move between microclimates to behaviorally thermoregulate (Kearney & Porter, 2009). In concreto, when the structures of both climate data and ecological models are such to allow variability in both space and time, then climate data of high proximity will become dramatically more useful for accurate ecological predictions.
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52 When generating, accessing, or processing climate data, researchers should consider not just spatiotemporal resolution, but also climate proximity: how well such data represent the conditions that organisms actually experience within their habitats. In our case studies, we

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3 428 found that increasing proximity can yield far greater improvements in ecological predictions than
4 429 increasing either spatial or temporal resolution. Accordingly, a unified definition of "microclimate"
5 430 should be couched in terms of proximity for a given organism or process, rather than just the
6 431 spatial and temporal resolution of climate observation or prediction.
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10 432 Re-evaluating climate scale in this manner questions the use of some generic
11 433 formulations of the relationship between climate and ecology as often employed in large-scale
12 biogeographical studies. For any individual organism, actual exposure is best represented with
13 434 physiologically-informed predictors (Gardner et al., 2019). Such predictors can range from
14 435 simple sums of the cumulative time beyond a threshold (e.g. the critical thermal maximum), to
15 436 calculating average performance based upon a thermal reaction norm (Figure 5c), or fully
16 437 embracing a biophysical model to link energy exchange between an organism and its
17 438 environment to the corresponding physiological impacts (Briscoe et al., 2023). When possible,
18 439 employing spatiotemporally-explicit data and models will also better capture the diverse
19 440 processes that shape climate and its roles in ecology and biogeography, and will best leverage
20 441 high-resolution proximal climate data.
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Re-evaluating climate scale in this manner questions the use of some generic formulations of the relationship between climate and ecology as often employed in large-scale biogeographical studies. For any individual organism, actual exposure is best represented with physiologically-informed predictors (Gardner et al., 2019). Such predictors can range from simple sums of the cumulative time beyond a threshold (e.g. the critical thermal maximum), to calculating average performance based upon a thermal reaction norm (Figure 5c), or fully embracing a biophysical model to link energy exchange between an organism and its environment to the corresponding physiological impacts (Briscoe et al., 2023). When possible, employing spatiotemporally-explicit data and models will also better capture the diverse processes that shape climate and its roles in ecology and biogeography, and will best leverage high-resolution proximal climate data.

Microclimate datasets and models make different tradeoffs to represent proximal microclimates that may serve certain applications better than others. The microclimf model employed here spatially predicts subcanopy and soil microclimate in a computationally efficient manner by simplifying treatment of some physical processes; NicheMapR (Kearney & Porter, 2017) trades computational speed for greater fidelity to physical processes; microclimc (Maclean & Klinges, 2021) includes more detail on canopy effects; and TrenchR (Buckley et al., 2023b) trades complexity for accessibility. Gridded microclimate datasets are also becoming increasingly available across broad expanses (Kearney et al., 2014; Lembrechts et al., 2022; Haesen et al., 2023). When selecting between models and datasets to represent proximal microclimate, we advise ecologists to especially consider the home range size (for animals), phenology and activity windows, and an organism's morphology, all of which determine climate exposure (Kearney & Porter, 2009; Potter et al., 2013). Body size in particular can be used to quantitatively define microclimates for a given organism (Pincebourde & Woods, 2020; Kearney et al., 2021). Using several tools in tandem may also represent multi-scale microclimates relevant to a species, such as variable tree bark surface temperatures nested within forest understories (Pincebourde & Woods, 2020).

Our case studies are inherently limited in scope (e.g. all are ectothermic animals with
restricted mobility), as they serve only as illustrative examples of several ecoclimatic
relationships worth exploring (phenology, thermal suitability, and range shifts). Similar to much

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462 of the climate change ecology literature, we focused primarily on temperature, yet climate
463 consists of many other relevant variables (Stocker et al., 2014). Furthermore, the spatial
464 resolution of climate data explored here matches that of many studies of species' distributions,
465 but is coarser than that of data collected in most field-based ecological studies (Estes et al.,
466 2018). Yet while increasing climate proximity is useful across scales, even down to operative or
467 body conditions (including leaf surface or phyllospheric temperatures; Pincebourde & Casas,
468 2019), finer resolution data can sometimes decrease prediction accuracy due to overfitting or
469 bias from a scale mismatch (also see Box 1). We recommend modelers to perform scale-of-
470 effect analyses (e.g. van de Pol et al., 2016), which identify the temporal periods and/or spatial
471 extents that are most correlated to a chosen ecological response, and therefore do not assume
472 that finer resolution entails higher accuracy. By doing so, one may find that the spatiotemporal
473 density of climate measurements and estimates may matter less than how those data were
474 collected or derived, and how they are integrated into models.

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475 Both climate and its ecological responses are multifaceted and scale-dependent. Here
476 we have highlighted some of the inadequacies of coarse or even fine resolution climate data,
477 and demonstrate the importance of proximity for spatial, temporal, and spatiotemporal
478 processes. By redefining climate scale with an emphasis on proximity to a target application, we
479 not only better distinguish the language used to express climate's impact on ecology and
480 improve modeling practices, but also advance theory. Considering climate proximity uncovers
481 important mechanisms by exploring how climate drives biology through the lens of the organism
482 itself. Rather than relying on analytical shortcuts that may be easily generalizable (e.g. standard
483 bioclimatic variables derived from distal data), it is every ecologist's duty to identify what
484 conditions are most proximal for their study system and species. Using available data and tools
485 that measure and model biologically relevant climate, ecologists can gain a more refined and
486 process-focused understanding of global change.

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599 Klinges, D.H. & Scheffers, B.R. (2021) Microgeography, Not Just Latitude, Drives Climate

600 Overlap on Mountains from Tropical to Polar Ecosystems. *The American Naturalist*, **197**, 75–92.

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607 Lembrechts, J.J., van den Hoogen, J., Aalto, J., Ashcroft, M.B., De Frenne, P., Kemppinen, J., Kopecký, M., Luoto, M., Maclean, I.M.D., Crowther, T.W., et al. (2022) Global maps of soil temperature. *Global Change Biology*, **28**, 3110–3144.

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612 Lenoir, J., Bertrand, R., Comte, L., Bourgeaud, L., Hattab, T., Murienne, J. & Grenouillet, G. (2020) Species better track climate warming in the oceans than on land. *Nature Ecology & Evolution*, 1–16.

615 Lepage, M.P., Bourgeois, G., Brodeur, J. & Boivin, G. (2012) Effect of Soil Temperature and Moisture on Survival of Eggs and First-Instar Larvae of Delia radicum. *Environmental Entomology*, **41**, 159–165.

618 Levin, S.A. (1992) The Problem of Pattern and Scale in Ecology: The Robert H. MacArthur Award Lecture. *Ecology*, **73**, 1943–1967.

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623 Maclean, I. (2023) ilyamaclean/microclimf.

624 Maclean, I.M.D. & Early, R. (2023) Macroclimate data overestimate range shifts of plants in response to climate change. *Nature Climate Change*, 1–7.

626 Maclean, I.M.D. & Klinges, D.H. (2021) Microclimc: A mechanistic model of above, below and within-canopy microclimate. *Ecological Modelling*, **451**, 109567.

628 Martin, T.L. & Huey, R.B. (2008) Why “Suboptimal” Is Optimal: Jensen’s Inequality and Ectotherm Thermal Preferences. *The American Naturalist*, **171**, E102–E118.

630 Meigen, J.W. (1826) Delia platura. *Systematische Beschreibung der bekannten europäische n*
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632 Mordecai, E.A., Cohen, J.M., Evans, M.V., Gudapati, P., Johnson, L.R., Lippi, C.A., Miazgowicz, K., Murdock, C.C., Rohr, J.R., Ryan, S.J., et al. (2017) Detecting the impact of temperature on transmission of Zika, dengue, and chikungunya using mechanistic models. *PLOS Neglected Tropical Diseases*, **11**, e0005568.

636 Nix, H.A. (1986) *A biogeographic analysis of Australian Elapid Snakes. Atlas of Elapid Snakes of Australia* Australian Flora and Fauna Series Number 7., pp. 4–15. Australian Government Publishing Service, Canberra.

639 Paaijmans, K.P., Heinig, R.L., Seliga, R.A., Blanford, J.I., Blanford, S., Murdock, C.C. & Thomas, M.B. (2013) Temperature variation makes ectotherms more sensitive to climate change. *Global Change Biology*, **19**, 2373–2380.

642 Pauli, J.N., Zuckerberg, B., Whiteman, J.P. & Porter, W. (2013) The subnivium: a deteriorating seasonal refugium. *Frontiers in Ecology and the Environment*, **11**, 260–267.

644 Pearl, R. & Reed, L.J. (1920) On the Rate of Growth of the Population of the United States since 1790 and Its Mathematical Representation. *Proceedings of the National Academy of Sciences*, **6**, 275–288.

647 Pincebourde, S. & Casas, J. (2019) Narrow safety margin in the phyllosphere during thermal extremes. *Proceedings of the National Academy of Sciences*, 201815828.

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655 Potter, K.A., Woods, H.A. & Pincebourde, S. (2013) Microclimatic challenges in global change biology. *Global Change Biology*, **19**, 2932–2939.

657 R Core Team (2022) A Language and Environment for Statistical Computing.

658 Renner, I.W., Elith, J., Baddeley, A., Fithian, W., Hastie, T., Phillips, S.J., Popovic, G. & Warton, D.I. (2015) Point process models for presence-only analysis. *Methods in Ecology and Evolution*, **6**, 366–379.

661 Saager, E.S., Iwamura, T., Jucker, T. & Murray, K.A. (2023) Deforestation for oil palm increases microclimate suitability for the development of the disease vector Aedes albopictus. *Scientific Reports*, **13**, 9514.

