Measured Canopy Cover Research Articles

Display court ecology in male Albert’s Lyrebirds

ABSTRACT Display courts are fundamental to the sexual displays of many species of bird; however, the ecology of display courts is poorly understood, representing an important gap in our understanding of the function and evolution of complex animal displays. Here, we describe the biophysical attributes (‘ecology’) of the display courts of male Albert’s Lyrebirds, a ground-dwelling rainforest species that performs a spectacular song and dance display on set courts, known as ‘display platforms’, that are composed of vines or fallen branches. Males rhythmically move these vegetative structures while singing, forming a unique ‘stage shaking’ component of their sexual display. We investigated whether display platforms differed from random locations within a male’s territory in terms of the number of vines or other vegetative structures, area of ground clearing, vegetation density, and canopy cover measurements. We primarily found that platforms had a larger number of vines or other vegetative structures. Platforms also tended to be in areas with more canopy openings. We suggest that platform features amplify the movement of the dancing male, and that male Albert’s Lyrebirds choose platforms in locations that increase their conspicuousness to female receivers.

Thinning relationships of woody encroachers in a US southwestern shrubland

Woody plant encroachment degrades the economic and environmental potential of drylands by altering processes such as nutrient fluxes, ecohydrology, and ecosystem services. Though past research has investigated the encroachment process, relatively little is known about post-encroachment shrub community ecology. To better quantify dynamics within post-woody encroachment shrub communities, we combined USGS lidar height data and multispectral imagery to estimate shrub density, shrub height, shrub cover, and shrub volume across the Jornada Basin Long Term Ecological Research (JRN-LTER) site in southern New Mexico, USA. Structural estimates were analyzed in search of telltale signs of community competition, specifically, the presence of thinning relationships. Results demonstrated density-dependent thinning relationships in shrub communities of creosote and mesquite, indicating a large role for competition in arid shrub communities even at relatively low shrub densities and cover (∼35% cover). In addition, shrub volume estimates better modeled the expected thinning dynamics of shrub communities than shrub canopy cover measurements. Overall, our results indicate the utility of lidar data in extending two-dimensional descriptions of woody vegetation structure (i.e., woody canopy cover) into the critical third dimension (i.e., woody plant volume), as well as the relative importance of competition and demographic bottlenecks to vegetation structure in drylands.

Leaf Area Index Estimation of Fully and Deficit Irrigated Alfalfa through Canopy Cover and Canopy Height

In arid and semiarid regions, crop production has high irrigation water demands due to low precipitation. Efficient irrigation water management strategies can be developed using crop growth models to assess the effect of different irrigation management practices on crop productivity. The leaf area index (LAI) is an important growth parameter used in crop modeling. Measuring LAI requires specialized and expensive equipment not readily available for producers. Canopy cover (CC) and canopy height (CH) measurements, on the other hand, can be obtained with little effort using mobile devices and a ruler, respectively. The objective of this study was to determine the relationships between LAI, CC, and CH for fully and deficit-irrigated alfalfa (Medicago sativa L.). The LAI, CC, and CH measurements were obtained from an experiment conducted at the Valley Road Field Lab in Reno, Nevada, starting in the Fall of 2020. Three irrigation treatments were applied to two alfalfa varieties (Ladak II and Stratica): 100%, 80%, and 60% of full irrigation demands. Biweekly measurements of CC, CH, and LAI were collected during the growing seasons of 2021 and 2022. The dataset was randomly split into training and testing subsets. For the training subset, an exponential model and a simple linear regression (SLR) model were used to determine the individual relationship of CC and CH with LAI, respectively. Also, a multiple linear regression (MLR) model was implemented for the estimation of LAI with CC and CH as its predictors. The exponential model was fitted with a residual standard error (RSE) and coefficient of determination (R2) of 0.97 and 0.86, respectively. A lower performance was obtained for the SLR model (RSE = 1.03, R2 = 0.81). The MLR model (RSE = 0.82, R2 = 0.88) improved the performance achieved by the exponential and SLR models. The results of the testing indicated that the MLR performed better (RSE = 0.82, R2 = 0.88) than the exponential model (RSE = 0.97, R2 = 0.86) and the SLR model (RSE = 1.03, R2 = 0.82) in the estimation of LAI. The relationships obtained can be useful to estimate LAI when CC, CH, or both predictors are available and assist with the validation of data generated by crop growth models.

