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Chaparral shrubs in southern California may be vulnerable to frequent fire and severe drought. Drought may diminish postfire recovery or worsen impact of short-interval fires. Field-based studies have not shown the extent and magnitude of drought effects on recovery, which may vary among chaparral types and climatic zones. We tracked regional patterns of shrub cover based on June-solstice Landsat Normalized Difference Vegetation Index series, compared between the periods 1984-1989 and 2014-2018. High spatial resolution ortho-imagery was used to map shrub cover in distributed sample plots, to empirically constrain the Landsat-based estimates of mature-stage lateral canopy recovery. We evaluated precipitation, climatic water deficit (CWD), and Palmer Drought Severity Index in summer and wet seasons preceding and following fire, as regional predictors of recovery in 982 locations between the Pacific Coast and inland deserts. Wet-season CWD was the strongest drought-metric predictor of recovery, contributing 34-43 % of explanatory power in multivariate regressions (R2 =0.16-0.42). Limited recovery linked to drought was most prevalent in transmontane chamise chaparral; impacts were minor in montane areas, and in mixed and montane chaparral types. Elevation was correlated negatively to recovery of transmontane chamise; this may imply acute drought sensitivity in resprouts which predominate seedlings at higher elevations. Landsat Visible Atmospherically Resistant Index (sensitive to live-fuel moisture) was evaluated as a landscape-scale predictor of recovery and explained the greatest amount of variance in a multivariate regression (R2 = 0.53). We find that drought severity was more closely related to recovery differences among twice-burned sites than was fire-return interval. Summarily, drought has a major role in long-term shrub cover reduction within xeric chaparral ecotones bounding the Mojave Desert and Colorado Desert, likely in tandem with other global change stressors.

Regrowth after fire is critical to the persistence of chaparral shrub communities in southern California, which has been subject to frequent fire events in recent decades. Fires that recur at short intervals of 10 years or less have been considered an inhibitor of recovery and the major cause of 'community type-conversion' in chaparral, primarily based on studies of small extents and limited time periods. However, recent sub-regional investigations based on remote sensing suggest that short-interval fire (SIF) does not have ubiquitous impact on postfire chaparral recovery. A region-wide analysis including a greater spatial extent and time period is needed to better understand SIF impact on chaparral. This study evaluates patterns of postfire recovery across southern California, based on temporal trajectories of Normalized Difference Vegetation Index (NDVI) derived from June-solstice Landsat image series covering the period 1984-2018. High spatial resolution aerial images were used to calibrate Landsat NDVI trajectory-based estimates of change in fractional shrub cover (dFSC) for 294 stands. The objectives of this study were (1) to assess effects of time between fires and number of burns on recovery, using stand-aggregate samples (n = 294) and paired single- and multiple-burn sample plots (n = 528), and (2) to explain recovery variations among predominant single-burn locations based on shrub community type, climate, soils, and terrain. Stand-aggregate samples showed a significant but weak effect of SIF on recovery (p

Regional maps of vegetation structure are necessary for delineating species habitats and for supporting conservation and ecological analyses. A systematic approach that can discriminate a wide range of meaningful and detailed vegetation classes is still lacking for neotropical savannas. Detailed vegetation mapping of savannas is challenged by seasonal vegetation dynamics and substantial heterogeneity in vegetation structure and composition, but fine spatial resolution imagery (

In mountain landscapes, surface temperatures vary over short distances due to interacting influences of topography and overstory vegetation on local energy and water balances. At two study landscapes in the Sierra Nevada of California, characterized by foothill oak savanna at 276–481 m elevation and montane conifer forest at 1977–2135 m, we deployed 288 near-surface (5 cm above the surface) temperature sensors to sample site-scale (30 m) temperature variation related to hillslope orientation and vegetation structure and microsite-scale (2–10 m) variation related to microtopography and tree overstory. Daily near-surface maximum and minimum temperatures for the 2013 calendar year were related to topographic factors and vegetation overstory characterized using small footprint LiDAR imagery acquired by the National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP). At both landscapes we recorded large site and microsite spatial variation in daily maximum temperatures, and less absolute variation in daily minimum temperatures. Generalized boosted regression trees were estimated to measure the influence of tree canopy density, understory solar radiation, cold-air drainage and pooling, ground cover and microtopography on daily maximum and minimum temperatures at site and microsite scales. Site-scale models based on indices of understory solar radiation and landscape position explained an average of 61–65% of daily variation in maximum temperature; site-scale models based on tree canopy density and landscape position explained 65–83% of variation in minimum temperatures. Models explained

The field of systematic conservation planning has grown substantially, with hundreds of publications in the peer-reviewed literature and numerous applications to regional conservation planning globally. However, the extent to which systematic conservation plans have influenced management is unclear. This paper analyses factors that facilitate the transition from assessment to implementation in conservation planning, in order to help integrate assessment and implementation into a seamless process. We propose a framework for designing implementation strategies, taking into account three critical planning aspects: processes, inputs, and context. Our review identified sixteen processes, which we broadly grouped into four themes and eight inputs. We illustrate how the framework can be used to inform context-dependent implementation strategies, using the process of 'engagement' as an example. The example application includes both lessons learned from successfully implemented plans across the engagement spectrum, and highlights key barriers that can hinder attempts to bridge the assessment-implementation gap.

