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At least 1,603 records · Page 89Linked to original sources
USGS2010

Genetic diversity of lake whitefish in lakes Michigan and Huron: sampling, standardization, and research priorities

We combined data from two laboratories to increase the spatial extent of a genetic data set for lake whitefish Coregonus clupeaformis from lakes Huron and Michigan and saw that genetic diversity was greatest between lakes, but that there was also structuring within lakes. Low diversity among stocks may be a reflection of relatively recent colonization of the Great Lakes, but other factors such as recent population fluctuation and localized stresses such as lamprey predation or heavy exploitation may also have a homogenizing effect. Our data suggested that there is asymmetrical movement of lake whitefish between Lake Huron and Lake Michigan; more genotypes associated with Lake Michigan were observed in Lake Huron. Adding additional collections to the calibrated set will allow further examination of diversity in other Great Lakes, answer questions regarding movement among lakes, and estimate contributions of stocks to commercial yields. As the picture of genetic diversity and population structure of lake whitefish in the Great Lakes region emerges, we need to develop methods to combine data types to help identify important areas for biodiversity and thus conservation. Adding genetic data to existing models will increase the precision of predictions of the impacts of new stresses and changes in existing pressures on an ecologically and commercially important species.

Journal of Great Lakes Research↗
USGS2021

Using decision analysis to collaboratively respond to invasive species threats: A case study of Lake Erie grass carp (Ctenopharyngodon idella)

Decisions about invasive species control and eradication can be difficult because of uncertainty in population demographics, movement ecology, and effectiveness of potential response actions. These decisions often include multiple stakeholders and management entities with potentially different objectives, management priorities, and jurisdictional authority. We provide a case study of using multi-party, collaborative decision analysis to aid decision makers in determining objectives and control actions for invasive grass carp ( Ctenopharyngodon idella ) in Lake Erie. Creating this process required binational (Canada-United States) and multi-state/provincial collaboration to craft a shared problem statement, establish objectives related to ecological, economic, and social concerns, determine potential response actions, and evaluate consequences and tradeoffs of these actions. We used participatory modeling and expert elicitation to evaluate the effectiveness of control scenarios that varied in action type (i.e., removal efforts and spawning barriers) and the temporal and spatial application of these actions. Using a matrix population model parameterized for western Lake Erie grass carp, we found that removal efforts concentrated in areas of high catchability, when paired with a spawning barrier on the Sandusky River, Ohio, USA, could effectively control grass carp in Lake Erie, if all assumptions are met. We determined a set of key uncertainties regarding gear catchability and current population size that have led to the transition to an adaptive management process. In addition, our work formed the basis for grass carp management plans for the states of Michigan and Ohio and has provided a means for collaboration among agencies for effective application of control efforts.

Michigan, Ohio↗
USGS2010

The bioenergetic consequences of invasive-induced food web disruption to Lake Ontario alewives

Alewives Alosa pseudoharengus are the dominant prey fish in Lake Ontario, and their response to ecological change can alter the structure and function of the Lake Ontario food web. Using stochastic population-based bioenergetic models of Lake Ontario alewives for 1987–1991 and 2001–2005, we evaluated changes to alewife production, consumption, and associated bioenergetic ratios after invasive-induced food web disruption. After the disruption, mean biomass of alewives declined from 28.0 to 14.6 g/m 2 , production declined from 40.8 to 13.6 g·m −2 ·year −1 , and consumption declined from 342.1 to 137.2 g·m −2 ·year −1 , but bootstrapping of error sources suggested that the changes were not statistically significant. Population-based bioenergetic ratios of production to biomass ( P/B ratio), total consumption to biomass ( Q/B ratio), and production efficiency did not change. Pathways of energy flow measured as prey-group-specific Q/B ratios changed significantly between the two time periods for invasive predatory cladocerans (from 0.6 to 1.3), Mysis diluviana (from 0.4 to 2.5), and other prey (from 0.8 to 0.1), but the observed decline in the zooplankton Q/B ratio (from 10.6 to 5.5) was not significant. Gross production efficiency did not change; values ranged from 8% to 15%. Age-group mean gross conversion efficiency (GCE) declined with age; GCE ranged from 7.5% to 11.0% for yearlings, was approximately 5% for age-2 alewives, and was less than 2% for age-3 and older alewives. The GCE increased significantly between the time periods for yearling alewives. Our analyses support the hypothesis that after 2003, alewives could not sustain their growth while feeding on zooplankton closer to shore. Modeling of observed spatial variation in diet and alternative occupied temperatures demonstrates the potential for reducing consumption by alewives. Our results suggest that Lake Ontario alewives can exploit spatial heterogeneity in resource patches and thermal habitat to partially mitigate the effects of food web disruption. Fish management implications are discussed.

