Effects of Changing Climate Extremes and Vegetation Phenology on Wildlife Associated with Grasslands in the Southwestern United States

Assessments of the potential responses of animal species to climate change often rely on correlations between long-term average temperature or precipitation and species\u27 occurrence or abundance. Such assessments do not account for the potential predictive capacity of either climate extremes and variability or the indirect effects of climate as mediated by plant phenology. By contrast, we projected responses of wildlife in desert grasslands of the southwestern United States to future climate means, extremes, and variability and changes in the timing and magnitude of primary productivity. We used historical climate data and remotely sensed phenology metrics to develop predictive models of climate-phenology relations and to project phenology given anticipated future climate. We used wildlife survey data to develop models of wildlife-climate and wildlife-phenology relations. Then, on the basis of the modeled relations between climate and phenology variables, and expectations of future climate change, we projected the occurrence or density of four species of management interest associated with these grasslands: Gambel\u27s Quail (Callipepla gambelii), Scaled Quail (Callipepla squamat), Gunnison\u27s prairie dog (Cynomys gunnisoni), and American pronghorn (Antilocapra americana). Our results illustrated that climate extremes and plant phenology may contribute more to projecting wildlife responses to climate change than climate means. Monthly climate extremes and phenology variables were influential predictors of population measures of all four species. For three species, models that included climate extremes as predictors outperformed models that did not include extremes. The most important predictors, and months in which the predictors were most relevant to wildlife occurrence or density, varied among species. Our results highlighted that spatial and temporal variability in climate, phenology, and population measures may limit the utility of climate averages-based bioclimatic niche models for informing wildlife management actions, and may suggest priorities for sustained data collection and continued analysis

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

Analysis of small-diameter wood supply in northern Arizona - Final report

Forest management to restore fire-adapted ponderosa pine ecosystems is a central priority of the Southwestern Region of the USDA Forest Service. Appropriately-scaled businesses are apt to play a key role in achieving this goal by harvesting, processing and selling wood products, thereby reducing treatment costs and providing economic opportunities. The manner in which treatments occur across northern Arizona, with its multiple jurisdictions and land management areas, is of vital concern to a diversity of stakeholder groups. To identify a level of forest thinning treatments and potential wood supply from restoration byproducts, a 20-member working group representing environmental non-governmental organizations (NGOs), private forest industries, local government, the Ecological Restoration Institute at Northern Arizona University (NAU), and state and federal land and resource management agencies was assembled. A series of seven workshops supported by Forest Ecosystem Restoration Analysis (ForestERA; NAU) staff were designed to consolidate geographic data and other spatial information and to synthesize potential treatment scenarios for a 2.4 million acre analysis area south of the Grand Canyon and across the Mogollon Plateau. A total of 94% of the analysis area is on National Forest lands. ForestERA developed up-to-date remote sensing-based forest structure data layers to inform the development of treatment scenarios, and to estimate wood volume in three tree diameter classes of 16" diameter at breast height (dbh, 4.5' above base). For the purposes of this report, the group selected a 16" dbh threshold due to its common use within the analysis landscape as a break point differentiating "small" and "large" diameter trees in the ponderosa pine forest type. The focus of this study was on small-diameter trees, although wood supply estimates include some trees >16" dbh where their removal was required to meet desired post-treatment conditions.4 There was no concurrence within the group that trees over 16" dbh should be cut and removed from areas outside community protection management areas (CPMAs)..

Masked Bobwhite Recovery: The Need for a Multifaceted Approach

Masked bobwhite (Colinus virginianus ridgwayi) is a critically endangered quail historically found in the Sonoran grasslands of southern Arizona, USA and Sonora, Mexico. Native populations of masked bobwhite may already be extinct in the wild, but captive populations exist in the United States at G. M. Sutton Avian Research Center (Oklahoma, USA), Buenos Aires National Wildlife Refuge (Arizona, USA), and various zoos. The 47,000-hectare Buenos Aires National Wildlife Refuge, located in south-central Arizona, was established primarily for reintroduction of this bird. Recovery efforts within the refuge boundary in the 1980s and 1990s were initially successful but suffered debilitating setbacks that ultimately resulted in failure. Substantial releases were suspended in 2005. Improved habitat restoration efforts and promising conditioning and release techniques led to the belief that reintroductions could again be attempted and successful. In 2016–2017 plans were developed to increase captive propagation and reinitiate release efforts. Releases began in 2018. Over-winter survival of birds released in 2018–2019 was encouraging, and reproduction of wild birds was documented in 2019. An existing base of wild birds established from these releases could help masked bobwhite populations recover in the state. Habitat restoration, better methods of rearing, release, and conditioning, and improved production from captive facilities also inspire hope that a full recovery of the species in Arizona is possible