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665 Summations for Seedcorn Maggot1 Development under Controlled Conditions2. *Annals of the Entomological Society of America*, **75**, 103–106.

667 Scheffers, B.R., Evans, T.A., Williams, S.E. & Edwards, D.P. (2014) Microhabitats in the tropics buffer temperature in a globally coherent manner. *Biology Letters*, **10**, 20140819.

669 Stocker, T.F., Qin, D., Plattner, G.-K., Tignor, M.M.B., Allen, S.K., Boschung, J., Nauels, A., Xia, Y., Bex, V. & Midgley, P.M. (2014) *Climate Change 2013: The Physical Science Basis*. Contribution of Working Group I to the Fifth Assessment Report of IPCC the Intergovernmental Panel on Climate Change, (ed. by T. Stocker), D. Qin), G.-K. Plattner), M. Tignor), S. Allen), J. Boschung), A. Nauels), Y. Xia), V. Bex), and P. Midgley) Cambridge University Press, Cambridge.

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678 Woodward, F.I. (1987) *Climate and Plant Distribution*, Cambridge University Press.

Data accessibility statement

682 All data and code for ecological case studies and figures are available via Zenodo at 10.5281/zenodo.8110832. Code for the microclimf model is available at

683 https://github.com/ilyamaclean/microclimf. Empirical temperature data used for the temperature product comparison will be made available as part of the SoilTemp database Version 1.0 (in preparation).

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699 Box 1. The proximity of climate data

700

701 Scientists have traditionally grounded the definition of scale based upon extent (spatial area, temporal duration) and resolution (grain size, temporal interval) (Wiens, 1989). However, for climate data, resolution and extent are not comprehensive in describing their scale, as data of the same resolution and extent may differ in their ecological relevance to different organisms or processes. Here we define "climate proximity" as the degree to which climate data represent the actual conditions that an organism or system is exposed to, as a third dimension of scale distinct from the spatiotemporal resolution of the climate data (Figure 1). While resolution describes the quantity or frequency of data, proximity describes how well climate data capture radiation, water, and heat exchange relevant to a given species for a time and location. When estimating distributions of a forest-dwelling species, a 1-km² temperature product that closely represents forest understory conditions is more proximal than a 100-m² product that represents free-air conditions as measured by weather stations and without accounting for the effects of vegetation. Similarly, mean monthly soil temperatures are more proximal for belowground organisms than daily air temperatures. We call the opposite of proximal climate, "distal" climate, i.e. only indirectly related to ecological responses (Austin, 2002; Gardner et al., 2019). Proximity is evaluated in a context-specific manner, which entails that although it can be qualified (e.g. one climate measurement is more proximal for a given species than another climate measurement) it is challenging to quantify in general terms (e.g. a 20% increase in proximity). Key to increasing climate proximity for a given organism is knowledge of the organism's microhabitats, physiology, and ecology, nested within the broader habitat and landscape (Figure 1). For a gridded climate dataset, high proximity may not always involve representing the same heights or microhabitats across space or time. For ants, proximal climate might be air temperatures in forests, but soil temperatures in barren environments; for many amphibians, proximity entails aquatic conditions for tadpoles, and terrestrial conditions for adults.

725 Although proximity is distinct from spatial or temporal resolution, highly proximal climate typically varies at fine spatial and temporal scales. For instance, across horizontal space, topographic heterogeneity will determine how much local temperatures differ from macroclimate; vertically, soil temperatures can decouple considerably from air temperatures; and temporally, high-frequency wind turbulence drives thermal gradients near the ground (see Figure 2 for other mechanisms). Yet there are also aspects of climate proximity that do not align well within the spatial or temporal dimensions, as proximity also depends upon the biotic context and how organisms "construct" their environments through their physical properties and
733 behavior (Kearney & Porter, 2009; Pincebourde & Woods, 2020). Understanding the
734 ecophysiology of tree frogs inhabiting epiphytic ferns in forest canopies requires measuring or
735 simulating fern microclimates (Scheffers et al., 2014), which are nested within the canopy
736 microclimate. Or for many parasites, the spatial location of climate data may matter less than
737 accounting for host body temperatures (Thomas & Blanford, 2003). Furthermore, not all climate-
738 forcing processes contribute to proximity for a given context; the attenuation of heat exchange
739 by snow creates an important subnivium microclimate for small-bodied species, but less so for
740 many large mammals or birds (Pauli et al., 2013).

741 Climate proximity should also be considered for understanding ecological processes for
742 which there may be many relevant species, such as soil carbon sequestration. Given the
743 impracticality of delineating proximal conditions for many taxa across regions, distal
744 macroclimate may occasionally be useful as a "mean field approximation" of climate exposure
745 across ecological communities (Bennie et al., 2014). Yet oftentimes this approximation is
746 inadequate given heterogeneous microclimates and non-linear responses to climate (Martin &
747 Huey, 2008). Ultimately, operative conditions of organisms (body temperatures) are the broker
748 between climate exposure and biological responses. Yet given the challenge of estimating body
749 conditions across space and time, generating or selecting highly proximal climate data serves
750 as a useful surrogate for understanding bioclimatic relationships.

751
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<figure><img src="image_2.png" /><figcaption>Figure 1. Top: the three dimensions of climate data resolution used for ecological modeling, none of which are true characteristics of climate in nature, but constructs for describing data. The proximity of climate describes its relevance to a given organism, process, or system. For example, the soil temperature surrounding a fossorial mouse is more proximal (i.e. directly impacts operative body temperatures) than the forest understory microclimate, which in turn is more proximal than ambient free-air conditions. Bottom: the benefits of increasing each of these dimensions of climate data may then depend on the domains predicted by the ecological model: a process that is spatial, temporal, or spatiotemporal.</figcaption></figure>
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<figure><img src="image_3.png" /><figcaption>(a)</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>(b)</figcaption></figure>

<table><thead><tr><th colspan="3">Proximal Mechanisms</th></tr></thead><tbody><tr><td>Solar</td><td>Latitude (solar radiation angle)</td><td>●●●</td></tr><tr><td>Solar</td><td>Season (axial tilt)</td><td>●●●</td></tr><tr><td>Solar</td><td>Earth's rotation</td><td>●●●</td></tr><tr><td>Air</td><td>Sensible heat flux (convective exchange)</td><td>●●●</td></tr><tr><td>Air</td><td>Vapour pressure gradient (latent heat exchange)</td><td>●●●</td></tr><tr><td>Air</td><td>Cloud cover (radiation scattering/interception)</td><td>●●●</td></tr><tr><td>Water</td><td>Coastal effects (latent heat exchange)</td><td>●●●</td></tr><tr><td>Terrain</td><td>Altitudinal lapse rate</td><td>●●●</td></tr><tr><td>Terrain</td><td>Radiation interception</td><td>●●</td></tr><tr><td>Terrain</td><td>Heat storage and conductance</td><td>●●</td></tr><tr><td>Terrain</td><td>Longwave radiation</td><td>●●</td></tr><tr><td>Terrain</td><td>Albedo</td><td>●●</td></tr><tr><td>Terrain</td><td>Topographic shading</td><td>●●</td></tr><tr><td>Terrain</td><td>Cold air drainage</td><td>●●</td></tr><tr><td>Terrain</td><td>Topographic wetness</td><td>●●</td></tr><tr><td>Vegetation</td><td>Stomatal conductance</td><td>●●</td></tr><tr><td>Vegetation</td><td>Woody conductance</td><td>●●</td></tr><tr><td>Vegetation</td><td>Radiation interception</td><td>●●</td></tr><tr><td>Vegetation</td><td>Albedo</td><td>●●</td></tr></tbody></table>

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Figure 2. Climate data can be described in spatial, temporal, and proximal domains, as illustrated by the contrasting scales of the data products employed in this study: ERA5, WorldClim, and microclimf (a). The proximity of climate describes its relevance to the actual exposure of a given organism, process, or system. To categorize the proximity of climate data, one must consider the meteorological and geographical mechanisms that such data adequately represent, which drive how radiation, moisture, and heat exchange (latent and sensible) determine the temperature of a time and location. In (b), we present a non-exhaustive ordered list of temperature-forcing mechanisms and categorize which are represented by each climate data product, either explicitly (for the process-based ERA5 and microclimf) or implicitly (through covariates used in the statistical interpolation employed by WorldClim).
<figure><img src="image_5.png" /><figcaption>Figure 3. Comparison of temperature predictions from ERA5, WorldClim, and microclimf using empirical temperature measurements from in-situ loggers. (a – f): panels display time series from six example locations (from 371 in total) representing different vegetation categories from four continents (see Figure S1 for all locations). Vegetation categories are 2015 estimates from the Copernicus Global Land Service Land Cover product. Black lines indicate empirical observations of soil temperature, colored lines indicate temperature predictions from each temperature product. (g – i): scatter plots of the root mean square error (RMSE) between predictions from each temperature product and empirical temperature data (RMSE calculated from monthly resolution predictions for comparisons against WorldClim, and hourly resolution predictions for the ERA5-microclimf comparison). Each point corresponds to RMSE from measurements of one logger during one year, and black lines represent the lines of equality (equal error between the two temperature products). The microclimf model performed best (lowest RMSE), followed by ERA5 and then WorldClim (also see Table S2).</figcaption></figure>

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<figure><img src="image_6.png" /><figcaption>Figure 4. Case study 1: temperature predictions (a), growing degree days (GDDs, b), and emergence rate predictions (c) for the soil-dwelling larvae of two crop pest insects in Alberta, Canada, all calculated separately from ERA5, WorldClim, and microclimf. GDDs are indices for the cumulative sum of temperatures above a minimum (base) temperature for organismal development, and we predicted emergence rates using a logistic model driven by GDDs. Black points in panel (c) indicate empirical observations for the two species (no empirical data for 10% and 95% emergence for *D. platura*). Although all three temperature products had similar temporal patterns in temperature (a; average difference in daily means across climate sources = 0.49 °C) and growing degree days (b; average difference across climate sources = 200.0 GDDs; displayed here are GDDs just for *D. radicum*), even such small differences yielded divergent predictions of nonlinear biological responses (c; Table S3). High proximity (*microclimf*) yielded the most accurate biological predictions. See Figure S3 for results with temporally aggregated/disaggregated climate data.</figcaption></figure>

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<figure><img src="image_7.png" /><figcaption>Figure 5. Case study 2: estimates of mean annual temperature (BIO1, column a), maximum temperature of the warmest month (BIO5, column b), and average fecundity as a metric of thermal performance for *Aedes aegypti* mosquitoes (column c), derived from ERA5, WorldClim, and microclimf predictions across Sabah in Malaysian Borneo. Differences in spatial resolution, temporal resolution, and proximity can result in shifts in thermal profiles (density plots at top), depending on what bioclimatic variable is calculated. Column (a): as an annual average, BIO1 was not sensitive to differences in temporal variability between microclimate and macroclimate, and therefore thermal distributions across temperature products were similar (mean overlap = 70.1%). Column (b): maximum temperatures of the warmest month (BIO5) diverge more among the three temperature products (mean overlap = 16.9%), with hourly-resolution products (ERA5</figcaption></figure>
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826 and microclimf) yielding higher warmest extremes than the monthly-resolution WorldClim.

