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Protecting or restoring habitat connectivity in landscapes undergoing rapid environmental change requires multiple conservation and restoration strategies. These strategies have different risk profiles, costs, and require various types of technical expertise to conduct. This diversity in landscape context and strategic approach requires more nuance and flexibility than traditional connectivity conservation plans have supported. We present a novel, spatially‐explicit framework for developing connectivity conservation priorities and strategies based on Omniscape, an adaptation of Circuitscape, a common tool for mapping habitat connectivity. Using California (USA) as a case study, we mapped structural connectivity and developed a classification of connectivity conservation and restoration categories across the gradient of land use intensities, as well as by land ownership. The most constrained areas with highly concentrated flow (movement potential) make up 3% of the state and occur primarily on private lands. Conversely, intact areas with diffuse flow that indicate multiple connectivity options cover 55% of the state, including the majority of the desert and mountain ecoregions. This "strategy mapping" approach can be used to identify priority areas for conservation investment and suites of potential implementation mechanisms and partners, which in turn may improve the efficiency and effectiveness of connectivity conservation in this era of global change. [ABSTRACT FROM AUTHOR]

Although natural habitats are being lost globally, the extent and fate of natural habitats on private lands in the United States have not been quantified at the resolution relevant for conservation planning. Here we provide information on the locations and risk of loss of areas of natural vegetation cover (NVC) on private lands across the northwestern United States to motivate discussion on needs and opportunities to slow their loss. Specific questions were as follows: (1) Where are the remaining areas of NVC on private lands? (2) Which regions and communities have had the highest loss rates of NVC? and (3) In which socioecological settings is NVC at greatest risk of loss? NVC location and change were mapped using two land cover classifications during 2001–2011, the most recent period with available data. Associations between NVC loss and market proximity, demographic, infrastructure, natural amenity, and climate factors were used to model probability of NVC loss in 2011. We found that NVC covered 64% of the study area in 2011. During 2001–2011, 2.5% of the area of NVC in 2001 was converted to development and croplands. Rates of loss were as high as 12% in some regions (e.g., western Washington). Housing development accounted for the majority of this NVC loss, increasing by 8% while croplands increased by 5%. Conversion of NVC for development and crops during 2001–2011 per capita varied 20–40 fold among "city spheres" (urban areas >10,000 people and 40‐min commuting distance). NVC loss was statistically associated with urban fringe development, forest edge vegetation, proximity to highways, public land, and waterbodies and was associated with New West demographic city spheres. Of the NVC on private lands in 2011, 11% was projected to have >20% probability of future loss over the next decade. We conclude that portions of the northwestern United States, one of the last stronghold for extensive natural habitats in the contiguous United States, are rapidly losing NVC to development, particularly in the New West communities that typically have the highest motivation and capacity to conserve them. [ABSTRACT FROM AUTHOR]

Data on the extent, patterns, and trends of human land use are critically important to support global and national priorities for conservation and sustainable development. To inform these issues, we created a series of detailed global datasets for 1990, 2000, 2010, and 2015 to evaluate temporal and spatial trends of land use modification of terrestrial lands (excluding Antarctica). We found that the expansion and increase of human modification between 1990 and 2015 resulted in 1.6 M km² of natural land lost. The percent change between 1990 and 2015 was 15.2 % or 0.61 % annually - roughly 178 km² daily. Over the pause of a deep breath, over 8 football pitches of natural lands were lost (- 17 per minute). Worrisomely, we found that the global rate of loss has increased over the past 25 years. The greatest loss of natural lands from 1990-2015 occurred in Oceania, Asia, and Europe, and the biomes with the greatest loss were mangroves, tropical & subtropical moist broadleaf forests, and tropical & subtropical dry broadleaf forests. We also created a contemporary (- 2017) estimate of human modification that included additional stressors and found that globally 14.5 % or 18.5 M km² of lands have been completely modified - an area greater than Russia. Our novel datasets are detailed (0.09 km² resolution), temporal (1990-2015), recent (- 2017), comprehensive (11 change stressors, 14 current), robust (using an established framework and incorporating classification errors and parameter uncertainty), and strongly validated. We believe these datasets will support better understanding of the profound transformation wrought by human activities and provide foundational data on the amounts, patterns, and rates of change to inform planning and decision making for environmental mitigation, protection, restoration, and adaptation to climate change. The datasets generated from this work are available at https://doi.org/10.5061/dryad.n5tb2rbs1 (Theobald et al., 2020). [ABSTRACT FROM AUTHOR]

