Global priorities for conservation across multiple dimensions of mammalian diversity

Conservation priorities that are based on species distribution, endemism, and vulnerability may underrepresent biologically unique species as well as their functional roles and evolutionary histories. To ensure that priorities are biologically comprehensive, multiple dimensions of diversity must be considered. Further, understanding how the different dimensions relate to one another spatially is important for conservation prioritization, but the relationship remains poorly understood. Here, we use spatial conservation planning to (i) identify and compare priority regions for global mammal conservation across three key dimensions of biodiversity-taxonomic, phylogenetic, and traits-and (ii) determine the overlap of these regions with the locations of threatened species and existing protected areas. We show that priority areas for mammal conservation exhibit low overlap across the three dimensions, highlighting the need for an integrative approach for biodiversity conservation. Additionally, currently protected areas poorly represent the three dimensions of mammalian biodiversity. We identify areas of high conservation priority among and across the dimensions that should receive special attention for expanding the global protected area network. These high-priority areas, combined with areas of high priority for other taxonomic groups and with social, economic, and political considerations, provide a biological foundation for future conservation planning efforts

Demographic trends, the wildland-urban interface, and wildfire management

Testimony of Roger B. Hammer, Assistant Professor, Department of Sociology, Sustainable Rural Communities Initiative, Oregon State University before the House Interior, Environment & Related Agencies Appropriations Subcommittee, Oversight Hearing on Wildfire Management, February 12, 200

Mapping agricultural land abandonment from spatial and temporal segmentation of Landsat time series

© 2018 Elsevier Inc. Agricultural land abandonment is a common land-use change, making the accurate mapping of both location and timing when agricultural land abandonment occurred important to understand its environmental and social outcomes. However, it is challenging to distinguish agricultural abandonment from transitional classes such as fallow land at high spatial resolutions due to the complexity of change process. To date, no robust approach exists to detect when agricultural land abandonment occurred based on 30-m Landsat images. Our goal here was to develop a new approach to detect the extent and the exact timing of agricultural land abandonment using spatial and temporal segments derived from Landsat time series. We tested our approach for one Landsat footprint in the Caucasus, covering parts of Russia and Georgia, where agricultural land abandonment is widespread. First, we generated agricultural land image objects from multi-date Landsat imagery using a multi-resolution segmentation approach. Second, we estimated the probability for each object that agricultural land was used each year based on Landsat temporal-spectral metrics and a random forest model. Third, we applied temporal segmentation of the resulting agricultural land probability time series to identify change classes and detect when abandonment occurred. We found that our approach was able to accurately separate agricultural abandonment from active agricultural lands, fallow land, and re-cultivation. Our spatial and temporal segmentation approach captured the changes at the object level well (overall mapping accuracy = 97 ± 1%), and performed substantially better than pixel-level change detection (overall accuracy = 82 ± 3%). We found strong spatial and temporal variations in agricultural land abandonment rates in our study area, likely a consequence of regional wars after the collapse of the Soviet Union. In summary, the combination of spatial and temporal segmentation approaches of time-series is a robust method to track agricultural land abandonment and may be relevant for other land-use changes as well

The conundrum of agenda-driven science in conservation

Conservation biology is a value-laden discipline predicated on conserving biodiversity (Soulé 1985), a mission that does not always sit easily with objective science (Lackey 2007; Pielke 2007; Scott et al. 2007). While some encourage scientists to be responsible advocates for conservation (Garrard et al. 2016), others worry that objectivity in conservation research may suffer (Lackey 2007). At this time, we believe advocacy by scientists is essential for environmental conservation and, indeed, humanity. It is difficult to envision the state of our environment had scientists failed to encourage policy makers and the public to address emerging conservation problems. Nevertheless, conservation scientists must avoid misusing the scientific process to promote specific conservation outcomes (Wilholt 2009); doing so erodes the credibility of science and can produce undesirable consequences (Thomas 1992; Mills 2000; Rohr and McCoy 2010). We consider intentionally engaging in activities outside of professional norms to promote desired outcomes, as part of either the production or dissemination of science, to constitute “agenda-driven science”. The issue of advocacy-related bias in conservation science merits renewed discussion because conservation conflicts in an increasingly polarized world might tempt some to engage in agenda-driven science to “win” a conflict

