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At least 343 records · Page 19Linked to original sources
USGS2024

Assessing giant sequoia mortality and regeneration following high-severity wildfire

Fire is a critical driver of giant sequoia ( Sequoiadendron giganteum [Lindl.] Buchholz) regeneration. However, fire suppression combined with the effects of increased temperature and severe drought has resulted in fires of an intensity and size outside of the historical norm. As a result, recent mega-fires have killed a significant portion of the world's sequoia population (13%–19%), and uncertainty surrounds whether severely affected groves will be able to recover naturally, potentially leading to a loss of grove area. To assess the likelihood of natural recovery, we collected spatially explicit data assessing mortality, crown condition, and regeneration within four giant sequoia groves that were severely impacted by the SQF- (2020) and KNP-Complex (2021) wildfires within Sequoia and Kings Canyon National Parks. In total, we surveyed 5.9 ha for seedlings and assessed the crown condition of 1104 giant sequoias. To inform management, we used a statistical methodology that robustly quantifies the uncertainty in inherently “noisy” seedling data and takes advantage of readily available remote sensing metrics that would make our findings applicable to other recently burned groves. A loss of giant sequoia grove area would be a consequence of giant sequoia tree mortality followed by a failure of natural regeneration. We found that areas that experienced very high-severity fire (above ~800 RdNBR) are at substantial risk for the loss of grove area, with tree mortality rapidly increasing and giant sequoia seedling density simultaneously decreasing with fire severity. Such high-severity areas comprised 17.8, 142.0, 14.6, 1.6 ha and ~90%, ~14%, ~53%, and ~27% of Board Camp, Redwood Mountain, Suwanee, and New Oriole Lake groves, respectively. In all sampling areas, we found that seedling densities fell far below the average density measured after prescribed fires, where seedling numbers were almost certainly adequate to maintain giant sequoia populations and postfire conditions were more in keeping with historical norms. Importantly, spatial pattern is also important in assessing the risk of grove loss, and in two groves, Suwanee and New Oriole Lake, the high-severity patches were not always contiguous, potentially making some areas more resilient to regeneration failure due to the proximity of surviving trees.

Ecosphere↗
USGS2024

Warm places, warm years, and warm seasons increase parasitizing of moose by winter ticks

Observed links between parasites, such as ticks, and climate change have aroused concern for human health, wildlife population dynamics, and broader ecosystem effects. The one-host life history of the winter tick ( Dermacentor albipictus ) links each annual cohort to environmental conditions during three specific time periods when they are predictably vulnerable: spring detachment from hosts, summer larval stage, and fall questing for hosts. We used mixed-effects generalized linear models to investigate the drivers of tick loads carried by moose ( Alces alces ) relative to these time periods and across 750 moose, 10 years, and 16 study areas in the western United States. We tested for the effects of biotic factors (moose density, shared winter range, vegetation, migratory behavior) and weather conditions (temperature, snow, humidity) during each seasonal period when ticks are vulnerable and off-host. We found that warm climatic regions, warm seasonal periods across multiple partitions of the annual tick life cycle, and warm years relative to long-term averages each contributed to increased tick loads. We also found important effects of snow and other biotic factors such as host density and vegetation. Tick loads in the western United States were, on average, lower than those where tick-related die-offs in moose populations have occurred recently, but loads carried by some individuals may be sufficient to cause mortality. Lastly, we found interannual variation in tick loads to be most correlated with spring snowpack, suggesting this environmental component may have the highest potential to induce change in tick load dynamics in the immediate future of this region.

Ecosphere↗
USGS2024

Multiple stressors mediate the effects of warming on leaf decomposition in a large regulated river

Predicting how increasing temperatures interact with other global change drivers to influence the structure and dynamics of Earth's ecosystems is a primary challenge in ecology. Our study made use of multiple simultaneous “natural experiments” to examine how rapid warming, declining nutrients, invasive consumers, and riparian invasive species management interact to influence leaf decomposition in a large and regulated river. Specifically, we compared the breakdown of cottonwood ( Populus fremontii ), willow ( Salix exigua ), and saltcedar ( Tamarix sp.) leaf litter in 2022 to a previous experiment from 1998 that occurred under much cooler water temperatures, and had higher water phosphorus concentrations, low numbers of invasive New Zealand mudsnails ( Potamopyrgus antipodarum ), and unaltered litter chemistry from the herbivory of saltcedar leaf beetles ( Diorhabda carinulata ). We found that the effects of up to 10°C warmer temperatures on leaf decomposition were mediated by the establishment and management of invasive species and declining water nutrient concentrations arising from upstream reservoir lowering. Such interactions led to accelerated breakdown of saltcedar, but relatively minor effects of warming on the rate of cottonwood and willow decomposition. Additionally, our results demonstrate the potential for favorable invasive species management outcomes in the terrestrial environment to produce unintended responses in adjacent freshwater ecosystems. As temperatures continue to rise, it is critical that future studies consider how warming interacts with multiple stressors and environmental factors to influence processes such as decomposition in freshwater ecosystems.