North American Journal of Fisheries Management↗
USGS2006

Population dynamics of Greater Scaup breeding on the Yukon-Kuskokwim Delta, Alaska

Populations of greater scaup ( Aythya marila ) remained relatively stable during a period when populations of lesser scaup ( A. affinis ) have declined from historic levels. To assist in describing these differences in population trends, from 1991 through 2000, we studied the survival, nesting ecology, and productivity of greater scaup on the Yukon-Kuskokwim Delta (Y-K Delta), Alaska, to develop a model of population dynamics. We located nests, radio-marked females for renesting studies, estimated duckling survival, and leg-banded females to examine nest site fidelity and annual survival. Greater scaup initiated egg laying later than other species, and most clutches (>80%) were initiated over 20 days each year. We located 1,056 nests; nest success ranged from 7 to 61 % among years. Following loss of their first clutch, 51 % of radio-tagged females attempted to renest. Duckling survival to 30 days of age was 37.5%. Our best model suggested that annual survival did not vary among years and averaged 81 %. Survival rate was positively related to structural body size. Only 8 of 214 banded individuals were reported as recovered (1 each in Maryland, Michigan, Minnesota, Washington, and Alaska and 3 in California). Using a stochastic model, we estimated that, on average, breeding females produced 0.57 young females/nesting season. We combined this estimate of productivity with our annual estimates of adult survival and an assumed population growth rate of 1.0, then solved for an estimate of first-year survival (0.40). Under these conditions the predicted stable age distribution of breeding females (i.e., the nesting population) was 15.1% 1-year-old, 4.1% 2-year-old first-time breeders, and 80.8% 2-year-old and older, experienced breeders. We subjected this stochastic model to perturbation analyses to examine the relative effects of demographic parameters on k. The relative effects of productivity and adult survival on the population growth rate were 0.26 and 0.72, respectively. Thus, compared to productivity, proportionally equivalent changes in annual survival would have 2.8 times the effect on k. However, when we examined annual variation in predicted population size using standardized regression coefficients, productivity explained twice as much variation as annual survival. Thus, management actions focused on changes in survival or productivity have the ability to influence population size; however, substantially larger changes in productivity are required to influence population trends.

Wildlife Monographs↗
USGS2017

Interactive effects of water temperature and salinity on growth and mortality of eastern oysters, Crassostrea virginica: A meta-analysis using 40 years of monitoring data

Despite nearly a century of exploitation and scientific study, predicting growth and mortality rates of the eastern oyster ( Crassostrea virginica ) as a means to inform local harvest and management activities remains difficult. Ensuring that models reflect local population responses to varying salinity and temperature combinations requires locally appropriate models. Using long-term (1988 to 2015) monitoring data from Louisiana's public oyster reefs, we develop regionally specific models of temperature- and salinity-driven mortality (sack oysters only) and growth for spat (<25 mm), seed (25–75 mm), and sack (>75 mm) oyster size classes. The results demonstrate that the optimal combination of temperature and salinity where Louisiana oysters experience reduced mortality and fast growth rates is skewed toward lower salinities and higher water temperatures than previous models have suggested. Outside of that optimal range, oysters are commonly exposed to combinations of temperature and salinity that are correlated with high mortality and reduced growth. How these combinations affect growth, and to a lesser degree mortality, appears to be size class dependent. Given current climate predictions for the region and ongoing large-scale restoration activities in coastal Louisiana, the growth and mortality models are a critical step toward ensuring sustainable oyster reefs for long-term harvest and continued delivery of the ecological services in a changing environment.