From simple linear regression to machine learning methods: Canopy cover modelling of a young forest using planet data

Accurate canopy cover estimation is essential for mature and early-stage young forests, as it guides forest management and silvicultural activities necessary for their growth and regeneration. However, obtaining precise measurements of canopy cover in the field is time-consuming and challenging, especially at the regional and landscape levels. Remote sensing techniques offer a promising alternative to traditional field-based measurements for estimating forest canopy cover. In this study, our objective is to estimate forest canopy cover using vegetation indices derived from the multispectral bands of PlanetScope (Planet Lab, Inc., San Francisco, CA, USA). To the best of our knowledge, this is the first study to utilise PlanetScope imagery data for estimating canopy cover in young boreal forests. Based on the analysis of four bands (green, blue, red, and near-infrared) from PlanetScope imagery, 43 vegetation indices, including four spectral bands and 13 salinity indices, were computed to select predictors in canopy cover modelling. Six regression models were employed to model canopy cover: linear, elastic net, support vector machine, random forest, extreme gradient boosting, and light gradient boosting machine. All the models demonstrated good performance for both the training dataset (R2 = 0.58–0.69) and the testing dataset (R2 = 0.59–0.64, RMSE = 0.16–0.18, rRMSE = 22%–23%, and MAE = 0.12–0.14). Based on the fit statistics in the training and testing datasets and the paired t-test, our study identified the light gradient boosting machine as the most suitable model for predicting canopy cover in young boreal forests. For the light gradient boosting machine, the R2 value was 0.69 (training), and for testing data, the R2 = 0.64, RMSE = 0.16, rRMSE = 22%, and MAE = 0.12. Therefore, we recommend that future researchers utilise Planet multispectral data and the light gradient boosting machine regression to estimate forest canopy cover at a higher spatial resolution. However, exploring additional machine learning algorithms and explicitly boosting methods when computing forest canopy cover using satellite remote sensing is strongly advised.

Improving Irrigation Management of Cotton with Small Unmanned Aerial Vehicle (UAV) in Texas High Plains

The rapid decline in water availability for irrigation on the Texas High Plains (THP) is a significant problem affecting crop production and the viability of a large regional economy worth approximately USD 7 billion annually. This region is the largest continuous cotton-producing area in the United States, and the timely delivery and efficient use of irrigation water are critical to the sustainability and profitability of cotton production in this region. Current irrigation scheduling must be improved to reduce water consumption without compromising crop production. Presently, irrigation scheduling based on reference evapotranspiration (ETo) is limited due to the lack of reliable and readily available in-field weather data and updated crop coefficients. Additionally, in-field variability in crop water demand is often overlooked, leading to lower irrigation efficiency. To address these challenges, we explored the potential use of an unmanned aerial vehicle (UAV)-based crop monitoring system to support irrigation management decisions. This study was conducted in Lubbock, Texas, in 2022, where high temporal and spatial resolution images were acquired using a UAV from a cotton field experiment with four irrigation levels. Soil moisture and canopy temperature sensors were deployed to monitor crop response to irrigation and rainfall. The results indicated a significant effect of water stress on crop growth (revealed by UAV-based canopy cover (CC) measurements), yield, and fiber quality. Strong correlations between multi-temporal CC and lint yield (R2 = 0.68 to 0.88) emphasized a clear trend: rainfed treatments with lower yields exhibited reduced CC, while irrigated plots with higher CC displayed increased yields. Furthermore, irrigated plots produced more mature and uniform fibers. This study also explored various evapotranspiration calculation approaches indicating that site-specific CC measurements obtained from a UAV could significantly reduce irrigation application. A regression model linking evapotranspiration to canopy cover demonstrated promising potential for estimating water demand in crops with an R2 as high as 0.68. The findings highlight the efficacy of UAV-based canopy features in assessing drought effects and managing irrigation water in water-limited production regions like the THP.

