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USGS2017

Evapotranspiration by remote sensing: An analysis of the Colorado River Delta before and after the Minute 319 pulse flow to Mexico

The unique hydrologic conditions characterizing riparian ecosystems in dryland (arid and semi-arid) areas help maintain high biodiversity and support high levels of primary productivity compared to associated uplands. In western North America, many riparian ecosystems have been damaged by altered flow regimes (e.g., impoundments and diversions) and over utilization of water resources (e.g., groundwater pumping for agriculture and human consumption). This has led some state and national governments to provide occasional environmental flows to address the declining condition of such riparian systems. In a historic agreement between the United States and Mexico, 130 million cubic meters (mcm) of water was released to the lower Colorado River Delta in Mexico, with the intent to evaluate the hydrological and biological response of the ecosystem. We used the Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) to estimate long term (2000–2014) and short term (pre- and post-pulse; 2013 and 2014) evapotranspiration (ET; used herein as an indicator of plant health) of the delta’s riparian corridor. We found the pulse flow helped reverse a decline in ET from 2011 to 2013, with a small, but statistically significant increase in 2014 (P < 0.05). ET was greater than 100 mcm in all years analyzed (even in years without surface flows) and exceeded surface flows in all years except 2000 (result of excess flows following an El Niño cycle in 1997) and 2014 (year of the pulse flow). Based on groundwater salinities and MODIS ET estimates, we estimated groundwater flow into the delta to be ∼103 mcm. Shallow groundwater salinities in the riparian zone increased from 1.30 g L −1 in the most upstream reach to 2.77 g L −1 in the most downstream reach we measured, partly due to uptake of water by riparian vegetation and partly to intrusion of saline agricultural return flows. The disparity between surface flows and ET can likely be explained by the predominantly phreatophytic plants characterizing the area, which draw water from the aquifer. These results also suggest that the deteriorated condition of vegetation within the riparian zone might not be reversed by a single pulse event and could instead require subsequent pulse flows as a long term strategy to restore vegetation in this riparian ecosystem.

Ecological Engineering↗

USGS2020

Acoustic space occupancy: Combining ecoacoustics and lidar to model biodiversity variation and detection bias across heterogeneous landscapes

There is global interest in quantifying changing biodiversity in human-modified landscapes. Ecoacoustics may offer a promising pathway for supporting multi-taxa monitoring, but its scalability has been hampered by the sonic complexity of biodiverse ecosystems and the imperfect detectability of animal-generated sounds. The acoustic signature of a habitat, or soundscape, contains information about multiple taxa and may circumvent species identification, but robust statistical technology for characterizing community-level attributes is lacking. Here, we present the Acoustic Space Occupancy Model, a flexible hierarchical framework designed to account for detection artifacts from acoustic surveys in order to model biologically relevant variation in acoustic space use among community assemblages. We illustrate its utility in a biologically and structurally diverse Amazon frontier forest landscape, a valuable test case for modeling biodiversity variation and acoustic attenuation from vegetation density. We use complementary airborne lidar data to capture aspects of 3D forest structure hypothesized to influence community composition and acoustic signal detection. Our novel analytic framework permitted us to model both the assembly and detectability of soundscapes using lidar-derived estimates of forest structure. Our empirical predictions were consistent with physical models of frequency-dependent attenuation, and we estimated that the probability of observing animal activity in the frequency channel most vulnerable to acoustic attenuation varied by over 60%, depending on vegetation density. There were also large differences in the biotic use of acoustic space predicted for intact and degraded forest habitats, with notable differences in the soundscape channels predominantly occupied by insects . This study advances the utility of ecoacoustics by providing a robust modeling framework for addressing detection bias from remote audio surveys while preserving the rich dimensionality of soundscape data, which may be critical for inferring biological patterns pertinent to multiple taxonomic groups in the tropics. Our methodology paves the way for greater integration of remotely sensed observations with high-throughput biodiversity data to help bring routine, multi-taxa monitoring to scale in dynamic and diverse landscapes.

Mato Grosso↗

USGS2023

Effects of landcover on mesocarnivore density and detection rate along an urban to rural gradient

Human development has major implications for wildlife populations. Urban-exploiter species can benefit from human subsidized resources, whereas urban-avoider species can vanish from wildlife communities in highly developed areas. Therefore, understanding how the density of different species varies in response to landcover changes associated with human development can provide important insight into how wildlife communities are likely to change and provide a starting point for predicting the consequences of those changes. Here, we estimated the population density of five common mesocarnivore species (coyote ( Canis latrans ), bobcat ( Lynx rufus ), red fox ( Vulpes vulpes ), raccoon ( Procyon lotor ), and Virginia opossum ( Didelphis virginiana) ) at 12 study sites along an urban to rural gradient in the greater Fayetteville Area, Northwest Arkansas, USA between November 2021, and March 2022. At each study site, we applied the Random Encounter Model (REM) to data from camera traps to calculate the density of five focal species. Coyote density ranged from 0.5 to 0.93 individuals/km 2 . Raccoon density ranged from 0.19 to 20.25 individuals/km 2 . Bobcat density ranged from 0 to 1.06 individuals/km 2 . Opossum density ranged from 0 to 3.43 individuals/km 2 . Red fox density ranged from 0 to 0.10 individuals/km 2 . Coyote and raccoon density showed a positive relationship with anthropogenic noise. Opossum density increased with HUD. Red Fox and bobcat density showed a negative relationship with forest area and a positive relationship with distance to water respectively, however confidence intervals for both species overlapped zero. The density estimates we report based on camera trap data of unmarked animals were consistent with reports from the literature for these same species derived from traditional methods, providing additional support to the REM as a viable, non-invasive method to calculate density of unmarked species. Our second analysis consisted of taking camera level density estimates and treating them as detection rates corrected for camera viewshed and animal movement. Coyote and raccoon detection rate showed a positive relationship with anthropogenic noise. Red Fox detection rate was positively related to developed open space, and negatively related to distance to water. Similarly to red fox, opossums detection rate was higher in areas with more developed open space. We found no evidence that bobcat density or detection rate varied with any of the landcover or anthropogenic variables we measured.