In this study, we automate tree species classification and mapping using field-based training data, high spatial resolution airborne hyperspectral imagery, and a convolutional neural network classifier (CNN). We tested our methods by identifying seven dominant trees species as well as dead standing trees in a mixed-conifer forest in the Southern Sierra Nevada Mountains, CA (USA) using training, validation, and testing datasets composed of spatially-explicit transects and plots sampled across a single strip of imaging spectroscopy. We also used a three-band 'Red-Green-Blue' pseudo true-color subset of the hyperspectral imagery strip to test the classification accuracy of a CNN model without the additional non-visible spectral data provided in the hyperspectral imagery. Our classifier is pixel-based rather than object based, although we use three-dimensional structural information from airborne Light Detection and Ranging (LiDAR) to identify trees (points > 5 m above the ground) and the classifier was applied to image pixels that were thus identified as tree crowns. By training a CNN classifier using field data and hyperspectral imagery, we were able to accurately identify tree species and predict their distribution, as well as the distribution of tree mortality, across the landscape. Using a window size of 15 pixels and eight hidden convolutional layers, a CNN model classified the correct species of 713 individual trees from hyperspectral imagery with an average F-score of 0.87 and F-scores ranging from 0.67-0.95 depending on species. The CNN classification model performance increased from a combined F-score of 0.64 for the Red-Green-Blue model to a combined F-score of 0.87 for the hyperspectral model. The hyperspectral CNN model captures the species composition changes across ~700 meters (1935 to 2630 m) of elevation from a lower-elevation mixed oak conifer forest to a higher-elevation fir-dominated coniferous forest. High resolution tree species maps can support forest ecosystem monitoring and management, and identifying dead trees aids landscape assessment of forest mortality resulting from drought, insects and pathogens. We publicly provide our code to apply deep learning classifiers to tree species identification from geospatial imagery and field training data.

The biodiversity and high productivity of coastal terrestrial and aquatic habitats are the foundation for important benefits to human societies around the world. These globally distributed habitats need frequent and broad systematic assessments, but field surveys only cover a small fraction of these areas. Satellite-based sensors can repeatedly record the visible and near-infrared reflectance spectra that contain the absorption, scattering, and fluorescence signatures of functional phytoplankton groups, colored dissolved matter, and particulate matter near the surface ocean, and of biologically structured habitats (floating and emergent vegetation, benthic habitats like coral, seagrass, and algae). These measures can be incorporated into Essential Biodiversity Variables (EBVs), including the distribution, abundance, and traits of groups of species populations, and used to evaluate habitat fragmentation. However, current and planned satellites are not designed to observe the EBVs that change rapidly with extreme tides, salinity, temperatures, storms, pollution, or physical habitat destruction over scales relevant to human activity. Making these observations requires a new generation of satellite sensors able to sample with these combined characteristics: (1) spatial resolution on the order of 30 to 100-m pixels or smaller; (2) spectral resolution on the order of 5 nm in the visible and 10 nm in the short-wave infrared spectrum (or at least two or more bands at 1,030, 1,240, 1,630, 2,125, and/or 2,260 nm) for atmospheric correction and aquatic and vegetation assessments; (3) radiometric quality with signal to noise ratios (SNR) above 800 (relative to signal levels typical of the open ocean), 14-bit digitization, absolute radiometric calibration

Climate change has emerged as a major threat to biodiversity, but there have been few assessments of variability in sensitivity to climate across species’ ranges. We examined the sensitivity of ponderosa pine (Pinus ponderosa), a common, commercially important tree species found throughout the western United States, to climate variability using tree rings mostly from the International Tree Ring Data Bank (161 chronologies) and multiple climate products for 1930–1979. We compared interannual tree ring variation to water-year precipitation (wy ppt), May-July vapor pressure deficit (MJJ VPD), April-July precipitation (AMJJ ppt) and average winter (Dec-Feb) minimum temperature (winter tmn). We mapped growth-climate correlations for each variable and applied hierarchical agglomerative cluster analysis to identify five main groups of climate sensitivities, one of which contained mostly climate-insensitive populations. Ponderosa pine growth was positively correlated with wy ppt and AMJJ ppt and negatively correlated with MJJ VPD throughout its range, but sensitivities were greater in the Southern Rockies and Northern Great Plains than in Pacific and Southwestern populations. Sensitivities to winter tmn were weaker than responses to other climate variables, but Pacific and interior populations mostly demonstrated positive and negative responses, respectively. In summary, our analyses revealed the general pattern that interior populations of ponderosa pine were more climate-sensitive than Pacific populations, but there is considerable heterogeneity in sensitivity throughout the species’ range. Sensitivities to climate variables were related to position along corresponding climate gradients for all variables except MJJ VPD, but the unevenness of these relationships suggests a role of local adaptation. Our results illustrate the variation in climate response for a uniquely wide-ranging species and suggest that “leading” and “trailing” range edge concepts can oversimplify patterns of species exposure to climate change.