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827 Column (c): estimates of fecundity rates from a temperature-driven reaction norm empirically parameterized for A. aegypti were fairly similar between the two macroclimate products (overlap = 47.2%), but higher and more accurate when derived from microclimf microclimate predictions.

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830 Dashed gray density plot indicates a distribution generated from empirical fecundity measurements, which overlapped more with microclimf predictions (37.8%) than ERA5 (6.1%)

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832 or WorldClim (4.9%). Averaging climate (e.g. to BIO1) suppresses the variability that characterizes high-resolution or proximal climate data, while biophysically-informed summaries (e.g. average fecundity from a thermal reaction norm) are more sensitive to such variability, and therefore result in greater divergence between macroclimate and microclimate.

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<figure><img src="image_8.png" /><figcaption>Figure 6. Case study 3: incorporating microclimate results in improved prediction accuracy of spatiotemporal prevalence shifts, as indicated by predicted changes in salamander prevalence in Great Smoky Mountains National Park. (a): Estimates of warming (slopes of lines) between the 1960s and 1990s were higher for WorldClim (0.597 °C per decade) than either ERA5 or microclimf. Furthermore, microclimf – which predicted topsoil temperatures both below canopies and exposed to full sun, as opposed to just free-air temperatures from ERA5 and WorldClim – expressed greater spatial variability in maximum temperature than the macroclimate products. (b): During the same four-decade timespan, areas of high salamander prevalence shifted towards higher elevations in the center of the park (top map). Predictions of changes in salamander prevalence from generalized additive models (GAMs) driven by the three temperature products had vastly different patterns, with microclimf-driven predictions (bottom map) visually and quantitatively capturing the spatial signal of empirical prevalence shifts (top map). (c): The GAM driven by 1-km resolution microclimf predictions performed best as indicated by the proportion of variance explained (R²), and this advantage was held by microclimf until spatially aggregated to 10-km resolution.</figcaption></figure>

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## Temporal Resolution

<figure><img src="image_9.png" /></figure>

Global Ecology and Biogeography

Decadal

Annual

Monthly

Daily

Hourly

<figure><img src="image_10.png" /></figure>

## Proximity

<figure><img src="image_11.png" /></figure>

## Spatial Resolution

<figure><img src="image_12.png" /></figure>

## Ecological Models

<figure><img src="image_13.png" /></figure>

Habitat Suitability

<figure><img src="image_14.png" /></figure>

<figure><img src="image_15.png" /></figure>
<figure><img src="image_16.png" /></figure>

<figure><img src="image_17.png" /></figure>

<figure><img src="image_18.png" /></figure>

<figure><img src="image_19.png" /></figure>

<figure><img src="image_20.png" /></figure>

ERA5

<figure><img src="image_21.png" /></figure>

WorldClim

<figure><img src="image_22.png" /></figure>

microclimf

<figure><img src="image_23.png" /></figure>

<table><thead><tr><th colspan="2">Proximal Mechanisms</th></tr></thead><tbody><tr><td>Solar</td><td>Latitude (solar radiation angle)</td></tr><tr><td>Solar</td><td>Season (axial tilt)</td></tr><tr><td>Solar</td><td>Earth's rotation</td></tr><tr><td>Air</td><td>Sensible heat flux (convective exchange)</td></tr><tr><td>Air</td><td>Vapour pressure gradient (latent heat exchange)</td></tr><tr><td>Air</td><td>Cloud cover (radiation scattering/interception)</td></tr><tr><td>Water</td><td>Coastal effects (latent heat exchange)</td></tr><tr><td>Terrain</td><td>Altitudinal lapse rate</td></tr><tr><td>Terrain</td><td>Radiation interception</td></tr><tr><td>Terrain</td><td>Heat storage and conductance</td></tr><tr><td>Terrain</td><td>Longwave radiation</td></tr><tr><td>Terrain</td><td>Albedo</td></tr><tr><td>Terrain</td><td>Topographic shading</td></tr><tr><td>Terrain</td><td>Cold air drainage</td></tr><tr><td>Terrain</td><td>Topographic wetness</td></tr><tr><td>Vegetation</td><td>Stomatal conductance</td></tr><tr><td>Vegetation</td><td>Woody conductance</td></tr><tr><td>Vegetation</td><td>Radiation interception</td></tr><tr><td>Vegetation</td><td>Albedo</td></tr></tbody></table>

<figure><img src="image_24.png" /></figure>

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<figure><img src="image_88.png" /><figcaption>RMSE of microclimf</figcaption></figure>

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(a)

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Prevalence Shifts (1960's to 1990's)

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microclimf-predicted shifts

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Loss
# Supporting Information

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## Proximal microclimate: moving beyond spatiotemporal resolution improves ecological predictions

### Contents:

## Appendix 1 – Methodological Details

### 1.1 – The microclimf model: structure and parameterization

### 1.2 – Temperature Data Comparison

### 1.3 – Case Study 1: Temporal Crop Pest Emergence

### 1.4 – Case study 2: Spatial bioclimate and mosquito thermal performance

### 1.5 – Case study 3: Spatiotemporal salamander abundance shifts

### 1.6 – Software

## Table S1 – Inputs for Microclimate Model Parameterization

## Table S2 – Temperature Data Product Validation

## Table S3 – Case Study 1 Validation Results

## Table S4 – Case Study 2 Significance Testing

## Table S5 – Case Study 2 Spatial Distributions

## Table S6 – Case Study 3 Validation Results, with Spatial Aggregation

## Table S7 – Case Study 3 Validation Results, with Initial Prevalence as a Covariate

## Figure S1 – Locations of Thermal Loggers for Validation

## Figure S2 – Temperature Data Product Validation across Vegetation Categories

## Figure S3 – Case Study 1 Validation with Temporal Aggregation

## References in Supporting Information

## Appendix 1 – Methodological Details

### 1.1 The microclimf model: structure and parameterization

The microclimf R package provides tools for efficient mechanistic modeling of fine-scale variation in temperature near the ground, building upon methods developed in microclima (Maclean et al., 2019) and microclimc (Maclean & Klinges, 2021). In brief, the model estimates near-ground air and soil temperatures principally based upon the net flux density absorbed by surfaces (vegetation canopies and soils) by emulating the Lagrangian localized near-field model of Raupach (1989). Specifically, air temperature and humidity are assumed to be governed by the vertical distribution of heat and vapor sources and sinks within the canopy, which are in turn determined primarily by the attenuation of radiation, quantified using the two-stream model of Sellers (1985). The transfer of heat and vapor within vegetation is approximated using an emulator that characterizes the concentration field of heat and vapor emanating from a spatially extensive source, expressed as the sum of a diffusive far-field contribution governed by gradient-diffusion, and a non-diffusive near-field contribution. This means that while the general air temperature and vapor profiles are determined by the vertical distribution of leaf foliage upwind of the location of interest, they are assumed also to be influenced by the presence of vegetation immediately adjacent to the location of interest. The microclimf model generates gridded outputs in hourly or twice-daily timesteps. The use of a model emulator enables
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calculations to be performed simultaneously on all pixels and time increments, thereby increasing computational efficiency.

A combination of remote sensing and model-derived data products were used to parameterize the microclimf microclimate model, primarily describing macroclimatic, vegetation, soil composition, and topographic conditions. See Table S1 for a complete list of all input sources used, along with their spatial and temporal resolutions and extents. Mechanistic models estimating near-surface meteorology such as those used here are primarily sensitive to the ambient meteorological conditions, and here the ECMWF ERA5 reanalysis product was used in order to incorporate moderate spatial resolution (0.25 decimal degrees), fine temporal resolution (hourly), and predictions for the entire spatiotemporal domain of interest. We used a subset of ERA5 variables relevant to microclimatic models that depict near-surface conditions. We used the mcera5 R package (Klinges et al., 2022) to download ERA5 meteorological data and perform the necessary unit transformations and corrections before integration into microclimate models.