Arkansas↗

USGS2022

Looking ahead, guided by the past: The role of U.S. national parks in amphibian research and conservation

Protected areas like national parks are essential elements of conservation because they limit human influence on the landscape, which protects biodiversity and ecosystem function. The role of national parks in conservation, however, often goes far beyond limiting human influence. The U.S. National Park Service and its system of land units contribute substantively to conservation by providing protected lands where researchers can document trends in species distributions and abundances, examine characteristics important for generating these trends, and identify and implement conservation strategies to preserve biodiversity. We reviewed the contribution of U.S. national parks to amphibian research and conservation and highlight important challenges and findings in several key areas. First, U.S. national parks were instrumental in providing strong support that amphibian declines were real and unlikely to be simply a consequence of habitat loss. Second, research in U.S. national parks provided evidence against certain hypothesized causes of decline, like UV-B radiation, and evidence for others, such as introduced species and disease. However, describing declines and identifying causes contributes to conservation only if it leads to management; importantly, U.S. national parks have implemented many conservation strategies and evaluated their effectiveness in recovering robust amphibian populations. Among these, removal of invasive species, especially fishes; conservation translocations; and habitat creation and enhancement stand out as examples of successful conservation strategies with broad applicability. Successful management for amphibians is additionally complicated by competing mandates and stakeholder interests; for example, past emphasis on increasing visitor enjoyment by introducing fish to formerly fishless lakes had devastating consequences for many amphibians. Other potential conflicts with amphibian conservation include increasing development, increased risk of introductions of disease and exotic species with increased visitation, and road mortality. Decision science and leveraging partnerships have proven to be key components of effective conservation under conflicting mandates in national parks. As resource managers grapple with large-scale drivers that are outside local control, public-private partnerships and adaptive strategies are increasing in importance. U.S. national parks have played an important role in many aspects of identifying and ameliorating the amphibian decline crisis and will continue to be essential for the conservation of amphibians in the future.

Ecological Indicators↗

USGS2022

Moisture abundance and proximity mediate seasonal use of mesic areas and survival of greater sage-grouse broods

Water is a critical and limited resource, particularly in the arid West, but water availability is projected to decline even while demand increases due to growing human populations and increases in duration and severity of drought. Mesic areas provide important water resources for numerous wildlife species, including the greater sage-grouse ( Centrocercus urophasianus ; hereafter, sage-grouse), an indicator for the health of sagebrush ecosystems. Understanding how wildlife use these crucial areas is necessary to inform management and conservation of sensitive species. Specifically, the influence of anthropogenic water subsidies such as irrigated pastures is not well-studied. We evaluated brood-rearing habitat selection and brood survival of sage-grouse in Long Valley, California, an area where the water rights are primarily owned by the city of Los Angeles and water is used locally to irrigate for livestock. This area thus represents a unique balance between the needs of wildlife and people that could increasingly define future water management. In this study, sage-grouse broods moved closer to the edge of mesic areas and used more interior areas during the late brood-rearing period, selecting for greener areas after 1 July. Mesic areas were particularly important during dry years, with broods using areas farther interior than in wet years. Brood survival was also positively influenced by the availability and condition of mesic resources, as indicated by variation in values of normalized difference vegetation index (NDVI), with survival peaking at moderate values of NDVI and just outside the edge but decreasing inside the mesic areas. Our results highlight the importance of quality edge habitat of large mesic areas for sage-grouse to balance habitat selection and survival, particularly during drier years and during the late brood-rearing period, which is a critical period because chick survival has been shown to influence population growth. This study highlights the implications of large-scale anthropogenic water manipulation, and the balance between local irrigation and water distribution to benefit other regions, from the context of a species of high conservation concern in North American sagebrush ecosystems.

California↗

USGS2006

Trade-offs across space, time, and ecosystem services

Ecosystem service (ES) trade-offs arise from management choices made by humans, which can change the type, magnitude, and relative mix of services provided by ecosystems. Trade-offs occur when the provision of one ES is reduced as a consequence of increased use of another ES. In some cases, a trade-off may be an explicit choice; but in others, trade-offs arise without premeditation or even awareness that they are taking place. Trade-offs in ES can be classified along three axes: spatial scale, temporal scale, and reversibility. Spatial scale refers to whether the effects of the trade-off are felt locally or at a distant location. Temporal scale refers to whether the effects take place relatively rapidly or slowly. Reversibility expresses the likelihood that the perturbed ES may return to its original state if the perturbation ceases. Across all four Millennium Ecosystem Assessment scenarios and selected case study examples, trade-off decisions show a preference for provisioning, regulating, or cultural services (in that order). Supporting services are more likely to be "taken for granted." Cultural ES are almost entirely unquantified in scenario modeling; therefore, the calculated model results do not fully capture losses of these services that occur in the scenarios. The quantitative scenario models primarily capture the services that are perceived by society as more important - provisioning and regulating ecosystem services - and thus do not fully capture trade-offs of cultural and supporting services. Successful management policies will be those that incorporate lessons learned from prior decisions into future management actions. Managers should complement their actions with monitoring programs that, in addition to monitoring the short-term provisions of services, also monitor the long-term evolution of slowly changing variables. Policies can then be developed to take into account ES trade-offs at multiple spatial and temporal scales. Successful strategies will recognize the inherent complexities of ecosystem management and will work to develop policies that minimize the effects of ES trade-offs. Copyright ?? 2006 by the author(s).