Louisiana↗
USGS2008

Relative importance of natural disturbances and habitat degradation on snail kite population dynamics

Natural disturbances and habitat degradation are major factors influencing the dynamics and persistence of many wildlife populations, yet few large-scale studies have explored the relative influence of these factors on the dynamics and persistence of animal populations. We used longterm demographic data and matrix population models to examine the potential effects of habitat degradation and natural disturbances on the dynamics of the endangered snail kite Rostrhamus sociabilis in Florida, USA. We found that estimates of stochastic population growth rate were low (0.90). Population growth rate (??) during the first half or our study period (1992 to 1998) was substantially greater than during the second half (1999 to 2005). These 2 periods were characterized by contrasting hydrological conditions. Although ?? was most sensitive to changes in adult survival, the analysis of life table response experiments revealed that a reduction in fertility of kites accounted for >80% of the observed decline in population growth rate. We examined the possibility that the reduction in ?? was caused by (1) habitat degradation due to management, (2) an increase in frequency of moderate drying events in recent years, and (3) both habitat degradation and an increase in frequency of moderate drying events. Our results suggest that both factors could potentially contribute to a large decrease in population growth rate. Our study highlights the importance of simultaneously considering short- and long-term effects of disturbances when modeling population dynamics. Indeed, focusing exclusively on one type of effect may be misleading to both our understanding of the ecological dynamics of the system and to management. The relevance of our results to management is heightened because the snail kite has been selected as a key performance measure of one of the most ambitious ecosystem restoration projects ever undertaken. ?? Inter-Research 2008.

Endangered Species Research↗
USGS2026

Building resilience in dryland ecosystems: A climate adaptation strategy menu for pinyon–juniper woodlands

Pinyon–juniper (PJ) woodlands, one of the most extensive mature and old-growth woodland types in the Western United States, provide critical ecological, cultural, and economic benefits but face increasing threats from climate change, altered disturbance regimes, invasive species, and pests. We developed the PJ Woodland Climate Adaptation Management Menu, a decision support tool designed to guide adaptive, climate-informed management of PJ ecosystems, particularly within the Colorado Plateau ecoregion. The menu was created through an iterative, collaborative process involving literature review, integration of strategies from existing adaptation frameworks, and extensive input from scientists, land managers, and community partners during workshops and focus groups. The menu links specific, evidence-based approaches to each of six broad strategies, including soliciting community input, mitigating disturbance, enhancing and maintaining biodiversity, conserving ecotones, timing actions for optimal outcomes, and accepting climate-driven changes when appropriate. It is intended for use with the Adaptation Workbook to help managers connect local goals and climate vulnerabilities to tailored management tactics. Hypothetical scenarios demonstrate the menu’s application to contrasting PJ woodland conditions, from die-off events to old-growth maintenance. Lessons learned during development underscore the value of early stakeholder engagement, cross-sector collaboration, and balancing diverse ecological objectives. This menu offers a flexible, transferable framework to strengthen climate resilience in PJ woodlands and serves as a model that could improve adaptation planning in other dryland forest ecosystems.