Spatially analysed expansion of individual street tree crowns enables species-specific crown expansion predictions in different rainfall zones

Many cities are developing ambitious future canopy cover targets in recognition of urban trees' numerous benefits. Selecting fast-growing and climate-adapted tree species is important to help achieve these targets. The assessment of growth performance of tree species is challenging, but essential for selecting species that grow in different environments. Often, remote sensing is used to measure canopy cover change at a landscape or neighbourhood scale in urban forests, but rarely at individual tree or species scales. In this study, we developed a novel spatial analysis method combining remotely sensed canopy cover mapping with georeferenced urban tree inventory data to identify individual tree crowns and measure crown expansion rates. We developed species-specific models of crown expansion rates for 20 most common street tree species growing in two rainfall zones (478–665 mm). Predicted crown areas at 10 years after planting ranged from 6.6 m2 to 43.7 m2. The species showed four different crown expansion responses at 10 years after planting: Fast and consistent growth; Fast but sensitive growth; Slow and consistent growth; Slow and sensitive growth. This study demonstrates a simple, but robust method to delineateindividual tree crowns that can be used to develop species-specific crown expansion models. It also shows the importance of developing species specific crown expansion models in different rainfall zones, as some tree species were clearly sensitive to rainfall differences. Using this spatial analysis method, urban forest managers can make informed decisions regarding tree species selection, considering rainfall zone specific environmental growth conditions, as well as space constraints and water availability.

Phenotypic characterization of drought responses in red clover (Trifolium pratense L.).

Red clover (Trifolium pratense) is a protein-rich, short-lived perennial forage crop that can achieve high yields, but suffers increasingly from drought in different cultivation areas. Breeding for increased adaptation to drought is becoming essential, but at this stage it is unclear which traits breeders should target to phenotype responses to drought that allow them to identify the most promising red clover genotypes. In this study, we assessed how prolonged periods of drought affected plant growth in field conditions, and which traits could be used to distinguish better adapted plant material. A diverse panel of 395 red clover accessions was evaluated during two growing seasons. We simulated 6-to-8-week drought periods during two consecutive summers, using mobile rain-out shelters, while an irrigated control field was established in an adjacent parcel. Plant growth was monitored throughout both growing seasons using multiple flights with a drone equipped with RGB and thermal sensors. At various observation moments throughout both growing seasons, we measured canopy cover (CC) and canopy height (CH). The crop water stress index (CWSI) was determined at two moments, during or shortly after the drought event. Manual and UAV-derived measurements for CH were well correlated, indicating that UAV-derived measurements can be reliably used in red clover. In both years, CC, CH and CWSI were affected by drought, with measurable growth reductions by the end of the drought periods, and during the recovery phase. We found that the end of the drought treatment and the recovery phase of approximately 20 days after drought were suitable periods to phenotype drought responses and to distinguish among genotypes. Multifactorial analysis of accession responses revealed interactions of the maturity type with drought responses, which suggests the presence of two independent strategies in red clover: 'drought tolerance' and 'drought recovery'. We further found that a large proportion of the accessions able to perform well under well-watered conditions were also the ones that were less affected by drought. The results of this investigation are interpreted in view of the development of breeding for adaptation to drought in red clover.

Effects of forest structural and compositional change on forest microclimates across a gradient of disturbance severity

Forest structural diversity and community composition are key in regulating forest microclimates. When disturbance affects structural diversity or composition, forest microclimates may be altered due to changes in soil temperature, soil water content, and light availability. It is unclear however which structural or compositional components, when changed or to what extent, result in microclimatic change. To address this question, we used data from a large scale, manipulative stem-girdling experiment in northern, lower Michigan—the Forest Resilience and Threshold Experiment (FoRTE). FoRTE follows a factorial design with multiple levels of disturbance severity (0, 45, 65, 85%) based on targeted reductions in gross leaf area index via stem-girdling induced mortality. These disturbance severity treatments are applied in two ways: either as top-down (largest trees are killed) or bottom-up (small to medium trees killed) treatments. We examined how multiple components of structural diversity and community composition changed as a product of disturbance severity and type, and then tested for resulting effects on forest microclimates (light availability, soil temperature, and soil water), using a multivariate, Random Forest framework. We found that measures of community composition (species richness, species evenness, and Shannon-Wiener Diversity Index) and stand structure (basal area, standard deviation of DBH, tree size diversity) declined more following disturbance than did measures of canopy cover, heterogeneity, arrangement, or height. However, when changes in each variable from pre- to post-disturbance, measured as log change, were employed in a multivariate, Random Forest regression framework, structural diversity measures of heterogeneity (rugosity, top rugosity), cover (canopy cover), and arrangement (porosity) were the most influential variables, but with differences among bottom-up and top-down treatments We found that the death of large trees from disturbance impacts soil temperature, water, and light environments more substantially and uniformly across disturbance gradients than does the death of smaller trees. Our results have implications for both statistical and process-based modeling of forest disturbance.