Recent guidance on environmental modeling and global land-cover validation stresses the need for a probability-based design. Additionally, spatial balance has also been recommended as it ensures more efficient sampling, which is particularly relevant for understanding land use change. In this paper I describe a global sample design and database called the Global Grid (GG) that has both of these statistical characteristics, as well as being flexible, multi-scale, and globally comprehensive. The GG is intended to facilitate collaborative science and monitoring of land changes among local, regional, and national groups of scientists and citizens, and it is provided in a variety of open source formats to promote collaborative and citizen science. Since the GG sample grid is provided at multiple scales and is globally comprehensive, it provides a universal, readily-available sample. It also supports uneven probability sample designs through filtering sample locations by user-defined strata. The GG is not appropriate for use at locations above ±85° because the shape and topological distortion of quadrants becomes extreme near the poles. Additionally, the file sizes of the GG datasets are very large at fine scale (resolution ~600 m × 600 m) and require a 64-bit integer representation. [ABSTRACT FROM AUTHOR]

Land cover maps reasonably depict areas that are strongly converted by human activities, but typically are unable to resolve low-density but widespread development patterns. Data products specifically designed to resolve land uses complement land cover datasets and likely improve our ability to understand the extent and complexity of human modification. Methods for developing a comprehensive land use classification system are described, and a map of land use for the conterminous United States is presented to reveal what we are doing on the land. The comprehensive, detailed and high-resolution dataset was developed through spatial analysis of nearly two-dozen publicly-available, national spatial datasets – predominately based on census housing, employment, and infrastructure, as well as land cover from satellite imagery. This effort resulted in 79 land use classes that fit within five main land use groups: built-up, production, recreation, conservation, and water. Key findings from this study are that built-up areas occupy 13.6% of mainland US, but that the majority of this occurs as low-density exurban/rural residential (9.1% of the US), while more intensive built-up land uses occupy 4.5%. For every acre of urban and suburban residential land, there are 0.13 commercial, 0.07 industrial, 0.48 institutional, and 0.29 acres of interstates/highways. This database can be used to address a variety of natural resource applications, and I provide three examples here: an entropy index of the diversity of land uses for smart-growth planning, a power-law scaling of metropolitan area population to developed footprint, and identifying potential conflict areas by delineating the urban interface. [ABSTRACT FROM AUTHOR]

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Abstract: Assessing threats to protected areas is a critical step to ensure effective resource conservation and to leverage future conservation actions. It is equally important to assess external and internal threats because human activities both in and around a protected area can impair the area''s ecological goals or impart important ecological benefits to adjacent lands. We applied a threat framework that accounts for both local and surrounding threats to identify areas in the conterminous United States that provide or offer opportunities for ecological conservation. We find that, of the lands that are least threatened locally, 49% have some existing formal protection, but that more than 35% of the existing protected portfolio is at risk from external threats. However, over 20% of currently unprotected lands provide potential opportunities for conservation. Of the area identified with highest potential conservation opportunities, over 50% is on private lands, highlighting the need to engage owners of private land in conservation. There is greater potential for large, buffered, core conservation areas in the West, but other areas require networks of stepping-stone conservation islands. We summarize our results by ecoregions and within 40 national parks, identifying areas that are notably unthreatened (Colorado Plateau and Northwestern Glaciated Plains ecoregions and Great Basin and Canyonlands parks) versus those that may require more intense management actions (Eastern Great Lakes and Hudson Lowlands and Southeastern Wisconsin Till Plains ecoregions and Cuyahoga Valley and Great Smoky Mountains parks). Finally, we discuss how the approach could be applied and improved for finer-grained, local assessments. [Copyright &y& Elsevier]

Quantifying landscape dynamics is a central goal of landscape ecology, and numerous metrics have been developed to measure the influence of human activities on natural landscapes. Composite scores that characterize human modifications to landscapes have gained widespread use. A parsimonious alternative is to estimate the proportion of a cover type (i.e. natural) within a spatial neighborhood to characterize both compositional and structural aspects of natural landscapes. Here I extend this approach into a multi-scale, integrated metric and apply it to national datasets on land cover, housing density, road existence, and highway traffic volume to measure the dynamics of natural landscapes in the conterminous US. Roughly one-third of the conterminous US (2.6 million km2) in 1992 was classified as human-dominated. By 2001 this expanded by 80,800 km2, and forecasted residential growth by 2030 will potentially lead to an additional loss of up to 92,200 km2. Wetland cover types were particularly affected. The natural landscapes metric developed here provides a simple, robust measure of landscape dynamics that has a direct physical interpretation related to proportion of natural habitat affected at a location, represents landscapes as a gradient of conditions rather predicated on patch/matrix definition, and measures the spatial context of natural areas. [ABSTRACT FROM AUTHOR]