Forests↗
USGS2004

Occupancy as a surrogate for abundance estimation

In many monitoring programmes it may be prohibitively expensive to estimate the actual abundance of a bird species in a defined area, particularly at large spatial scales, or where birds occur at very low densities. Often it may be appropriate to consider the proportion of area occupied by the species as an alternative state variable. However, as with abundance estimation, issues of detectability must be taken into account in order to make accurate inferences: the non?detection of the species does not imply the species is genuinely absent. Here we review some recent modelling developments that permit unbiased estimation of the proportion of area occupied, colonization and local extinction probabilities. These methods allow for unequal sampling effort and enable covariate information on sampling locations to be incorporated. We also describe how these models could be extended to incorporate information from marked individuals, which would enable finer questions of population dynamics (such as turnover rate of nest sites by specific breeding pairs) to be addressed. We believe these models may be applicable to a wide range of bird species and may be useful for investigating various questions of ecological interest. For example, with respect to habitat quality, we might predict that a species is more likely to have higher local extinction probabilities, or higher turnover rates of specific breeding pairs, in poor quality habitats.

Animal Biodiversity and Conservation↗
USGS2014

Error propagation in energetic carrying capacity models

Conservation objectives derived from carrying capacity models have been used to inform management of landscapes for wildlife populations. Energetic carrying capacity models are particularly useful in conservation planning for wildlife; these models use estimates of food abundance and energetic requirements of wildlife to target conservation actions. We provide a general method for incorporating a foraging threshold (i.e., density of food at which foraging becomes unprofitable) when estimating food availability with energetic carrying capacity models. We use a hypothetical example to describe how past methods for adjustment of foraging thresholds biased results of energetic carrying capacity models in certain instances. Adjusting foraging thresholds at the patch level of the species of interest provides results consistent with ecological foraging theory. Presentation of two case studies suggest variation in bias which, in certain instances, created large errors in conservation objectives and may have led to inefficient allocation of limited resources. Our results also illustrate how small errors or biases in application of input parameters, when extrapolated to large spatial extents, propagate errors in conservation planning and can have negative implications for target populations.

Journal of Conservation Planning↗
USGS2015

The role of floodplain restoration in mitigating flood risk, Lower Missouri River, USA

Recent extreme floods on the Lower Missouri River have reinvigorated public policy debate about the potential role of floodplain restoration in decreasing costs of floods and possibly increasing other ecosystem service benefits. The first step to addressing the benefits of floodplain restoration is to understand the interactions of flow, floodplain morphology, and land cover that together determine the biophysical capacity of the floodplain. In this article we address interactions between ecological restoration of floodplains and flood-risk reduction at 3 scales. At the scale of the Lower Missouri River corridor (1300 km) floodplain elevation datasets and flow models provide first-order calculations of the potential for Missouri River floodplains to store floods of varying magnitude and duration. At this same scale assessment of floodplain sand deposition from the 2011 Missouri River flood indicates the magnitude of flood damage that could potentially be limited by floodplain restoration. At the segment scale (85 km), 1-dimensional hydraulic modeling predicts substantial stage reductions with increasing area of floodplain restoration; mean stage reductions range from 0.12 to 0.66 m. This analysis also indicates that channel widening may contribute substantially to stage reductions as part of a comprehensive strategy to restore floodplain and channel habitats. Unsteady 1-dimensional flow modeling of restoration scenarios at this scale indicates that attenuation of peak discharges of an observed hydrograph from May 2007, of similar magnitude to a 10 % annual exceedance probability flood, would be minimal, ranging from 0.04 % (with 16 % floodplain restoration) to 0.13 % (with 100 % restoration). At the reach scale (15–20 km) 2-dimensional hydraulic models of alternative levee setbacks and floodplain roughness indicate complex processes and patterns of flooding including substantial variation in stage reductions across floodplains depending on topographic complexity and hydraulic roughness. Detailed flow patterns captured in the 2-dimensional model indicate that most floodplain storage occurs on the rising limb of the flood as water flows into floodplain bottoms from downstream; at a later time during the rising limb this pattern is reversed and the entire bottom conveys discharge down the valley. These results indicate that flood-risk reduction by attenuation is likely to be small on a large river like the Missouri and design strategies to optimize attenuation and ecological restoration should focus on frequent floods (20–50 % annual exceedance probability). Local stage reductions are a more certain benefit of floodplain restoration but local effects are highly dependent on magnitude of flood discharge and how floodplain vegetation communities contribute to hydraulic roughness. The most certain flood risk reduction benefit of floodplain restoration is avoidance of flood damages to crops and infrastructure.

