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Proactively identifying where land conversion might occur is critical to targeted and effective conservation planning. Previous efforts to map future habitat loss have largely focused on forested systems and have been limited in their consideration of drivers of loss. We developed a 1-km resolution, global map of land conversion pressure from multiple drivers, referred to as the conversion pressure index (CPI). The CPI combines past rates of anthropogenic change, as measured by temporal human modification maps, with suitability maps for potential future expansion by large-scale development. The CPI thus offers a new way to measure a cumulative gradient of anthropogenic pressure as opposed to categorical land cover change. We find that nearly 23% of land across 200 countries have relatively high conversion pressure, potentially impacting over 460 million ha of intact natural lands. We illustrate how this information can be used to identify areas for proactive conservation to avoid future loss and ensure that national commitments under the Kunming-Montreal Global Biodiversity and Paris Agreement Climate Frameworks are upheld. [ABSTRACT FROM AUTHOR]

Context: With underrepresentation of habitats in publicly protected areas, attention has focused on the function of alternative land conservation mechanisms. Private conservation easements (CEs) have proliferated in the United States, yet assessing landscape-level function is confounded by varying extent, resolution, and temporal scale. Objectives: We developed and tested an assessment tool to evaluate interacting spatial, social, and environmental attributes of easements relative to the degree of human modification (HM). We hypothesized that on both private and public conservation properties HM would be lower than on non-conserved parcels, and that for fine-scale features (most CEs), the level of HM would be driven by the variables used to create the coarser scale HM measure. Methods: Variation in HM between private, public, and non-conserved was tested via pairwise parcel sampling. Composition was evaluated using multiple geographic bounds and edge characteristics. We assessed both environmental and social predictors using multinomial logistic regression. Results: Privately conserved lands did not differ significantly from non-conserved lands. Publicly conserved lands had lower HM than both privately conserved and non-conserved lands. Edge contrast was similar between private and matched non-conserved patches. The level of HM was not driven by distance to roads, or by elevation in this mixed-use setting. Conclusions: Variation in tests for differences, land characteristics, and HM variables confirmed the significantly lower HM of publicly protected lands, and opens the question as to naturalness of easements in some contexts. CEs in this location may be representative of the mixed rural-forested landscape instead of more natural land cover. [ABSTRACT FROM AUTHOR]

Context: Projected increases in human population size are expected to increase forest loss and fragmentation in the next century at the expense of forest-dwelling species.Objectives: We estimated landscape carrying capacity (Nk) for Ovenbirds in urban, suburban, exurban, and rural areas for the years 2000 and 2050, and compared changes in Nk with changes in occupancy probability.Methods: Maximum clique analysis, a branch of mathematical graph theory, was used to estimate landscape carrying capacity, the maximum potential number of territories a given landscape is capable of supporting (Nk). We used occupancy probability maps as inputs for calculating Ovenbird Nk in the northeastern USA and a spatially explicit growth model to forecast future development patterns in 2050. We compared occupancy probability with estimates of Nk for urban, suburban, exurban, and rural areas for the years 2000 and 2050.Results: In response to human population growth and development, Ovenbird Nk was predicted to decrease 23% in urban landscapes, 28% in suburban landscapes, 43% in exurban landscapes, and 20% in rural landscapes. These decreases far exceeded decreases in mean occupancy probabilities that ranged between 2 and 5% across the same development categories. Thus, small decreases in occupancy probability between 2000 and 2050 translated to much larger decreases in Nk.Conclusions: For the first time, our study compares occupancy probability with a species population metric, Nk, to assess the impact of future development. Maximum clique analysis is a tool that can be used to estimate Nk and inform landscape management and communication with stakeholders. [ABSTRACT FROM AUTHOR]

Increasingly, natural resources agencies and organizations are using measures of ecological integrity to monitor and evaluate the status and condition of their landscapes, and numerous methods have been developed to map the pattern of human activities. In this paper I apply formal methods from decision theory to develop a transparent ecological indicator of landscape integrity. I developed a parsimonious set of stressors using an existing framework to minimize redundancy and overlap, mapping each variable as an individual data layer with values from 0 to 1.0, and then combined them using an “increasive” function called fuzzy sum. A novel detailed land use dataset is used to generate empirical measures of the degree of human modification to map important stressors such as land use, land cover, and presence, use, and distance from roads. I applied this general framework to the US and found that the overall average degree of human modification was 0.375. Regional variation was fairly predictable, but aggregation of these raw values into terrestrial or watershed units resulted in large differences at local to regional scales. I discuss three uses of these data by land managers to manage protected areas within a dynamic landscape context. This approach generates an internally-valid model that has a direct, empirical, and physical basis to estimate the degree of human modification. [ABSTRACT FROM AUTHOR]