Spatial and temporal residential density patterns from 1940 to 2000 in and around the Northern Forest of the Northeastern United States

Over the past 60 years, housing growth has outpaced population growth in the United States. Conservationists are concerned about the far-reaching environmental impacts of housing development, particularly in rural areas. We use clustering analysis to examine the pattern and distribution of housing development since 1940 in and around the Northern Forest, a heavily forested region with high amenity and recreation use in the Northeastern United States. We find that both proximity to urban areas and an abundance of natural amenities are associated with housing growth at the neighborhood level in this region. In the 1970s, counterurbanization led to higher rates of growth across rural areas. The Northern Forest now has extensive interface between forest vegetation and residential development, which has the potential to profoundly alter the ecological and social benefits of these forests.Keywords: Housing density, Amenity growth, Sprawl, Housing growth, Cluster analysis, Northern ForestKeywords: Housing density, Amenity growth, Sprawl, Housing growth, Cluster analysis, Northern Fores

Wildfire ignition-distribution modelling: a comparative study in the Huron-Manistee National Forest, Michigan, USA

Abstract. Wildfire ignition distribution models are powerful tools for predicting the probability of ignitions across broad areas, and identifying their drivers. Several approaches have been used for ignition-distribution modelling, yet the performance of different model types has not been compared. This is unfortunate, given that conceptually similar speciesdistribution models exhibit pronounced differences among model types. Therefore, our goal was to compare the predictive performance, variable importance and the spatial patterns of predicted ignition-probabilities of three ignition-distribution model types: one parametric, statistical model (Generalised Linear Models, GLM) and two machine-learning algorithms (Random Forests and Maximum Entropy, Maxent). We parameterised the models using 16 years of ignitions data and environmental data for the Huron-Manistee National Forest in Michigan, USA. Random Forests and Maxent had slightly better prediction accuracies than did GLM, but model fit was similar for all three. Variables related to human population and development were the best predictors of wildfire ignition locations in all models (although variable rankings differed slightly), along with elevation. However, despite similar model performance and variables, the map of ignition probabilities generated by Maxent was markedly different from those of the two other models. We thus suggest that when accurate predictions are desired, the outcomes of different model types should be compared, or alternatively combined, to produce ensemble predictions

Using structure locations as a basis for mapping the wildland urban interface

The wildland urban interface (WUI) delineates the areas where wildland fire hazard most directly impacts human communities and threatens lives and property, and where houses exert the strongest influence on the natural environment. Housing data are a major problem for WUI mapping. When housing data are zonal, the concept of a WUI neighborhood can be captured easily in a density measure, but variations in zone (census block) size and shape introduce bias. Other housing data are points, so zonal issues are avoided, but the neighborhood character of the WUI is lost if houses are evaluated individually. Our goal was to develop a consistent method to map the WUI that is able to determine where neighborhoods (or clusters of houses) exist, using just housing location and wildland fuel data. We used structure and vegetation maps and a moving window analysis, with various window sizes representing neighborhood sizes, to calculate the neighborhood density of both houses and wildland vegetation. Mapping four distinct areas (in WI, MI, CA and CO) the method resulted in amounts of WUI comparable to those of zonal mapping, but with greater precision. We conclude that this hybrid method is a useful alternative to zonal mapping from the neighborhood to the landscape scale, and results in maps that are better suited to operational fire management (e.g., fuels reduction) needs, while maintaining consistency with conceptual and U.S. policy-specific WUI definitions.Keywords: Structure locations, Wildland urban interface, MappingKeywords: Structure locations, Wildland urban interface, Mappin