Iowa, Kansas, Minnesota, Missouri, Nebraska, South↗
USGS2022

Democratizing macroecology: Integrating unoccupied aerial systems with the National Ecological Observatory Network

Macroecology research seeks to understand ecological phenomena with causes and consequences that accumulate, interact, and emerge across scales spanning several orders of magnitude. Broad-extent, fine-grain information (i.e., high spatial resolution data over large areas) is needed to adequately capture these cross-scale phenomena, but these data have historically been costly to acquire and process. Unoccupied aerial systems (UAS or drones carrying a sensor payload) and the National Ecological Observatory Network (NEON) make the broad-extent, fine-grain observational domain more accessible to researchers by lowering costs and reducing the need for highly specialized equipment. Integration of these tools can further democratize macroecological research, as their strengths and weaknesses are complementary. However, using these tools for macroecology can be challenging because mental models are lacking, thus requiring large up-front investments in time, energy, and creativity to become proficient. This challenge inspired a working group of UAS-using academic ecologists, NEON professionals, imaging scientists, remote sensing specialists, and aeronautical engineers at the 2019 NEON Science Summit in Boulder, Colorado, to synthesize current knowledge on how to use UAS with NEON in a mental model for an intended audience of ecologists new to these tools. Specifically, we provide (1) a collection of core principles for collecting high-quality UAS data for NEON integration and (2) a case study illustrating a sample workflow for processing UAS data into meaningful ecological information and integrating it with NEON data collected on the ground—with the Terrestrial Observation System—and remotely—from the Airborne Observation Platform. With this mental model, we advance the democratization of macroecology by making a key observational domain—the broad-extent, fine-grain domain—more accessible via NEON/UAS integration.

Ecosphere↗
USGS2008

Annual variation in habitat-specific recruitment success: Implications from an individual-based model of Lake Michigan alewife (Alosa pseudoharengus)

The identification of important spawning and nursery habitats for fish stocks can aid fisheries management, but is complicated by various factors, including annual variation in recruitment success. The alewife (Alosa pseudoharengus) is an ecologically important species in Lake Michigan that utilizes a variety of habitats for spawning and early life growth. While productive, warm tributary mouths (connected to Lake Michigan) may contribute disproportionately more recruits (relative to their habitat volume) to the adult alewife population than cooler, less productive nearshore habitats, the extent of interannual variation in the relative contributions of recruits from these two habitat types remains unknown. We used an individual-based bioenergetics simulation model and input data on daily temperatures to estimate alewife recruitment to the adult population by these different habitat types. Simulations suggest that nearshore lake habitats typically produce the vast majority of young alewife recruits. However, tributary habitats may contribute the majority of alewife recruits during years of low recruitment. We suggest that high interannual variation in the relative importance of habitats for recruitment is a common phenomenon, which should be considered when developing habitat management plans for fish populations. ?? 2008 NRC.

Canadian Journal of Fisheries and Aquatic Sciences↗
USGS2014

Geographic coincidence of richness, mass, conservation value, and response to climate of U.S. land birds

