Post-Fire Seed Dispersal of a Wind-Dispersed Shrub Declined with Distance to Seed Source, yet had High Levels of Unexplained Variation

Plant-population recovery across large disturbance areas is often seed-limited. An understanding of seed dispersal patterns is fundamental for determining natural-regeneration potential. However, forecasting seed dispersal rates across heterogeneous landscapes remains a challenge. Our objectives were to determine (i) the landscape patterning of post-disturbance seed dispersal, and underlying sources of variation and the scale at which they operate, and (ii) how the natural seed dispersal patterns relate to a seed augmentation strategy. Vertical seed trapping experiments were replicated across 2 years and five burned and/or managed landscapes in sagebrush steppe. Multi-scale sampling and hierarchical Bayesian models were used to determine the scale of spatial variation in seed dispersal. We then integrated an empirical and mechanistic dispersal kernel for wind-dispersed species to project rates of seed dispersal and compared natural seed arrival to typical post-fire aerial seeding rates. Seeds were captured across the range of tested dispersal distances, up to a maximum distance of 26 m from seed-source plants, although dispersal to the furthest traps was variable. Seed dispersal was better explained by transect heterogeneity than by patch or site heterogeneity (transects were nested within patch within site). The number of seeds captured varied from a modelled mean of ~13 m−2 adjacent to patches of seed-producing plants, to nearly none at 10 m from patches, standardized over a 49-day period. Maximum seed dispersal distances on average were estimated to be 16 m according to a novel modelling approach using a ‘latent’ variable for dispersal distance based on seed trapping heights. Surprisingly, statistical representation of wind did not improve model fit and seed rain was not related to the large variation in total available seed of adjacent patches. The models predicted severe seed limitations were likely on typical burned areas, especially compared to the mean 95–250 seeds per m2 that previous literature suggested were required to generate sagebrush recovery. More broadly, our Bayesian data fusion approach could be applied to other cases that require quantitative estimates of long-distance seed dispersal across heterogeneous landscapes

Detecting Gold Mining Impacts on Insect Biodiversity in a Tropical Mining Frontier with SmallSat Imagery

Gold mining is a major driver of Amazonian forest loss and degradation. As mining activity encroaches on primary forest in remote and inaccessible areas, satellite imagery provides crucial data for monitoring mining-related deforestation. High-resolution imagery, in particular, has shown promise for detecting artisanal gold mining at the forest frontier. An important next step will be to establish relationships between satellite-derived land cover change and biodiversity impacts of gold mining. In this study, we set out to detect artisanal gold mining using high-resolution imagery and relate mining land cover to insects, a taxonomic group that accounts for the majority of faunal biodiversity in tropical forests. We applied an object-based image analysis (OBIA) to classify mined areas in an Indigenous territory in Guyana, using PlanetScope imagery with ~3.7 m resolution. We complemented our OBIA with field surveys of insect family presence or absence in field plots (n = 105) that captured a wide range of mining disturbances. Our OBIA was able to identify mined objects with high accuracy (\u3e90% balanced accuracy). Field plots with a higher proportion of OBIA-derived mine cover had significantly lower insect family richness. The effects of mine cover on individual insect taxa were highly variable. Insect groups that respond strongly to mining disturbance could potentially serve as bioindicators for monitoring ecosystem health during and after gold mining. With the advent of global partnerships that provide universal access to PlanetScope imagery for tropical forest monitoring, our approach represents a low-cost and rapid way to assess the biodiversity impacts of gold mining in remote landscapes

Lidar and Deep Learning Reveal Forest Structural Controls on Snowpack

Forest structure has a strong relationship with abiotic components of the environment. For example, canopy morphology controls snow depth through interception and modifies incoming thermal radiation. In turn, snow water availability affects forest growth, carbon sequestration, and nutrient cycling. We investigated how structural diversity and topography affect snow depth patterns across scales. The study site, Grand Mesa, Colorado, is representative of many areas worldwide where declining snowpack and its consequences for forest ecosystems are increasingly an environmental concern. On the basis of a convolution neural network model (R2 of 0.64; root mean squared error of 0.13 m), we found that forest structural and topographic metrics from airborne light detection and ranging (lidar) at fine scales significantly influence snow depth during the accumulation season. Moreover, complex vertically arranged foliage intercepts more snow and results in shallower snow depths below the canopy. Assessing forest structural controls on snow distribution and depth will aid efforts to improve understanding of the ecological and hydrological impacts of changing snow patterns