A Methodological Framework for Mapping Canopy Cover Using ICESat-2 in the Southern USA

NASA’s Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) provides exceptional opportunities for characterizing the structure of ecosystems through the acquisition of along-track, three-dimensional observations. Focusing on canopy cover as a fundamental parameter for assessing forest conditions, the overall goal of this study was to establish a framework for generating a gridded 30 m canopy cover product with ICESat-2. Specifically, our objectives were to (1) Determine and compare ICESat-2-derived canopy cover with airborne lidar-derived and the 2016 National Land Cover Database (NLCD) cover product estimates, and (2) Evaluate a methodology for wall-to-wall mapping of canopy cover. Using two Southern US sites, the Sam Houston National Forest (SHNF) in south-east Texas and the Solon Dixon Forestry Education Center (SDFEC) in southern Alabama, four measures of canopy cover estimated with ICESat-2′s Land-Vegetation Along-Track Product, or ATL08, were evaluated at the 30 m pixel scale. Comparisons were made using spatially coinciding NLCD pixels and airborne lidar-derived reference canopy cover. A suite of Landsat and Landsat-derived parameters were then used as predictors to model and map each measure of canopy cover with Random Forests (RF), and their accuracies were assessed and compared. Correlations (r) between ICESat-2-derived and airborne lidar canopy cover at the pixel scale ranged from 0.57 to 0.78, and R2 up to 0.81 was produced between NLCD and ICESat-2-derived canopy cover. RF models developed for extrapolating ICESat-2-derived canopy cover estimate yielded R2 values between 0.50 and 0.61 (RMSEs between 16% and 20%) when evaluated with airborne lidar-derived canopy cover. With a demonstrated capability of ICESat-2 to estimate vegetation biophysical parameters, the findings serve to support the spatially comprehensive mapping of other vegetation attributes, especially forest aboveground biomass, and contribute to the development of an up-to-date gridded canopy cover product.

CanopyCAM – an edge-computing sensing unit for continuous measurement of canopy cover percentage of dry edible beans

Canopy cover (CC) is an important indicator for crop development. Currently, CC can be estimated indirectly by measuring leaf area index (LAI) using commercially available hand-held meters. However, it does not capture the dynamics of CC. Continuous CC monitoring is essential for dry edible beans production since it can affect crop water use, weed, and disease control. It also helps growers to closely monitor “yellowness”, or senescence of dry beans to decide proper irrigation cutoff timing to allow the crop to dry down for harvest. Therefore, the goal of this study was to develop a device – CanopyCAM, containing software and hardware that can monitor dry bean CC continuously. CanopyCAM utilized an in-house developed image-based algorithm, edge-computing, and Internet of Things (IoT) telemetry to process and transmit CC in real-time. In the 2021 growing season, six CanopyCAMs were developed with three installed in fully irrigated dry edible beans research plots and three installed at commercial farm fields, respectively. CC measurements were recorded at 15 min interval from 7:00 am to 7:00 pm in each day. Initially, the overall trend of CC development increased over time but fluctuations in daily readings were noticed due to changing lighting conditions which caused some overexposed images. A simple filtering algorithm was developed to remove the “noisy images”. CanopyCAM measured CC (CCCanopyCAM) were compared with CC obtained from a LI-COR Plant Canopy Analyzer (CCLAI). The average error between CCCanopyCAM and CCLAI was 2.3 %, and RMSE and R2 were 2.95 % and 0.99, respectively. In addition, maximum CC (CCmax) and duration of the maximum CC (tmax_canopy) were identified at each installation location using the generalized reduced gradient (CRG) algorithm with nonlinear optimization. An improvement of correlation was found between dry bean yield and combination of CCmax and tmax_canopy (R2 = 0.77, Adjusted R2 = 0.62) as compared to yield versus CCmax (R2 = 0.58) or yield versus tmax_canopy (R2 = 0.45) only. This edge-computing, IoT enabled CanopyCAM, provided accurate and continuous CC readings for dry edible beans which could be used by growers and researchers for different purposes.