Ecology and Society↗

USGS2002

Comparisons of likelihood and machine learning methods of individual classification

Classification methods used in machine learning (e.g., artificial neural networks, decision trees, and k -nearest neighbor clustering) are rarely used with population genetic data. We compare different nonparametric machine learning techniques with parametric likelihood estimations commonly employed in population genetics for purposes of assigning individuals to their population of origin (“assignment tests”). Classifier accuracy was compared across simulated data sets representing different levels of population differentiation (low and high F ST ), number of loci surveyed (5 and 10), and allelic diversity (average of three or eight alleles per locus). Empirical data for the lake trout ( Salvelinus namaycush ) exhibiting levels of population differentiation comparable to those used in simulations were examined to further evaluate and compare classification methods. Classification error rates associated with artificial neural networks and likelihood estimators were lower for simulated data sets compared to k -nearest neighbor and decision tree classifiers over the entire range of parameters considered. Artificial neural networks only marginally outperformed the likelihood method for simulated data (0–2.8% lower error rates). The relative performance of each machine learning classifier improved relative likelihood estimators for empirical data sets, suggesting an ability to “learn” and utilize properties of empirical genotypic arrays intrinsic to each population. Likelihood-based estimation methods provide a more accessible option for reliable assignment of individuals to the population of origin due to the intricacies in development and evaluation of artificial neural networks. In recent years, characterization of highly polymorphic molecular markers such as mini- and microsatellites and development of novel methods of analysis have enabled researchers to extend investigations of ecological and evolutionary processes below the population level to the level of individuals (e.g., Bowcock et al. 1994 ; Estoup and Angers 1998 ; Jarne and Lagoda 1996 ). Analyses of individual-based genotypic information could substantially improve our understanding of evolutionary phenomena and contribute to effective management of natural populations (review in Bernatchez and Duchesne 2000 ). The use of individual-based methods remained largely unexplored in animal populations until recently due to a lack of highly polymorphic markers ( Bernatchez and Duchesne 2000 ; Smouse and Chevillon 1998 ). Traditional analytical methods in population genetics rely almost exclusively on descriptors of genetic characterizations of populations ( Bernatchez and Duchesne 2000 ) and not on individual genotypes. “Assignment tests” are designed to determine population membership for individuals. One particular application based on a likelihood estimate (LE) was introduced by Paetkau et al. (1995 ; see also Vásquez-Domínguez et al. 2001) to assign an individual to the population of origin on the basis of multilocus genotype and expectations of observing this genotype in each potential source population. The LE approach can be implemented statistically in a Bayesian framework as a convenient way to evaluate hypotheses of plausible genealogical relationships (e.g., that an individual possesses an ancestor in another population) ( Dawson and Belkhir 2001 ; Pritchard et al. 2000 ; Rannala and Mountain 1997 ). Other studies have evaluated the confidence of the assignment ( Almudevar 2000 ) and characteristics of genotypic data (e.g., degree of population divergence, number of loci, number of individuals, number of alleles) that lead to greater population assignment ( Bernatchez and Duchesne 2000 ; Cornuet et al. 1999 ; Haig et al. 1997 ; Shriver et al. 1997; Smouse and Chevillon 1998 ). Main statistical and conceptual differences between methods leading to the use of an assignment test are given in, for example, Cornuet et al. (1999) and Rosenberg et al. (2001) . However, the relative power of those tests has certainly not been fully appreciated and empirical comparisons are scarce ( Eldridge et al. 2001 ). Assignment tests can also be considered as surrogates at the individual level (sensu Hansen et al. 2001a ) for other statistical tools developed earlier, such as mixed-stock analysis (e.g., Pella and Masuda 2001 ; Pella and Milner 1987 ). Detailed theoretical comparison of the interests and limitations of both methods are still lacking, but empirical studies have revealed correlations between outputs of methods ( Knutsen et al. 2001 ; Potvin and Bernatchez 2001 ). Assignment tests have been widely used in different applications, including determination of degree of population differentiation or to establish the relationship among individuals within and among various taxonomic groupings (e.g., Bogdanowicz et al. 1997 ; Koskinen et al. 2001 ; Marshall et al. 2000 ; Müller 2000 ; Neraas and Spruell 2001 ; Nielsen et al. 2001b ; Polzhien et al. 2000 ; Primmer et al. 1999 ; Roeder et al. 2001 ; Roques et al. 1999 ; Schulte-Hostedde et al. 2001 ; Sefc et al. 2000 ; Spidle et al. 2001 ; Vásquez-Domínguez et al. 2001 ), including hybrids (e.g., Beaumont et al. 2001 ; Congiu et al. 2001 ; Randi et al. 2001 ), introgressed individuals (e.g., Martinez et al. 2001 ; Randi and Lucchini 2002 ), and ecotypes (e.g., Taylor et al. 2000 ). Applications of assignment tests also include [human] forensics (e.g., Evett and Weir 1998 ; Primmer et al. 2000 ), identification and/or source of dispersers (e.g., Davies et al. 1999 ; Eldridge et al. 2001 ; Galbusera et al. 2000 ; Petersson et al. 2001 ; Tsutsui et al. 2001 ; Vasemägi et al. 2001), phylogeographical analyses (e.g., King et al. 2001 ; Zeisset and Beebee 2001 ), and the evaluation of the contribution of stocked individuals to natural populations (e.g., Fritzner et al. 2001 ; Hansen et al. 2000 , 2001b ) and of supportive breeding programs ( Nielsen et al. 2001a ; Olsen et al. 2000 ). Fish are among the organisms that have received considerable attention using such tools (see Hansen et al. [2001a] for a review). Moreover, these techniques are now used for profiles of traits outside the limited scope of population genetics ( Thorrold et al. 2001 ). Methods of classification vary widely based on several criteria (e.g., Jain et al. 2000 ) ( Figure 1 ). Two basic classification processes are traditionally recognized in machine learning: supervised classifiers and unsupervised classifiers ( Figure 1 ; e.g., Duda et al. 2000 ; Jain et al. 2000 ). Supervised classifiers represent a group of methods whereby individual assignment is made to predefined classes (i.e., populations of origin). Unsupervised classification classes are unknown and are defined a posteriori on the basis of the degree of difference or similarity in attributes characterized from sampled individuals. Clustering methods (e.g., multidimensional scaling, principal component analysis) are examples of unsupervised classification. Applications of assignment testing in population genetics first used supervised parametric likelihood-based approaches ( Figure 1 ). Other machine learning classification methods are widely used in the physical and social sciences and in other biological disciplines (e.g. Boddy et al. 2000 ; Leung and Tran 2000 ; Manel et al. 1999 ; Raymer et al. 1997 ). Artificial neural networks (ANNs) are a popular technique used in machine learning (e.g., Boddy and Morris 1999 ; Duda et al. 2000 ; Lek and Guégan 2000 ; Ripley 1996 ). However, while recognized ( Hansen et al. 2001a ), ANN methods rarely have been employed for population genetics applications ( Aurelle 1999 ; Aurelle et al. 1999 ; Cornuet et al. 1996 ; Curtis et al. 2001 ; Giraudel et al. 2000 ; Grigull et al. 2001 ; Taylor et al. 1994 ; Whitler et al. 1994 ). Other popular classification methods in machine learning, such as decision trees (e.g., Bell 1996 , 1999 ; Duda et al. 2000 ; Mitchell 1997 ) and k -nearest neighbor analysis ( k -NN; e.g., Dasarathy 1991 ; Duda et al. 2000 ) have yet to be applied in population genetics ( Figure 1 ). Moreover, there has not been a directed effort to compare machine learning methodologies with the likelihood-based procedures widely used in population genetics. Cornuet et al. (1996) compared the relative merits of ANNs to discriminant analysis in an empirical study involving different populations and subspecies of honeybee ( Apis mellifera ). However, they did not compare LE and ANN supervised classifiers. Aurelle (1999) used the approach of Rannala and Mountain (1997) ( Figure 1 ) and ANN analysis using brown trout ( Salmo trutta ) microsatellite data; however, he did not provide a direct comparison of classification results or accuracies. Hansen et al. (2001a) briefly presented ANNs, but rejected their use without really testing their ability to classify individuals. The objective of this article is to describe several of the more widely used machine learning classifiers that may have utility when used with empirical population genetics data. We compare likelihood-based “assignment tests” ( Paetkau et al. 1995 ) with supervised machine learning classifiers including ANN, decision tree, and a k -NN clustering. Simulations were conducted which estimated and compared the assignment accuracy associated with different classifiers using ranges of parameter values (number of loci, allelic diversity, and interpopulation variance in allele frequency) typically encountered in natural populations. Comparative analyses were extended to empirical examples using lake trout ( Salvelinus namaycush ; Salmonidae).