Seedling establishment is a critical step that may ultimately govern tree species' distribution shifts under environmental change. Annual variation in the location of seed rain and microclimates results in transient "windows of opportunity" for tree seedling establishment across the landscape. These establishment windows vary at fine spatiotemporal scales that are not considered in most assessments of climate change impacts on tree species range dynamics and habitat displacement. We integrate field seedling establishment trials conducted in the southern Sierra Nevada and western Tehachapi Mountains of southern California with spatially downscaled grids of modeled water-year climatic water deficit (CWDwy) and mean August maximum daily temperature (Tmax) to map historical and projected future microclimates suitable for establishment windows of opportunity for Quercus douglasii, a dominant tree species of warm, dry foothill woodlands, and Q. kelloggii, a dominant of cooler, more mesic montane woodlands and forests. Based on quasi-binomial regression models, Q. douglasii seedling establishment is significantly associated with modeled CWDwy and to a lesser degree with modeled Tmax. Q. kelloggii seedling establishment is most strongly associated with Tmax and best predicted by a two-factor model including CWDwy and Tmax. Establishment niche models are applied to explore recruitment window dynamics in the western Tehachapi Mountains, where these species are currently widespread canopy dominants. Establishment windows are projected to decrease by 50-95%, shrinking locally to higher elevations and north-facing slopes by the end of this century depending on the species and climate scenario. These decreases in establishment windows suggest the potential for longer-term regional population declines of the species. While many additional processes regulate seedling establishment and growth, this study highlights the need to account for topoclimatic controls and interannual climatic variation when assessing how seedling establishment and colonization processes could be affected by climate change.

In this study pollinators visiting highbush blueberry fields set in landscapes with differing land use pattern in south-central Chile were investigated. Effects of spatial buffers from 0.5 to 8 km around each blueberry field on the abundance of the main wild pollinator, Bombus terrestris queens, were tested. Wild B. terrestris abundances were positively affected by natural forest area and negatively affected by high-food resource area, and these effects were strongest at a buffer radius of 1 and 3.5 km, respectively. Possibly, continuous food resources provided by natural forest areas favor colony establishment and growth, and/or increase overwintering survival of bumblebee queens. Also, pollinator dependent crop area can generate a “transient dilution effect” by decreasing the density of bumblebees in simultaneously flowering crops. Management strategies might increase crop pollination services by considering the importance of nesting and overwintering habitat quality/amount and area of simultaneously flowering crops requiring insect pollination.

We synthesize insights from current understanding of drought impacts at stand-to-biogeographic scales, including management options, and we identify challenges to be addressed with new research. Large stand-level shifts underway in western forests already are showing the importance of interactions involving drought, insects, and fire. Diebacks, changes in composition and structure, and shifting range limits are widely observed. In the eastern US, the effects of increasing drought are becoming better understood at the level of individual trees, but this knowledge cannot yet be confidently translated to predictions of changing structure and diversity of forest stands. While eastern forests have not experienced the types of changes seen in western forests in recent decades, they too are vulnerable to drought and could experience significant changes with increased severity, frequency, or duration in drought. Throughout the continental United States, the combination of projected large climate-induced shifts in suitable habitat from modeling studies and limited potential for the rapid migration of tree populations suggests that changing tree and forest biogeography could substantially lag habitat shifts already underway. Forest management practices can partially ameliorate drought impacts through reductions in stand density, selection of drought-tolerant species and genotypes, artificial regeneration, and the development of multistructured stands. However, silvicultural treatments also could exacerbate drought impacts unless implemented with careful attention to site and stand characteristics. Gaps in our understanding should motivate new research on the effects of interactions involving climate and other species at the stand scale and how interactions and multiple responses are represented in models. This assessment indicates that, without a stronger empirical basis for drought impacts at the stand scale, more complex models may provide limited guidance.

