DOI: 10.5061/dryad.kd51c5b2z
title: Structural and compositional heterogeneity influences the thermal environment across multiple scales

description: Heterogeneity is becoming increasingly recognized as a critical driver of
 biodiversity and ecosystem processes. While the influence of heterogeneity
 on species diversity and abundance is well documented, how heterogeneity
 influences the distribution and arrangement of important resources across
 a landscape is still unclear. In particular, the mechanistic relationship
 between temperature and heterogeneity remains to be explored.
 Heterogeneity in vegetation structure and composition is often cited as
 important drivers of the near ground thermal environment. Due to a
 relative lack of comparative studies across landscapes that differ in
 their degree of vegetation heterogeneity, we lack an understanding of the
 underlying mechanisms that drive variation in the thermal environment. To
 better understand this relationship, we assessed the thermal environment
 in nine grasslands that differ in their degree of structural and
 compositional heterogeneity. At the landscape level, we used a variance
 partitioning approach with linear mixed models to assess the link between
 four different metrics of vegetation heterogeneity and temperature
 variability. At the microsite (individual I-buttons) level, we used
 piecewise Structural Equations Models to assess the fine scale drivers of
 temperature in these landscapes, and developed a causal model describing
 the relationship between vegetation variables and temperature. We found
 that landscape temperature variance was strongly related to diversity of
 plant functional group, heterogeneity in plant species composition, and
 variation in vegetation height. At finer scales, species richness,
 vegetation height, and overhead obstruction were the best predictors of
 temperature once weather was accounted for. Additionally, vegetation
 composition variables primarily had an indirect influence on fine scale
 temperature. These results suggest that scale has a strong influence on
 the observed relationship between temperature variance and different
 metrics of grassland heterogeneity. Our results provide strong support for
 the role of landscape heterogeneity in shaping the thermal landscape, and
 offer insights into the possible impacts of habitat homogenization on the
 thermal environment.
description: We sampled the thermal environment and vegetation in September of 2019.
 This month was selected for sampling as September is characterized by warm
 and stable weather patterns, meaning patterns of thermal heterogeneity
 would likely be the most pronounced and easily detected if they existed.
 Additionally, September is at the peak of annual biomass accumulation and
 most perennial plants are identifiable and available to sample during this
 time. To sample thermal conditions, we generated 30 random locations
 (hereafter referred to as microsites) within each of the nine grassland
 landscapes with the restrictions that each site had to be greater than 50
 meters from woodland borders to minimize the effect of shading, and at
 least 10 meters from the next closest sample point. We used Maxim
 Integrated data loggers (Maxim Integrates Products, Sunnyville,
 California, USA; hereafter, I-button) to collect thermal data. We secured
 each I-button to a steel spike using double-sided mounting tape, and we
 drove the spikes into the ground such that each I-button was approximately
 5-10 centimeters above the ground surface. We did this to avoid insulation
 of the I-buttons by grass litter at ground level, allowing us to better
 characterize the effects near ground vegetation structure. Field tests
 were performed before data collection to ensure that the steel spikes had
 minimal effect on I-button data. Each I-button was programmed to record
 temperature every 15 minutes. We selected days that had minimal to low
 cloud cover, no precipitation, and near average ambient temperatures to
 collect thermal data. Each thermal sample period was 48 hours in length so
 that we could capture a wider range of weather conditions within and
 across sample days. We randomly assigned one grassland from each grassland
 type (one grassland with low, moderate, and high heterogeneity) to one of
 three sample groups, and we collected thermal data at all three grasslands
 in each sample group simultaneously. We did this to minimize variation in
 weather conditions among the three grassland types. We used onsite weather
 stations at both the Stillwater and Perkins sites to compare ambient
 temperature to the I-button temperatures (Oklahoma Mesonet Stations; Brock
 et al. 1995). After collection of the temperature data was complete, we
 revisited each site to collect vegetation measurements to assess
 vegetation composition, structure, and plant diversity at the location of
 each I-button. At each site, we centered a standard 20 x 50 cm Daubenmire
 frame over the I-button location and recorded the percent cover of plant
 functional group (grass, litter, forb, and shrub) and bare ground, and we
 identified and recorded every plant species observed within the frame.
 Forbs are defined as herbaceous (non-woody) broad leaved plants. To assess
 vegetation structure, we recorded plant height directly over the I-button
 site and four angle of obstruction measurements. Angle of obstruction
 provides an index of the amount of cover directly above a point, which
 would influence the amount of solar radiation and airflow at a site. We
 measured the angle of obstruction by attaching a digital level to a meter
 stick and tilting the digital level at an angle until it came into contact
 with the vegetation layer (Carroll et al. 2016). We recorded an angle of
 obstruction measurement in each of four cardinal directions at each site,
 and we averaged the four obstruction measurements to obtain a single
 overhead obstruction metric per site (Carroll et al. 2016).
description: Temperature data and vegetation data was collected in 9 pastures with each
 pasture having 25-30 sample locations. In the spread sheets the pastures
 and sample sites are designated by the columns with the same names and the
 pasture type is given in the column type. Cover class data for grass,
 forbs, litter, bare and shrub in the Site Description sheet were recorded
 using standard Daubenmire cover classes and are coded such that 0= none
 present 1=0-5% 2=6-25% 3=26-50 4=51-75%, 5=76-95% 6=96-100% cover. Cover
 classes were converted to class mid-points for analysis. Richness is the
 number of species in each plot, and those species are recorded in the
 site_species_list sheet. The temperature data sheet includes the I-button
 temperature and the corresponding temperature and solar radiation
 measurement from the nearest weather station.