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.

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).

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.