Tree cover is a fundamental structural characteristic and driver of ecosystem processes in terrestrial ecosystems, and trees are a major global carbon (C) sink. Fire and herbivores have been ...hypothesized to play dominant roles in regulating trees in African savannas, but the evidence for this is conflicting. Moving up a trophic scale, the factors that regulate fire occurrence and herbivores, such as disease and predation, are poorly understood for any given ecosystem. We used a Bayesian state-space model to show that the wildebeest population eruption that followed disease (rinderpest) eradication in the Serengeti ecosystem of East Africa led to a widespread reduction in the extent of fire and an ongoing recovery of the tree population. This supports the hypothesis that disease has played a key role in the regulation of this ecosystem. We then link our state-space model with theoretical and empirical results quantifying the effects of grazing and fire on soil carbon to predict that this cascade may have led to important shifts in the size of pools of C stored in soil and biomass. Our results suggest that the dynamics of herbivores and fire are tightly coupled at landscape scales, that fire exerts clear top-down effects on tree density, and that disease outbreaks in dominant herbivores can lead to complex trophic cascades in savanna ecosystems. We propose that the long-term status of the Serengeti and other intensely grazed savannas as sources or sinks for C may be fundamentally linked to the control of disease outbreaks and poaching.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Electricity generation from renewable-energy sources has increased dramatically worldwide in recent decades. Risks associated with wind-energy infrastructure are not well understood for endangered ...Whooping Cranes (Grus americana) or other vulnerable Crane populations. From 2010 to 2016, we monitored 57 Whooping Cranes with remote-telemetry devices in the United States Great Plains to determine potential changes in migration distribution (i.e., avoidance) caused by presence of wind-energy infrastructure. During our study, the number of wind towers tripled in the Whooping Crane migration corridor and quadrupled in the corridor’s center. Median distance of Whooping Crane locations from nearest wind tower was 52.1 km, and 99% of locations were >4.3 km from wind towers. A habitat selection analysis revealed that Whooping Cranes used areas ≤5.0 km (95% confidence interval CI 4.8–5.4) from towers less than expected (i.e., zone of influence) and that Whooping Cranes were 20 times (95% CI 14–64) more likely to use areas outside compared to adjacent to towers. Eighty percent of Whooping Crane locations and 20% of wind towers were located in areas with the highest relative probability of Whooping Crane use based on our model, which comprised 20% of the study area. Whooping Cranes selected for these places, whereas developers constructed wind infrastructure at random relative to desirable Whooping Crane habitat. As of early 2020, 4.6% of the study area and 5.0% of the highest-selected Whooping Crane habitat were within the collective zone of influence. The affected area equates to habitat loss ascribed to wind-energy infrastructure; losses from other disturbances have not been quantified. Continued growth of the Whooping Crane population during this period of wind infrastructure construction suggests no immediate population-level consequences. Chronic or lag effects of habitat loss are unknown but possible for long-lived species. Preferentially constructing future wind infrastructure outside of the migration corridor or inside of the corridor at sites with low probability of Whooping Crane use would allow for continued wind-energy development in the Great Plains with minimal additional risk to highly selected habitat that supports recovery of this endangered species.
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BFBNIB, FZAB, GIS, IJS, INZLJ, KILJ, NLZOH, NMLJ, NUK, OILJ, PNG, SAZU, SBCE, SBMB, UL, UM, UPUK, ZRSKP
Effective habitat management for rare and endangered species requires a thorough understanding of their specific habitat requirements. Although machine learning models have been increasingly used in ...the analyses of habitat use by wildlife, the primary focus of these models has been on generating spatial predictions. In this study, we used machine learning models in combination with simulated management actions to guide planning and inform managers. We used data from 61 whooping cranes (Grus americana) tagged with GPS telemetry collars between 2009 and 2018 near Aransas National Wildlife Refuge in coastal Texas. We included variables based on topography, land use classification, vegetation height, plant phenology, drought, storm surge events, and both wild and prescribed fires. We then built models at multiple scales: population level, home range level, and roosting and daytime within home range level. We simulated responses to the two primary management actions used to enhance whooping crane habitat on Aransas National Wildlife Refuge: prescribed fire and removal of woody vegetation. At the population and home range scales, land use classification variables had the highest importance values, whereas the combined elevation and bathymetry layer was the most important predictor at both roosting and daytime within home range scales. Our findings revealed that the effects of fire, although generally modest, varied spatially. Areas dominated by estuarine wetlands exhibited higher predicted use within the first months after a fire, whereas those dominated by palustrine wetlands were more likely to be avoided in the immediate postfire years. Our simulation of vegetation removal identified the areas on Aransas National Wildlife Refuge where whooping cranes were predicted to benefit the most if vegetation were removed. These techniques can be used by other researchers wanting to examine and predict the effects of potential management actions on target species habitat.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
