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

Quantifying ecological variation across jurisdictional boundaries in a management mosaic landscape

Context Large landscapes exhibit natural heterogeneity. Land management can impose additional variation, altering ecosystem patterns. Habitat characteristics may reflect these management factors, potentially resulting in habitat differences that manifest along jurisdictional boundaries. Objectives We characterized the patchwork of habitats across a case study landscape, the Grand Canyon Protected Area-Centered Ecosystem. We asked: how do ecological conditions vary across different types of jurisdictional boundaries on public lands? We hypothesized that differences in fire and grazing, because they respond to differences in management over time, contribute to ecological differences by jurisdiction. Methods We collected plot-scale vegetation and soils data along boundaries between public lands units surrounding the Grand Canyon. We compared locations across boundaries of units managed differently, accounting for vegetation type and elevation differences that pre-date management unit designations. We used generalized mixed effects models to evaluate differences in disturbance and ecology across boundaries. Results Jurisdictions varied in evidence of grazing and fire. After accounting for these differences, some measured vegetation and soil properties also differed among jurisdictions. The greatest differences were between US Forest Service wilderness and Bureau of Land Management units. For most measured variables, US Forest Service non-wilderness units and National Park Service units were intermediate. Conclusions In this study, several ecological properties tracked jurisdictional boundaries, forming a predictable patchwork of habitats. These patterns likely reflect site differences that pre-date jurisdictions as well as those resulting from different management histories. Understanding how ecosystem differences manifest at jurisdictional boundaries can inform resource management, conservation, and cross-boundary collaborations

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

Mapped Quadrats in Sagebrush Steppe: Long-Term Data for Analyzing Demographic Rates and Plant-Plant Interactions

This historical data set consists of 26 permanent 1-m2 quadrats located on sagebrush steppe in eastern Idaho, USA. During most growing seasons from 1923 to 1957, and again in 1973, all individual plants in each quadrat were identified and mapped. This combination of a long time-series with full spatial resolution allows analyses of demographic processes and intra- and interspecific interactions among individual plants. The data provide unique opportunities to test theory about the effect of environmental variation on population and community dynamics and to describe empirical relationships between climate variables and demographic rates. We provide the following data and data formats: (1) the digitized maps in shapefile format; (2) a tabular version of the entire data set (a table with no spatial information except an x,y coordinate for each individual plant record); (3) a species list, containing information on plant growth forms and shapefile geometry type; (4) a record of changes to species names; (5) quadrat information; (6) grazing treatment information; (7) an inventory of the years each quadrat was sampled; (8) monthly precipitation, temperature, and snowfall records; and (9) counts of annuals in the quadrats

