This page provides information for the Tree and Shade Canopy performance measure.

This map is intended to allow for analysis of average tree canopy coverage in the City of Los Angeles, California, United States in comparison with air quality data for the 18 years from 2000 to 2018, and temperature data for a particularly hot day of September 5, 2020.

This operations dashboard shows historic and current data related to this performance measure.The performance measure dashboard is available at 4.11 Tree and Shade Canopy. Data Dictionary

High resolution tree canopy data from 2022 were accessed from Google Environmental Insights Explorer, and aggregated to the census tract (U.S. Census Bureau) as a percentage. Building footprint area was removed from the analysis, in order to calculate tree canopy as a percentage of plantable ground area. This is expressed as the formula: tree canopy area/(total area - building area)*100. Tree canopy values modified from Google Environmental Insights Explorer (accessed July 2023). Building footprint values modified from Microsoft Building Footprints - Features (accessed July 2023).

This dataset contains tree canopy cover layers as derived and calculated via a land cover classification for the City of Tempe and Guadalupe using 2019 NAIP Imagery. The land cover classification utilized a Support Vector Machine Classifier and was calculated for various areas including city boundary, census tracts, census blocks, character areas, etc.This dataset also contains the point locations and attributes of trees maintained by the City of Tempe. The point dataset was obtained by WCA from WCA in Oct 2021. The attributes of interest to this study included unique TreeID, Exact DBH, DBH Range, Height Range, Botanical Name, Common Name, Latitude, and Longitude. Updates to the tree layer were made by joining the results from the Oct 2021 i-Tree report. An i-Tree Eco Analysis was run in Oct 2021 using i-Tree Eco v6.0.22 and the results were joined based on unique tree ID to the Tempe’s tree inventory. Attributes added were: Structural Value ($), Carbon Storage (lb), Carbon Storage ($), Gross Carbon Sequestration (lb/yr), Gross Carbon Sequestration ($/yr), Avoided Runoff (cubicFT/yr), Avoided Runoff ($/yr), Pollution Removal (oz/yr), Pollution Removal ($/yr), Total Annual Benefits ($/yr), Height (ft), Canopy Cover (sqft), Tree Condition, Leaf Area (sqft), Leaf Biomass (lb), Leaf Area Index Basal Area (sqft), Cond, i-Tree_ID_BotName, i-Tree_ID_ComName and i-Tree_ID Genus. The exact definitions, meanings, calculations, etc. for the i-Tree Values can be found on i-Tree’s website via the i-Tree Eco User Manual. For certain layers the individual i-Tree values were aggregated by census tract, census block, zip code, etc. These results can be seen in the polygon layers with the following attribute values: CanopyCoverPer_Final, COUNT_Tree_ID, SUM_Replacement_Value_, SUMCarbon_Storagelb_, SUM_Carbon_Storage_, SUMGross_Carbon_Sequestrationlb_, SUM_Gross_Carbon_Sequestration_y, SUMAvoided_RunoffftÂ_yr_, SUM_Avoided_Runoff_yr, SUM_Pollution_Removaloz_yr_, SUM_Pollution_Removal_yr, and SUM_Total_Annual_Benefits_yr_This dataset also contains the Tree Equity Score from American Forests. The Tree Equity Score is a product of American Forests and is a metric that helps cities assess how well they are delivering equitable tree canopy cover to all residents. The score combines measures of tree canopy cover need and priority for trees in urban neighborhoods. It is derived from tree canopy cover, climate, demographic and socioeconomic data. For more information please visit American Forests Tree Equity ScoreProjected Coordinate System: NAD 1983 StatePlane Arizona Central FIPS 0202 (Intl Feet)

🇺🇸 미국 English This page provides data for the Tree and Shade Canopy performance measure.Sustainable growth for the City of Tempe is influenced by many factors, including the development and maintenance of our tree canopy. An adequate canopy provides shade, cooling, reduces water evaporation and is an indication of a healthy and well-maintained environment. As per the Urban Forestry Master Plan, this data represents tree canopy in parks, street right-of-ways and municipally owned and managed facilities.The performance measure dashboard is available at 4.11 Tree and Shade Canopy.Additional Information

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See published article in the Journal of Ecology for method details.