Context: Predicting climate-driven species’ range shifts depends substantially on species’ exposure to climate change. Mountain landscapes contain a wide range of topoclimates and soil characteristics that are thought to mediate range shifts and buffer species’ exposure. Quantifying fine-scale patterns of exposure across mountainous terrain is a key step in understanding vulnerability of species to regional climate change. Objectives: We demonstrated a transferable, flexible approach for mapping climate change exposure in a moisture-limited, mountainous California landscape across 4 climate change projections under phase 5 of the Coupled Model Intercomparison Project (CMIP5) for mid-(2040–2069) and end-of-century (2070–2099). Methods: We produced a 149-year dataset (1951–2099) of modeled climatic water deficit (CWD), which is strongly associated with plant distributions, at 30-m resolution to map climate change exposure in the Tehachapi Mountains, California, USA. We defined climate change exposure in terms of departure from the 1951–1980 mean and historical range of variability in CWD in individual years and 3-year moving windows. Results: Climate change exposure was generally greatest at high elevations across all future projections, though we encountered moderate topographic buffering on poleward-facing slopes. Historically dry lowlands demonstrated the least exposure to climate change. Conclusions: In moisture-limited, Mediterranean-climate landscapes, high elevations may experience the greatest exposure to climate change in the 21st century. High elevation species may thus be especially vulnerable to continued climate change as habitats shrink and historically energy-limited locations become increasingly moisture-limited in the future.

Aim: Forest regeneration data provide an early signal of the persistence and migration of tree species, so we investigated whether species shifts due to climate change exhibit a common signal of response or whether changes vary by species. Location: California Floristic Province, United States; mediterranean biome. Methods: We related Forest Inventory and Analysis (FIA) data from 2000-07 for 13 tree species to high-resolution climate and geographical data. Using methods from invasion ecology, we derived indices of species-specific regeneration overlap and central tendency change (range-wide global indicators) based on kernel density estimation of presence and absence of regeneration. We then built regeneration surfaces to identify areas of occurrence of high regeneration (regeneration hotspots, local indicators) in both geographical and climate space for 13 common tree species. Results: Differences between presence and absence of regeneration in forests varied in magnitude across species, with little evidence that tree regeneration is shifting to higher latitudes and elevations, the expected geographical fingerprint of climate change. We also identified potential topographic mediators of regeneration dynamics. Multiple regeneration hotspots were found for many species, suggesting the influence of non-climatic factors on regeneration. Differences between the presence and absence of regeneration in geographic and climate spaces were not always congruent, suggesting that shifting climate space and range area are not entirely coupled. Main conclusions: The distributions of regeneration in Californian forests show diverse signals, not always tracking the higher latitudinal-elevation fingerprint of climate change. Local regeneration hotspots are common in our analysis, suggesting spatially varying persistence of forest linked to natural and anthropogenic disturbances. Our results emphasize that projections of tree range shifts in the context of climate change should consider the variation of regeneration drivers within species ranges, beyond the overall climate signal.

A crucial gap exists between the static nature of the United States' existing protected areas and the dynamic impacts of 21st century stressors, such as habitat loss and fragmentation and climate change. Connectivity is a valuable element for bridging that gap and building the ecological resilience of existing protected areas. However, creating terrestrial connectivity by designing individual migration corridors across fragmented landscapes is arguably untenable at a national scale. We explore the potential for use of riverine corridors in a Riparian Connectivity Network (RCN) as a potential contributor to a more resilient network of protected areas. There is ample scientific support for the conservation value of riparian areas, including their habitat, their potential to connect environments, and their ecosystem services. Our spatial analysis suggests that they could connect protected areas and have a higher rate of conservation management than terrestrial lands. Our results illustrate that the spatial backbone for an RCN is already in place, and existing policies favor riparian area protection. Furthermore, existing legal and regulatory goals may be better served if governance requirements and incentives are aligned with conservation efforts focused on riparian connectivity, as part of a larger landscape connectivity strategy. While much research on the effectiveness of riparian corridors remains to be done, the RCN concept provides a way to improve connectivity among currently protected areas. With focused attention, increased institutional collaboration, and improved incentives, these pieces could coalesce into a network of areas for biological conservation.

As natural habitats become fragmented by human activities, animals must increasingly move through human-dominated systems, particularly agricultural landscapes. Mapping areas important for animal movement has therefore become a key part of conservation planning. Models of landscape connectivity are often parameterized using expert opinion and seldom distinguish between the risks and barriers presented by different crop types. Recent research, however, suggests different crop types, such as row crops and orchards, differ in the degree to which they facilitate or impede species movements. Like many mammalian carnivores, bobcats (Lynx rufus) are sensitive to fragmentation and loss of connectivity between habitat patches. We investigated how distinguishing between different agricultural land covers might change conclusions about the relative conservation importance of different land uses in a Mediterranean ecosystem. Bobcats moved relatively quickly in row crops but relatively slowly in orchards, at rates similar to those in natural habitats of woodlands and scrub. We found that parameterizing a connectivity model using empirical data on bobcat movements in agricultural lands and other land covers, instead of parameterizing the model using habitat suitability indices based on expert opinion, altered locations of predicted animal movement routes. These results emphasize that differentiating between types of agriculture can alter conservation planning outcomes.

