An iterative and targeted sampling design informed by habitat suitability models for detecting focal plant species over extensive areas

Prioritizing areas for management of non-native invasive plants is critical, as invasive plants can negatively impact plant community structure. Extensive and multi-jurisdictional inventories are essential to prioritize actions aimed at mitigating the impact of invasions and changes in disturbance regimes. However, previous work devoted little effort to devising sampling methods sufficient to assess the scope of multi-jurisdictional invasion over extensive areas. Here we describe a large-scale sampling design that used species occurrence data, habitat suitability models, and iterative and targeted sampling efforts to sample five species and satisfy two key management objectives: 1) detecting non-native invasive plants across previously unsampled gradients, and 2) characterizing the distribution of non-native invasive plants at landscape to regional scales. Habitat suitability models of five species were based on occurrence records and predictor variables derived from topography, precipitation, and remotely sensed data. We stratified and established field sampling locations according to predicted habitat suitability and phenological, substrate, and logistical constraints. Across previously unvisited areas, we detected at least one of our focal species on 77% of plots. In turn, we used detections from 2011 to improve habitat suitability models and sampling efforts in 2012, as well as additional spatial constraints to increase detections. These modifications resulted in a 96% detection rate at plots. The range of habitat suitability values that identified highly and less suitable habitats and their environmental conditions corresponded to field detections with mixed levels of agreement. Our study demonstrated that an iterative and targeted sampling framework can address sampling bias, reduce time costs, and increase detections. Other studies can extend the sampling framework to develop methods in other ecosystems to provide detection data. The sampling methods implemented here provide a meaningful tool when understanding the potential distribution and habitat of species over multi-jurisdictional and extensive areas is needed for achieving management objectives

Drivers of Plant Population Dynamics in Three Arid to Subhumid Ecosystems

Understanding the relative importance of density-dependent and density-independent factors in driving population dynamics is one of the oldest challenges in ecology, and may play a critical role in predicting the effects of climate change on populations. We used long-term observational data to describe patterns in plant population regulation for 57 forb and grass species from three different ecosystems (arid desert grassland, semiarid sagebrush steppe, and subhumid mixed-grass prairie). Using a hierarchical partitioning approach, we (i) quantified the relative influence of conspecific density, heterospecific composition, and climate on temporal variation in population growth rates, and (ii) asked how the relative importance of these drivers depends on site aridity, species growth form and life expectancy, and abundance and spatial patterns. The data from one of the sites in this analysis are presented in one of the chapters of this thesis. We found that density-dependence had the strongest effect on species. Climate often had a significant effect, but its strength depended on growth form. Community composition rarely explained significant variation in growth rates. The relative importance of density, composition, and climate did not vary among sites, but was related to species\u27 life histories: compared to forbs, grasses were more sensitive to climate drivers. Abundance and spatial clustering were negatively correlated with the importance of density dependence, suggesting that local rarity is a consequence of self-limitation. Our results show that interspecific interactions play a weaker role than intraspecific interactions and climate variability in regulating plant populations. Forecasting the impacts of climate change on populations may require understanding how changes in climate variables will affect the strength of density-dependence, especially for rare species

Chimera: A Multi-Task Recurrent Convolutional Neural Network for Forest Classification and Structural Estimation

More consistent and current estimates of forest land cover type and forest structural metrics are needed to guide national policies on forest management, carbon sequestration, and ecosystem health. In recent years, the increased availability of high-resolution (<30 m) imagery and advancements in machine learning algorithms have opened up a new opportunity to fuse multiple datasets of varying spatial, spectral, and temporal resolutions. Here, we present a new model, based on a deep learning architecture, that performs both classification and regression concurrently, thereby consolidating what was previously several independent tasks and models into one stream. The model, a multi-task recurrent convolutional neural network that we call the Chimera, integrates varying resolution, freely available aerial and satellite imagery, as well as relevant environmental factors (e.g., climate, terrain) to simultaneously classify five forest cover types (‘conifer’, ‘deciduous’, ‘mixed’, ‘dead’, ‘none’ (non-forest)) and to estimate four continuous forest structure metrics (above ground biomass, quadratic mean diameter, basal area, canopy cover). We demonstrate the performance of our approach by training an ensemble of Chimera models on 9967 georeferenced (true locations) Forest Inventory and Analysis field plots from the USDA Forest Service within California and Nevada. Classification diagnostics for the Chimera ensemble on an independent test set produces an overall average precision, recall, and F1-score of 0.92, 0.92, and 0.92. Class-wise F1-scores were high for ‘none’ (0.99) and ‘conifer’ (0.85) cover classes, and moderate for the ‘mixed’ (0.74) class samples. This demonstrates a strong ability to discriminate locations with and without trees. Regression diagnostics on the test set indicate very high accuracy for ensembled estimates of above ground biomass ( R 2 = 0.84 , RMSE = 37.28 Mg/ha), quadratic mean diameter ( R 2 = 0.81 , RMSE = 3.74 inches), basal area ( R 2 = 0.87 , RMSE = 25.88 ft 2 /ac), and canopy cover ( R 2 = 0.89 , RMSE = 8.01 percent). Comparative analysis of the Chimera ensemble versus support vector machine and random forest approaches demonstrates increased performance over both methods. Future implementations of the Chimera ensemble on a distributed computing platform could provide continuous, annual estimates of forest structure for other forested landscapes at regional or national scales