Wildland-urban interface maps vary with purpose and context

Maps of the Wildland-Urban Interface (WUI) are both pragmatic policy tools and powerful visual images with broad appeal. While the growing number of WUI maps serve the same general purpose, this paper demonstrates that WUI maps based on the same data can differ in ways related to their purpose, and discusses the use of ancillary data in modifying census data. A comparison of two methods suggests GIS methods used for mapping the WUI be tailored to specific questions. Dasymetric mapping to improve census data precision is useful but dependent on data quality, and land ownership datasets suffer problems that argue for caution in their use. No single mapping approach is “best,” and analysts must be clear about the problem addressed, the methods used, and data quality. These considerations should apply to any analysis, but are especially important to analyses of the WUI upon which public-sector decisions will be made

Human and biophysical influences on fire occurrence in the United States

National-scale analyses of fire occurrence are needed to prioritize fire policy and management activities across the United States. However, the drivers of national-scale patterns of fire occurrence are not well understood, and how the relative importance of human or biophysical factors varies across the country is unclear. Our research goal was to model the drivers of fire occurrence within ecoregions across the conterminous United States. We used generalized linear models to compare the relative influence of human, vegetation, climate, and topographic variables on fire occurrence in the United States, as measured by MODIS active fire detections collected between 2000 and 2006. We constructed models for all fires and for large fires only and generated predictive maps to quantify fire occurrence probabilities. Areas with high fire occurrence probabilities were widespread in the Southeast, and localized in the Mountain West, particularly in southern California, Arizona, and New Mexico. Probabilities for large-fire occurrence were generally lower, but hot spots existed in the western and southcentral United States The probability of fire occurrence is a critical component of fire risk assessments, in addition to vegetation type, fire behavior, and the values at risk. Many of the hot spots we identified have extensive development in the wildland–urban interface and are near large metropolitan areas. Our results demonstrated that human variables were important predictors of both all fires and large fires and frequently exhibited nonlinear relationships. However, vegetation, climate, and topography were also significant variables in most ecoregions. If recent housing growth trends and fire occurrence patterns continue, these areas will continue to challenge policies and management efforts seeking to balance the risks generated by wildfires with the ecological benefits of fire.Keywords: MODIS active fires, Wildfire risk, Wildland–urban interface, Fire occurrenc

Systematic Temporal Patterns in the Relationship Between Housing Development and Forest Bird Biodiversity

As people encroach increasingly on natural areas, one question is how this affects avian biodiversity. The answer to this is partly scale-dependent. At broad scales, human populations and biodiversity concentrate in the same areas and are positively associated, but at local scales people and biodiversity are negatively associated with biodiversity. We investigated whether there is also a systematic temporal trend in the relationship between bird biodiversity and housing development. We used linear regression to examine associations between forest bird species richness and housing growth in the conterminous United States over 30 years. Our data sources were the North American Breeding Bird Survey and the 2000 decennial U.S. Census. In the 9 largest forested ecoregions, housing density increased continually over time. Across the conterminous United States, the association between bird species richness and housing density was positive for virtually all guilds except ground nesting birds. We found a systematic trajectory of declining bird species richness as housing increased through time. In more recently developed ecoregions, where housing density was still low, the association with bird species richness was neutral or positive. In ecoregions that were developed earlier and where housing density was highest, the association of housing density with bird species richness for most guilds was negative and grew stronger with advancing decades. We propose that in general the relationship between human settlement and biodiversity over time unfolds as a 2-phase process. The first phase is apparently innocuous; associations are positive due to coincidence of low-density housing with high biodiversity. The second phase is highly detrimental to biodiversity, and increases in housing density are associated with biodiversity losses. The long-term effect on biodiversity depends on the final housing density. This general pattern can help unify our understanding of the relationship of human encroachment and biodiversity response.Keywords: Animals, Forest, Housing, Woodland, Functional groups, Temporal pattern, North America, Bird