Mapping Species Composition of Forests and Tree Plantations in Northeastern Costa Rica with an Integration of Hyperspectral and Multitemporal Landsat Imagery

An efficient means to map tree plantations is needed to detect tropical land use change and evaluate reforestation projects. To analyze recent tree plantation expansion in northeastern Costa Rica, we examined the potential of combining moderate-resolution hyperspectral imagery (2005 HyMap mosaic) with multitemporal, multispectral data (Landsat) to accurately classify (1) general forest types and (2) tree plantations by species composition. Following a linear discriminant analysis to reduce data dimensionality, we compared four Random Forest classification models: hyperspectral data (HD) alone; HD plus interannual spectral metrics; HD plus a multitemporal forest regrowth classification; and all three models combined. The fourth, combined model achieved overall accuracy of 88.5%. Adding multitemporal data significantly improved classification accuracy (p less than 0.0001) of all forest types, although the effect on tree plantation accuracy was modest. The hyperspectral data alone classified six species of tree plantations with 75% to 93% producer's accuracy; adding multitemporal spectral data increased accuracy only for two species with dense canopies. Non-native tree species had higher classification accuracy overall and made up the majority of tree plantations in this landscape. Our results indicate that combining occasionally acquired hyperspectral data with widely available multitemporal satellite imagery enhances mapping and monitoring of reforestation in tropical landscapes

Land cover dynamics following a deforestation ban in northern Costa Rica

Forest protection policies potentially reduce deforestation and re-direct agricultural expansion to already-cleared areas. Using satellite imagery, we assessed whether deforestation for conversion to pasture and cropland decreased in the lowlands of northern Costa Rica following the 1996 ban on forest clearing, despite a tripling of area under pineapple cultivation in the last decade. We observed that following the ban, mature forest loss decreased from 2.2% to 1.2% per year, and the proportion of pineapple and other export-oriented cropland derived from mature forest declined from 16.4% to 1.9%. The post-ban expansion of pineapples and other crops largely replaced pasture, exotic and native tree plantations, and secondary forests. Overall, there was a small net gain in forest cover due to a shifting mosaic of regrowth and clearing in pastures, but cropland expansion decreased reforestation rates. We conclude that forest protection efforts in northern Costa Rica have likely slowed mature forest loss and succeeded in re-directing expansion of cropland to areas outside mature forest. Our results suggest that deforestation bans may protect mature forests better than older forest regrowth and may restrict clearing for large-scale crops more effectively than clearing for pasture

Remote sensing and machine learning to improve aerial wildlife population surveys

Technological and methodological advances in remote sensing and machine learning have created new opportunities for advancing wildlife surveys. We assembled a Community of Practice (CoP) to capitalize on these developments to explore improvements to the efficiency and effectiveness of aerial wildlife monitoring from a management perspective. The core objective of the CoP is to organize the development and testing of remote sensing and machine learning methods to improve aerial wildlife population surveys that support management decisions. Beginning in 2020, the CoP collaboratively identified the natural resource management decisions that are informed by wildlife survey data with a focus on waterbirds and marine wildlife. We surveyed our membership to establish 1) what management decisions they were using wildlife count data to inform; 2) how these count data were collected prior to the advent of remote sensing/machine learning methods; 3) the impetus for transitioning to a remote sensing/machine learning methodological framework; and 4) the challenges practitioners face in transitioning to this framework. This paper documents these findings and identifies research priorities for moving toward operational remote sensing-based wildlife surveys in service of wildlife management

A geospatial data integration framework for mapping and monitoring tropical landscape diversity in Costa Rica's San Juan-La Selva Biological Corridor

Ilus. Tab. Bib.Landcover change has substantially reduced the amount of tropical rain forests since the 1950s. Little is known about the extent of remaining forest types. A multivariate analysis of 144 forest plots across Costa Rica's San Juan-La Selva Biological Corridor resulted in eight floristically defined old-growth forest categories. Spectral separability was tested between categories using Landsat TM bands and vegetation indices for old-growth types, palm swamps, tree plantations and regrowth. Image filtering and NDVI increased spectral separability among categories by 30 per cent. Separability tests resulted in seven well-discriminated forest categories. Factors driving forest beta-diversity are not well quantified for wet tropical environments. We examined the relationship between rain forest composition and environmental variation for a 3000 km2 area in northeastern Costa Rica

Le Petit Troyen : journal démocratique régional ["puis" journal quotidien de la démocratie de l'Est "puis" grand quotidien de la Champagne]

09 novembre 19001900/11/09 (A20,N6505).Appartient à l’ensemble documentaire : ChArdenn

Correction: Sesnie et al. In-Situ and Remote Sensing Platforms for Mapping Fine-Fuels and Fuel-Types in Sonoran Semi-Desert Grasslands. <i>Remote Sens.</i> 2018, <i>10</i>, 1358

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