Characterizing canopy cover with ICESat-2: A case study of southern forests in Texas and Alabama, USA

The acquisition of elevation measurements from NASA's Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) and availability of airborne lidar data over the US present an exceptional opportunity to understand vegetation structural estimates provided by ICESat-2. Although a critical forest biophysical attribute, canopy cover is not yet reported from ICESat-2. Thus, there is a need to better understand the application of ICESat-2 for providing canopy cover information. The overall goal of this study was to investigate methods to derive canopy cover and characterize the predictive capability, with ICESat-2. Given availability of dedicated vegetation products (ATL08) and custom noise filtering algorithms developed for ICESat-2 photon data, multiple datasets were evaluated in this study. With focus on two study sites, the Sam Houston National Forest (SHNF) in south-east Texas and the Solon Dixon Forestry Education Center (SDFEC) in southern Alabama, specific objectives were to: (1) Evaluate equations for estimating canopy cover using custom-processed geolocated photon data, ATL03, and ICESat-2's vegetation product data, ATL08, (2) Compare ICESat-2-derived estimates of canopy cover with airborne lidar canopy cover, and (3) Evaluate a modeling-based approach to improve predictions of canopy cover by leveraging available canopy metrics from ATL08 and custom-processed datasets. A total of six (6) potential measures of canopy cover were investigated in this study, based on 100-m ATL08 segments: (i) percentage of photons above 2 m, (ii) percentage of photons above 4.6 m, (iii) percent of canopy (inside canopy) and top-of-canopy photons of total canopy and ground photons, (iv) percent of canopy of total ground and canopy photons, (v) percent of top-of-canopy of total top-of-canopy and ground photons, and (vi) mean of (iii), (iv) and (v). While strongest correlations were produced from canopy cover computed with (ii) (r between 0.72 and 0.84), similar relationships were indicated from using (iii) and (v) (r values between 0.70 and 0.79). Considering a suite of available canopy parameters, the best R2 and RMSE values from canopy cover estimation models were produced from the use of custom-processed ICESat-2-derived metrics (R2 and RMSE values of 0.77 and 13%). Findings from this study highlight methods for estimating canopy cover with ICESat-2 that may be suitable to a range of cover types, in addition to vegetation settings where cut-offs based on tree heights (e.g., 2 m and 4.6 m) are not applicable. With multiple approaches to characterizing canopy cover, results serve to better understand the applicability of ICESat-2 as a data source for canopy cover information and ultimately inform ongoing efforts for deriving an updated gridded product.

Development of a geomorphic monitoring strategy for stage 0 restoration in the South Fork McKenzie River, Oregon, USA

AbstractThe South Fork McKenzie River (SFMR) in western Oregon, USA hosts one of the largest Stage 0 stream restoration projects implemented to date. Stage 0 refers to a multichannel planform with strong hydrologic connectivity to the adjacent floodplain and surface–subsurface connectivity. Stage 0 restoration was implemented on a 900‐m‐long reach of the SFMR by re‐grading the channel and floodplain using 65,000 m3 of sediment to raise the channel bed. Thousands of large logs were added and the ends of some logs were buried in the sediment to provide foundations for future log jams. Our primary objective is to present a monitoring protocol based on randomly located sampling plots. We also analyze results from 2 years of data collection since project implementation. Within each plot, we measured canopy cover, wood volume, flow depth and velocity, organic cover (area covered by coarse and fine organic material), and substrate grain size. We used intracluster correlation coefficients and variance of measured variables to assess heterogeneity at three spatial scales: within plots, between adjacent plots, and across the entire site. Here, we evaluate changes in the first 2 years after restoration (i.e., not pre‐ vs. post‐restoration). We hypothesized that heterogeneity within a plot would decrease as the plot adjusted to local‐scale hydraulics and sediment and particulate material transport. We hypothesized that heterogeneity would increase between adjacent plots and across the entire site. We found that spatial heterogeneity of geomorphic variables decreased within plots. Heterogeneity of organic cover, sediment size, and flow depth increased between adjacent plots, although other variables did not change. Site‐scale heterogeneity decreased for all variables except organic cover and substrate. We interpret the observed geomorphic responses to reflect decreased longitudinal connectivity and increased lateral and vertical connectivity at the restoration site.