Journal of Heredity↗

USGS2021

Interrupted incubation: How dabbling ducks respond when flushed from the nest

Nesting birds must provide a thermal environment sufficient for egg development while also meeting self‐maintenance needs. Many birds, particularly those with uniparental incubation, achieve this balance through periodic incubation recesses, during which foraging and other self‐maintenance activities can occur. However, incubating birds may experience disturbances such as predator or human activity which interrupt natural incubation patterns by compelling them to leave the nest. We characterized incubating mallard Anas platyrhynchos and gadwall Mareca strepera hens’ responses when flushed by predators and investigators in Suisun Marsh, California, USA. Diurnal incubation recesses initiated by investigators approaching nests were 63% longer than natural diurnal incubation recesses initiated by the hen (geometric mean: 226.77 min versus 142.04 min). Nocturnal incubation recesses, many of which were likely the result of predators flushing hens, were of similar duration regardless of whether the nest was partially depredated during the event (115.33 [101.01;131.68] minutes) or not (119.62 [111.96;127.82] minutes), yet were 16% shorter than natural diurnal incubation recesses. Hens moved further from the nest during natural diurnal recesses or investigator‐initiated recesses than during nocturnal recesses, and the proportion of hen locations recorded in wetland versus upland habitat during recesses varied with recess type (model‐predicted means: natural diurnal recess 0.77; investigator‐initiated recess 0.82; nocturnal recess 0.31). Hens were more likely to take a natural recess following an investigator‐initiated recess earlier that same day than following a natural recess earlier that same day, and natural recesses that followed an investigator‐initiated recess were longer than natural recesses that followed an earlier natural recess, suggesting that hens may not fulfill all of their physiological needs during investigator‐initiated recesses. We found no evidence that the duration of investigator‐initiated recesses was influenced by repeated visits to the nest, whether by predators or by investigators, and trapping and handling the hen did not affect investigator‐initiated recess duration unless the hen was also fitted with a backpack‐harness style GPS–GSM transmitter at the time of capture. Hens that were captured and fitted with GPS–GSM transmitters took recesses that were 26% longer than recesses during which a hen was captured but a GPS–GSM transmitter was not attached. Incubation interruptions had measurable but limited and specific effects on hen behavior.