Because of the importance of vegetation characteristics for estimating the amount of downwelling direct and diffuse radiation, we used several satellite-derived products to characterize vegetation, primarily plant area index (PAI, the total one-sided plant area per unit ground area) and canopy height. We derived estimates of daily PAI from a combination of the MODIS MOD15A2H leaf area index (LAI) product and temporally-explicit estimates of PAI for broad habitat categories as provided in the microctools package (Maclean & Klinges, 2021). We assumed that seasonal variation in PAI is principally due to changes in leafy foliage rather than woody growth or senescence, and that the annual minimum PAI corresponds to the fixed amount of woody mass. Therefore, for each MODIS pixel, we extracted the annual minimum LAI value, subtracted this minimum value from the entire LAI time series, and then added the annual minimum microctools PAI value corresponding to the habitat type of that pixel, to acquire temporally-explicit estimates of PAI corresponding to MODIS resolution (500-m, 8-day intervals). We then performed a cubic spline as provided by the "fmm" method in 'spline)' (Forsythe et al., 1977) to interpolate 8-day estimates to a time series of daily PAI for each spatial pixel.

In addition, we incorporated satellite-derived estimates of canopy height. For microclimate modeling at latitudes within 52°N and 52°S, we used 30-m resolution estimates of canopy height generated from integration of the Global Ecosystem Dynamics Investigation (GEDI) lidar forest structure measurements with Landsat surface phenology (Potapov et al., 2021). For higher latitudes, for which GEDI measurements are not available, we instead used ICESat-derived 1-km resolution estimates of canopy height (Simard et al., 2011).

The microclimf model accepts a variety of other parameters to describe the composition and configuration of vegetation, yet these parameters are difficult to directly estimate at large spatial scales from available remote sensing. Conveniently, microclimf has built-in parameterization for estimating these parameters from broad habitat classifications, provided in the `vegpfromhab()` function and also provided in the microclimc R package (see Maclean & Klinges, 2021 for details on vegetation parameterization). We therefore used this function to provide estimates of the ratio of vertical to horizontal projections of leaf foliage, the maximum stomatal conductances, leaf reflectance, canopy clumpiness, and mean leaf width. We also used the function `PAIfromhabitat()` from microctools for estimates of the zero plane displacement correction factor, leaf orientation, the roughness height, and a correction factor to simulate vegetation wind buffering. To inform selection of broad habitat classifications for each spatial pixel, we used several global, temporally-explicit land cover products: the ESA CCI land cover (300-m and annual) for predictions 2014 or earlier (ESA, 2017), and the Copernicus Global Land Service Land Cover (100-m and annual, Buchhorn et al., 2020) for predictions 2015 or onwards.
For characterizing landform, we employed the ASTER Global Digital Elevation Map (GDEM V3) (Fujisada et al., 2005), which provides 30-m resolution estimates of elevation, and used this to derive estimates of slope and aspect at the same spatial resolution using the `terrain()` function from the package `terra`. To describe soil composition characteristics, we used the ISRIC-WISE soil properties map (Batjes, 2012) at 5 arc-minute resolution (9.26-km resolution at the equator), extracting bulk density and percents clay, silt and sand at six depths (0-cm, 5-cm, 15-cm, 30-cm, 60-cm, and 100-cm). All other parameters were left at default values as provided in the `temphumE()` and `below_hr()` functions in `microclimf`.

All spatial layers were reprojected to the WGS 1984 coordinate system. Given that we parameterized `microclimf` using spatially gridded data products, `microclimf` predictions for both the temperature data comparison and the first case study represented estimates of average conditions within a 1-km² pixel surrounding each coordinate rather than exact conditions at the coordinate.

## 1.2 Temperature Data Comparison

To assess how closely ERA5 air temperatures, WorldClim air temperatures, and `microclimf` topsoil temperatures match near-surface temperature measurements, we used hourly time series from 371 temperature loggers placed in a range of habitats across four continents (Figure S1) drawn from an early version of the SoilTemp database (Lembrechts et al., 2020). We selected for logger time series that had hourly or finer temporal resolution (to compare against hourly temperature predictions of ERA5 and `microclimf`), and collector-contributed estimates of elevation, slope, aspect, canopy height, and leaf area index (in order to assess performance across a range of habitats). We chose only belowground loggers rather than aboveground loggers, as the temperature of aboveground thermometers may differ considerably from air temperature owing to the absorption of radiation by the thermometer (WMO, 2008), while shielding of an aboveground thermometer alters measurements due to shading and reducing wind speeds (Maclean et al., 2021). This query of SoilTemp yielded 371 logger time series across four continents, which varied in belowground depth of loggers (min/mean/max: 1, 13, 271 cm) and consisted of the following makes and models: TOMST TMS loggers (n = 323), Campbell Scientific thermocouples (n = 30), ad-hoc Type T copper thermocouples (n = 7), Decagon 5TE (n = 5), EMS Brno MicroLog T3 with Pt1000/8 temperature sensors (n = 3), iButton DS1921G (n = 2), and EMS Brno MicroLog SP3 (n = 1). Given that some of the 371 loggers were within one kilometer of each other (thereby sometimes, but not always, falling within a single WorldClim grid cell), measurements of multiple loggers were sometimes compared against the same WorldClim predictions. The same applied for ERA5 predictions when loggers fell within a single 0.25° grid cell.

For each logger, we chose the most complete year of measurements, and for the logger's location and year we extracted the corresponding data from ERA5, WorldClim, and `microclimf` temperature predictions (the latter of which were made for the same depth as the logger). Then, we measured accuracy of the predictions as compared to the empirical measurements according to bias, root mean square error (RMSE) and Pearson's correlation coefficients (Willmott & Matsuura, 2006). We measured error for ERA5 and `microclimf` at the hourly level, and also aggregated ERA5, `microclimf`, and empirical data to the monthly mean/minima/maxima to match the temporal resolution of WorldClim, and then measured error for all three temperature products. To analyze the results for broad land cover categories, we extracted the classification for each logger coordinate from the Copernicus Global Land Service 100-m² 2015 layer (Buchhorn et al., 2020). Given that ERA5 and WorldClim are not intended to represent microclimates, this comparison does not serve as a validation of these temperature
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12 150 separately drive a simple mathematical model for emergence rates of two fly species: the
cabbage maggot (Delia radicum) and the seedcorn maggot (Delia platura). First, this entailed
calculating growing degree days (GDDs) from each climate data product. Given that WorldClim
g only provides monthly instead of daily values, we processed WorldClim data to GDDs via two
different methods: (1) replicating the monthly minima and maxima for each day of the month,
and (2) disaggregating to daily minima (maxima) via random draws from a normal distribution
centered on the average of the monthly mean and minimum (maximum), with a standard
deviation of 4.5°C (5.75 °C) as derived from the standard deviations in daily minima (maxima) in
temperature observations from a weather station near the study sites in Alberta (ACIS, 2023).
We also aggregated ERA5 and microclimf predictions to monthly resolution by calculating the
monthly minima and maxima from hourly data, and then following the same method in (1)
outlined for WorldClim.

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Historically, researchers have used two different methods for interpreting the base
temperature threshold (T<sub>base</sub>) for setting daily minimum temperature (T<sub>min</sub>) and daily maximum
temperature (T<sub>max</sub>) values: 1) if either T<sub>min</sub> or T<sub>max</sub> is below T<sub>base</sub>, set it equal to T<sub>base</sub>; 2) if the
average of T<sub>min</sub> and T<sub>max</sub> is below T<sub>base</sub>, set it equal to T<sub>base</sub> (McMaster & Wilhelm, 1997). Here,
we employed both methods separately and then averaged GDD values between the two
methods, before providing GDD values to logistic emergence models.

To calculate insect emergence as a function of temperature, we used the model
originally developed by Pearl and Reed (1920) and parameterized by Broatch et al. (2006),
which estimates the proportion of insects that emerges as a winged adult per day, using a
logistic function driven by GDDs.

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1
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3 192 Where Y is the percent emergence, x is GDDs, a is the lower asymptote, c is the upper asymptote, m is the degree-day requirement for maximum emergence (or inflection point), and b is the slope. Constant parameters a, b, c, and m are all species-specific estimates as provided by Broatch et al. (2006): 0, 0.02, 97.64, and 524.57 respectively for *D. platura*, and 0, 0.02, 125.23, and 290.42 respectively for *D. radicum*. Given that this model was developed to inform the timing of pesticide application, it does not necessitate, or provide, absolute values of adult abundance, only proportions of adults emerging over time for identifying the timing of peak emergence.
11 200

For validating emergence predictions by climate-driven models, we used empirical
observations of the proportions of total adult insects that emerged over time (10%, 25%, 50%,
75%, and 95% of total emergence) as reported by Broatch et al. (2006; Tables 5 and 8). We
measured error as the number of days between the climate-predicted timing of an emergence
threshold (e.g. 50% emergence predicted by day of year 180) and the observed day of year
when that threshold was actually crossed (e.g. 50% emergence by day of year 184, which
would entail prediction error of four days).
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20 207 1.4 Case study 2: Spatial bioclimate and mosquito thermal performance

21 208
22 209 We calculated *A. aegypti* fecundity rates (eggs laid / female / day) for each spatial pixel from an
23 210 empirically-derived thermal reaction norm for the mosquito (Mordecai *et al.*, 2017) drawn from
24 211 Briere's asymmetric equation (Briere *et al.*, 1999):

$$
26 \qquad 212 \qquad F = aT(T - T_0)(T_m - T)^{1/b}
27 \qquad 213
28 \qquad 214
$$