Pollination service delivery is complex: Urban garden crop yields are best explained by local canopy cover and garden scale plant species richness

Abstract Pollination is an important ecosystem service to agriculture, however, the factors influencing pollination in urban food gardens are poorly understood. We investigated how features within urban environments, including floral resources and canopy cover, impacted (a) flower visitation and seed production of a model plant (Brassica rapa) and (b) total yields of produce from urban gardens in Sydney, Australia. Floral visitation to model plants was dominated by a few common species, with honeybees responsible for 53.4% of visitation and native stingless bees and two hoverfly species accounting for another 37.1% of visitors; 28 other species were collectively responsible for less than 10% of visitation. While insect pollinators were abundant, the relationship between floral visitation and model plant reproductive output was weak and not always positive. The pollination success of model plants was only marginally impacted by environmental features, while pollinator visitation had a small negative impact. However, total crop yields across urban gardens were strongly influenced by local canopy cover and garden scale plant species richness. Synthesis and applications. The factors influencing pollination service delivery to urban gardens are complex; pollinator visitation, richness and/or floral resource availability may not always reflect positive, plant reproductive outcomes. Yields of garden crops, whether pollinator dependent or not, were more strongly influenced by surrounding environmental variables, including broad scale measures of canopy cover and local plant diversity, than pollination‐related metrics. This implies policies to support pollinator richness will likely differ to those supporting crop yields in urban gardens and will require assessment and management of external environmental factors.

Coupling Remote Sensing Data and AquaCrop Model for Simulation of Winter Wheat Growth under Rainfed and Irrigated Conditions in a Mediterranean Environment

The coupling of remote sensing technology and crop growth models represents a promising approach to support crop yield prediction and irrigation management. In this study, five vegetation indices were derived from the Copernicus-Sentinel 2 satellite to investigate their performance monitoring winter wheat growth in a Mediterranean environment in Lebanon’s Bekaa Valley. Among those indices, the fraction of canopy cover was integrated into the AquaCrop model to simulate biomass and yield of wheat grown under rainfed conditions and fully irrigated regimes. The experiment was conducted during three consecutive growing seasons (from 2017 to 2019), characterized by different precipitation patterns. The AquaCrop model was calibrated and validated for different water regimes, and its performance was tested when coupled with remote sensing canopy cover. The results showed a good fit between measured canopy cover and Leaf Area Index (LAI) data and those derived from Sentinel 2 images. The R2 coefficient was 0.79 for canopy cover and 0.77 for LAI. Moreover, the regressions were fitted to relate biomass with Sentinel 2 vegetation indices. In descending order of R2, the indices were ranked: Fractional Vegetation Cover (FVC), LAI, the fraction of Absorbed Photosynthetically Active Radiation (fAPAR), the Normalized Difference Vegetation Index (NDVI), and the Enhanced Vegetation Index (EVI). Notably, FVC and LAI were highly correlated with biomass. The results of the AquaCrop calibration showed that the modeling efficiency values, NSE, were 0.99 for well-watered treatments and 0.95 for rainfed conditions, confirming the goodness of fit between measured and simulated values. The validation results confirmed that the simulated yield varied from 2.59 to 5.36 t ha−1, while the measured yield varied from 3.08 to 5.63 t ha−1 for full irrigation and rainfed treatments. After integrating the canopy cover into AquaCrop, the % of deviation of simulated and measured variables was reduced. The Root Mean Square Error (RMSE) for yield ranged between 0.08 and 0.69 t ha−1 before coupling and between 0.04 and 0.42 t ha−1 after integration. This result confirmed that the presented integration framework represents a promising method to improve the prediction of wheat crop growth in Mediterranean areas. Further studies are needed before being applied on a larger scale.

Within‐corridor heterogeneity is more important than corridor design for maintaining butterfly functional and taxonomic diversity