Ecology and Evolution↗

USGS1997

Stream habitat characteristics of fixed sites in the western Lake Michigan drainages, Wisconsin and Michigan, 1993-95

Habitat characteristics of 11 fixed sites in the Western Lake Michigan Drainages were examined by the U.S. Geological Survey from 1993 through 1995 as part of the ecological assessment of the National Water-Quality Assessment Program. Evaluation of habitat consisted of more than 75 measurements at three spatial levels: drainage basin, stream segment between major tributaries (length from 1 to 14 kilometers), and stream reach (approximately 150 meters). The 11 fixed sites consisted of 8 "indicator" sites with drainage basins that differ in bedrock type, surficial deposits, and land use; and 3 "integrator" sites with drainage basins that contain a mixture of bedrock type, surficial deposits, and land use. Spatial and temporal variations in habitat characteristics are described and compared. Comparisons are limited to indicator sites except for comparisons amongbasin characteristics, which include all fixed sites. Two habitat classification schemes used in Wisconsin and Michigan were used to rank the quality of habitat in indicator streams. Reach-level data were collected at two additional reaches at three of the indicator sites to assess the representativeness of the reach for overall stream conditions. Although the number of sites is small, statistical analyses indicate that spatial distribution of several characteristics can be related to land use, geology, topography, and width of the riparian zone. Land use and geology, in combination, appeared to be important factors in controlling flood magnitudes. Annual mean flow was correlated with basin shape and drainage density and low flow was correlated with permeability of soils in the basin. At the reach level, a wide variety of characteristics were observed at the eight indicator sites, with many of the characteristics significantly different between sites. Spatial differences in some reach characteristics can be attributed to the percentage of agriculture in the drainage basin, type of surficial deposits, and width of the riparian zone. Temporal variability in width, depth, and velocity can be attributed to variable flow conditions; whereas temporal variability in streambank measurements are attributed to problematic identification of the boundary between the flood plain and streambanks. Data from multiple-reach sites indicate that the primary reach adequately represented the variability found within the stream segment for depth, streambank stability index, and canopy angle. However, velocity, dominant substrate type, embeddedness, streambank height, streambank angle, and streambank vegetative stability differed among the multiple reaches at one or more of the three sites. Correlation analyses of habitat characteristics with median concentrations of four nutrients, pH, and specific conductance indicates that dissolved nitrate plus nitrite concentrations are related to percentage of agriculture in the basin and fine-grained sediment deposition in the reach. Geology and land use appear to be major influences on pH, but their influence on specific conductance, although expected, was not confirmed in this study. Habitat evaluation scores at the eight indicator sites ranged from poor to good. Scores were correlated to the percentage of agricultural or urban land in the drainage basins, width of the riparian zone, and streambank stability index. Results from this study illustrate the need for collection of habitat data at multiple scales along with water-chemistry data for determining major influences on distribution of aquatic communities. These results also indicate the importance of collecting land use, geological, and geomorphic information at the drainage-basin level to adequately describe how natural and human factors influence local aquatic habitat conditions.

Michigan, Wisconsin↗

USGS2013

Habitat, wildlife and one health: Arcanobacterium pyogenes in Maryland and Upper Eastern Shore white-tailed deer populations

Background : Understanding the distribution of disease in wildlife is key to predicting the impact of emerging zoonotic one health concerns, especially for wildlife species with extensive human and livestock interfaces. The widespread distribution and complex interactions of white - tailed deer ( Odocoileus virginianus ) with humans suggest deer population health and management may have implications beyond stewardship of the animals. The intracranial abscessation suppurative meningitis (IASM) disease complex in deer has been linked to Arcanobacterium pyogenes , an under - diagnosed and often misdiagnosed organism considered commensal in domestic livestock but associated with serious disease in numerous species, including humans. Methods : Our study used standard bacterial culture techniques to assess A. pyogenes prevalence among male deer sampled across six physiogeographic regions in Maryland and male and female deer in the Upper Eastern Shore under Traditional Deer Management (TDM) and Quality Deer Management (QDM), a management protocol that alters population demographics in favor of older male deer. Samples were collected from antler pedicles for males, the top of the head where pedicles would be if present for females, or the whole dorsal frontal area of the head for neonates. We collected nasal samples from all animals by swabbing the nasopharyngeal membranes. A gram stain and catalase test were conducted, and aerobic bacteria were identified to genus and species when possible. We evaluated the effect of region on whether deer carried A. pyogenes using Pearson's chi-square test with Yates’ continuity correction. For the white-tailed deer management study, we tested whether site, age class and sex predisposed animals to carrying A. pyogenes using binary logistic regression. Results : A. pyogenes was detected on deer in three of the six regions studied, and was common in only one region, the Upper Eastern Shore. In the Upper Eastern Shore, 45% and 66% of antler and nasal swabs from deer were positive for A. pyogenes , respectively. On the Upper Eastern Shore, prevalence of A. pyogenes cultured from deer did not differ between management areas, and was abundant among both sexes and across all age classes. No A. pyogenes was cultured from a small sample of neonates. Conclusion : Our study indicates A. pyogenes may be carried widely among white-tailed deer regardless of sex or age class, but we found no evidence the pathogen is acquired in utero. The distribution of A. pyogenes across regions and concentration in a region with low livestock levels suggests the potential for localized endemicity of the organism and the possibility that deer may serve as a maintenance reservoir for an emerging one health concern.

Infection Ecology and Epidemiology↗

USGS2023

Crossing the threshold: Invasive grasses inhibit forest restoration on Hawaiian islands

