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Climate connectivity, the ability of a landscape to promote or hinder the movement of organisms in response to a changing climate, is contingent on multiple factors including the distance organisms need to move to track suitable climate over time (i.e. climate velocity) and the resistance they experience along such routes. An additional consideration which has received less attention is that human land uses increase resistance to movement or alter movement routes and thus influence climate connectivity. Here we evaluate the influence of human land uses on climate connectivity across North America by comparing two climate connectivity scenarios, one considering climate change in isolation and the other considering climate change and human land uses. In doing so, we introduce a novel metric of climate connectivity, ‘human exposure’, that quantifies the cumulative exposure to human activities that organisms may encounter as they shift their ranges in response to climate change. We also delineate potential movement routes and evaluate whether the protected area network supports movement corridors better than non-protected lands. We found that when incorporating human land uses, climate connectivity decreased; climate velocity increased on average by 0.3 km/year and cumulative climatic resistance increased for ~83% of the continent. Moreover, ~96% of movement routes in North America must contend with human land uses to some degree. In the scenario that evaluated climate change in isolation, we found that protected areas do not support climate corridors at a higher rate than non-protected lands across North America. However, variability is evident, as many ecoregions contain protected areas that exhibit both more and less representation of climate corridors compared to non-protected lands. Overall, our study indicates that previous evaluations of climate connectivity underestimate climate change exposure because they do not account for human impacts.
US fire scientists are developing Potential Wildfire Operational Delineations, also known as ‘PODs’, as a pre-fire season planning tool to promote safe and effective wildland fire response, strengthen risk management approaches in fire management and better align fire management objectives. PODs are a collaborative planning approach based on spatial analytics to identify potential wildfire control lines and assess the desirability of fire before ignition. They offer the opportunity to apply risk management principles with partners before the compressed timeframe of incident response. We sought to understand the potential utility of PODs and factors that may affect their use through semi-structured interviews with personnel on several national forests. Interviewees said PODs offer a promising shift in the wildland fire management dynamic, particularly by facilitating proactive communication and coordination about wildfire response. Successfully employing PODs will require leadership commitment, stakeholder and partner engagement and interdisciplinary staff involvement. Our work offers insights for national forests and other jurisdictions where managers are looking to strengthen coordination and strategic approaches for wildland fire response by utilizing pre-season collaboration and data analytics.
This study used a value of information approach to demonstrate the cost-effectiveness of using satellite imagery as part of the Burn Area Emergency Response (BAER), a US federal program that identifies imminent post-wildfire threats to human life and safety, property and critical natural or cultural resources. It compared the costs associated with producing a Burn Area Reflectance Classification map and implementing a BAER when imagery from satellites (either Landsat or a commercial satellite) was available to when the response team relied on information collected solely by aerial reconnaissance. The case study included two evaluations with and without Burn Area Reflectance Classification products: (a) savings of up to US$51 000 for the Elk Complex wildfire incident request and (b) savings of a multi-incident map production program. Landsat is the most cost-effective way to input burn severity information into the BAER program, with savings of up to US$35 million over a 5-year period.
Managed wildfire is an increasingly relevant management option to restore variability in vegetation structure within fire-suppressed montane forests in western North America. Managed wildfire often reduces tree cover and density, potentially leading to increases in soil moisture availability, water storage in soils and groundwater, and streamflow. However, the potential hydrologic impacts of managed wildfire in montane watersheds remain uncertain and are likely context dependent. Here, we characterize the response of vegetation and soil moisture to 47 years (1971–2018) of managed wildfire in Sugarloaf Creek Basin (SCB) in Sequoia-Kings Canyon National Park in the Sierra Nevada, California, USA, using repeat plot measurements, remote sensing of vegetation, and a combination of continuous in situ and episodic spatially distributed soil moisture measurements. We find that, by comparison to a nearby watershed with higher vegetation productivity and greater fire frequency, the managed wildfire regime at SCB caused relatively little change in dominant vegetation over the 47 year period and relatively little response of soil moisture. Fire occurrence was limited to drier mixed-conifer sites; fire-caused overstory tree mortality patches were generally less than 10 ha, and fires had little effect on removing mid- and lower strata trees. Few dense meadow areas were created by fire, with most forest conversion leading to sparse meadow and shrub areas, which had similar soil moisture profiles to nearby mixed-conifer vegetation. Future fires in SCB could be managed to encourage greater tree mortality adjacent to wetlands to increase soil moisture, although the potential hydrologic benefits of the program in drier basins such as this one may be limited.