Abstract Local and landscape features influence the spatial distribution of biodiversity. At the landscape scale, heterogeneity has compositional and configurational components that can be measured at different resolutions of conservation planning. We assess the importance of local and landscape features measured at different planning resolutions for maintaining taxonomic and functional butterfly diversity. In remnant conservation corridors that dissect alien tree plantations, butterflies were sampled along a gradient of successional stages, from open and recently burned grasslands to closed‐canopy woody habitats. Changes in local vegetation structure were represented by measures of canopy cover. At the landscape scale, compositional and configurational heterogeneity were measured at two resolutions: a simple binary classification of suitable habitat versus unsuitable transformed habitat, and a classification that accounts for the different successional stages within corridors (i.e. within‐corridor heterogeneity). Butterfly assemblages were characterized using taxonomic (Simpson's diversity index) and functional indices (Rao's quadratic entropy). The relationship between environmental filters and species traits was further assessed using multivariate modelling. Taxonomic diversity did not reflect functional diversity, as the latter showed differential trait specific responses to local vegetation structure. For both functional and taxonomic diversity, local and landscape variables representing within‐corridor heterogeneity were more important than landscape variables measured at the coarser planning resolution. Butterfly diversity showed a negative relationship with increasing edge density between grassy and woody habitats. Synthesis and applications. To promote the taxonomic and functional dimensions of biodiversity, management should aim to maintain both open‐ and closed‐canopy habitats within corridors. These habitats should be contiguous, and corridors as wide as possible, to increase the patch size of contiguous habitat while reducing the proportion of edge.

Shade-Grown Coffee in Colombia Benefits Soil Hydraulic Conductivity

Secondary tropical forests and coffee agroforestry systems contain fewer trees than native forests but can positively impact soil hydrological functions, such as water infiltration compared to the pasture land that they replace. However, for both land uses it remains in how far the soil hydraulic characteristics are comparable to that of native forest. Therefore, we investigated the saturated hydraulic conductivity (Ks) and some hydrophysical soil attributes in four land-use types: (i) a shade-grown coffee; (ii) a natural regenerated forest 15 years ago; (iii) a pasture; and (iv) a reference forest, in the municipality of La Jagua de Ibirico, César department, Colombia. We determined historical land use and conducted soil sampling, using the Beerkan method to determine the Ks values. We also measured canopy cover, vegetation height, diameter at breast height and total number of trees in the forest covers. Our results indicate that Ks values were similar for the coffee and the reference forest, reflecting the positive effect of trees on soil hydrological functioning in agroforestry systems. Our results suggest that 15 years of forest regeneration after land abandonment in Sub-Andean Forest, can improve the soil hydraulic attributes. Additionally, soil water repellency was observed for the reference forest soil.

Estimating Forest Canopy Cover by Multiscale Remote Sensing in Northeast Jiangxi, China

This research was focused on estimation of tree canopy cover (CC) by multiscale remote sensing in south China. The key aim is to establish the relationship between CC and woody NDVI (NDVIW) or to build a CC-NDVIW model taking northeast Jiangxi as an example. Based on field CC measurements, this research used Google Earth as a complementary source to measure CC. In total, 63 sample plots of CC were created, among which 45 were applied for modeling and the remaining 18 were employed for verification. In order to ascertain the ratio R of NDVIW to the satellite observed NDVI, a 20-year time-series MODIS NDVI dataset was utilized for decomposition to obtain the NDVIW component, and then the ratio R was calculated with the equation R = (NDVIW/NDVI) *100%, respectively, for forest (CC > 60%), medium woodland (CC = 25–60%) and sparse woodland (CC 1–25%). Landsat TM and OLI images that had been orthorectified by the provider USGS were atmospherically corrected using the COST model and used to derive NDVIL. R was multiplied for the NDVIL image to extract the woody NDVI (NDVIWL) from Landsat data for each of these plots. The 45 plots of CC data were linearly fitted to the NDVIWL, and a model with CC = 103.843 NDVIW + 6.157 (R2 = 0.881) was obtained. This equation was applied to predict CC at the 18 verification plots and a good agreement was found (R2 = 0.897). This validated CC-NDVIW model was further applied to the woody NDVI of forest, medium woodland and sparse woodland derived from Landsat data for regional CC estimation. An independent group of 24 measured plots was utilized for validation of the results, and an accuracy of 83.0% was obtained. Thence, the developed model has high predictivity and is suitable for large-scale estimation of CC using high-resolution data.