Open Access1. Agroforestry systems are refuges for biodiversity and provide multiple ecosystem functions and services. Diverse multispecies shade tree canopies are increasingly replaced by monospecific shade, often dominated by non-native tree species. The loss of tree diversity and the nature of the dominating tree can have strong implications for ecosystem functions, e.g. nutrient cycling ultimately reducing crop production. 2. To understand direct and indirect impacts of shade trees on nutrient cycling and crop production, we studied coffee agroforestry systems in India along a gradient from native multispecies canopies to Grevillea robusta (Proteaceae) -dominated canopy cover. We identified 25 agroforests, across a broad rainfall and management gradient and assessed litter quantity and quality, decomposition, nutrient release, soil fertility and coffee nutrient limitations. 3. Increasing G. robusta dominance affected nutrient cycling predominantly by; (1) changing of litter phenology, (2) reducing phosphorus (P), potassium (K), magnesium (Mg), boron (B), and zinc (Zn) inputs via litterfall, decelerated litter decomposition and immobilization of P and Zn due to low quality litter, (3) reducing soil carbon (C) and micronutrients (especially sulphur (S), Mg and B). Coffee plants were deficient in several nutrients (nitrogen (N), calcium (Ca), manganese (Mn), Mg and S in organic and B in conventional management). (4) Overall G. robusta dominated agroforests were characterized by a reduction of P cycling due to low inputs, strong immobilization while decomposition and antagonistic effects on its release in litter mixtures with coffee. 4. Synthesis and applications. The conversion of shade cover in coffee agroforestry systems from diverse tree canopies to canopies dominated by Grevillea robusta (Proteaceae) reduces the inputs and cycling of several micro- and macronutrients. Soil fertility is therefore expected to decline in G. robusta dominated systems, with likely impacts on coffee production. These negative effects might increase under the longer dry periods projected by regional climate change scenarios due to the pronounced litter phenology of G. robusta. Maintaining diverse shade canopies can more effectively sustain micro- and macronutrients in a more seasonal climate.

Diversified coffee agroforests modify microclimate conditions in comparison to monocultures, impacting the success of significant plant pathogens, such as Hemileia vastatrix, which causes coffee leaf rust (CLR). However, research is often limited to the dichotomous analysis of shaded agroforestry systems or unshaded monocultures, often overlooking the nuanced effect of shade tree trait diversity. Our study aims to determine the cumulative effects of shade tree canopy architectural characteristics and leaf functional traits in biodiverse agroforests on microclimate modifications and CLR incidence. We measured plot-level microclimate conditions (air temperature, relative humidity, leaf wetness duration, throughfall kinetic energy) in three single-stratum and two double-strata shade tree canopy treatments, including Erythrina poeppigiana, Terminalia amazonia, and Chloroleucon eurycyclum. Commonly reported canopy characteristics and leaf traits were compared to average microclimate conditions and CLR incidence levels. We found that shade tree trait expression significantly explained most microclimate conditions, and that two key shade tree traits (canopy openness, leaf area) significantly explain CLR incidence levels (R2 = 0.211, p = 0.036). Our results highlight the differences in microclimate conditions and CLR incidence among biodiverse agroforests, as well as the important explanatory power of shade tree traits. Specific effects of shade tree traits on pathogen dynamics can cirectly inform agroforestry system design (i.e. shade tree species selection) and sustainable coffee farm management practices (i.e. pruning practices).

Methods Thirty circular plots were selected at the international coffee agroforestry research trial by the Centro Agronómico Tropical de Investigación y Enseñanza (CATIE), in Turrialba, Costa Rica located at 09°53´44” N and 83°40´7” W, at 685 m a.s.l. Measurements took place from May to July 2017. Within the site, Coffea arabica cv. Caturra (herein referred to as coffee) is planted in agroforestry systems under various management schemes. We included five distinct types of shade tree treatments in our study, including three single-stratum and two double-strata canopies: Erythrina poeppigiana (E), Terminalia amazonia (T), Chloroleucon eurycyclum (C), E. poeppigiana + T. amazonia (ET) and E. poeppigiana + C. eurycyclum (EC). All shade tree treatments were repeated in two amendment regimes: moderate conventional (MC) and intensive organic (IO). All shade tree and amendment treatment combinations were repeated in three distinct blocks within the CATIE farm. Plots were confined to the area directly beneath a single representative shade tree in the single-stratum canopy treatments (E, T, and C), or beneath the overlapping of both shade tree canopies in the double-strata canopy treatments (ET, EC).