Forest removal for livestock grazing is a striking example of human-caused state change leading to a stable, undesirable invasive grass system that is resistant to restoration efforts. Understanding which factors lead to resilience to the alternative grass state can greatly benefit managers when planning forest restoration. We address how thresholds of grass cover and seed rain might influence forest recovery in a restoration project on Hawaiʻi Island, USA. Since the 1980s, over 400,000 Acacia koa (koa) trees have been planted across degraded pasture, and invasive grasses still dominate the understory with no native woody-plant recruitment. Between this koa/grass matrix are remnant native Metrosideros polymorpha (ʻōhiʻa) trees beneath which native woody plants naturally recruit. We tested whether there were threshold levels of native woody understory that accelerate recruitment under both tree species by monitoring seed rain at 40 trees (20 koa and ʻōhiʻa) with a range of native woody understory basal area (BA). We found a positive relationship between total seed rain (but not bird-dispersed seed rain) and native woody BA and a negative relationship between native woody BA and grass cover, with no indication of threshold dynamics. We also experimentally combined grass removal levels with seed rain density (six levels) of two common understory species in plots under koa ( n = 9) and remnant ʻōhiʻa ( n = 9). Few seedlings emerged when no grass was removed despite adding seeds at densities two to 75 times higher than naturally occurring. However, seedling recruitment increased two to three times once at least 50% of grass was removed. Existing survey data of naturally occurring seedlings also supported a threshold of grass cover below which seedlings were able to establish. Thus, removal of all grasses is not necessary to achieve system responses: Even moderate reductions (~50%) can increase rates of native woody recruitment. The nonlinear thresholds found here highlight how incremental changes to an inhibitory factor lead to limited restoration success until a threshold is crossed. The resources needed to fully eradicate an invasive species may be unwarranted for state change, making understanding where thresholds lie of the utmost importance to prioritize resources.

Hawaii↗

USGS2022

The wildland-urban interface in the United States based on 125 million building locations

The wildland-urban interface (WUI) is the focus of many important land management issues, such as wildfire, habitat fragmentation, invasive species, and human-wildlife conflicts. Wildfire is an especially critical issue, because housing growth in the WUI increases wildfire ignitions and the number of homes at risk. Identifying the WUI is important for assessing and mitigating impacts of development on wildlands and for protecting homes from natural hazards, but data on housing development for large areas are often coarse. We created new WUI maps for the conterminous U.S. based on 125 million individual building locations, offering higher spatial precision compared to existing maps based on U.S. census housing data. Building point locations were based on a building footprint dataset from Microsoft®. We classified WUI across the conterminous U.S. at 30-m resolution using a circular neighborhood mapping algorithm with a variable radius to determine thresholds of housing density and vegetation cover. We used our maps to (1) determine the total area of the WUI and number of buildings included, (2) assess the sensitivity of WUI area included and spatial pattern of WUI maps to choice of neighborhood size, (3) assess regional differences between building-based WUI maps and census-based WUI maps, and (4) determine how building location accuracy affected WUI map accuracy. Our building-based WUI maps identified 5.6% – 18.8% of the conterminous U.S. as being in the WUI, with larger neighborhoods increasing WUI area but excluding isolated building clusters. Building-based maps identified more WUI area relative to census-based maps for all but the smallest neighborhoods, particularly in the north-central states, and large differences were attributable to high numbers of non-housing structures in rural areas. Overall WUI classification accuracy was 98.0%. For wildfire risk mapping and for general purposes, WUI maps based on the 500-m neighborhood represent the original Federal Register definition of the WUI; these maps include clusters of buildings in and adjacent to wildlands and exclude remote, isolated buildings. Our approach for mapping the WUI offers flexibility and high spatial detail, and can be widely applied to take advantage of the growing availability of high-resolution building footprint datasets and classification methods.

Conterminous United States↗

USGS2003

Spring hunting changes the regional movements of migrating greater snow geese

1. Human-induced disturbance such as hunting may influence the migratory behaviour of long-distance migrants. In 1999 and 2000 a spring hunt of greater snow geese Anser caerulescens atlanticus occurred for the first time in North America since 1916, aimed at stopping population growth to protect natural habitats. 2. We evaluated the impact of this hunt on the staging movements of geese along a 600-km stretch of the St Lawrence River in southern Quebec, Canada. 3. We tracked radio-tagged female geese in three contiguous regions of the staging area from the south-west to the north-east: Lake St Pierre, Upper Estuary and Lower Estuary, in spring 1997 (n = 37) and 1998 (n = 70) before the establishment of hunting , and in 1999 (n = 60) and 2000 (n = 59) during hunting . 4. We used multi-state capture-recapture models to estimate the movement probabilities of radio-tagged females among these regions. To assess disturbance level, we tracked geese during their feeding trips and estimated the probability of completing a foraging bout without being disturbed. 5. In the 2 years without hunting , migration was strongly unidirectional from the south-west to the north-east, with very low westward movement probabilities. Geese gradually moved from Lake St Pierre to Upper Estuary and then from Upper Estuary to Lower Estuary. 6. In contrast, during the 2 years with hunting westward movement was more than four times more likely than in preceding years. Most of these backward movements occurred shortly after the beginning of the hunt, indicating that geese moved back to regions where they had not previously experienced hunting . 7. Overall disturbance level increased in all regions in years with hunting relative to years without hunting . 8. Synthesis and applications. We conclude that spring hunting changed the stopover scheduling of this long-distance migrant and might further impact population dynamics by reducing prenuptial fattening. The spring hunt may also have increased crop damage. We propose that staggered hunt opening dates could attenuate secondary effects of such management actions.