1. Anthrax is endemic throughout Africa, causing considerable livestock and wildlife losses and severe, sometimes fatal, infection in humans. Predicting the risk of infection is therefore important ...for public health, wildlife conservation and livestock economies. However, because of the intermittent and variable nature of anthrax outbreaks, associated environmental and climatic conditions, and diversity of species affected, the ecology of this multihost pathogen is poorly understood. 2. We explored records of anthrax from the Serengeti ecosystem in north-west Tanzania where the disease has been documented in humans, domestic animals and a range of wildlife. Using spatial and temporal case-detection and seroprevalence data from wild and domestic animals, we investigated spatial, environmental, climatic and species-specific associations in exposure and disease. 3. Anthrax was detected annually in numerous species, but large outbreaks were spatially localized, mostly affecting a few focal herbivores. 4. Soil alkalinity and cumulative weather extremes were identified as useful spatial and temporal predictors of exposure and infection risk, and for triggering the onset of large outbreaks. 5. Interacting ecological and behavioural factors, specifically functional groups and spatiotemporal overlap, helped to explain the variable patterns of infection and exposure among species. 6. Synthesis and applications. Our results shed light on ecological drivers of anthrax infection and suggest that soil alkalinity and prolonged droughts or rains are useful predictors of disease occurrence that could guide risk-based surveillance. These insights should inform strategies for managing anthrax including prophylactic livestock vaccination, timing of public health warnings and antibiotic provision in high-risk areas. However, this research highlights the need for greater surveillance (environmental, serological and case-detection-orientated) to determine the mechanisms underlying anthrax dynamics.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NMLJ, NUK, OILJ, PNG, SAZU, SBCE, SBMB, UL, UM, UPUK
•We modeled relationships between whooping crane demographics and winter drought.•Winter mortality increases with drought severity and is partially compensatory.•With average recruitment (0.145), ...population growth is 1.2% after extreme drought.•No recruitment during 3 moderate–severe drought years will delay species’ recovery.•Breeding and migration periods influence population growth more than winter.
Focusing conservation strategies requires identifying the demographic parameters and environmental conditions affecting the growth of animal populations most. Therefore, we examined relationships between population demographics and winter drought (1950–2011) for endangered whooping cranes (Grus americana) wintering in Texas, USA. We modeled winter loss and its contribution to annual mortality as functions of winter drought, determined recruitment needed to maintain population growth after drought, and identified which demographic parameters underpin this population’s growth. Previous research assumed winter loss (i.e., birds missed in subsequent surveys) represented mortality. We show that loss includes temporary emigration to upland habitats, early migration, and incomplete detection. Despite this, we maintained this assumption to evaluate the relevance of winter mortality to population growth. We found that winter loss (β^=-0.308, SE=0.042) and its contribution to annual mortality (β^=-0.318, SE=0.047) increased with drought severity (Palmer hydrological drought index; PHDI). Given average recruitment (0.145, SD=0.090), this population increases 1.2% (95% CI=−2.9% to 4.2%) after extreme drought (PHDI=−4). No recruitment must occur for 3years with moderate to severe drought (PHDI<−2.5) to delay species’ recovery ≈7years. This scenario has not occurred since population monitoring began in 1938. Of the demographic parameters we examined, winter loss explained population growth least (14.4%; 95% CI=3.6–35.8%), and it was partially compensatory. Breeding–migratory mortality explained 42.2% (95% CI=19.1–61.5%) of population growth and recruitment 49.9% (95% CI=20.6–75.2%). Our results focus conservation on breeding and migratory periods, and deemphasize winter mortality and drought. On the wintering grounds, conservation of whooping cranes should emphasize maintaining coastal, upland, and interior habitats for this population.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Savannas are globally important ecosystems of great significance to human economies. In these biomes, which are characterized by the co-dominance of trees and grasses, woody cover is a chief ...determinant of ecosystem properties. The availability of resources (water, nutrients) and disturbance regimes (fire, herbivory) are thought to be important in regulating woody cover, but perceptions differ on which of these are the primary drivers of savanna structure. Here we show, using data from 854 sites across Africa, that maximum woody cover in savannas receiving a mean annual precipitation (MAP) of less than approximately 650 mm is constrained by, and increases linearly with, MAP. These arid and semi-arid savannas may be considered 'stable' systems in which water constrains woody cover and permits grasses to coexist, while fire, herbivory and soil properties interact to reduce woody cover below the MAP-controlled upper bound. Above a MAP of approximately 650 mm, savannas are 'unstable' systems in which MAP is sufficient for woody canopy closure, and disturbances (fire, herbivory) are required for the coexistence of trees and grass. These results provide insights into the nature of African savannas and suggest that future changes in precipitation may considerably affect their distribution and dynamics.