Arizona↗
USGS2024

Comparison of sediment and water column nutrient processing rates in agricultural streams of contrasting buffer land use

Watershed nutrient management often focuses on actions that reduce the movement of nitrogen (N) and phosphorus (P) from agricultural lands into streams. One area of management focus is the buffer of land adjacent to streams. Wetlands and forests in this buffer can intercept and retain N and P from the landscape. In addition to directly intercepting agricultural nutrients, natural habitats in the buffer can alter stream geomorphology and influence the in-stream processing and transformation of N and P to less labile and mobile forms. Here, we assess the influence of buffer land cover on in-stream processing of N and P. We measured nutrient dynamics in the water column and sediments of agricultural streams in the Fox River and Duck Creek watersheds (WI, USA) during the growing season. In these streams, water column processing was low, possibly due to a lack of primary producers in the water column. Water column P processing was weakly associated with wetland land cover in the buffer, but buffer land cover had no clear effect on inorganic N processing. On the other hand, sediments were almost always a source of inorganic P and a sink for inorganic N. Sediment P release was higher in streams with more agricultural land cover in the buffer. Sediments in streams with agricultural land cover in the buffer also removed more nitrate, even after accounting for the greater availability of nitrate in those streams. The buffer land cover conditions we quantified occupy a very small portion of the overall watershed (100 m wide, for 1 km upstream of the study site) but nevertheless appear to influence in-stream cycling of N and P. For P management, reducing agricultural land cover in buffers is already a priority due to the ability of wetlands and forests to intercept nutrients, but this study suggests there may be some additional benefit due to changes in in-stream P processing.

Wisconsin↗
USGS2024

Estimating multivariate ecological variables at high spatial resolution using a cost-effective matching algorithm

Simulation models are valuable tools for estimating ecosystem response to environmental conditions and are particularly relevant for investigating climate change impacts. However, because of high computational requirements, models are often applied over a coarse grid of points or for representative locations. Spatial interpolation of model output can be necessary to guide decision-making, yet interpolation is not straightforward because the interpolated values must maintain the covariance structure among variables. We present methods for two key steps for utilizing limited simulations to generate detailed maps of multivariate and time series output. First, we present a method to select an optimal set of simulation sites that maximize the area represented for a given number of sites. Then, we introduce a multivariate matching approach to interpolate simulation results to detailed maps for the represented area. This approach links simulation output to environmentally analogous matched sites according to user-defined criteria. We demonstrate the methods with case studies using output from (1) an individual-based plant simulation model to illustrate site selection, and (2) an ecosystem water balance simulation model to illustrate interpolation. For the site selection case study, we identified 200 simulation sites that represented 96% of a large study area (1.12 × 10 6 km 2 ) at a ~1-km resolution. For the interpolation case study, we generated ~1-km resolution maps across 4.38 × 10 6 km 2 of drylands in North America from a 10 × 10 km grid of simulated sites. Estimates of interpolation errors using cross validation were low (less than 10% of the range of each variable). Our point selection and interpolation methods, which are available as an easy-to-use R package, provide a means of cost-effectively generating detailed maps of expensive, complex simulation output (e.g., multivariate and time series) at scales relevant for local conservation planning. Our methods are flexible and allow the user to identify relevant matching criteria to balance interpolation uncertainty with areal coverage to enhance inference and decision-making at management-relevant scales across large areas.

Ecosphere↗
USGS2024

Management implications of habitat selection by whooping cranes (Grus americana) on the Texas coast

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.

Texas↗
USGS2024

Translocation in a fragmented river provides demographic benefits for imperiled fishes

Fragmentation isolates individuals and restricts access to valuable habitat with severe consequences for populations, such as reduced gene flow, disruption of recolonization dynamics, reduced resiliency to disturbance, and changes in aquatic community structure. Translocations to mitigate the effects of fragmentation and habitat loss are common, but few are rigorously evaluated, particularly for fishes. Over six years, we translocated 1215 individuals of four species of imperiled fish isolated below a barrier on the San Juan River, Utah, USA, that restricts access to upstream habitat. We used re-encounter data (both passive integrated transponder tag and telemetry detections and physical recaptures) collected between 2016 and 2023, to inform a spatially explicit multistate mark–recapture model that estimated survival and transition probabilities of translocated and non-translocated individuals, both below and above the barrier. Individuals of all four species moved large (>200 km) distances upstream following translocation, with the maximum upstream encounter distance varying by species. Results from the multistate mark–recapture model suggested translocated fish survived at a higher rate compared with non-translocated fish below the barrier for three of the four species. Above the barrier, translocated individuals survived at similar rates as non-translocated fish for bluehead sucker ( Catostomus discobolus ) and flannelmouth sucker ( Catostomus latipinnis ), while survival rates of translocated endangered Colorado pikeminnow ( Ptychocheilus lucius ; mean, 95% CI: 0.75, 0.55–0.88) and endangered razorback sucker ( Xyrauchen texanus ; 0.86, 0.75–0.92) were higher relative to non-translocated individuals (Colorado pikeminnow: 0.52, 0.51–0.54; razorback sucker: 0.75, 0.74–0.75). Transition probabilities from above the barrier to below the barrier were generally low for three of the four species (all upper 95% CI ≤ 0.23), but they were substantially higher for razorback sucker. Our results suggest translocation to mitigate fragmentation and habitat loss can have demographic benefits for large-river fish species by allowing movements necessary to complete their life history in heterogeneous riverscapes. Further, given the costs or delays in providing engineered fish passage structures or in achieving dam removal, we suggest translocations may provide an alternative conservation strategy in fragmented river systems.