Microclimate measurements

Microclimate conditions were measured in five plots (each of the shade tree treatments) in the same block simultaneously for seven consecutive days, rotating between each of the amendment regimes and blocks, for a total of nine weeks between early May and late July 2017 (n = 63 days per treatment). Microclimate measurements were recorded every five minutes. We used HOBO® datalogger (Onset Computer Corporation, Bourne, MA) and HOBO® microclimate sensors (air temperature and relative humidity sensor (S-THB-M008) and leaf wetness sensor (S-LWA-M003)), which were installed next to the selected central shade tree between the main stem and canopy’s outer edge between the coffee plant rows. All air temperature and relative humidity sensors were installed on a stable vertical post and positioned above the average coffee plant height at about 2 m. Daily air temperature and relative humidity values were calculated based on data collected from midnight CST (or time of microclimate station installation) until 11:55 pm CST (or time of microclimate station removal). Start and stop times were kept consistent across all sampling plots. Leaf wetness sensors were positioned on the same vertical post at mid-coffee plant height (about 1 m), between the coffee plant row directed either north or south, laying horizontally and tilted at a 45o angle facing east. Leaf wetness duration was calculated as the amount of time (hours) that the leaf wetness sensor was wet between 6:00 am CST and 7:00 pm CST, as all wetting and drying processes were captured within this timeframe.

Total throughfall kinetic engery (TKE) of rain events was measured using Tübingen splash cups (Scholten, Geißler, Goc, Kühn, & Wiegand, 2011) with sand calibrated to 150-250 µm (Avelino et al., 2020), where total sand lost after each individual rain event was converted to the total TKE (J/m2) of the rain event using the equation developed by Scholten et al. (2011). Three splash cups were installed within each study plot: close to the central tree stem, mid-way between the central tree stem and canopy edge, and near the central tree’s canopy edge. Each cup was positioned on a stable vertical post at height of about 2 m (above the average coffee plant height). Measurements were collected in five shade tree plots (each of the shade tree treatments) in the same block simultaneously, rotating between each of the amendment regimes and blocks, for a total of two or four events, for a total of 18 days of TKE measurements per treatment. All measurements were collected simultaneously across the shade tree treatments on each sampling day.

Shade tree traits

For each shade tree, we measured total canopy height (m), canopy base height (CBH; m), canopy diameter (m) and canopy openness (%). In plots with one shade tree, the central shade tree was used for all measurements. In double-strata canopy plots, total canopy height was recorded as the tallest crown height, CBH was recorded as the shortest tree’s CBH, and canopy diameter was recorded as the largest crown diameter. Canopy openness was captured using hemispherical photography and analyzed using Gap Light Analyzer (Simon Fraser University, 1999). Hemispherical photographs were collected at a height above the average coffee plant height (about 2 m) at three positions under the central shade tree: close to the tree stem, in the middle of the canopy, at the edge of the canopy. The average canopy openness value of the three photographs, determined from the zenith angles 0o to 45o (Park & Cameron, 2008), were used in subsequent analyses.

We used a subset of six representative trees per species to measure leaf functional traits. Following protocols outlined by Pérez-Harguindeguy et al. (2013) and Pisek, Ryu, & Alikas (2011), five replicate leaf samples were collected from the lower canopy of each tree (n = 30 leaves per shade tree species), which were used to determine leaf area (cm2), specific leaf area (SLA; mg/mm2), leaf dry matter content (LDMC; mg/g) and leaf angle. The large leaflets of T. amazonia and E. poeppigiana were analyzed as separate leaves. In plots with double-strata canopies, final leaf trait values were calculated using the relative abundance-weighted mean of the shade tree species present in the plot, similar to Geißler et al. (2013), calculated based on the percent canopy area occupied by each shade tree canopy within the given plot.

Coffee leaf rust incidence

Within the boundaries each plot, six coffee plants of similar age were selected. CLR incidence was measured in July 2017, where coffee plants were assessed on the same day in each of the treatments. CLR incidence was estimated as the average percentage of leaves with CLR chlorotic spots, both with and without emerging mature uredospores, from three branches of varying height (Avelino, Romero-Gurdián, Cruz-Cuellar, & Declerck, 2012).

This dataset contains information on ‘Tree Montgomery’ which is a new program to plant shade trees for free. Please note that only planted and completed trees are included. The Montgomery County Department of Environmental Protection (DEP) is looking for places to plant, especially in yards of single family homes, parking lots, and multi-family communities. We’re also targeting areas where there is a lot of development, little tree canopy, or a real need for shade. The County will install shade trees and give them some after care; all for free. When installed, the trees will be 10 to 12 feet tall and will eventually be more than 50 feet tall, providing you with decades of shade. Funding for ‘Tree Montgomery’ is provided through the Tree Canopy Law. Update Frequency - As Needed