29 215 Where *F* is fecundity, *a* is a scale parameter, *T* is environmental temperature (here, thermal
time series from ERA5, WorldClim, or microclimf), *T*<sub>0</sub> is the critical minimum temperature, *T*<sub>m</sub> is
30 216 the critical maximum, and *b* is a shape parameter. All constant parameters (*a*, *T*<sub>0</sub>, *T*<sub>m</sub>, *b*) were fit
31 217 by Mordecai et al. (2017) based upon their exhaustive search of laboratory studies of
temperature-driven *A. aegypti* fecundity: *a* = 0.00856, *T*<sub>0</sub> = 14.58, *T*<sub>m</sub> = 34.61, *b* = 2, with
32 218 fecundity bounded at 0. Fecundity was calculated for each time step of thermal data (hourly for
33 219 ERA5 and microclimf, monthly for WorldClim) and then averaged for each pixel to generate
34 220 spatial maps of mean mosquito fecundity across Sabah.
35 221
36 222
37 223
38 224 To validate fecundity predictions, we extracted the mean and standard deviation of *A.*
39 225 *aegypti* fecundity measurements from two laboratory studies (Day *et al.*, 1994; Braks *et al.*,
40 226 2006) in which mosquitoes were held in chambers at 25 °C and 30 °C, respectively, which
41 227 match the annual mean daytime and nighttime temperatures as measured by six weather
42 228 stations across Sabah (German Weather Service, 2023; The World Bank, 2023). With values
43 229 from both studies equally weighted, we then generated a normal distribution of fecundity. Given
44 230 that empirical fecundities were lab-measured, values were not paired spatially with fecundity
45 231 predictions; rather the empirical values represented a range of realistic fecundity rates to which
46 232 to compare the range of predictions by each temperature product. We therefore measured the
47 233 area of integration (overlap, Klinges & Scheffers, 2021) between this empirical distribution with
48 234 kernel density-estimated distributions from each of ERA5, WorldClim, and microclimf fecundity
49 235 predictions. We also took two steps to compare the spatial distributions of BIO1, BIO5, and
50 236 fecundity generated by the three climate products: (1) we performed three Kruskal-Wallis tests
51 237 by rank (one each for BIO1, BIO5, and fecundity) to test for differences between predictions of
52 238 the three temperature products, with Dunn post-hoc analyses (non-parametric tests as
distributions of thermal predictions did not fit to any family of parametric distributions), and (2)
53 239 for BIO1, BIO5, and fecundity separately, we measured the area of integration (as outlined
54 240 above) between each pairwise set of climate data predictions.
55
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60

To validate fecundity predictions, we extracted the mean and standard deviation of *A.*
224 *aegypti* fecundity measurements from two laboratory studies (Day *et al.*, 1994; Braks *et al.*,
225 2006) in which mosquitoes were held in chambers at 25 °C and 30 °C, respectively, which
226 match the annual mean daytime and nighttime temperatures as measured by six weather
227 stations across Sabah (German Weather Service, 2023; The World Bank, 2023). With values
228 from both studies equally weighted, we then generated a normal distribution of fecundity. Given
229 that empirical fecundities were lab-measured, values were not paired spatially with fecundity
230 predictions; rather the empirical values represented a range of realistic fecundity rates to which
231 to compare the range of predictions by each temperature product. We therefore measured the
232 area of integration (overlap, Klinges & Scheffers, 2021) between this empirical distribution with
233 kernel density-estimated distributions from each of ERA5, WorldClim, and microclimf fecundity
234 predictions. We also took two steps to compare the spatial distributions of BIO1, BIO5, and
235 fecundity generated by the three climate products: (1) we performed three Kruskal-Wallis tests
236 by rank (one each for BIO1, BIO5, and fecundity) to test for differences between predictions of
237 the three temperature products, with Dunn post-hoc analyses (non-parametric tests as
distributions of thermal predictions did not fit to any family of parametric distributions), and (2)
238 for BIO1, BIO5, and fecundity separately, we measured the area of integration (as outlined
239 above) between each pairwise set of climate data predictions.
240
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241 1.5 Case study 3: Spatiotemporal salamander abundance shifts

242

243 *Species Distribution Model Calibration, Tuning, and Evaluation*

244 Species distribution models (SDMs) were used to measure changes in abundance of *P. jordani* across space and over time, from the periods of 1961 – 1970 to 1991 – 2000 (occurrence data

245 obtained via NPS research permit with IRMA Study IDs: GRSM-00104 GRSM-00105, GRSM-02139). However, given the relatively small geographic area that *P. jordani* occupies, traditional

246 SDMs – which attempt to quantify changes in population occupancy (colonization/extinction) – are likely insufficient to capture changes in their abundances. Therefore, to characterize

247 variation in both the spatial extent of occurrence and relative abundance of these salamanders, we implemented an Inhomogeneous Point Process Modeling (IPPM) framework. Under this

248 framework presence-only data y are realized as a spatial Poisson point process via an intensity function f(y). The quantity modeled is the spatial density of point presences (here referred to as

249 prevalence), which is interpreted as being proportional to the true abundance of the species (Renner & Warton, 2013). While homogenous PPMs generate a kernel density estimate of point

250 locations across a spatial window, IPPMs assume that points are not randomly distributed, but their locations are explained by a series of covariates.

251 we implemented an Inhomogeneous Point Process Modeling (IPPM) framework. Under this

252 framework presence-only data y are realized as a spatial Poisson point process via an intensity function f(y). The quantity modeled is the spatial density of point presences (here referred to as

253 prevalence), which is interpreted as being proportional to the true abundance of the species (Renner & Warton, 2013). While homogenous PPMs generate a kernel density estimate of point

254 locations across a spatial window, IPPMs assume that points are not randomly distributed, but their locations are explained by a series of covariates.

255

256 IPPMs were fitted using a MaxEnt algorithm as outlined by Renner et al. (2015), which requires a regular mesh of quadrature points be created using the centroid of each pixel in the study extent. This was done using the function 'maxent' from the R package *dismo* (Hijmans et al., 2022b) by instructing the model with the specific arguments: "noremoveduplicates" (which

257 does not allow removed duplicates to be part of the background sample), "noaddsamplestobackground" (which does not allow locations of records to be part of

258 "noaddsamplestobackground" (which does not allow locations of records to be part of background samples), and "maximumbackground=40000" (with a maximum of 40,000

259 background samples), and "maximumbackground=40000" (with a maximum of 40,000 background points). The model was then calibrated with 5 random test points across 5 replicates, using 16 threads, and a regularization multiplier equal to 1.0. Finally, model

260 background points). The model was then calibrated with 5 random test points across 5 replicates, using 16 threads, and a regularization multiplier equal to 1.0. Finally, model

261 predictions were obtained, in accordance with Renner et al. (2015), as raw (untransformed) numerical outputs (outputs default to logistic transformation). These models were evaluated

262 using five-fold cross validation using spatial and environmental blocks, and summarized based upon the Area under the Receiver Operator Curve (AUC), the True Skill Statistic (TSS), and

263 Boyce's Index (CBI). AUC is a standard measure of the success in predicting true positives by measuring the ratio of sensitivity to specificity, and was calculated non-parametrically using the

264 Wilcoxon statistic (Hanley & McNeil, 1982; Swets, 1988) using the built-in functionality of *dismo::maxent()*. TSS is a measure of model performance based upon presence and absence

265 predictions while correcting for dependence on prevalence (Allouche et al., 2006). Using the R package *dismo*, models were evaluated via cross-validation using presence points and a

266 random sample of 1000 background points (and therefore not true absences), and then TSS was calculated as the threshold at which the sum of the sensitivity (true positive rate) and

267 specificity (true negative rate) is highest (Allouche et al., 2006). CBI is typically considered most appropriate for habitat suitability or distribution predictions generated from presence-only data, as was the case here, and measures how much predictions differ from a random sample of the

268 numerical outputs (outputs default to logistic transformation). These models were evaluated using five-fold cross validation using spatial and environmental blocks, and summarized based upon the Area under the Receiver Operator Curve (AUC), the True Skill Statistic (TSS), and

269 Boyce's Index (CBI). AUC is a standard measure of the success in predicting true positives by measuring the ratio of sensitivity to specificity, and was calculated non-parametrically using the

270 Wilcoxon statistic (Hanley & McNeil, 1982; Swets, 1988) using the built-in functionality of *dismo::maxent()*. TSS is a measure of model performance based upon presence and absence

271 predictions while correcting for dependence on prevalence (Allouche et al., 2006). Using the R package *dismo*, models were evaluated via cross-validation using presence points and a

272 numerical outputs (outputs default to logistic transformation). These models were evaluated using five-fold cross validation using spatial and environmental blocks, and summarized based upon the Area under the Receiver Operator Curve (AUC), the True Skill Statistic (TSS), and

273 Wilcoxon statistic (Hanley & McNeil, 1982; Swets, 1988) using the built-in functionality of *dismo::maxent()*. TSS is a measure of model performance based upon presence and absence

274 predictions while correcting for dependence on prevalence (Allouche et al., 2006). Using the R package *dismo*, models were evaluated via cross-validation using presence points and a

275 random sample of 1000 background points (and therefore not true absences), and then TSS was calculated as the threshold at which the sum of the sensitivity (true positive rate) and

276 specificity (true negative rate) is highest (Allouche et al., 2006). CBI is typically considered most appropriate for habitat suitability or distribution predictions generated from presence-only data, as was the case here, and measures how much predictions differ from a random sample of the

277 observed presences (Boyce et al., 2002). We calculated CBI using a moving window with a width 1/10th of the suitability range and a resolution of 100 focals (the default values provided in the *ecospat::ecospat.boyce()* function), which provides a continuous measure of CBI rather than a calculation based upon a set of discrete suitability classes (Hirzel et al., 2006).

278 Maxent models of *P. jordani* salamander distributions in the 1960s and 1990s had good fit to empirical hold-out data as indicated by their CBI, AUC and TSS (CBI: 0.963 for 1960s model, 0.988 for 1990s model; AUC: 0.952 for 1960s model, 0.936 for the 1990s model; TSS: 0.896 for 1960s model, 0.783 for 1990s model). The mean change in salamander prevalence
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290 decreased negligibly (by 0.9%) across the Great Smoky Mountains National Park from the 1960s to 1990s. Yet, the total area occupied by salamanders decreased notably: the number of pixels with at least 30% prevalence decreased by 11.2%, and the number of pixels with at least 80% prevalence decreased by 52.2%. Moreover, salamanders appeared to be migrating moderately upslope from the 1960s to the 1990s: the elevations of pixels with at least 80% prevalence on average shifted up by 136 meters.