Restoration plantings of non-pioneer tree species in open fields, young secondary forests, and rubber plantations in Bahia, Brazil

The Atlantic forest of Brazil is a biodiversity hotspot that retains less than 12% of its original area. In this biome, non-pioneer tree species with limited dispersal are the most impacted by recent habitat loss and fragmentation. As attempts to establish non-pioneer tree species in pastures and agricultural fields in the Atlantic forest have failed, restoration strategies that consider planting these species in other habitats, such as the understory of secondary forests and tree plantations may be more successful. To test this, five native non-pioneer tree species (Sloanea obtusifolia, Garcinia macrophylla, Copaifera lucens, Symphonia globulifera, and Pouteria reticulata) were planted in complete randomized blocks in April 2009, with four site replications and four treatments (open fields, open fields with pioneers, young secondary forests, and rubber plantations). Survival, height, and dbh (diameter at the breast height) were measured every year for six years. We also measured canopy cover and soil physical and chemical parameters. Six years after planting, we found similar survival of non-pioneer tree species in open fields, young secondary forests and within rubber plantations. However, height and dbh growth were, in general, significantly higher in open fields. Planting pioneer species in mixture with non-pioneers did not improve the growth of non-pioneers. We also found differences among species, with Copaifera lucens showing higher survival and growth than the other species. Even though enriched young secondary forests and rubber plantations could be important contributors to biodiversity conservation, higher growth rates of non-pioneer species in open fields have the benefit of accelerating the recovery of wildlife activity in restoration plantings.

Her Majesty's Desert Throne: The Ecology of Queen Butterfly Oviposition on Mojave Milkweed Host Plants.

Butterfly–host plant relationships can inform our understanding of ecological and trophic interactions that contribute to ecosystem function, resiliency, and services. The ecology of danaid–milkweed (Apocynaceae) host plant interactions has been studied in several biomes but is neglected in deserts. Our objective was to determine effects of plant traits, seasonality, and landscape-level host plant availability on selection of Mojave milkweed (Asclepias nyctaginifolia A. Gray) by ovipositing monarch butterflies (Danaus plexippus plexippus) and queen butterflies (Danaus gilippus thersippus) in the Californian Mojave Desert. We surveyed all known Mojave milkweed locations in the Ivanpah Valley, California (n = 419) during early, mid-, and late spring in 2017. For each survey, we counted monarch and queen butterfly eggs on each Mojave milkweed plant. We also measured canopy cover, height, volume, and reproductive stage of each Mojave milkweed plant. We counted a total of 276 queen butterfly eggs and zero monarch butterfly eggs on Mojave milkweed host plants. We determined that count of queen butterfly eggs significantly increased with increasing Mojave milkweed canopy cover. Additionally, count of queen butterfly eggs was: (1) greater on adult Mojave milkweed plants than on juvenile and seedling plants and greater on juvenile Mojave milkweed plants than on seedling plants; and (2) greater during early spring than mid-spring—we recorded no eggs during late spring. Based on aggregation indices, queen butterfly eggs occurred on Mojave milkweed plants in a nonrandom, clustered pattern throughout the Ivanpah Valley. We provide the first evidence of trophic interactions between queen butterflies and Mojave milkweed at multiple spatial scales in the Mojave Desert, suggesting that conservation and management practices for both species should be implemented concurrently. Given its role as an herbivore, pollinator and prey, the queen butterfly may serve as a model organism for understanding effects of anthropogenic disturbance (e.g., solar energy development) on “bottom-up” and trophic interactions among soils, plants and animals in desert ecosystems.

Thinning enhances stool resistance to an extreme drought in a Mediterranean Quercus ilex L. coppice: insights for adaptation

Mediterranean holm oak forests are subjected to chronic seasonal droughts coinciding with the warmest conditions during the summer. Importantly, climate change projections support increased frequency and intensity of droughts in the future. In order to evaluate whether thinning practices can be used as efficient adaptation strategies to climate change, we measured canopy cover as a surrogate of defoliation in 49 Quercus ilex L. subsp. ilex individual genets subjected to experimental ramet thinning. Canopy cover measurements were obtained before, during and after the exceptionally intense and prolonged drought of 2016. Our results show that intensity of basal area reduction determined higher resistance, but not resilience, to this intense-long drought period. Independently of shoot thinning intensity, every holm oak exhibited crown defoliation during the dry period but most of them recovered their leaves the next spring. Holm oaks subjected to thinning with more than over 50% of basal area removed, were less vulnerable to severe drought, however, and supported more leaves later on. Additionally, our results highlight the positive influence of canopy shading from neighbouring trees preventing defoliation after drought events. A greater canopy shading per stool led to a higher resistance in holm oak trees exposed to severe drought. These findings suggest that, in order to cope with concomitant climate change and to optimize genet-level stress tolerance, coppice selection should be done by removing shoots with fewer leaves per unit area, preferably those unexposed to shading from neighbouring trees until a reduction around 50% of basal area per stool is achieved.