This mosaic of visible to shortwave infrared (VSWIR) data was derived from the assignable asset NEON AOP radiance data that was collected by LBNL’s Watershed Function SFA during the summer of 2018 (DOI: 10.15485/1617204). This atmospheric correction was completed to take into account site-specific terrain variability in the 334 km2 survey area centered around Crested Butte, CO. The atmospheric correction was completed using ACORN atmospheric correction software executed on 200 x 200 pixel kernels rather than line by line in order to capture local flight altitude conditions. Manual cloud delineation removed any small shaded areas that occurred within the data collection areas. Mosaics were developed based on preference for days that were in close timing proximity to the coincident ground campaign and with mosaicing criteria that minimized the angle between the sun and the sensor to retrieve the most consistent reflectance (min_phase_refl_tiled). Finally, we generated shade masks based on the geometry between the sun angle at time of flight, the ground surface, and the sensor. Here we provide the orthomosaiced estimated reflectance data (https://portal.nersc.gov/wfsfa/doi-10-15485-16181314/), canopy water content (wtrl), estimated atmospheric water vapor (wtrv), the observational data (obs) for this particular mosaic, the estimated visibility (vis), shade masks derived from the digital surface elevation model (shade) and the digital terrain model (shade_tch) added to the canopy height model (DOI: 10.15485/1617203), and a wavelength metadata file. Each image file is provided as a GeoTiff with internal tiling and LZW compression. These data can also be found on Google Earth Engine for extraction, download, and analysis of smaller extents: DSM Shade: https://code.earthengine.google.com/?asset=users/pgbrodrick/SFA/collections/shade_priority. DTM + CHM Shade: https://code.earthengine.google.com/?asset=users/pgbrodrick/SFA/collections/shade_tch_priority. Custom Reflectance: https://code.earthengine.google.com/?asset=users/pgbrodrick/SFA/collections/ciacorn_priority OBS data: https://code.earthengine.google.com/?asset=users/pgbrodrick/SFA/collections/obs_priority Water vapor estimates: https://code.earthengine.google.com/?asset=users/pgbrodrick/SFA/collections/wtrv_priority Canopy water content: https://code.earthengine.google.com/?asset=users/pgbrodrick/SFA/collections/wtrl_priority All data visualization: https://code.earthengine.google.com/5c96bbc96ffd50e3c8b1433b34a0bb86

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Overview This data set, a collaboration between the GLAD (Global Land Analysis & Discovery) lab at the University of Maryland (UMD), Google, USGS, and NASA, measures areas of tree cover loss across all global land (except - Antarctica and other Arctic islands) at approximately 30 × 30 meter resolution. The data were generated using multispectral satellite imagery from the Landsat 5 thematic mapper (TM), the Landsat 7 thematic mapper plus (ETM+), and the Landsat 8 Operational Land Imager (OLI) sensors. Over 1 million satellite images were processed and analyzed, including over 600,000 Landsat 7 images for the 2000-2012 interval, and more than 400,000 Landsat 5, 7, and 8 images for updates for the 2011-2022 interval, and additional images used for 2023 and 2024. The clear land surface observations in the satellite images were assembled and a supervised learning algorithm was applied to identify per pixel tree cover loss. In this data set, “tree cover” is defined as all vegetation greater than 5 meters in height, and may take the form of natural forests or plantations across a range of canopy densities. Tree cover loss is defined as “stand replacement disturbance” which is considered to be clearing of at least half of tree cover within a 30-meter pixel. The exact threshold is variable both through space and time, and is biome-dependent. Tree cover loss may be the result of human activities, including forestry practices such as timber harvesting or deforestation (the conversion of natural forest to other land uses), as well as natural causes such as disease or storm damage. Fire is another widespread cause of tree cover loss, and can be either natural or human-induced. This data set has been updated five times since its creation, and now includes loss up to 2024 (Version 1.12). The analysis method has been modified in numerous ways, including new data for the target year, re-processed data for previous years (2011 and 2012 for the Version 1.1 update, 2012 and 2013 for the Version 1.2 update, and 2014 for the Version 1.3 update), and improved modelling and calibration. These modifications improve change detection for 2011-2024, including better detection of boreal loss due to fire, smallholder rotation agriculture in tropical forests, selective losing, and short cycle plantations. Since the entire historical timeseries was not reprocessed with the updated methodology, time-series assessments should be performed with caution. Read more about the Version 1.12 update here and access on GEE here. When zoomed out (< zoom level 13), pixels of loss are shaded according to the density of loss at the 30 x 30 meter scale. Pixels with darker shading represent areas with a higher concentration of tree cover loss, whereas pixels with lighter shading indicate a lower concentration of tree cover loss. There is no variation in pixel shading when the data is at full resolution (≥ zoom level 13). The tree cover canopy density of the displayed data varies according to the selection - use the legend on the map to change the minimum tree cover canopy density threshold. Resolution: 30mGeographic Coverage: Global land area (excluding Antarctica and other Arctic islands).Update Frequency: AnnualContent Date: 2001-2024