Significant efforts are underway to translate improved understanding of how climate change is altering ecosystems into practical actions for sustaining ecosystem functions and benefits. We explore this transition in California, where adaptation and mitigation are advancing relatively rapidly, through four case studies that span large spatial domains and encompass diverse ecological systems, institutions, ownerships, and policies. The case studies demonstrate the context specificity of societal efforts to adapt ecosystems to climate change and involve applications of diverse scientific tools (e.g., scenario analyses, downscaled climate projections, ecological and connectivity models) tailored to specific planning and management situations (alternative energy siting, wetland management, rangeland management, open space planning). They illustrate how existing institutional and policy frameworks provide numerous opportunities to advance adaptation related to ecosystems and suggest that progress is likely to be greatest when scientific knowledge is integrated into collective planning and when supportive policies and financing enable action.

Balancing society's competing needs of development and conservation requires careful consideration of tradeoffs. Renewable energy development and biodiversity conservation are often considered beneficial environmental goals. The direct footprint and disturbance of renewable energy, however, can displace species' habitat and negatively impact populations and natural communities if sited without ecological consideration. Offsets have emerged as a potentially useful tool to mitigate residual impacts after trying to avoid, minimize, or restore affected sites. Yet the problem of efficiently designing a set of offset sites becomes increasingly complex where many species or many sites are involved. Spatial conservation prioritization tools are designed to handle this problem, but have seen little application to offset siting and analysis. To address this need we designed an offset siting support tool for the Desert Renewable Energy Conservation Plan (DRECP) of California, and present a case study of hypothetical impacts from solar development in the Western Mojave subsection. We compare two offset scenarios designed to mitigate a hypothetical 15,331 ha derived from proposed utility-scale solar energy development (USSED) projects. The first scenario prioritizes offsets based precisely on impacted features, while the second scenario offsets impacts to maximize biodiversity conservation gains in the region. The two methods only agree on 28% of their prioritized sites and differ in meeting species-specific offset goals. Differences between the two scenarios highlight the importance of clearly specifying choices and priorities for offset siting and mitigation in general. Similarly, the effects of background climate and land use change may lessen the durability or effectiveness of offsets if not considered. Our offset siting support tool was designed specifically for the DRECP area, but with minor code modification could work well in other offset analyses, and could provide continuing support for a potentially innovative mitigation solution to environmental impacts.

Many aspects of the Californian approach to controlling the greenhouse gases that cause climate change now have a sufficient track record to provide potential models or lessons for national and even international action. In comparison, the state's efforts on climate change adaptation, although multifaceted, are less well developed and thus far have focused largely on information sharing, impact assessments, and planning. Still, adaptation could advance more quickly in California than in many other regions, given relatively high public awareness and concern, extensive scientific information, a strong tradition of local and regional planning, and some enabling policies and institutions. Much more political support and sufficient financing will have to be mustered at state and local levels to enable new projects and initiatives to cope with sea level rise, water management, and ecosystem adaptation, not to mention public health and other key areas of concern. Even so, California's initial efforts to adapt to unavoidable changes in climate may offer insights for other governments that will, inevitably, need to fashion their own adaptation strategies. © The Author(s) 2014 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

Quantitative methods of spatial conservation prioritization have traditionally been applied to issues in conservation biology and reserve design, though their use in other types of natural resource management is growing. The utility maximization problem is one form of a covering problem where multiple criteria can represent the expected social benefits of conservation action. This approach allows flexibility with a problem formulation that is more general than typical reserve design problems, though the solution methods are very similar. However, few studies have addressed optimization in utility maximization problems for conservation planning, and the effect of solution procedure is largely unquantified. Therefore, this study mapped five criteria describing elements of multifunctional agriculture to determine a hypothetical conservation resource allocation plan for agricultural land conservation in the Central Valley of CA, USA. We compared solution procedures within the utility maximization framework to determine the difference between an open source integer programming approach and a greedy heuristic, and find gains from optimization of up to 12%. We also model land availability for conservation action as a stochastic process and determine the decline in total utility compared to the globally optimal set using both solution algorithms. Our results are comparable to other studies illustrating the benefits of optimization for different conservation planning problems, and highlight the importance of maximizing the effectiveness of limited funding for conservation and natural resource management.