Data Paper. Data Paper

<h2>File List</h2><blockquote> <table> <tbody><tr> <td><a href="shapefiles.zip">shapefiles.zip </a></td> <td>--</td> <td>a zipped directory containing every individual shapefile for each year that each quadrat was mapped </td> </tr> <tr> <td> </td> <td> </td> <td> </td> </tr> <tr> <td><a href="allrecords_density.csv">allrecords_density.csv</a></td> <td>--</td> <td>ASCII text, comma separated, 40837 records </td> </tr> <tr> <td> </td> <td> </td> <td> </td> </tr> <tr> <td><a href="allrecords_cover.csv">allrecords_cover.csv</a></td> <td>--</td> <td>ASCII text, comma separated, 80233 records</td> </tr> <tr> <td> </td> <td> </td> <td> </td> </tr> <tr> <td><a href="quad_info.csv">quad_info.csv</a></td> <td>--</td> <td>ASCII text, comma separated, 26 records</td> </tr> <tr> <td> </td> <td> </td> <td> </td> </tr> <tr> <td><a href="grazing_info.csv">grazing_info.csv</a></td> <td>--</td> <td>ASCII text, comma separated, 25 records</td> </tr> <tr> <td> </td> <td> </td> <td> </td> </tr> <tr> <td><a href="quad_inventory.csv">quad_inventory.csv</a></td> <td>--</td> <td>ASCII text, comma separated, 29 records</td> </tr> <tr> <td> </td> <td> </td> <td> </td> </tr> <tr> <td><a href="species_list.csv">species_list.csv</a></td> <td>--</td> <td>ASCII text, comma separated, 97 records</td> </tr> <tr> <td> </td> <td> </td> <td> </td> </tr> <tr> <td><a href="taxonomic_grouping.csv">taxonomic_grouping.csv</a></td> <td>--</td> <td>ASCII text, comma separated, 26 records</td> </tr> <tr> <td> </td> <td> </td> <td> </td> </tr> <tr> <td><a href="monthly_mean_temp.csv">monthly_mean_temp.csv</a></td> <td>--</td> <td>ASCII text, comma separated, 83 records</td> </tr> <tr> <td> </td> <td> </td> <td> </td> </tr> <tr> <td><a href="total_monthly_ppt.csv">total_monthly_ppt.csv</a></td> <td>--</td> <td>ASCII text, comma separated, 83 records</td> </tr> <tr> <td> </td> <td> </td> <td> </td> </tr> <tr> <td><a href="total_monthly_sno.csv">total_monthly_sno.csv</a></td> <td>--</td> <td>ASCII text, comma separated, 84 records</td> </tr> <tr> <td> </td> <td> </td> <td> </td> </tr> <tr> <td><a href="annuals_counts_v3.csv">annuals_counts_v3.csv</a></td> <td>--</td> <td>ASCII text, comma separated, 1361 records</td> </tr> </tbody></table> <p> <a href="shapefiles.zip"></a></p> </blockquote><h2>Description</h2><blockquote> <p>This historical data set consists of 26 permanent 1-m² quadrats located on sagebrush steppe in eastern Idaho, USA. During most growing seasons from 1923 to 1957, and again in 1973, all individual plants in each quadrat were identified and mapped. This combination of a long time-series with full spatial resolution allows analyses of demographic processes and intra- and interspecific interactions among individual plants. The data provide unique opportunities to test theory about the effect of environmental variation on population and community dynamics and to describe empirical relationships between climate variables and demographic rates. We provide the following data and data formats: (1) the digitized maps in shapefile format; (2) a tabular version of the entire data set (a table with no spatial information except an <i>x,y</i> coordinate for each individual plant record); (3) a species list, containing information on plant growth forms and shapefile geometry type; (4) a record of changes to species names; (5) quadrat information; (6) grazing treatment information; (7) an inventory of the years each quadrat was sampled; (8) monthly precipitation, temperature, and snowfall records; and (9) counts of annuals in the quadrats.</p> <p><i>Key words: <i>climate; demography; Geographic Information Systems (GIS); Idaho; plant community; plant population; sagebrush steppe; species interaction</i></i>.</p> </blockquote

Weekly Large Wildfire Probability in Western US Forests andWoodlands, 2005-2017

<div>This dataset is a weekly time-series of images from 2005-2017 that depict the probability of large fire across western US forests and woodlands. Specifically, the images depict the probability that an area on the landscape will burn in a large (i.e., > 405 ha) fire following an ignition event, on the given date. Each product in the dataset is a three-band GeoTIFF image (250-m resolution) in the WGS84 geographic coordinate reference system (EPSG:4326). Names of each product correspond to the image prediction date. Band 1 values are the output of a Random Forest classification algorithm, trained on 10 independent, random samples of small and large wildfires that occurred from 2005-2014, and represent the mean predicted probability of an individual pixel burning in a large fire. Band 1 values range from 0-100 (probability scaled by 100). Band 2 values represent the standard deviation of predicted probability of an individual pixel burning in a large fire, and values also range from 0-100 (standard deviation scaled by 100). Band 3 values indicate the quality of MODIS predictor variables. Multiple MODIS products were used as predictor variables to describe the vegetation and land surface immediately preceding a fire event. Only good quality pixels were retained for model training, but all pixels were retained when creating spatial predictions. Therefore, Band 3 indicates if one of these MODIS predictors had unreliable quality, where</div><div>0 = All MODIS pixels were processed and good quality and 1 = At least one MODIS pixel was not processed or had bad quality. No Data values in each image are set to 255. </div><div><br></div

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 (&lt;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 (&#8216;conifer&#8217;, &#8216;deciduous&#8217;, &#8216;mixed&#8217;, &#8216;dead&#8217;, &#8216;none&#8217; (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 &#8216;none&#8217; (0.99) and &#8216;conifer&#8217; (0.85) cover classes, and moderate for the &#8216;mixed&#8217; (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