Quebec↗

USGS2003

Sea otter studies in Glacier Bay National Park and Preserve: annual report 2002

Since 1995, the number of sea otters in Glacier Bay proper has increased from around 5 to more than 1200. Sea otter distribution is mostly limited to the Lower Bay, south of Sandy Cove, and is not continuous within that area. Concentrations occur in the vicinity of Sita Reef and Boulder Island and between Pt. Carolus and Rush Pt. on the west side of the Bay, although there have been occasional sightings north of Sandy Cove (Figure 1). Large portions of the Bay remain unoccupied by sea otters, but recolonization is occurring rapidly. Most prey recovered by sea otters in Glacier Bay are ecologically, commercially, or socially important species. In 2002 sea otter diet consisted of 35% clam, 26% mussel, 3% crab, 3.0% snail, 2% starfish, 11% urchins, 2% other, and 20% unidentified. Dominant clam species include the butter clam, Saxidomus gigantea , the Greenland cockle, Serripes groenlandicus , and the littleneck clam, Protothaca staminea . Urchins are primarily green urchins, Strongylocentrotus droebachiensis , and the mussel is Modiolus modiolus . Crabs observed in 2002 include the Dungeness, Cancer magister , the kelp crab Pugettia gracilis , and the helmet crab, Telmessus cherigonus . Although we characterize diet at broad geographic scales, we have previously found diet to vary between sites separated by as little as several hundred meters. Dietary variation among and within sites can reflect differences in prey availability as well as individual specialization. We estimated species composition, density, biomass, and sizes of subtidal clams, urchins, and mussels at 13 sites in Glacier Bay and 5 sites in nearby Port Althorp, where sea otters have been present for at least 20 years. All sites were selected based on the presence of abundant clam siphons and the absence of sea otters (Glacier Bay) or abundant shell litter and the presence of sea otters (Port Althorp). Glacier Bay sites were selected to achieve a broad geographic sample of dense subtidal clam beds within Glacier Bay prior to occupation and foraging by sea otters. Port Althorp sites were chosen to achieve a representative sample of subtidal clam beds already under prolonged foraging pressure by sea otters. There was no direct evidence of otter foraging at any of our Glacier Bay sampling sites. In Glacier Bay, we sampled 15,338 bivalves (average of 1,180/site) representing 14 species of clam, 2 species of mussel, and a single scallop and we sampled 6,917 urchins (average of 513/site). In Port Althorp, we sampled 1,034 bivalves (average of 207/site) representing 14 species of clam. We found only 5 urchins, all S. droebachiensis . Mean densities and biomass of all subtidal clams were significantly greater in Glacier Bay (59.2 and 99/0.25m 2 compared to Port Althorp (10.3 and 5.8/0.25m 2 (p<0.002 for both). Our contrasts of subtidal clam populations between Glacier Bay and Port Althorp suggest that clam densities will likely decline by about a factor of six and that clam biomass estimates will decline by more than a factor of ten. Numerically dominant species of clams, P. staminea , S. gigantea , Macoma sp. and Mya sp. were all significantly greater in density and biomass in Glacier Bay, while C. nutalli density was low but significantly higher in Port Althorp. Subtidal clam species diversity was significantly greater in Port Althorp compared to Glacier Bay, although this may simply reflect habitat differences. Sea urchin densities were high in Glacier Bay, while in Port Althorp urchins were virtually absent. Sea otters are now well established in limited areas of the lower portions of Glacier Bay. It is likely that distribution and numbers of sea otters will continue to increase in Glacier Bay in the near future. Glacier Bay supports large and diverse populations of clams that are largely unexploited by sea otters at present. It is predictable that the density and sizes of clam populations will decline in response to otter predation. This will result in fewer opportunities for human harvest, but will also trigger ecosystem level changes, as prey for other predators, such as octopus, sea stars, fishes, birds and mammals are modified. Sea otters will also modify benthic habitats through excavation of sediments required to extract burrowing infauna such as clams. Effects of sediment disturbance by foraging sea otters are not understood. Glacier Bay also supports large populations of other preferred sea otter prey, such as king ( Paralithodes sp. ), tanner ( Chionoecetes sp. ) and dungeness ( Cancer magister ) crabs and green sea urchins ( S. droebachiensis ). As the colonization of Park waters by sea otters continues, it is also likely that dramatic changes will occur in the species composition, abundance, and size class distribution of many components of the nearshore marine ecosystem. Many of the changes will occur as a direct result of predation by sea otters. Others will result from indirect or cascading effects of sea otter foraging, such as increased kelp production and modified prey availability for other nearshore predators. Without recognizing and quantifying the extent of change initiated by the colonization of Glacier Bay by sea otters, management of nearshore resources will be severely constrained for many decades.

Alaska↗

USGS2021

The Chesapeake Bay program modeling system: Overview and recommendations for future development

The Chesapeake Bay is the largest, most productive, and most biologically diverse estuary in the continental United States providing crucial habitat and natural resources for culturally and economically important species. Pressures from human population growth and associated development and agricultural intensification have led to excessive nutrient and sediment inputs entering the Bay, negatively affecting the health of the Bay ecosystem and the economic services it provides. The Chesapeake Bay Program (CBP) is a unique program formally created in 1983 as a multi-stakeholder partnership to guide and foster restoration of the Chesapeake Bay and its watershed. Since its inception, the CBP Partnership has been developing, updating, and applying a complex linked modeling system of watershed, airshed, and estuary models as a planning tool to inform strategic management decisions and Bay restoration efforts. This paper provides a description of the 2017 CBP Modeling System and the higher trophic level models developed by the NOAA Chesapeake Bay Office, along with specific recommendations that emerged from a 2018 workshop designed to inform future model development. Recommendations highlight the need for simulation of watershed inputs, conditions, processes, and practices at higher resolution to provide improved information to guide local nutrient and sediment management plans. More explicit and extensive modeling of connectivity between watershed landforms and estuary sub-areas, estuarine hydrodynamics, watershed and estuarine water quality, the estuarine-watershed socioecological system, and living resources will be important to broaden and improve characterization of responses to targeted nutrient and sediment load reductions. Finally, the value and importance of maintaining effective collaborations among jurisdictional managers, scientists, modelers, support staff, and stakeholder communities is emphasized. An open collaborative and transparent process has been a key element of successes to date and is vitally important as the CBP Partnership moves forward with modeling system improvements that help stakeholders evolve new knowledge, improve management strategies, and better communicate outcomes.