After solar and other renewable energy developers select generally suitable sites for exploration, they frequently encounter conflict over biodiversity conservation values that were not factored into the initial suitability rating methods. This paper presents a spatial multicriteria analysis method for modeling risk of conflict with biological resources and applies the model in the California deserts where such conflicts are rapidly rising. The premise of the model is that the least conflict will occur on sites that are the most ecologically degraded with low conservation value and that would engender low off-site impacts when connecting to existing transmission infrastructure. Model results suggest sufficient compatible land exists in flat, non-urban areas to meet state solar energy targets of 8.7 GW of installed capacity in the California deserts for 2040. The model is a promising tool to fill the gap between site suitability analysis for renewable energy and regional biodiversity conservation planning to identify areas where rapid impact assessment and permitting will generate the least regrets. © 2013 Elsevier Ltd.

Recent studies suggest that species distribution models (SDMs) based on fine-scale climate data may provide markedly different estimates of climate-change impacts than coarse-scale models. However, these studies disagree in their conclusions of how scale influences projected species distributions. In rugged terrain, coarse-scale climate grids may not capture topographically controlled climate variation at the scale that constitutes microhabitat or refugia for some species. Although finer scale data are therefore considered to better reflect climatic conditions experienced by species, there have been few formal analyses of how modeled distributions differ with scale. We modeled distributions for 52 plant species endemic to the California Floristic Province of different life forms and range sizes under recent and future climate across a 2000-fold range of spatial scales (0.008-16 km(2) ). We produced unique current and future climate datasets by separately downscaling 4 km climate models to three finer resolutions based on 800, 270, and 90 m digital elevation models and deriving bioclimatic predictors from them. As climate-data resolution became coarser, SDMs predicted larger habitat area with diminishing spatial congruence between fine- and coarse-scale predictions. These trends were most pronounced at the coarsest resolutions and depended on climate scenario and species' range size. On average, SDMs projected onto 4 km climate data predicted 42% more stable habitat (the amount of spatial overlap between predicted current and future climatically suitable habitat) compared with 800 m data. We found only modest agreement between areas predicted to be stable by 90 m models generalized to 4 km grids compared with areas classified as stable based on 4 km models, suggesting that some climate refugia captured at finer scales may be missed using coarser scale data. These differences in projected locations of habitat change may have more serious implications than net habitat area when predictive maps form the basis of conservation decision making.

Wide-ranging species cannot persist in reserves alone. Consequently, there is growing interest in the conservation value of agricultural lands that separate or buffer natural areas. The value of agricultural lands for wildlife habitat and connectivity varies as a function of the crop type and landscape context, and quantifying these differences will improve our ability to manage these lands more effectively for animals. In southern California, many species are present in avocado orchards, including mammalian carnivores. We examined occupancy of avocado orchards by mammalian carnivores across agricultural-wildland gradients in southern California with motion-activated cameras. More carnivore species were detected with cameras in orchards than in wildland sites, and for bobcats and gray foxes, orchards were associated with higher occupancy rates. Our results demonstrate that agricultural lands have potential to contribute to conservation by providing habitat or facilitating landscape connectivity.

State wildlife conservation programs provide a strong foundation for biodiversity conservation in the United States, building on state wildlife action plans. However, states may miss the species that are at the most risk at rangewide scales, and threats such as novel diseases and climate change increasingly act at regional and national levels. Regional collaborations among states and their partners have had impressive successes, and several federal programs now incorporate state priorities. However, regional collaborations are uneven across the country, and no national counterpart exists to support efforts at that scale. A national conservation-support program could fill this gap and could work across the conservation community to identify large-scale conservation needs and support efforts to meet them. By providing important information-sharing and capacity-building services, such a program would advance collaborative conservation among the states and their partners, thus increasing both the effectiveness and the efficiency of conservation in the United States. © 2012 by American Institute of Biological Sciences. All rights reserved.

Biofuels from agricultural sources are an important part of California's strategy to reduce greenhouse gas emissions and dependence on foreign oil. Land conversion for agricultural and urban uses has already imperiled many animal species in the state. This study investigated the potential impacts on wildlife of shifts in agricultural activity to increase biomass production for transportation fuels. We applied knowledge of the suitability of California's agricultural landscapes for wildlife species to evaluate wildlife effects associated with plausible scenarios of expanded production of three potential biofuel crops (sugar beets, bermudagrass, and canola). We also generated alternative, spatially explicit scenarios that minimized loss of habitat for the same level of biofuel production. We explored trade-offs to compare the marginal changes per unit of energy for transportation costs, wildlife, land and water-use, and total energy produced, and found that all five factors were influenced by crop choice. Sugar beet scenarios require the least land area: 3.5 times less land per liter of gasoline equivalent than bermudagrass and five times less than canola. Canola scenarios had the largest impacts on wildlife but the greatest reduction in water use. Bermudagrass scenarios resulted in a slight overall improvement for wildlife over the current situation. Relatively minor redistribution of lands converted to biofuel crops could produce the same energy yield with much less impact on wildlife and very small increases in transportation costs. This framework provides a means to systematically evaluate potential wildlife impacts of alternative production scenarios and could be a useful complement to other frameworks that assess impacts on ecosystem services and greenhouse gas emissions. © 2011 Blackwell Publishing Ltd.