In situ measurements of sagebrush have traditionally been expensive and time consuming. Currently, improvements in small Unmanned Aerial Systems (sUAS) technology can be used to quantify sagebrush morphology and community structure with high resolution imagery on western rangelands, especially in sensitive habitat of the Greater sage-grouse (Centrocercus urophasianus). The emergence of photogrammetry algorithms to generate 3D point clouds from true color imagery can potentially increase the efficiency and accuracy of measuring shrub height in sage-grouse habitat. Our objective was to determine optimal parameters for measuring sagebrush height including flight altitude, single- vs. double- pass, and continuous vs. pause features. We acquired imagery using a DJI Mavic Pro 2 multi-rotor Unmanned Aerial Vehicle (UAV) equipped with an RGB camera, flown at 30.5, 45, 75, and 120 m and implementing single-pass and double-pass methods, using continuous flight and paused flight for each photo method. We generated a Digital Surface Model (DSM) from which we derived plant height, and then performed an accuracy assessment using on the ground measurements taken at the time of flight. We found high correlation between field measured heights and estimated heights, with a mean difference of approximately 10 cm (SE = 0.4 cm) and little variability in accuracy between flights with different heights and other parameters after statistical correction using linear regression. We conclude that higher altitude flights using a single-pass method are optimal to measure sagebrush height due to lower requirements in data storage and processing time.
This study quantified seasonal second‐order habitat selection for sage‐grouse across the state of Utah to produce spatio‐temporal predictions of their distribution at the southern periphery of the species range. We used location data obtained from sage‐grouse marked with very‐high‐frequency radio‐transmitters and lek location data collected between 1998 and 2013 to quantify species habitat selection in relation to a suite of topographic, edaphic, climatic, and anthropogenic variables using random forest algorithms. Sage‐grouse selected for greater sagebrush (Artemisia spp.) cover, higher elevations, and gentler slopes and avoided lower precipitations and higher temperatures. The strength of responses to habitat variables varied across seasons. Anthropogenic variables previously reported as affecting their range‐wide distribution (i.e., roads, powerlines, communication towers, and agricultural development) were not ranked as top predictors at our focal scale. Other than strong selection for sagebrush cover, the responses we observed differed from what has been reported at the range‐wide scale. These differences likely reflect the unique climatic, geographic, and topographic context found in the southern peripheral area of the species distribution compared to range‐wide environmental gradients. Our results highlight the importance of considering appropriateness of scale when planning conservation actions for wide‐ranging species.
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Climate change is increasing the risk of extreme events, resulting in social and economic challenges. I examined recent past (1971–2000), current and near future (2010-2039), and future (2040-2069) fire and heat hazard combined with population growth by different regions and residential densities (i.e., exurban low and high densities, suburban, and urban low and high densities). Regional values for extreme fire weather days varied greatly. Temperature and number of extreme fire weather days increased over time for all residential density categories, with the greatest increases in the exurban low-density category. The urban high-density category was about 0.8 to 1 °C cooler than the urban low-density category. The areas of the urban and suburban density categories increased relative to the exurban low-density category. Holding climate change constant at 1970-2000 resulted in a temperature increase of 0.4 to 0.8 °C by 2060, indicating future population increases in warmer areas. Overall, U.S. residents will experience greater exposure to fire hazard and heat over time due to climate change, and compound risk emerges because fire weather and heat are coupled and have effects across sectors. Movement to urban centers will help offset exposure to fire but not heat, because urban areas are heat islands; however, urban high-density areas had lower base temperatures, likely due to city locations along coastlines. This analysis provides a timely look at potential trends in fire and heat risk by residential density classes due to the expansion and migration of US populations.
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This study shows that simultaneous wildfire is at least as correlated with preparedness levels as other burned area measures and identify changes in simultaneous wildfire occurrence within the western and southern United States. Seasonal variation and spatial autocorrelation in simultaneous wildfire occurrence provide evidence of coupling of wildfire activity in portions of the western United States. Best-approximating models of simultaneity suggest that high levels of simultaneous wildfire often coincided with low fuel moisture and high levels of lightning occurrence. Model uncertainty was high in some contexts but, with only a few exceptions, there was strong evidence that the best model should include both a dryness and lightning indicator.
Across the western US, severe wildfires fueled by tinder-dry vegetation have already burned more than 3.2 million hectares (8 million acres) — an area the size of Maryland — as of the end of October, 2020, and nearly six times that area burned this year in Australia. And even though neither country’s worst-ever fire year is not yet over, concerns are already mounting regarding the next hazard these regions will face: dangerous and destructive debris flows.
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This study showed that all models provided higher predictive accuracy than chance, with an average AUC across the 20 forage species of 0.84 for distal and proximal variables and 0.81 for proximal variables only. This indicated that the addition of distal variables improved model performance. We validated the models using two independent datasets from two regions of Idaho. We found that predicted forage species occurrence was on average within 10% of observed occurrence at both sites. However, predicted occurrences had much less variability between habitat patches than the validation data, implying that the models did not fully capture fine-scale heterogeneity. We suggest that future efforts will benefit from additional fine resolution (i.e., less than 30 m) environmental predictor variables and greater accounting of environmental disturbances (i.e., wildfire, grazing) in the training data. Our approach was novel both in methodology and spatial scale (i.e., resolution and extent). Our models can inform ungulate nutrition by predicting the occurrence of forage species and aide habitat management strategies to improve nutritional quality.