Socio-Ecological Interactions Promote Outbreaks of a Harmful Invasive Plant in an Urban Landscape

Urban landscapes often harbour organisms that harm people and threaten native biodiversity. These landscapes are characterized by differences in socioeconomic context, habitat suitability and patch connectedness. Identifying which spatial differences enable outbreaks of pests, pathogens and invasive species will improve targeted control efforts. We tested hypotheses to explain the distribution and demography of puncturevine Tribulus terrestris, a human-dispersed invasive plant in Boise, a city in the western United States. We hypothesized an increase in puncturevine infestations near low-valued properties with a high proportion of bare ground, the species\u27 preferred microhabitat, that are well connected on the urban road network. To test these hypotheses, we collected data on the abundance, emergence and persistence of reproductive plants in transects spanning \u3e100 km of our study city. We then used hierarchical Bayesian models to evaluate the impacts of spatial covariates on puncturevine distribution and demography. Bare ground cover consistently increased abundance, emergence and persistence of puncturevine, indicating the overarching importance of suitable establishment sites for this invasive species. Property value had the strongest impact on puncturevine abundance and was the most important main effect in the model for puncturevine emergence. In both models, lower-valued properties had a higher risk of puncturevine occurrence. The effects of road network connectivity depended on bare ground cover, with the highest predicted abundance and emergence of puncturevine in patches with low connectivity on the road network and high bare ground cover. Understanding these relationships will require data that can disentangle seed dispersal from establishment limitations

Data from: Divergent rates of change between tree cover types in a tropical pastoral region

Context: Forest cover change analyses have revealed net forest gain in many tropical regions. While most analyses have focused solely on forest cover, trees outside forests are vital components of landscape integrity. Quantifying regional-scale patterns of tree cover change, including non-forest trees, could benefit forest and landscape restoration (FLR) efforts. Objectives: We analyzed tree cover change in Southwestern Panama to quantify: 1) patterns of change from 1998-2014, 2) differences in rates of change between forest and non-forest classes, and 3) the relative importance of social-ecological predictors of tree cover change between classes. Methods: We digitized tree cover classes, including dispersed trees, live fences, riparian forest, and forest, in very high resolution images from 1998-2014. We then applied hurdle models to relate social-ecological predictors to the probability and amount of tree cover gain. Results: All tree cover classes increased in extent, but gains were highly variable between classes. Non-forest tree cover accounted for 21% of tree cover gains, while riparian trees constituted 31% of forest cover gains. Drivers of tree cover change varied widely between classes, with opposite impacts of some social-ecological predictors on non-forest and forest cover. Conclusions: We demonstrate that key drivers of forest cover change, including topography, road distance and historical forest cover, do not explain rates of non-forest tree cover change. Consequently, predictions from medium-resolution forest cover change analyses may not apply to finer-scale patterns of tree cover. We highlight the opportunity for FLR projects to target tree cover classes adapted to local social and ecological conditions

Digitized tree cover 1998

This shapefile consists of polygons representing tree cover in 150 x 150 m plots. Columns in the attribute table indicate the year during which the aerial image used to digitize tree cover was acquired (1998), the tree cover type (Dispersed, Fallow, Riparian, Fence, and Forest), and the area of each polygon (in square meters)

Sampling squares

This shapefile represents sampling units for measuring tree cover. Each sampling unit is a 150 x 150 m square. Squares were stratified to properties using a cadastral data set, such that one square represents one property. Within the sampling unit, all tree cover was digitized and categorized into tree cover types. Areas without tree cover were not digitized