## Climate Processing for Salamander Range Shifts

297 We used bioclimatic variables from ERA5, WorldClim, and microclimf as predictors in Generalized Additive Models (GAMs) employed for predicting spatiotemporal changes in salamander abundances. For each climate data product, data were downloaded for the complete range of the salamander *Plethodon jordani* (corresponding to the area of Great Smoky Mountains National Park, GSMNP). We employed the historic monthly weather data archive of WorldClim (a downscaled version of CRU-TS-4.03, Harris et al., 2014), which provides estimates of monthly minimum and maximum temperature on a decadal basis (here, using estimates for 1960-1969 and 1990-1999). We extracted ERA5 hourly predictions of two-meter air temperature for each of these years, calculated BIO5 and BIO6 for each year separately, and then averaged bioclimatic variables for each decade. We also generated microclimate predictions via microclimf in the same manner as ERA5 (hourly predictions aggregated to annual bioclimatic layers, then averaged to decades) with two adjustments as compared to predictions for other case studies. First, given that 1992 is the initial available year of ESA land cover estimates used to designate several vegetation parameters of microclimf, for all prediction years prior to 1992 we used 1992 land cover estimates; secondly, we did the same with plant area index (PAI) estimates for years prior to 2000, the first year of MODIS imagery used for calculating PAI. However, between our chosen study decades of the 1960s and 1990s, GSMNP experienced some moderate changes in land and vegetation cover – namely, the recovery and succession of forest following early-century clearcut logging prior to the park’s designation in 1934 (Pyle, 1988; Hansen et al., 2014). Thus our use of nearly static land cover and vegetation layers for microclimate predictions makes our estimates of the differences between macroclimate and microclimate reported here conservative, as in reality microclimate likely was even more decoupled from free-air conditions given increasing vegetation height and density. More dynamic land cover and vegetation estimates would then likely improve predictions of models fitted with microclimf data even more so than as found in this study.

All spatial disaggregation and aggregation was performed on temperature data prior to calculation of bioclimatic variables (BIO5 and BIO6) with the exception of WorldClim data, for which public data are already pre-processed to such variables.

## Fitting GAMs for modeling salamander distribution changes

326 Given that spatiotemporal changes in salamander abundances likely reflect not just climate change, but also static spatial patterns in climate, we also included as covariates average BIO5 and BIO6 layers for the 1990s. Furthermore to account for regression towards the mean (where pixels of low prevalence are by chance more likely to increase than decrease, and vice versa for pixels of high prevalence), we also included the initial 1960s prevalence values as a covariate in all models, and for a null model used a GAM with just the initial prevalence as a covariate. In addition, to isolate the predictive abilities of climate data, we fit a separate set of models with only climate layers as covariates (without initial prevalence as a covariate) and an intercept-only null model. Model residuals confirmed that Gaussian-distributed models were most appropriate. Model performance was judged using deviance explained, R², generalized cross-validation scores (GCV), and ΔAIC scores. Upon model evaluation, the ranked order of models was the same across both model sets (with and without initial prevalence as a
<table><caption>384 Table S1. Input data products, and the variables extracted from them, used to parameterize the microclimf microclimate model.
385
386</caption><thead><tr><th>Product</th><th>Variable(s) used</th><th>Spatial Resolution</th><th>Spatial Extent</th><th>Temporal Resolution</th><th>Temporal Extent</th><th>Remarks</th><th>Citation</th></tr></thead><tbody><tr><td>ECMWF ERA5<br>reanalysis</td><td>2m temperature, 2m dewpoint temperature, surface pressure, 10-m u wind, 10-m v wind, total precipitation, total cloud cover, mean surface net longwave radiation flux, mean surface downward longwave radiation flux, total sky direct solar radiation at surface, surface downward solar radiation</td><td>0.25 decimal degrees</td><td>global</td><td>hourly</td><td>1950 - present</td><td>Used for predictions in years before 2015</td><td>Hersbach et al., 2020</td></tr><tr><td>ESA CCI Land<br>Cover</td><td>Land cover categories</td><td>300-m</td><td>global</td><td>annual</td><td>1992 - one year from present</td><td>Used for predictions in years 2015 or later</td><td>ESA 2017</td></tr><tr><td>Copernicus Global<br>Land Service Land<br>Cover</td><td>Land cover categories</td><td>100-m</td><td>global</td><td>annual</td><td>2015 - two years from present</td><td>Used for predictions in years 2015 or later</td><td>Buchhorn et al., 2020</td></tr><tr><td>Potapov et al. 2020</td><td>Vegetation Canopy Height</td><td>30-m</td><td>within 52°N and 52°S globally</td><td>static</td><td>2019</td><td>Used for latitudes between 52°N and 52°S</td><td>Potapov et al., 2020</td></tr><tr><td>Simard et al. 2011</td><td>Vegetation Canopy Height</td><td>1000-m</td><td>global (employed for above 52°N and 52°S)</td><td>static</td><td>2005</td><td>Used for latitudes outside of 52°N and 52°S</td><td>Simard et al., 2011</td></tr><tr><td>MODIS<br>MOD15A2H</td><td>LAI (Leaf Area Index) Stage 2</td><td>500-m</td><td>global</td><td>8 days</td><td>2000 - 2020</td><td>spatial data sources from 1970s to 2000s</td><td>Myneni et al., 2015</td></tr><tr><td>SRIC-WISE</td><td>Soil bulk density and percent clay, silt, and sand at 0cm, 5cm, 15cm, 30cm, 60cm, and 100cm depths</td><td>0.05 decimal degrees</td><td>global</td><td>static</td><td></td><td></td><td>Batjes et al., 2012</td></tr><tr><td>ASTER Global<br>Digital Elevation<br>Map</td><td>Elevation (GDEM V3)</td><td>30-m</td><td>global</td><td>static</td><td>Launched in 1999</td><td></td><td>Fujisada et al., 2005</td></tr></tbody></table>

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3 391 Table S2. Mean root-mean square error (RMSE), root-mean square deviation (RMSD), and
4 absolute bias between empirical soil temperature data and temperature predictions from ERA5,
5 WorldClim, and microclimf; lower values indicate higher accuracy (here, the averages from 371
6 time series). Lowest values across all three temperature data sources are bolded. Given that
7 WorldClim is provided at monthly resolution, comparison with WorldClim could not be performed
8 at the hourly level, therefore predictions from other temperature products were summarized to
9 monthly means, minima, and maxima to compare to WorldClim. Across both temporal
10 resolutions, the microclimate predictions of microclimf performed the best, followed by ERA5
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<table><thead><tr><th>Temporal Resolution</th><th>Statistic</th><th>ERA5</th><th>WorldClim</th><th>microclimf</th></tr></thead><tbody><tr><td>Hourly</td><td>RMSE</td><td>4.62</td><td>NA</td><td><strong>4.34</strong></td></tr><tr><td>Hourly</td><td>RMSD</td><td>8</td><td>NA</td><td><strong>7.52</strong></td></tr><tr><td>Hourly</td><td>Bias</td><td>4.43</td><td>NA</td><td><strong>4.05</strong></td></tr><tr><td>Monthly</td><td>RMSE</td><td>5.23</td><td>6.04</td><td><strong>4.7</strong></td></tr><tr><td>Monthly</td><td>RMSD</td><td>9.06</td><td>10.46</td><td><strong>8.14</strong></td></tr><tr><td>Monthly</td><td>Bias</td><td>3.59</td><td>3.6</td><td><strong>1.97</strong></td></tr></tbody></table>

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Table S3. Biases in predictions of adult insect emergence rates for two cabbage maggot species (*Delia platura* and *Delia radicum*), as predicted by three temperature products (ERA5, WorldClim, and microclimf). Bias is expressed as the mean number of calendar days away from the true dates at which 10%, 25%, 50%, 75% and 95% of total observed adults had emerged. Lowest bias values indicated in bold. Climate predictions with both high proximity and fine temporal resolution (microclimf-hourly) had the lowest error, followed by predictions with either high proximity or fine temporal resolution (microclimf-monthly and ERA5-hourly), then after which performance decreased with temporal resolution.

<table><thead><tr><th>Species</th><th>Temperature Product</th><th>Temporal resolution</th><th>Bias</th></tr></thead><tbody><tr><td><i>D. platura</i></td><td><i>microclimf</i></td><td>hourly</td><td><b>10.92</b></td></tr><tr><td><i>D. platura</i></td><td>ERA5</td><td>hourly</td><td>18.75</td></tr><tr><td><i>D. platura</i></td><td><i>microclimf</i></td><td>monthly</td><td>22.20</td></tr><tr><td><i>D. platura</i></td><td>WorldClim</td><td>daily</td><td>24.23</td></tr><tr><td><i>D. platura</i></td><td>WorldClim</td><td>monthly</td><td>24.93</td></tr><tr><td><i>D. platura</i></td><td>ERA5</td><td>monthly</td><td>39.63</td></tr><tr><td><i>D. radicum</i></td><td><i>microclimf</i></td><td>hourly</td><td><b>2.22</b></td></tr><tr><td><i>D. radicum</i></td><td><i>microclimf</i></td><td>monthly</td><td>13.00</td></tr><tr><td><i>D. radicum</i></td><td>ERA5</td><td>hourly</td><td>15.25</td></tr><tr><td><i>D. radicum</i></td><td>WorldClim</td><td>daily</td><td>16.62</td></tr><tr><td><i>D. radicum</i></td><td>WorldClim</td><td>monthly</td><td>16.88</td></tr><tr><td><i>D. radicum</i></td><td>ERA5</td><td>monthly</td><td>26.87</td></tr><tr><td>Average</td><td><i>microclimf</i></td><td>hourly</td><td><b>6.57</b></td></tr><tr><td>Average</td><td>ERA5</td><td>hourly</td><td>17.00</td></tr><tr><td>Average</td><td><i>microclimf</i></td><td>monthly</td><td>17.60</td></tr><tr><td>Average</td><td>WorldClim</td><td>daily</td><td>20.42</td></tr><tr><td>Average</td><td>WorldClim</td><td>monthly</td><td>20.91</td></tr><tr><td>Average</td><td>ERA5</td><td>monthly</td><td>33.25</td></tr></tbody></table>
Table S4. Significance testing for differences in distributions of bioclimatic variables and mosquito thermal performance generated from each of ERA5, WorldClim, and microclimf for case study 2 in Malaysian Borneo. As quantified via Z-values from Dunn post-hoc analyses of Kruskal-Wallis ranked sum tests as well as KDE-derived overlap, spatial distributions of BIO1 overlapped considerably between the three climate data products, yet distributions of BIO5 and average thermal performance differed to a larger degree between microclimf and both of ERA5 and WorldClim. Furthermore, microclimf-derived fecundity rates were more similar to the empirical distribution of fecundity rates (lower Z-value, higher overlap) than either ERA5 or WorldClim (higher Z-values, lower overlaps).