Distributional patterns across the United States of five avian community breeding-season characteristics&mdash;community biomass, richness, constituent species' vulnerability to extirpation, percentage of constituent species' global abundance present in the community (conservation index, CI), and the community's position along the ecological gradient underlying species composition (principal curve ordination score, PC)&mdash;were described, their covariation was analyzed, and projected effects of climate change on the characteristics and their covariation were modeled. Higher values of biomass, richness, and CI were generally preferred from a conservation perspective. However, higher values of these characteristics often did not coincide geographically; thus regions of the United States would differ in their value for conservation depending on which characteristic was chosen for setting conservation priorities. For instance, correlation patterns between characteristics differed among Landscape Conservation Cooperatives. Among the five characteristics, community richness and the ecological gradient underlying community composition (PC) had the highest correlations with longitude, with richness declining from east to west across the contiguous United States. The ecological gradient underlying composition exhibited a demarcation near the 100th meridian, separating the contiguous United States grossly into two similar-sized avian ecological provinces. The combined score (CS), a measure of species' threat of decline or extirpation, exhibited the strongest latitudinal pattern, declining from south to north. Over &sim;75% of the lower United States, projected changes in June temperature and precipitation to year 2080 were associated with decreased averaged values of richness, biomass, and CI, implying decreased conservation value for birds. The two ecological provinces demarcated near the 100th meridian diverged from each other, with projected changes in June temperatures and precipitation from the year 2000 to 2080 suggesting increased ecological dissimilarity between the eastern and western halves of the lower United States with changing climate. Anticipated climate-related changes in the five characteristics by 2080 were more weakly correlated with latitude or longitude then the responses themselves, indicating less distinct geographic patterns of characteristic change than in the characteristics themselves. Climate changes projected for 2080 included geographic shifts in avian biomass, CS, and PC values, a moderate overall decline in CI, and general decline in species richness per site.

Ecological Applications↗
USGS2012

Explaining differences between bioaccumulation measurements in laboratory and field data through use of a probabilistic modeling approach

In the regulatory context, bioaccumulation assessment is often hampered by substantial data uncertainty as well as by the poorly understood differences often observed between results from laboratory and field bioaccumulation studies. Bioaccumulation is a complex, multifaceted process, which calls for accurate error analysis. Yet, attempts to quantify and compare propagation of error in bioaccumulation metrics across species and chemicals are rare. Here, we quantitatively assessed the combined influence of physicochemical, physiological, ecological, and environmental parameters known to affect bioaccumulation for 4 species and 2 chemicals, to assess whether uncertainty in these factors can explain the observed differences among laboratory and field studies. The organisms evaluated in simulations including mayfly larvae, deposit-feeding polychaetes, yellow perch, and little owl represented a range of ecological conditions and biotransformation capacity. The chemicals, pyrene and the polychlorinated biphenyl congener PCB-153, represented medium and highly hydrophobic chemicals with different susceptibilities to biotransformation. An existing state of the art probabilistic bioaccumulation model was improved by accounting for bioavailability and absorption efficiency limitations, due to the presence of black carbon in sediment, and was used for probabilistic modeling of variability and propagation of error. Results showed that at lower trophic levels (mayfly and polychaete), variability in bioaccumulation was mainly driven by sediment exposure, sediment composition and chemical partitioning to sediment components, which was in turn dominated by the influence of black carbon. At higher trophic levels (yellow perch and the little owl), food web structure (i.e., diet composition and abundance) and chemical concentration in the diet became more important particularly for the most persistent compound, PCB-153. These results suggest that variation in bioaccumulation assessment is reduced most by improved identification of food sources as well as by accounting for the chemical bioavailability in food components. Improvements in the accuracy of aqueous exposure appear to be less relevant when applied to moderate to highly hydrophobic compounds, because this route contributes only marginally to total uptake. The determination of chemical bioavailability and the increase in understanding and qualifying the role of sediment components (black carbon, labile organic matter, and the like) on chemical absorption efficiencies has been identified as a key next steps.

Integrated Environmental Assessment and Management↗
USGS2022

Medium spatial resolution mapping of global land cover and land cover change across multiple decades from Landsat