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This mosaic of visible to shortwave infrared (VSWIR) data was derived from the assignable asset NEON AOP radiance data that was collected by LBNL’s Watershed Function SFA during the summer of 2018 (DOI: 10.15485/1617204). This atmospheric correction was completed to take into account site-specific terrain variability in the 334 km2 survey area centered around Crested Butte, CO. The atmospheric correction was completed using ACORN atmospheric correction software executed on 200 x 200 pixel kernels rather than line by line in order to capture local flight altitude conditions. Manual cloud delineation removed any small shaded areas that occurred within the data collection areas. Mosaics were developed based on preference for days that were in close timing proximity to the coincident ground campaign and with mosaicing criteria that minimized the angle between the sun and the sensor to retrieve the most consistent reflectance (min_phase_refl_tiled). Finally, we generated shade masks based on the geometry between the sun angle at time of flight, the ground surface, and the sensor. Here we provide the orthomosaiced estimated reflectance data (min_phase_refl_tiled.tif), canopy water content (wtrl), estimated atmospheric water vapor (wtrv), the observational data (obs) for this particular mosaic, the estimated visibility (vis), shade masks derived from themore » digital surface elevation model (shade) and the digital terrain model (shade_tch) added to the canopy height model (DOI: 10.15485/1617203), and a wavelength metadata file. Each image file is provided as a GeoTiff with internal tiling and LZW compression.These data can also be found on Google Earth Engine for extraction, download, and analysis of smaller extents (Google Earth Engine data is linked in external link table).« less

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Spring ephemeral herb species in temperate deciduous forests are active above-ground only briefly each year. This study tested experimentally how two countervailing constraints – cold and darkness – influence the phenology of six spring herb species. Dormancy of underground structures, maintained by cold temperatures in a growth chamber, was broken at six 25-day intervals from January or February to June in two consecutive years. Upon emergence, survival and flowering were measured on cohorts grown outdoors. Shade cloth was added at the time of normal canopy closure. Cardamine concatenata, Dicentra cucullaria, Erythronium albidum and Trillium recurvatum had no or low 2-year survival in the two or three earliest cohorts and no or low survival in the latest cohort, relative to their natural cohort. Allium canadense and Claytonia virginica had survival in all cohorts. Flowering never occurred in the first two or three cohorts for three species and never occurred or declined in later cohorts in all species. Despite widely differing emergence dates, senescence was completed within a 40-day period soon after shade was imposed for all cohorts for all species. Consequently, leaf life span became shorter as date of emergence was delayed among cohorts. In general, the brief growth period of spring herb species is an adaptation to avoid winter cold and late-spring canopy shade. These constraints are species-specific and differ for survival and flowering for some species. Claytonia virginica is the most tolerant among the species to a wider range of conditions. Synthesis. Knowing that cold and shade constrain a plant's non-dormant period is important because of the significant role plant phenology plays in responses to climate change.