Chesapeake Bay estuary and watershed↗

USGS2006

Denitrification across landscapes and waterscapes: A synthesis

Denitrification is a critical process regulating the removal of bioavailable nitrogen (N) from natural and human-altered systems. While it has been extensively studied in terrestrial, freshwater, and marine systems, there has been limited communication among denitrification scientists working in these individual systems. Here, we compare rates of denitrification and controlling factors across a range of ecosystem types. We suggest that terrestrial, freshwater, and marine systems in which denitrification occurs can be organized along a continuum ranging from (1) those in which nitrification and denitrification are tightly coupled in space and time to (2) those in which nitrate production and denitrification are relatively decoupled. In aquatic ecosystems, N inputs influence denitrification rates whereas hydrology and geomorphology influence the proportion of N inputs that are denitrified. Relationships between denitrification and water residence time and N load are remarkably similar across lakes, river reaches, estuaries, and continental shelves. Spatially distributed global models of denitrification suggest that continental shelf sediments account for the largest portion (44%) of total global denitrification, followed by terrestrial soils (22%) and oceanic oxygen minimum zones (OMZs; 14%). Freshwater systems (groundwater, lakes, rivers) account for about 20% and estuaries 1% of total global denitrification. Denitrification of land-based N sources is distributed somewhat differently. Within watersheds, the amount of land-based N denitrified is generally highest in terrestrial soils, with progressively smaller amounts denitrified in groundwater, rivers, lakes and reservoirs, and estuaries. A number of regional exceptions to this general trend of decreasing denitrification in a downstream direction exist, including significant denitrification in continental shelves of N from terrestrial sources. Though terrestrial soils and groundwater are responsible for much denitrification at the watershed scale, per-area denitrification rates in soils and groundwater (kg N·km −2 ·yr −1 ) are, on average, approximately one-tenth the per-area rates of denitrification in lakes, rivers, estuaries, continental shelves, or OMZs. A number of potential approaches to increase denitrification on the landscape, and thus decrease N export to sensitive coastal systems exist. However, these have not generally been widely tested for their effectiveness at scales required to significantly reduce N export at the whole watershed scale.

Ecological Applications↗

USGS2018

Broad‐scale occurrence of a subsidized avian predator: reducing impacts of ravens on sage‐grouse and other sensitive prey

Expanding human enterprise across remote environments impacts numerous wildlife species. Anthropogenic resources provide subsidies for generalist predators that can lead to cascading effects on prey species at lower trophic levels. A fundamental challenge for applied ecologists is to disentangle natural and anthropogenic influences on species occurrence, and subsequently develop spatially explicit models to help inform management and conservation decisions. Using Bayesian hierarchical occupancy models, we mapped the broad‐scale occurrence of common ravens Corvus corax as a function of natural and anthropogenic landscape covariates using >15,000 point count surveys performed during 2007–2016 within the Great Basin region, USA. Raven abundance and distribution is substantially increasing across the American west due to unintended anthropogenic resource subsidies. Importantly, ravens prey on eggs and chicks of numerous species including greater sage‐grouse Centrocercus urophasianus , an indicator species whose decline is at the centre of national conservation strategies and land use policies.Anthropogenic factors that contributed to greater raven occurrence were: increased road density, presence of transmission lines, agricultural activity, and presence of roadside rest areas. Natural landscape characteristics included lower elevations with greener vegetation (NDVI), greater stream and habitat edge densities, and lower percentages of big sagebrush A. tridentata spp . Interactions between anthropogenic sources of nesting substrate and food subsidies suggested that raven occurrence increased multiplicatively when these resource subsidies co‐occurred. Overall, the average probability of raven occurrence estimated within sagebrush ecosystems of the study area was ~0.83. Synthesis and applications . We demonstrate how anthropogenic factors can be disentangled from natural effects when making spatially‐explicit predictions of subsidized predators occurring across expansive landscapes. This approach can guide management decisions where subsidized predators overlap sensitive prey habitats. For example, we identify areas where elevated raven occurrence coincides with breeding sage‐grouse concentration areas and appears to be largely driven by anthropogenic factors. Management applications could focus on reducing raven access to anthropogenic subsidies in these areas, while prioritizing habitat improvements for sage‐grouse elsewhere. Our approach is applicable to other species where widespread survey data are available.

Journal of Applied Ecology↗

USGS2021

Development and validation of a spatially-explicit agent-based model for space utilization by African savanna elephants (Loxodonta africana) based on determinants of movement

African elephants (Loxodonta africana) are well-studied and inhabit diverse landscapes that are being transformed by both humans and natural forces. Most tools currently in use are limited in their ability to predict how elephants will respond to novel changes in the environment. Individual-, or agent-based modeling (ABM), may extend current methods in addressing and predicting spatial responses to environmental conditions over time. We developed a spatially explicit agent-based model to simulate elephant space use and validated the model with movement data from elephants in Kruger National Park (KNP) and Chobe National Park (CNP). We simulated movement at an hourly scale, as this scale can reflect switches in elephant behavior due to changes in internal states and short-term responses to the local availability and distribution of critical resources, including forage, water, and shade. Known internal drivers of elephant movement, including perceived temperature and the time since an individual last visited a water source, were linked to the external environment through behavior-based movement rules. Simulations were run on model landscapes representing the wet season and the hot, dry season for both parks. The model outputs, including home range size, daily displacement distance, net displacement distance, and maximum distance traveled from a permanent water source, were evaluated through qualitative and quantitative comparisons to actual elephant movement data from both KNP and CNP. The ABM was successful in reproducing the differences in daily displacements between seasons in each park, and in distances traveled from a permanent water source between parks and seasons. Other movement characteristics, including differences in home range sizes and net daily displacements, were partially reproduced. Out of the all the statistical comparisons made between the empirical and simulated movement patterns, the majority were classified as discrepancies of medium or small effect size. We have shown that a resource-driven model with relatively simple decision rules generates trajectories with movement characteristics that are mostly comparable to those calculated from empirical data. Simulating hourly movement (as our model does) may be useful in predicting how finer-scale patterns of space use, such as those created by foraging movements, are influenced by finer spatio-temporal changes in the environment.

Chobe National Park, Kruger National Park↗