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]

Many organisms persist in fragmented habitat where movement between patches is essential for long-term demographic and genetic stability. In the absence of direct observation of movement, connectivity or isolation metrics are useful to characterize potential patch-level connectivity. However, multiple metrics exist at varying levels of complexity, and empirical data on species distribution are rarely used to compare performance of metrics. We compared 12 connectivity metrics of varying degrees of complexity to determine which metric best predicts the distribution of prairie dog colonies along an urban gradient of 385 isolated habitat patches in Denver, Colorado, USA. We found that a modified version of the incidence function model including area-weighting of patches and a cost-weighted distance surface best predicted occupancy, where we assumed roads were fairly impermeable to movement, and low-lying drainages provided dispersal corridors. We also found this result to be robust to a range of cost weight parameters. Our results suggest that metrics should incorporate both patch area and the composition of the surrounding matrix. These results provide guidance for improved landscape habitat modeling in fragmented landscapes and can help identify target habitat for conservation and management of prairie dogs in urban systems. [ABSTRACT FROM AUTHOR]

Geostatistical models are typically based on symmetric straight-line distance, which fails to represent the spatial configuration, connectivity, directionality, and relative position of sites in a stream network. Freshwater ecologists have explored spatial patterns in stream networks using hydrologic distance measures and new geostatistical methodologies have recently been developed that enable directional hydrologic distance measures to be considered. The purpose of this study was to quantify patterns of spatial correlation in stream water chemistry using three distance measures: straight-line distance, symmetric hydrologic distance, and weighted asymmetric hydrologic distance. We used a dataset collected in Maryland, USA to develop both general linear models and geostatistical models (based on the three distance measures) for acid neutralizing capacity, conductivity, pH, nitrate, sulfate, temperature, dissolved oxygen, and dissolved organic carbon. The spatial AICC methodology allowed us to fit the autocorrelation and covariate parameters simultaneously and to select the model with the most support in the data. We used the universal kriging algorithm to generate geostatistical model predictions. We found that spatial correlation exists in stream chemistry data at a relatively coarse scale and that geostatistical models consistently improved the accuracy of model predictions. More than one distance measure performed well for most chemical response variables, but straight-line distance appears to be the most suitable distance measure for regional geostatistical modeling. It may be necessary to develop new survey designs that more fully capture spatial correlation at a variety of scales to improve the use of weighted asymmetric hydrologic distance measures in regional geostatistical models. [ABSTRACT FROM AUTHOR]

Focuses on role of biological information in local land-use decision making. Changes affecting fundamental ecological processes; Decisions governing land-use changes; Challenges faced by several landscape ecologists.

Biotic and abiotic factors are increasingly acknowledged to synergistically shape broad-scale species distributions. However, the relative importance of biotic and abiotic factors in predicting species distributions is unclear. In particular, biotic factors, such as predation and vegetation, including those resulting from anthropogenic land-use change, are underrepresented in species distribution modeling, but could improve model predictions. Using generalized linear models and model selection techniques, we used 129 estimates of population density of wild pigs (Sus scrofa) from 5 continents to evaluate the relative importance, magnitude, and direction of biotic and abiotic factors in predicting population density of an invasive large mammal with a global distribution. Incorporating diverse biotic factors, including agriculture, vegetation cover, and large carnivore richness, into species distribution modeling substantially improved model fit and predictions. Abiotic factors, including precipitation and potential evapotranspiration, were also important predictors. The predictive map of population density revealed wide-ranging potential for an invasive large mammal to expand its distribution globally. This information can be used to proactively create conservation/management plans to control future invasions. Our study demonstrates that the ongoing paradigm shift, which recognizes that both biotic and abiotic factors shape species distributions across broad scales, can be advanced by incorporating diverse biotic factors. [ABSTRACT FROM AUTHOR]