When amphibians thermoregulate, they face a fundamental trade-off between the ability to maintain activity and an increased rate of dehydration at higher temperatures. Canopy coverage affects both the thermal and hydric conditions of the environment and can therefore influence amphibian thermoregulation. Frogs require proper conditions to thermoregulate to successfully grow, survive, and reproduce. But while we know how canopy and environmental variables typically affect operative temperature, less is known about the effects on amphibian water loss rates. In this study, we measure the effect of canopy coverage on the conditions available for thermoregulation at a breeding pond of the California red-legged frog, Rana draytonii. We use agar frog models to estimate the thermal and hydric capacities frogs would experience in locations with different canopy coverage and microhabitats. At each site, we deployed models under four microhabitat treatments: wet/sun, wet/shade, dry/sun, and dry/shade. We modeled how environmental variables affected operative temperature and evaporative water loss from agar frogs. We found positive effects of air temperature, the sun treatment, and reduced canopy cover on operative temperature, and negative direct or indirect effects of these variables on evaporative water loss, consistent with the hypothesized trade-off between thermoregulatory behavior to increased temperature and the increased desiccation risk due to higher water loss. Additionally, our results indicate that the availability of wet microhabitats can allow frogs to reduce water loss, potentially mitigating the risk of desiccation when thermoregulating to achieve higher operative temperatures. Our findings suggest, that with access to proper microhabitats, amphibians can mitigate the fundamental trade-off and receive benefits of thermoregulating at high temperatures. Methods Study Design To measure the thermoregulatory conditions at the pond, we divided up the pond into 8 wedges, each with an area of approximately 51.28m2. We deployed agar frogs in each site’s shoreline for 24-hour periods from March to September. We used natural differences in canopy coverage to test how percent canopy coverage affects EWL and Te on agar frogs. For a list of variables and their definitions, see Table 1. To measure the full range of thermal and hydric conditions an amphibian could experience, agar frogs were placed in different microhabitats using a 2 x 2 factorial design with two levels of light treatment (sun vs shade) and two levels of water treatment (wet vs dry). We tracked Te with internal dataloggers, EWL from mass changes in agar frogs, and other environmental conditions. With this design, we tested how percent canopy coverage affects agar frogs’ Te and EWL individually. Then, we tested the bi-directional interaction between Te and EWL using structural equation modeling. Finally, we tested a real-life example of thinning excess vegetation and its effect on EWL and Te. Canopy Coverage Holding a camera at 1 meter height and pointing the lens up vertically during the daytime, we took photographs of the canopy overstory at each site to calculate the percent canopy coverage. We took two photos at each site: one at the densest canopy coverage where shade agar frogs were deployed and the second at the thinnest canopy coverage where sun agar frogs were deployed (approximately 5m apart). We analyzed photos in Adobe Photoshop software and converted pixels of the sky to white and pixels of vegetation to black following the procedure in Stewart et al. 2007. Using ImageJ software (Rasband 1997-2018), we calculated the number of black vs white pixels estimated percent canopy coverage by comparing the number of pixels of vegetation to the total pixels. The average percent canopy coverage between the two photos was calculated for each site. Agar Creation and Validation Agar frogs are regularly used to estimate the Te and EWL an amphibian could potentially access in its environment. The free evaporation from agar captures the evaporative properties experienced by live frogs, including the cooling aspects caused by EWL, since we are unable to use live frogs due to their threatened status. Agar EWL has been validated in comparison to living frogs, and agar models exhibit internal temperatures that match live amphibians. While there has been recent debate about using agar models to compare to live thermoregulating amphibians, we minimized identified limitations by using agar frogs that represent live frogs in size, shape, posture, and inactivity, and replace models facing extreme dehydration to avoid inaccurate readings of EWL. We also acknowledge that agar frogs cannot replicate live frog behavior and movements, so we instead use agar frogs to study the available microhabitats with different thermal and hydric opportunities for frogs, as opposed to simulating what a live frog would experience. We made agar frog models in latex molds, which