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DOBA, IJS, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Understanding ecosystem processes as they relate to wildfire and vegetation dynamics is of growing importance as fire frequency and extent increase throughout the western United States. However, the ...effects of severe, stand-replacing wildfires are poorly understood. We studied inorganic nitrogen pools and mineralization rates after stand-replacing wildfires in the Greater Yellowstone Ecosystem, Wyoming. After fires that burned in summer 2000, soil ammonium concentration peaked in 2001 (33 mg NH₄-N· kgFormula: see text); soil nitrate increased subsequently (2.7 mg NO₃-N·kgFormula: see text in 2003) but was still low. However, annual net ammonification rates were largely negative from 2001 to 2004, indicating ammonium depletion. Thus, although net nitrification rates were positive, annual net nitrogen mineralization (net ammonification plus net nitrification) remained low. Aboveground net primary production (ANPP) increased from 0.25 to 1.6 Mg·ha⁻¹·yr⁻¹ from 2001 to 2004, but variation in ANPP among stands was not related to net nitrogen mineralization rates. Across a broader temporal gradient (stand age zero to >250 yr), negative rates of net annual ammonification were especially pronounced in the first postfire year. Laboratory incubations using ¹⁵N isotope pool dilution revealed that gross production of ammonium was reduced and ammonium consumption greatly exceeded gross production during the initial postfire years. Our results suggest a microbial nitrogen sink for several years after severe, stand-replacing fire, confirming earlier hypotheses about postdisturbance succession and nutrient cycling in cold, fire-dominated coniferous forests. Postfire forests in Yellowstone seem to be highly conservative for nitrogen, and microbial immobilization of ammonium plays a key role during early succession.
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BFBNIB, NMLJ, NUK, PNG, SAZU, UL, UM, UPUK
Migratory birds use numerous strategies to successfully complete twice-annual movements between breeding and wintering sites. Context for conservation and management can be provided by characterizing ...these strategies. Variations in strategy among and within individuals support population persistence in response to changes in land use and climate. We used location data from 58 marked Whooping Cranes (Grus americana) from 2010 to 2016 to characterize migration strategies in the U.S. Great Plains and Canadian Prairies and southern boreal region, and to explore sources of heterogeneity in their migration strategy, including space use, timing, and performance. Whooping Cranes completed ∼3,900-km migrations that averaged 29 days during spring and 45 days during autumn, while making 11–12 nighttime stops. At the scale of our analysis, individual Whooping Cranes showed little consistency in stopover sites used among migration seasons (i.e. low site fidelity). In contrast, individuals expressed a measure of consistency in timing, especially migration initiation dates. Whooping Cranes migrated at different times based on age and reproductive status, where adults with young initiated autumn migration after other birds, and adults with and without young initiated spring migration before subadult birds. Time spent at stopover sites was positively associated with migration bout length and negatively associated with time spent at previous stopover sites, indicating Whooping Cranes acquired energy resources at some stopover sites that they used to fuel migration. Whooping Cranes were faithful to a defined migration corridor but showed less fidelity in their selection of nighttime stopover sites; hence, spatial targeting of conservation actions may be better informed by associations with landscape and habitat features rather than documented past use at specific locations. The preservation of variation in migration strategies existing within this species that experienced a severe population bottleneck suggests that Whooping Cranes have maintained a capacity to adjust strategies when confronted with future changes in land use and climate.
Accelerated sea-level rise and intensifying hurricanes highlight the need to better understand surface elevation change in coastal wetlands. We used the surface elevation table-marker horizon ...approach to measure surface elevation change in 14 coastal marshes along the northwestern Gulf of Mexico, within five National Wildlife Refuges in Texas (USA). During the 2014–2019 study period, the mean rate of surface elevation change was 1.96 ± 0.87 mm yr
−1
(range: -1.57 to 8.37 mm yr
−1
). Vertical accretion rates varied due to landscape proximity relative to sediment inputs from Hurricane Harvey. At most sites, vertical accretion offset subsurface losses due to shallow subsidence. However, net elevation gains were often lower than recent relative sea-level rise rates, and much lower than rates expected under future sea-level rise. Because these marshes are not keeping pace with recent sea-level rise, it is unlikely that they will be able to adjust to future accelerations. Climate change threatens these Texas coastal wetlands and the ecological and economic services they provide. By characterizing the status and prospective loss of coastal marshes, our study reinforces the value of identifying local and landscape-level adaptation mechanisms that can enhance the ability of coastal marshes to adapt to threats posed by climate change.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Available stopover habitats with quality foraging opportunities are essential for migrating waterbirds, including Whooping Crane (Grus americana). Several studies have evaluated habitats used by ...Whooping Crane for roosting throughout its migration corridor; however, habitats associated with foraging and other diurnal activities have received less attention. We used data collected from 42 Whooping Crane individuals that included 2169 diurnal use locations within 395 stopover sites evaluated during spring 2013 to fall 2015 to assess diurnal habitat selection throughout the U.S. portion of the migration corridor. We found that Whooping Crane selected wetland land-cover types (i.e., open water, riverine, and semipermanent wetlands) and lowland grasslands for diurnal activities over all other land-cover types that we evaluated, including croplands. Whooping Crane generally avoided roads, and avoidance varied based on land-cover class. There has been considerable alteration and destruction of natural wetlands and rivers that serve as roosting and foraging sites for migrating Whooping Crane. Given recent droughts and the likelihood of future landscape changes within the migration corridor, directing conservation efforts toward protecting and enhancing wetland stopover areas may prove critical for continued growth of the last remaining wild population of Whooping Crane. Future studies of this Whooping Crane population should focus on specific wetland complexes and riverine sites throughout the migration corridor to identify precise management actions that could be taken to enhance and protect these imperilled land-cover types.
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