<table><thead><tr><th>Bioclimatic Variable</th><th>Climate Source Comparison</th><th>Dunn's Test Z-Value</th><th>KDE-derived overlap</th></tr></thead><tbody><tr><td rowspan="3">BIO1 (Mean Annual Temperature)</td><td>ERA5 - WorldClim</td><td>-150.0</td><td>0.595</td></tr><tr><td>ERA5 - microclimf</td><td>-66.7</td><td>0.765</td></tr><tr><td>WorldClim - microclimf</td><td>81.9</td><td>0.742</td></tr><tr><td rowspan="3">BIO5 (Max Temperature of the Warmest Month)</td><td>ERA5 - WorldClim</td><td>-352.0</td><td>0.000473</td></tr><tr><td>ERA5 - microclimf</td><td>-125.0</td><td>0.445</td></tr><tr><td>WorldClim - microclimf</td><td>224.0</td><td>0.0614</td></tr><tr><td rowspan="6">Average Thermal Performance (Mosquito Fecundity)</td><td>ERA5 - WorldClim</td><td>-98.2</td><td>0.472</td></tr><tr><td>ERA5 - microclimf</td><td>193.0</td><td>0.0434</td></tr><tr><td>WorldClim - microclimf</td><td>289.0</td><td>0.0153</td></tr><tr><td>ERA5 - empirical</td><td>265.0</td><td>0.0608</td></tr><tr><td>WorldClim - empirical</td><td>361.0</td><td>0.0487</td></tr><tr><td>microclimf - empirical</td><td>71.9</td><td>0.378</td></tr></tbody></table>

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Table S5. Means and standard deviations of thermal distributions for each bioclimatic prediction calculated in case study 2 in Malaysian Borneo, from ERA5, WorldClim, and *microclimf*. Data from each bioclimatic layer were standardized between -1 and 1 in order to intercompare across bioclimatic indices. Mean BIO1 calculated from ERA5, WorldClim, and *microclimf* did not diverge considerably, however BIO5 predictions had larger differences in means, and average thermal performance calculated from *microclimf* was substantially different from predictions calculated from ERA5 and WorldClim.

<table><thead><tr><th>Bioclimatic Variable</th><th>Climate Source</th><th>Mean</th><th>Standard Deviation</th><th>Mean (Scaled)</th><th>Standard Deviation (Scaled)</th></tr></thead><tbody><tr><td rowspan="3">BIO1 (Mean Annual Temperature)</td><td>ERA5</td><td>26.5</td><td>1.27</td><td>0.0844</td><td>0.837</td></tr><tr><td>WorldClim</td><td>25.0</td><td>1.76</td><td>0.117</td><td>0.738</td></tr><tr><td>*microclimf*</td><td>25.9</td><td>1.27</td><td>0.085</td><td>0.799</td></tr><tr><td rowspan="3">BIO5 (Max Temperature of the Warmest Month)</td><td>ERA5</td><td>36.4</td><td>1.46</td><td>0.065</td><td>0.782</td></tr><tr><td>WorldClim</td><td>29.4</td><td>1.96</td><td>0.088</td><td>0.468</td></tr><tr><td>*microclimf*</td><td>34.6</td><td>1.30</td><td>0.058</td><td>0.702</td></tr><tr><td rowspan="3">Average Thermal Performance (Fecundity)</td><td>ERA5</td><td>5.30</td><td>0.25</td><td>0.034</td><td>0.633</td></tr><tr><td>WorldClim</td><td>4.92</td><td>0.47</td><td>0.063</td><td>0.581</td></tr><tr><td>*microclimf*</td><td>6.96</td><td>0.59</td><td>0.080</td><td>0.855</td></tr></tbody></table>
<table><caption>Table S6. Performance of generalized additive models (GAMs) predicting salamander (*Plethodon jordani*) prevalence shifts with ERA5, WorldClim, and microclimf climate data as covariates, both at native and (dis)aggregated spatial resolutions. The microclimf GAM outperformed all other models, and although spatially aggregating climate data generally eroded model performance, microclimf still outperformed ERA5 and WorldClim even when aggregated to a 3x coarser resolution. Best performance for each parameter highlighted in bold.</caption><thead><tr><th>Model</th><th>Deviance explained</th><th>R<sup>2</sup></th><th>Generalized Cross Validation Score</th><th>AIC</th></tr></thead><tbody><tr><td>microclimf (1-km)</td><td>0.5808</td><td>0.5732</td><td>0.4362</td><td>3753.10</td></tr><tr><td>microclimf (3-km)</td><td>0.4167</td><td>0.4088</td><td>0.6019</td><td>4354.10</td></tr><tr><td>WorldClim (1-km)</td><td>0.3914</td><td>0.3821</td><td>0.6274</td><td>4460.49</td></tr><tr><td>WorldClim (3-km)</td><td>0.3446</td><td>0.3370</td><td>0.6734</td><td>4564.23</td></tr><tr><td>ERA5 (1-km)</td><td>0.2730</td><td>0.2604</td><td>0.7525</td><td>4802.04</td></tr><tr><td>ERA5 (3-km)</td><td>0.2586</td><td>0.2472</td><td>0.7673</td><td>4807.87</td></tr><tr><td>WorldClim (10-km)</td><td>0.2186</td><td>0.2074</td><td>0.8071</td><td>4902.52</td></tr><tr><td>microclimf (10-km)</td><td>0.1851</td><td>0.1785</td><td>0.8315</td><td>4958.22</td></tr><tr><td>microclimf (27.75-km)</td><td>0.1720</td><td>0.1652</td><td>0.8451</td><td>4988.52</td></tr><tr><td>ERA5 (10-km)</td><td>0.1783</td><td>0.1649</td><td>0.8521</td><td>5003.64</td></tr><tr><td>WorldClim (27.75-km)</td><td>0.1725</td><td>0.1646</td><td>0.8467</td><td>4992.14</td></tr><tr><td>ERA5 (27.75-km)</td><td>0.1718</td><td>0.1645</td><td>0.8463</td><td>4991.16</td></tr><tr><td>Null (intercept-only)</td><td>0.0000</td><td>0.0000</td><td>1.0002</td><td>5337.54</td></tr></tbody></table>

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<table><thead><tr><th>Model</th><th>Deviance explained</th><th>R<sup>2</sup></th><th>Generalized Cross Validation Score</th><th>AIC</th></tr></thead><tbody><tr><td>microclimf (1-km)</td><td>0.6682</td><td>0.6605</td><td>0.3474</td><td>3348.63</td></tr><tr><td>microclimf (3-km)</td><td>0.5436</td><td>0.5356</td><td>0.4726</td><td>3927.74</td></tr><tr><td>WorldClim (1-km)</td><td>0.5412</td><td>0.5317</td><td>0.4779</td><td>3948.45</td></tr><tr><td>WorldClim (3-km)</td><td>0.5122</td><td>0.5033</td><td>0.5077</td><td>4036.33</td></tr><tr><td>WorldClim (10-km)</td><td>0.4819</td><td>0.4748</td><td>0.5346</td><td>4132.87</td></tr><tr><td>ERA5 (1-km)</td><td>0.4857</td><td>0.4751</td><td>0.5360</td><td>4163.89</td></tr><tr><td>ERA5 (3-km)</td><td>0.4800</td><td>0.4703</td><td>0.5418</td><td>4157.58</td></tr><tr><td>ERA5 (10-km)</td><td>0.4729</td><td>0.4645</td><td>0.5463</td><td>4173.26</td></tr><tr><td>microclimf (10-km)</td><td>0.4695</td><td>0.4621</td><td>0.5476</td><td>4177.73</td></tr><tr><td>ERA5 (27.75-km)</td><td>0.4520</td><td>0.4462</td><td>0.5598</td><td>4246.12</td></tr><tr><td>microclimf (27.75-km)</td><td>0.4525</td><td>0.4460</td><td>0.5606</td><td>4249.00</td></tr><tr><td>WorldClim (27.75-km)</td><td>0.4502</td><td>0.4448</td><td>0.5608</td><td>4249.71</td></tr><tr><td>Null (initial prevalence only)</td><td>0.4331</td><td>0.4305</td><td>0.5723</td><td>4288.10</td></tr></tbody></table>

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<figure><img src="image_131.png" /><figcaption>Figure S1. Locations of microclimatic loggers used for comparing temperature data products (ERA5, WorldClim, and *microclimf*), representing four continents from 38.0 °S to 72.4 °N. Points are jittered by 1° to reduce overlap. Vegetation categories are 2015 estimates from the Copernicus Global Land Service Land Cover product.</figcaption></figure>

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<figure><img src="image_132.png" /><figcaption>Figure S2. Root mean square error (RMSE) of climate predictions from ERA5, WorldClim, and microclimf as compared to empirical soil temperature loggers across broadly-classified Copernicus 2015 land cover categories, displaying the number of plots of empirical data for each category.</figcaption></figure>

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<figure><img src="image_133.png" /></figure>

Figure S3. Case study 1: predictions of emergence rates for two fossorial larvae of crop pest insects (both cabbage maggots) in Alberta, Canada, using a logistic model driven by growing degree days (GDDs) calculated separately from ERA5, WorldClim, and microclimf at native as well as aggregated/disaggregated temporal resolution; GDDs are indices for tracking durations of species-specific suitable growing periods. Black points in panel C indicate empirical observations for the two species (no empirical data for 10% and 95% emergence for *D. platura*). Although all three temperature products had similar temporal patterns in temperature (panel A; average difference in daily means across climate sources = 2.48 °C) and growing degree days (panel B; average difference across climate sources = 224.6 GDDs), even such small differences yielded divergent predictions of non-linear biological responses (panel C, Table S3). Here, high proximity (*microclimf*) improved biological predictions, although decreasing temporal resolution of any of the temperature products decreased accuracy.