were initially created with plaster casts from museum specimens spanning the size range of anuran in the Pacific Northwest: two large-bodied frogs (15-48 grams), Bufo boreas and Rana draytonii, one small frog (6-22 grams), Pseudacris regilla, and a small ellipse shape (3 cm diameter, 5-16 grams). We filled molds with agar (4.74 g in 100 ml) tinted green with acrylic paint and allowed them to solidify around a thermocouple probe in the center. The agar formula we used was denser than previous studies to achieve a similar density to live amphibians, obtain more accurate Te, and be a better proxy for simulating evaporative properties. Agar Frog Deployment We deployed the agar frogs for 24 hours every two weeks during the 2017 breeding season March-September (N = 13 total weeks of deployment). For the first 7 deployment weeks, agar frogs were deployed at all 8 sites. However, as the pond water levels naturally went down, agar was only deployed at sites still holding water, with four sites measured in weeks 8 and 9, three sites in weeks 10-12, and one site in week 13. Within each of the 8 wedges, one small (5–22 g) and one large (15–48 g) agar frog were deployed under four microhabitat treatments: wet/sun (N = 432 deployment periods), wet/shade (N = 438), dry/sun (N = 428), and dry/shade (N = 448). Agar frogs in the sunlight treatment were placed in the site under the location of lowest canopy coverage and shade light treatment frogs were placed under fallen branches to be fully shaded. Agar frogs in the dry water treatment were placed on a dry substrate near the pond bank (1-2 meters from the water’s edge), and wet water treatment frogs were placed on the water line, so half the agar was in the water and half was on the saturated substrate. As the season progressed and the water level changed, we adjusted the wet treatment location to keep agar frogs on the water line, but dry treatment locations remained in the original location. Every agar frog was deployed with an internal thermocouple (Onset HOBO ProV2 U23-003) that measured internal model temperature at one-minute intervals to calculate Te. To determine EWL for each deployment period, agar frogs were initially weighed with a Pesola PPS200 digital scale at each site location. Agar frogs were then deployed in the evening (15:00-19:30 hours) and reweighed three times in situ the following day: morning (between 07:30-11:00 hours), noon (between 11:00-15:00 hours) and evening (between 14:00-20:00 hours). This creates three deployment periods each day (overnight, morning, and afternoon), defined as the 4–14 hour period between models being set in the field and the time when the next weighing occurred (weigh point). The first 15 minutes after each weigh point were removed from calculations to ensure agar frogs had achieved equilibrium again following handling. Any agar frogs that had lost 10% or more of their body weight were replaced with fresh agar frogs because agar that has lost more than 10% mass no longer maintains consistent EWL with live amphibians. Cages, constructed from chicken wire, were placed over agar frogs to prevent damage from birds. A relative humidity datalogger (Onset HOBO ProV2 U23-001) was placed at the main site 2 meters above the ground in shade conditions to log ambient air temperature and relative humidity at one-minute intervals. EWL and Te Calculations EWL was measured as percent water loss per hour, calculated as the change in agar mass over the deployment period. Operative temperature, Te, was calculated from the internal thermocouple data. We calculated the average, minimum, and maximum temperature for each weigh period as well as the standard deviation and full range difference of temperatures. The ambient data logger was used to calculate air temperature and relative humidity for each weight period. We calculated the average, minimum, maximum, standard deviation, and range of both air temperature and relative humidity. Effects of Vegetation Trimming At one wedge site along the pond, site managers trimmed the canopy and removed dead vegetation to simulate historical management. We used this site in our study, but we additionally compared how this artificially trimmed canopy affected EWL and T e, compared to sites with naturally thin canopy coverage. We aimed to test if artificial thinning can return potential thermoregulatory benefits, such as increased Te, that naturally thin canopy provides. Statistical Analysis Environmental Variable Effects on Average Te and EWL To examine how percent canopy coverage, water treatment, and light treatment impacted agar frogs, we fit a linear mixed model using the lmer package in R ver 3.6.1 (www.r-project.org). This modeling framework allowed us to examine the effect of many environmental variables (i.e., fixed effects) on multiple agar response variables and include two nested random effects of “session/period” and “site/microhabitat." We created two models to test how these