Decisions about which places to conserve are based upon the geographic heterogeneity of three types of information: public goods or benefits, their vulnerability to threats, and the costs to avert those threats. The choice of public goods depends on the mission of the conservation organization (e.g., biodiversity, open space, cultural values, or farmland). For spatial targeting of conservation at the regional scale, practitioners must estimate the values of these types of information. The quality of the estimations will vary by the primary data used, the assumptions made, and the practitioner's technical ability to analyze complex data. This paper contributes to the growing literature by presenting a systematic evaluation of effect of the quality of the estimation on the cost-effectiveness of the set of sites selected for conservation based upon those estimates. The specific case study targets farmland for preservation from urban development in California's Central Valley where a new land trust was recently established to purchase conservation easements. In one analysis, we compared the cost-effectiveness of farmland benefits using our most sophisticated estimation procedures to those that ignored costs and/or potential loss (i.e., assumed they were equal among sites). Excluding information about the potential loss of resources caused only a slight decrease in cost-effectiveness. On the other hand, ignoring cost information was extremely inefficient. The second analysis compared the performance of the sophisticated estimate to increasingly simpler estimates, such as those that are representative of the methods used by many American farmland preservation programs. The simplification of the estimates caused a 5- to 20-fold decline in the benefits that could be retained for a given budget. To make more cost-effective targeting strategies accessible to farmland preservation programs, we recommend that researchers develop new spatial targeting tools to overcome obstacles in data processing. © 2010 Elsevier Ltd.

For centuries humans have reduced and transformed Mediterranean-climate oak woodland and savanna ecosystems, making it difficult to establish credible baselines for ecosystem structure and composition that can guide ecological restoration efforts. We combined historical data sources, with particular attention to mid-1800s General Land Office witness tree records and maps and twentieth century air photos, to reconstruct 150 years of decline in extent and stand density of Valley oak (Quercus lobata Neé) woodlands and savannas in the Santa Clara Valley of central coastal California. Nineteenth century Valley oak woodlands here were far more extensive and densely stocked than early twentieth century air photos would suggest, although reconstructed basal areas (7.5 m2/ha) and densities (48.9 trees/ha) were not outside the modern range reported for this ecosystem type. Tree densities and size distribution varied across the landscape in relation to soil and topography, and trees in open savannas were systematically larger than those in denser woodlands. For the largest woodland stand, we estimated a 99% decline in population from the mid-1800s to the 1930s. Although most of the study area is now intensely developed, Valley oaks could be reintroduced in urban and residential areas as well as in surrounding rangelands at densities comparable to the native oak woodlands and savannas, thereby restoring aspects of ecologically and culturally significant ecosystems, including wildlife habitat and genetic connectivity within the landscape. © 2010 Society for Ecological Restoration International.

Geospatial details about land use are necessary to assess its potential impacts on biodiversity. Geographic information systems (GIS) are adept at modeling land use in a spatially explicit manner, while life cycle assessment (LCA) does not conventionally utilize geospatial information. This study presents a proof-of-concept approach for coupling GIS and LCA for biodiversity assessments of land use and applies it to a case study of ethanol production from agricultural crops in California.In Part 2 of this paper series, four biodiversity impact indicators are presented and discussed, which use the inventory data on habitat composition and sizes from the GIS-based inventory modeling in Part 1 (Geyer et al. 2010). The concepts used to develop characterization models are hemeroby, species richness, species abundance, and species evenness. The biodiversity assessments based on species richness, abundance, and evenness use a species–habitat suitability matrix which relates 443 terrestrial vertebrate species native to California to the 29 habitat types that occur in the study area.The structural similarities and differences of all four characterization models are discussed in some detail. Characterization factors and indicator results are calculated for each of the four characterization models and the 11 different land use scenarios from Part 1 of this paper series. For the sugar beet production scenarios, the indicator results are in fairly good agreement. For the corn production scenarios, however, they come to fundamentally different results. The overall approach of using GIS-based inventory data on land use together with information on species–habitat relationships is not only feasible but also grounded in ecological science and well connected with existing life cycle impact assessment efforts.Excluding biodiversity impacts from land use significantly limits the scope of LCA. Accounting for land use in inventory modeling is dramatically enhanced if LCA is coupled with GIS. The resulting inventory data are a sound basis for biodiversity impact assessments, in particular if coupled with information on species–habitat relationships. However, much more case studies and structural analysis of indicators is required, together with an evaluation framework that enables comparisons and ranking of indicators.