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708 Wyman, J.A., Libby, J.L. & Chapman, R.K. (1977) Cabbage Maggot Management Aided by Predictions of Adult Emergence12. *Journal of Economic Entomology*, **70**, 327–331.

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709 Zeileis, A., Grothendieck, G., Ryan, J.A., Ulrich, J.M. & Andrews, F. (2022) zoo: S3 Infrastructure for Regular and Irregular Time Series (Z's Ordered Observations).

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710 Wyman, J.A., Libby, J.L. & Chapman, R.K. (1977) Cabbage Maggot Management Aided by Predictions of Adult Emergence12. *Journal of Economic Entomology*, **70**, 327–331.

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711 Zeileis, A., Grothendieck, G., Ryan, J.A., Ulrich, J.M. & Andrews, F. (2022) zoo: S3 Infrastructure for Regular and Irregular Time Series (Z's Ordered Observations).

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712 Wyman, J.A., Libby, J.L. & Chapman, R.K. (1977) Cabbage Maggot Management Aided by Predictions of Adult Emergence12. *Journal of Economic Entomology*, **70**, 327–331.

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713 Zender, C.S. (2008) Analysis of self-describing gridded geoscience data with netCDF Operators (NCO). *Environmental Modelling & Software*, **23**, 1338–1342.

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714 Wyman, J.A., Libby, J.L. & Chapman, R.K. (1977) Cabbage Maggot Management Aided by Predictions of Adult Emergence12. *Journal of Economic Entomology*, **70**, 327–331.

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715 Zeileis, A., Grothendieck, G., Ryan, J.A., Ulrich, J.M. & Andrews, F. (2022) zoo: S3 Infrastructure for Regular and Irregular Time Series (Z's Ordered Observations).

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716 Wyman, J.A., Libby, J.L. & Chapman, R.K. (1977) Cabbage Maggot Management Aided by Predictions of Adult Emergence12. *Journal of Economic Entomology*, **70**, 327–331.

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717 Zeileis, A., Grothendieck, G., Ryan, J.A., Ulrich, J.M. & Andrews, F. (2022) zoo: S3 Infrastructure for Regular and Irregular Time Series (Z's Ordered Observations).

30

718 Wyman, J.A., Libby, J.L. & Chapman, R.K. (1977) Cabbage Maggot Management Aided by Predictions of Adult Emergence12. *Journal of Economic Entomology*, **70**, 327–331.

31

719 Zender, C.S. (2008) Analysis of self-describing gridded geoscience data with netCDF Operators (NCO). *Environmental Modelling & Software*, **23**, 1338–1342.

32

720 Wyman, J.A., Libby, J.L. & Chapman, R.K. (1977) Cabbage Maggot Management Aided by Predictions of Adult Emergence12. *Journal of Economic Entomology*, **70**, 327–331.

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721 Zeileis, A., Grothendieck, G., Ryan, J.A., Ulrich, J.M. & Andrews, F. (2022) zoo: S3 Infrastructure for Regular and Irregular Time Series (Z's Ordered Observations).

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722 Wyman, J.A., Libby, J.L. & Chapman, R.K. (1977) Cabbage Maggot Management Aided by Predictions of Adult Emergence12. *Journal of Economic Entomology*, **70**, 327–331.

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723 Wyman, J.A., Libby, J.L. & Chapman, R.K. (1977) Cabbage Maggot Management Aided by Predictions of Adult Emergence12. *Journal of Economic Entomology*, **70**, 327–331.

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724 Zeileis, A., Grothendieck, G., Ryan, J.A., Ulrich, J.M. & Andrews, F. (2022) zoo: S3 Infrastructure for Regular and Irregular Time Series (Z's Ordered Observations).

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725 Wyman, J.A., Libby, J.L. & Chapman, R.K. (1977) Cabbage Maggot Management Aided by Predictions of Adult Emergence12. *Journal of Economic Entomology*, **70**, 327–331.

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726 Zeileis, A., Grothendieck, G., Ryan, J.A., Ulrich, J.M. & Andrews, F. (2022) zoo: S3 Infrastructure for Regular and Irregular Time Series (Z's Ordered Observations).

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727 Wyman, J.A., Libby, J.L. & Chapman, R.K. (1977) Cabbage Maggot Management Aided by Predictions of Adult Emergence12. *Journal of Economic Entomology*, **70**, 327–331.

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728 Zeileis, A., Grothendieck, G., Ryan, J.A., Ulrich, J.M. & Andrews, F. (2022) zoo: S3 Infrastructure for Regular and Irregular Time Series (Z's Ordered Observations).

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729 Wyman, J.A., Libby, J.L. & Chapman, R.K. (1977) Cabbage Maggot Management Aided by Predictions of Adult Emergence12. *Journal of Economic Entomology*, **70**, 327–331.

42

730 Wyman, J.A., Libby, J.L. & Chapman, R.K. (1977) Cabbage Maggot Management Aided by Predictions of Adult Emergence12. *Journal of Economic Entomology*, **70**, 327–331.

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731 Zeileis, A., Grothendieck, G., Ryan, J.A., Ulrich, J.M. & Andrews, F. (2022) zoo: S3 Infrastructure for Regular and Irregular Time Series (Z's Ordered Observations).

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732 Wyman, J.A., Libby, J.L. & Chapman, R.K. (1977) Cabbage Maggot Management Aided by Predictions of Adult Emergence12. *Journal of Economic Entomology*, **70**, 327–331.

45

733 Zeileis, A., Grothendieck, G., Ryan, J.A., Ulrich, J.M. & Andrews, F. (2022) zoo: S3 Infrastructure for Regular and Irregular Time Series (Z's Ordered Observations).

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734 Wyman, J.A., Libby, J.L. & Chapman, R.K. (1977) Cabbage Maggot Management Aided by Predictions of Adult Emergence12. *Journal of Economic Entomology*, **70**, 327–331.

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735 Zeileis, A., Grothendieck, G., Ryan, J.A., Ulrich, J.M. & Andrews, F. (2022) zoo: S3 Infrastructure for Regular and Irregular Time Series (Z's Ordered Observations).

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736 Wyman, J.A., Libby, J.L. & Chapman, R.K. (1977) Cabbage Maggot Management Aided by Predictions of Adult Emergence12. *Journal of Economic Entomology*, **70**, 327–331.

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737 Zeileis, A., Grothendieck, G., Ryan, J.A., Ulrich, J.M. & Andrews, F. (2022) zoo: S3 Infrastructure for Regular and Irregular Time Series (Z's Ordered Observations).

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738 Wyman, J.A., Libby, J.L. & Chapman, R.K. (1977) Cabbage Maggot Management Aided by Predictions of Adult Emergence12. *Journal of Economic Entomology*, **70**, 327–331.

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739 Zeileis, A., Grothendieck, G., Ryan, J.A., Ulrich, J.M. & Andrews, F. (2022) zoo: S3 Infrastructure for Regular and Irregular Time Series (Z's Ordered Observations).

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740 Wyman, J.A., Libby, J.L. & Chapman, R.K. (1977) Cabbage Maggot Management Aided by Predictions of Adult Emergence12. *Journal of Economic Entomology*, **70**, 327–331.

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741 Zeileis, A., Grothendieck, G., Ryan, J.A., Ulrich, J.M. & Andrews, F. (2022) zoo: S3 Infrastructure for Regular and Irregular Time Series (Z's Ordered Observations).

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742 Wyman, J.A., Libby, J.L. & Chapman, R.K. (1977) Cabbage Maggot Management Aided by Predictions of Adult Emergence12. *Journal of Economic Entomology*, **70**, 327–331.

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743 Wyman, J.A., Libby, J.L. & Chapman, R.K. (1977) Cabbage Maggot Management Aided by Predictions of Adult Emergence12. *Journal of Economic Entomology*, **70**, 327–331.

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744 Zeileis, A., Grothendieck, G., Ryan, J.A., Ulrich, J.M. & Andrews, F. (2022) zoo: S3 Infrastructure for Regular and Irregular Time Series (Z's Ordered Observations).

57

745 Wyman, J.A., Libby, J.L. & Chapman, R.K. (1977) Cabbage Maggot Management Aided by Predictions of Adult Emergence12. *Journal of Economic Entomology*, **70**, 327–331.

58

746 Zeileis, A., Grothendieck, G., Ryan, J.A., Ulrich, J.M. & Andrews, F. (2022) zoo: S3 Infrastructure for Regular and Irregular Time Series (Z's Ordered Observations).

59

747 Wyman, J.A., Libby, J.L. & Chapman, R.K. (1977) Cabbage Maggot Management Aided by Predictions of Adult Emergence12. *Journal of Economic Entomology*, **70**, 327–331.

60