Agroecological restoration aims to restore biodiversity and ecosystem function in agricultural landscapes while sustaining crop production. Adopting native plants as cover crops may restore ecological value to cropping systems such as nut orchards. We focused on Oregon hazelnut orchards and compared how four seed mixes (native annuals, native perennials, conventional cover crops, and unseeded controls) performed under three levels of orchard floor disturbance (flailing, flailing and scraping, and unmanaged/none) across three different orchard ages with corresponding differences in canopy shade over a two-year period. We evaluated cover crop performance by three criteria: the survival criteria (response to disturbance and shading), the production criteria (effects on weeds, erosion potential, and soil moisture) and the ecological functioning criteria (abundance and diversity of native plants and pollinator visitations). We found that native species generally outperformed conventional cover crops and bare ground across these criteria. Plant survival was not affected by disturbance but shading reduced survival of most species. Native annuals had high cover in the first year, and native perennials had high cover across both years. Native perennial species provided the best weed reduction and erosion control while not reducing soil moisture, and hosted the highest pollinator abundances and diversity. Synthesis and Applications: Our results suggest that agroecological restoration of orchards through native cover cropping is a viable strategy for improving ecological outcomes without compromising production needs. Methods Data collection To evaluate cover crop survival, we visually estimated percent cover of each species at peak biomass in May 2020 and 2021. To quantify shading, we measured the canopy cover of each subplot in July each year using Canopeo (Patrignani & Ochsner 2015), a mobile application that analyzes fractional green canopy cover from digital images. Imagery was gathered in July 2020 at the subplot-level by taking upward-facing photos at chest height. This was repeated in July 2021, following an ice storm in February 2021 that broke tree limbs and reduced canopy cover, especially in the 40-year-old orchard. Because shading in a portion of the 15-year-old orchard was increased by rows of taller vegetation to the south (Figure S1) we estimated the proportion of plots that were in shade from adjacent trees at noon during mid-spring monitoring. Dense shade affected approximately 60% of three plots, leading us to calculate canopy cover as a weighted average within the subplot (40%) and the adjacent vegetation (60%). We addressed our production criteria by monitoring weed (any non-planted, volunteer vegetation) cover, total winter vegetation cover, and soil moisture in each subplot. We visually estimated plant cover of each present species each spring. We estimated winter vegetation and bare ground in January of the second year, after perennial plants had established. Winter vegetation cover was used as a proxy for erosion as it affects the extent to which soil is bare and vulnerable to winter rains. We measured soil moisture as volumetric water content (VWC) to a depth of 15cm over two time periods: weekly from April-May 2020 and monthly from March-August 2021 (Figure S4). In this critical period rainfall is reduced but cover crops are abundant, and thus most likely increase water stress in trees. To minimize spatial variability in VWC, we replaced the probes of Campbell Scientific HydroSenseII moisture meter with 1 cm threaded bolts which were touched to two six-inch steel box nails permanently embedded in the center of each subplot (Grinath et al. 2019). To calibrate the nail measurements, we took a subset of measurements with both nails and probes at the same location over time and fit a linear regression (Figure S2). To assess how the timing of species flowering and senescence aligned with management practices (such as flailing and pesticide application), which is relevant for all three criteria, we evaluated phenology from April through August 2021. Each month, we recorded the life stage of each species by the following categories: pre-flowering vegetative, first flower, peak flowering, last flower, post-flower vegetative, or senesced. First and last flower were both defined as approximately 10% of flower buds open. At peak flower, we estimated floral abundance as the number of inflorescences per target species to the nearest category: 1, 2, 5, 10, 25, 50, and intervals of 50 thereafter. We asked the farm to observationally record whether any combination of species and treatment interfered with harvest. Finally, to evaluate our ecological functioning criteria we monitored pollinator visitations within each cover crop subplot in June and July of 2020, and monthly from April through August 2021. We used observational surveys to quantify abundance and aerial net collections to quantify diversity. We surveyed during dry conditions with partial or no cloud cover between 12pm and 6pm, recording each pollinator morphospecies visitation and its host plant for two minutes. We also conducted two-minute aerial net collections of pollinators in each subplot, excluding honeybees and queen bumblebees. Samples were classified to order using a dissecting microscope, except the primary contributors to pollination, Anthophila and Syrphidae (Ssymank et al. 2008; Youngsteadt 2020), which were identified to genus using Jackson (2019) and Miranda et al. (2013). Data Analysis All data cleaning and analyses were carried out using R version 4.2.3 (R Core Team 2023). We used field data to separately evaluate the three cover crop success criteria in this study, namely criteria related to survival, agricultural production, and ecological functioning. First, to evaluate survival criteria, we focused on the aggregate cover of seed mixes (summed percent cover of each species included within a seed mix) and its change across time. Then, we created a model using the lmer function from package lme4 (Bates et al. 2015) using aggregate seed mix cover as a response variable and seed mix, management treatment, and orchard age (as a rough proxy for shading), as fixed factors, and year and block as random factors. We evaluated the model using check_model and other functions from package performance (Lüdecke at al. 2021). We detected some non-normality of residuals. Consequently, we used package glmmTMB (Brooks et al. 2017) to create models with the same set of predictors but with different error distributions (in particular, Tweedie and generalized Poisson distributions). These models resulted in qualitatively similar results, indicating that the results of our original model were robust. We proceeded with the original lmer model, because of its ease of interpretation. We evaluated differences between groups with post-hoc Tukey tests using emmeans from package lsmeans (Lenth 2016). We also evaluated survival criteria by assessing the survival of individual species within our seed mixes across orchard canopy density. We built separate models for each species and each year of the study to describe the relationship between species cover (response) and orchard canopy cover (predictor), including both a linear and quadratic term for orchard canopy (using the lm and poly functions from package stats (R Core Team 2023)). If the quadratic predictor was not significant, we proceeded to use only the linear term; otherwise we used both the linear and quadratic predictor. Because of species dispersal between year one and year two, our data from the second year includes observations made outside of each species’ subplots but within their respective main plot. To evaluate production criteria, we analyzed cover crop effects on spring weed cover and total winter vegetation cover (as a proxy for erosion reduction). For both spring weeds and winter ground cover, we created linear mixed models using lmer. Models used orchard canopy density and seed mix treatment as fixed effects and year as a random effect. For spring weeds, the response variable was the difference in spring weed cover between each respective seed mix and the control treatment (lower values represent greater weed control compared to the control treatment). For winter vegetation, the response variable was the difference in bare ground cover between each respective seed mix and the control treatment (lower values represent increased ground cover relative to the unseeded control and therefore reduced potential for soil erosion). We included only winter vegetation data from 2021, to evaluate perennial plant establishment in the second year of the study. We included orchard canopy cover and year in the models, because associated environmental data can affect baseline weed pressure (Figure S3). We also checked model assumptions and created alternative models, similar to as described above, but we again proceeded with our original model as the most parsimonious and easiest to interpret. We also assessed production criteria by evaluating the effect of cover crops on available soil moisture. We used mixed models to compare the effect of seed mix treatment on VWC using for each year separately. We used seed mix, time period (week in 2020, month in 2021) and their interaction as fixed effects and orchard and block as random effects. We highlighted a common reference period of April 1 through May 17 for comparison between years (Figure S4, red boxes). To contextualize soil moisture with climate, we downloaded daily precipitation data from PRISM and calculated weekly mean precipitation during each sampling period (PRISM Climate Group 2022). Because shading has a strong influence on soil moisture, we could not disentangle the affects of shading and seed mix in the inconsistently shaded 15-year-old orchard, which we dropped from all moisture analyses. To assess how cover crops altered ecological