Land cover maps are essential for characterizing the biophysical properties of the Earth’s land areas. Because land cover information synthesizes a rich array of information related to both the ecological condition of land areas and their exploitation by humans, they are widely used for basic and applied research that requires information related to land surface properties (e.g., terrestrial carbon models, water balance models, weather, and climate models) and are core inputs to models and analyses used by natural resource scientists and land managers. As the Earth’s global population has grown over the last several decades rates of land cover change have increased dramatically, with enormous impacts on ecosystem services (e.g., biodiversity, water supply, carbon sequestration, etc.). Hence, accurate information related to land cover is essential for both managing natural resources and for understanding society’s ecological, biophysical, and resource management footprint. To address the need for high-quality land cover information we are using the global record of Landsat observations to compile annual maps of global land cover from 2001 to 2020 at 30 m spatial resolution. To create these maps we use features derived from time series of Landsat imagery in combination with ancillary geospatial data and a large database of training sites to classify land cover at annual time step. The algorithm that we apply uses temporal segmentation to identify periods with stable land cover that are separated by breakpoints in the time series. Here we provide an overview of the methods and data sets we are using to create global maps of land cover. We describe the algorithms used to create these maps and the core land cover data sets that we are creating through this effort, and we summarize our approach to accuracy assessment. We also present a synthesis of early results and discuss the strengths and weaknesses of our early map products and the challenges that we have encountered in creating global land cover data sets from Landsat. Initial accuracy assessment for North America shows good overall accuracy (77.0 ± 2.0% correctly classified) and 79.8% agreement with the European Space Agency (ESA) WorldCover product. The land cover mapping results we report provide the foundation for robust, repeatable, and accurate mapping of global land cover and land cover change across multiple decades at 30 m spatial resolution from Landsat.

Frontiers in Remote Sensing↗
USGS2011

Using maximum entropy modeling for optimal selection of sampling sites for monitoring networks

Environmental monitoring programs must efficiently describe state shifts. We propose using maximum entropy modeling to select dissimilar sampling sites to capture environmental variability at low cost, and demonstrate a specific application: sample site selection for the Central Plains domain (453,490 km 2 ) of the National Ecological Observatory Network (NEON). We relied on four environmental factors: mean annual temperature and precipitation, elevation, and vegetation type. A “sample site” was defined as a 20 km × 20 km area (equal to NEON’s airborne observation platform [AOP] footprint), within which each 1 km 2 cell was evaluated for each environmental factor. After each model run, the most environmentally dissimilar site was selected from all potential sample sites. The iterative selection of eight sites captured approximately 80% of the environmental envelope of the domain, an improvement over stratified random sampling and simple random designs for sample site selection. This approach can be widely used for cost-efficient selection of survey and monitoring sites.

Diversity↗
USGS2006

Ecological and sampling constraints on defining landscape fire severity

Ecological definition and detection of fire severity are influenced by factors of spatial resolution and timing. Resolution determines the aggregation of effects within a sampling unit or pixel (alpha variation), hence limiting the discernible ecological responses, and controlling the spatial patchiness of responses distributed throughout a burn (beta variation). As resolution decreases, alpha variation increases, extracting beta variation and complexity from the spatial model of the whole burn. Seasonal timing impacts the quality of radiometric data in terms of transmittance, sun angle, and potential contrast between responses within burns. Detection sensitivity candegrade toward the end of many fire seasons when low sun angles, vegetation senescence, incomplete burning, hazy conditions, or snow are common. Thus, a need exists to supersede many rapid response applications when remote sensing conditions improve. Lag timing, or timesince fire, notably shapes the ecological character of severity through first-order effects that only emerge with time after fire, including delayed survivorship and mortality. Survivorship diminishes the detected magnitude of severity, as burned vegetation remains viable and resprouts, though at first it may appear completely charred or consumed above ground. Conversely, delayed mortality increases the severity estimate when apparently healthy vegetation is in fact damaged by heat to the extent that it dies over time. Both responses dependon fire behavior and various species-specific adaptations to fire that are unique to the pre-firecomposition of each burned area. Both responses can lead initially to either over- or underestimating severity. Based on such implications, three sampling intervals for short-term burn severity are identified; rapid, initial, and extended assessment, sampled within about two weeks, two months, and depending on the ecotype, from three months to one year after fire, respectively. Spatial and temporal conditions of sampling strategies constrain data quality and ecological information obtained about fire severity. Though commonly overlooked, such considerations determine the objectives and hypotheses that are appropriate for each application, and are especially important when building comparative studies or long-term reference databases on fire severity.

Fire Ecology↗