Geospatial details about land use are necessary to assess its potential impacts on biodiversity. Geographic information systems (GIS) are adept at modeling land use in a spatially explicit manner, while life cycle assessment (LCA) does not conventionally utilize geospatial information. This study presents a proof-of-concept approach for coupling GIS and LCA for biodiversity assessments of land use and applies it to a case study of ethanol production from agricultural crops in California.GIS modeling was used to generate crop production scenarios for corn and sugar beets that met a range of ethanol production targets. The selected study area was a four-county region in the southern San Joaquin Valley of California, USA. The resulting land use maps were translated into maps of habitat types. From these maps, vectors were created that contained the total areas for each habitat type in the study region. These habitat compositions are treated as elementary input flows and used to calculate different biodiversity impact indicators in a second paper (Geyer et al., submitted).Ten ethanol production scenarios were developed with GIS modeling. Current land use is added as baseline scenario. The parcels selected for corn and sugar beet production were generally in different locations. Moreover, corn and sugar beets are classified as different habitat types. Consequently, the scenarios differed in both the habitat types converted and in the habitat types expanded. Importantly, land use increased nonlinearly with increasing ethanol production targets. The GIS modeling for this study used spatial data that are commonly available in most developed countries and only required functions that are provided in virtually any commercial or open-source GIS software package.This study has demonstrated that GIS-based inventory modeling of land use allows important refinements in LCA theory and practice. Using GIS, land use can be modeled as a geospatial and nonlinear function of output. For each spatially explicit process, land use can be expressed within the conventional structure of LCA methodology as a set of elementary input flows of habitat types.

Biofuels from agricultural and other biomass are an important part of California’s strategy to reduce greenhouse gas emissions and dependence on foreign oil. Ongoing land conversion for agricultural and urban uses has already imperiled many animal species in the state. This study investigated the potential impacts on wildlife of shifts in agricultural activity to increase biomass production. We applied knowledge of the suitability of California’s agricultural landscapes for wildlife species to evaluate wildlife effects associated with plausible scenarios of expanded production of five potential biofuel crops (sweet sorghum, sugar beets, bermudagrass, canola, and safflower). We generated alternative, spatially-explicit scenarios that minimized loss of habitat for a given level of biofuel production. Trade-off analysis compared the marginal changes per unit of energy for transportation costs, wildlife, and water-use. The choice of crop matters. Sweet sorghum and sugar beets require three times less land area per gallon of gasoline equivalent than bermudagrass and five to six times less than canola and safflower. Canola and safflower scenarios had the largest impacts on wildlife but the greatest reduction in water use. The bermudagrass scenarios would result in a slight overall improvement for wildlife over the current situation. Relatively minor redistribution of lands converted to biofuel could produce the same energy yield with much less impact on wildlife and very small increases in transportation costs for any crop. This framework provides a means to systematically evaluate potential wildlife impacts of alternative production scenarios. The accuracy of the predicted impacts could be improved with additional field study of wildlife use of agricultural landscapes, especially habitats associated with biofuel crops.

We analyzed recent survey data and mapped environmental variables integrated over a home range scale of 10 km(2) to model the distribution of fisher ( Martes pennanti) habitat in California, USA. Our goal was to identify habitat factors associated with the current distribution of fishers in California, and to test whether those factors differ for widely disjunct northern and southern populations. Our analyses were designed to probe whether poor habitat quality can explain the current absence of fishers in the historically occupied central and northern Sierra Nevada region that separates these two populations. Fishers were detected at 64/433 (14.8%) sample units, including 35/111 (32%) of sample units in the Klamath/Shasta region and 28/88 (32%) of sample units in the southern Sierra Nevada. Generalized additive models (GAM) that included mean annual precipitation, topographic relief, forest structure, and a spatial autocovariate term best predicted fisher detections over the species' recent historical range in California. Models derived using forest structure data from ground plots were comparable to models derived from Landsat Thematic Mapper imagery. Models for the disjunct Klamath/Cascades and southern Sierra Nevada populations selected different environmental factors and showed low agreement in the spatial pattern of model predictions. Including a spatial autocovariate term significantly improved model fits for all models except the southern Sierra Nevada. We cannot rule out dispersal or habitat in explaining the absence of fishers in the northern and central Sierra Nevada, but mapped habitat quality is low over much of the region. Landscapes with good fisher habitat may exist in rugged forested canyons of the currently unoccupied northern Sierra Nevada, but these areas are fragmented and at least 60 km from the nearest recent fisher detections.