Nested pixel plot design used to sample plants in the Sonoran Desert of Arizona.

<p>A) Plot were co-registered with the resolution and location of a MODIS image pixel, and included five nested subplots, each co-registered with the resolution and location of a Landsat TM image pixel. Target and alternate (used when the target subplot was inaccessible) subplots are in red and gray, respectively. B) Within each subplot, five point-intercept transects were established to measure attributes of species composition at 5 m intervals.</p

Proportion of sampled subplots in 2011 across habitat suitability ranges for each species.

<p>X-axis shows average habitat suitability predicted by five models for each focal species. Y-axis indicates the proportion of subplots that fell within a given range of predicted habitat suitability. We sampled all focal species in habitats that ranged from low to very high suitability to increase chances of detecting unknown populations or unknown areas of species distribution.</p

List of environmental variables used in habitat suitability models at cell size = 30 m for stratifying our sampling locations in the Sonoran Desert of Arizona in the 2011 field season.

<p>TM  =  Landsat Thematic Mapper imagery; NDVI  =  Normalized Difference Vegetation Index.</p

Number and percentage of detections of five focal species by plot and subplot sampled in the Sonoran Desert of Arizona during our 2011–2012 field seasons.

<p>Number and percentage of detections of five focal species by plot and subplot sampled in the Sonoran Desert of Arizona during our 2011–2012 field seasons.</p

Boundary and land jurisdictions of our study area in the Sonoran Desert of Arizona.

<p>Specific land ownerships highlighted by abbreviations and include: the U.S. Army Yuma Proving Ground (YPG), Barry M. Goldwater Air Force Range (BMGR), Kofa National Wildlife Refuge (KNWR), Cabeza Prieta National Wildlife Refuge (CPNWR), Organ Pipe Cactus National Monument (OPCNM), and the Tohono O'odham Nation (TON).</p

Number of species (black, gray, and white circles) detected in our study area in 2011.

<p>Colored areas show the number of habitat suitability models (Model 4 for winter annuals and Model 5 for <i>Pennisetum</i>) with predicted high habitat suitability (70^th percentile). Darker colors indicate greater spatial overlap of high suitability across species.</p

Achieving conservation targets by jointly addressing climate change and biodiversity loss

Abstract Unprecedented rates of climate change and biodiversity loss have galvanized efforts to expand protected areas (PAs) globally. However, limited spatial overlap between the most important landscapes for mitigating climate change and those with the highest value for biodiversity may impede efforts to simultaneously address both issues through new protections. At the same time, there is a need to understand how lands with high conservation value align with existing patterns of land management, both public and private, which will inform strategies for developing new conservation areas. To address these challenges, we developed three composite indices to identify the highest conservation value lands across the conterminous United States (CONUS) and Alaska, drawing on a suite of key ecological and environmental indicators. Two indices characterize the most important conservation lands for addressing climate change (based on climate accessibility, climate stability, and total carbon storage) and biodiversity (based on species richness, ecological integrity, and ecological connectivity), while a third, combined index simultaneously addresses both conservation challenges. We found that existing PAs in the United States have relatively low overlap with the highest conservation value lands, regardless of the index used (10%–13% in CONUS, 27%–34% in Alaska), suggesting limited effectiveness of current protections but substantial opportunity for expanding conservation into high‐value, unprotected areas. In unprotected landscapes, the highest value lands for addressing climate change generally diverged from those identified as most important for protecting biodiversity (22%–38% overlap, depending on index and geography). Our combined index reconciled these spatial trade‐offs through high overlap with both the climate and biodiversity indices (66%–72%). Of the unprotected high conservation value lands identified by each of our three indices, we found ≥70% are privately managed in CONUS, while 16%–27% are privately managed in Alaska, underscoring the need to engage private landowners and land trusts in efforts to substantially increase the total footprint of conservation lands in the United States. Our findings highlight the importance of balancing climate and biodiversity objectives when identifying new lands for conservation and provide guidance on where to target new protections to simultaneously address both goals. To facilitate planning using the indices, we developed an interactive web application