In this paper, we estimated impervious surface from United States census housing density data sets for the conterminous United States to examine the distribution and extent of impaired watersheds, and to estimate the risk to watersheds from development in the near future. We used regression tree methods to relate estimates of current housing density to the 2001 National Land Cover Database (NLCD) percent urban imperviousness. As of 2000, we estimate 83,749 km2 of impervious surface (IS) cover across the United States (about 9.6% lower than the NLCD). We estimate that IS cover will expand to 114,070 km2 by 2030. About 7% of eight-digit Hydrologic Unit Code (HUC) watersheds (3.6% of the conterminous United States) were stressed or degraded (>5% IS) in 2000, and we estimated that this will increase to nearly double to 8.5% of watersheds by 2030 (6.3% of area). We explored the subtle differences of fine-grain pattern for different urban land use types by comparing our national estimates of IS to those developed for the Chesapeake Bay watershed. We also found important nonlinear affects of watershed scale and aggregation, whereby estimates of IS could differ by roughly ten-fold. [ABSTRACT FROM AUTHOR]

The article presents a study of land use by human beings. It highlights its influence on the prevalence of chronic wasting disease (CWD) in the population of mule deers (Odocoileus hemionus). It also outlines the alteration of landscape, urbanization and epidemiology of the disease as well as model selection.

Throughout the American West, private open lands are being converted to a human-dominated residential landscape. This conversion is the foremost threat to wildlife habitat. Pitkin County, Colorado, home to the City of Aspen, is an internationally known example of this land use change. In this article, we describe patterns of development in Pitkin County from the late 1800s through the 1990s. Using field data, we estimate the overall loss of songbird habitat due to two types of residential development, clustered and dispersed, and then examine the types of vegetation most affected. From these data we demonstrate that clustered development, in contrast to dispersed development, can reduce the impact of exurban residential development on wildlife habitat. [ABSTRACT FROM AUTHOR]

Describes a general approach to forecasting spatially explicit rural land-use change at the landscape scale. Two ways to model the spatial distribution of land-use change; Cellular automata theory; Basis of traditional urban and rural development models.

Discusses a general approach to estimate the cumulative effects of land use change on wildlife habitat using Summit County, Colorado, as a case study. Effects of development on wildlife and wildlife habitat; Development density and pattern; Density-disturbance functional relationship; Fragmentation of habitat caused by development.

A variety of land-use and land-cover (LULC) models operating at scales from local to global have been developed in recent years, including a number of models that provide spatially explicit, multi-class LULC projections for the conterminous United States. This diversity of modeling approaches raises the question: how consistent are their projections of future land use? We compared projections from six LULC modeling applications for the United States and assessed quantitative, spatial, and conceptual inconsistencies. Each set of projections provided multiple scenarios covering a period from roughly 2000 to 2050. Given the unique spatial, thematic, and temporal characteristics of each set of projections, individual projections were aggregated to a common set of basic, generalized LULC classes (i.e., cropland, pasture, forest, range, and urban) and summarized at the county level across the conterminous United States. We found very little agreement in projected future LULC trends and patterns among the different models. Variability among scenarios for a given model was generally lower than variability among different models, in terms of both trends in the amounts of basic LULC classes and their projected spatial patterns. Even when different models assessed the same purported scenario, model projections varied substantially. Projections of agricultural trends were often far above the maximum historical amounts, raising concerns about the realism of the projections. Comparisons among models were hindered by major discrepancies in categorical definitions, and suggest a need for standardization of historical LULC data sources. To capture a broader range of uncertainties, ensemble modeling approaches are also recommended. However, the vast inconsistencies among LULC models raise questions about the theoretical and conceptual underpinnings of current modeling approaches. Given the substantial effects that land-use change can have on ecological and societal processes, there is a need for improvement in LULC theory and modeling capabilities to improve acceptance and use of regional- to national-scale LULC projections for the United States and elsewhere. [ABSTRACT FROM AUTHOR]