Colorado, New Mexico, Utah↗
USGS2024

Using integrated step selection to determine effects of predation risk on bison habitat selection and movement

Animal movement is a fundamental mechanism that shapes communities and ecosystems. Ungulates alter the ecosystems they inhabit and understanding their movements and distribution is critical for linking habitat with population dynamics. Predation risk has been shown to strongly influence ungulate movement patterns, such that ungulates may select habitat where predation risk is lower (refugia), adjust movement rates, temporal patterns, or selection of cover variables in areas with greater predation risk. We evaluated potential predation avoidance behavior in a population of plains bison inhabiting the north rim of Grand Canyon National Park (GRCA) and adjacent Kaibab National Forest (KNF). The KNF has year-round hunting managed by Arizona Game and Fish Department, whereas hunting is not allowed in GRCA. Human-maintained water sources on the KNF are particularly important resources for bison wherein they may be exposed to higher predation risk to access these resources. We used 2-h GPS locations for three years from 31 bison ( n = 9 males; n = 22 females), and integrative step selection analysis to test four hypotheses about the potential for bison to reduce their risk from human predation by avoiding areas of high predation risk; moving faster in areas with high predation risk; entering high-risk areas at night when risk is reduced; and entering high-risk areas in habitats that provide cover (coniferous forest). The highest performing model indicated bison movement was 1.3 times faster per 2-h step interval than in areas with no hunting across all vegetation classes (coniferous forest, shrub, quaking aspen, grass-forb meadow) and across all topography classes (valley, slope, ridge). Bison moved more slowly in grass-forb meadows than all other vegetation types, and in valleys relative to slopes and ridges. Several radio-collared individuals had no GPS locations in KNF for the duration of the study. Bison avoided predation risk using two strategies: moving faster while in the KNF, and fully avoiding high-risk areas by remaining within GRCA. Management that manipulates or reduces timing of hunting seasons may reduce perceived predation risk and encourage bison to distribute into the KNF and across a broader range of available habitat.

Arizona↗
USGS2024

Evaluating mountain lion diet before and after a removal of feral horses in a semiarid environment

Non-native species can affect ecosystems by influencing native predator-prey dynamics. Therefore, management interventions designed to remove non-natives may inadvertently lead to increased predation on native species. Feral horses are widely distributed throughout the arid parts of western North America. A growing body of research indicates that horses can be an important prey species to mountain lions in ecosystems where they overlap. In December 2020, the Bureau of Land Management removed 455 horses from the Delamar Mountains, Nevada, USA. We leveraged this management intervention to implement a before–after–control–impact study to test hypotheses about predation on horses and native ungulates. We predicted (1) that horses would comprise an important part of the diet in this mixed-prey community, (2) following removal, the proportion of horses in the diet would decrease and native ungulates would increase, and (3) mountain lion home ranges overlapping the treatment areas would increase in response to decreased prey availability. From 2018 to 2022, we investigated 1360 clusters from 29 GPS-collared lions and identified 1056 prey items. To model the probability of a predation event (a kill), we fit a mixed-effects logistic regression model for ungulate prey as a function of lion sex, treatment area (in/out), and treatment period (pre-/post-removal). We used a log-linear regression model to evaluate changes in home range size. The most common prey were mule deer (55%), feral horses (32%), and coyotes (4%). Twenty-two of 29 lions consumed horses, although the rate of horse consumption was highly variable across individuals. Horses of both sexes and all age classes were predated. In contrast to predictions, our models detected no effect of removals on diet composition (β interaction = 0.30 ± 1.1), nor did the removal influence home range size (β interaction = 0.02 ± 0.02). Despite a 46% reduction in horse abundance, we found no evidence for prey-switching following the horse removal treatment. Removal magnitude, rapid horse immigration, and/or behavioral specialization of individual mountain lions may help explain these results. Our findings have important implications for mountain lion and feral horse management in arid environments characterized by high prey diversity, but low prey abundance.

Nevada↗
USGS2024

Resource availability and heterogeneity affect space use and resource selection of a feral ungulate

Animals adjust their habitat use patterns in response to changes in their physiological needs and environmental conditions. Understanding the mechanisms underlying resource selection and space use across time and space reveals effects of the environment on animals' decisions. We explored the effects of habitat availability and heterogeneity on the seasonal and annual space use and resource selection of two free-roaming feral burro ( Equus asinus ) populations in the United States within distinct climate and habitat conditions: the Sonoran Desert and the Colorado Plateau. As an introduced yet protected species in the United States, understanding burros' interactions with habitat elements is important for their conservation and management, as well as the conservation of sympatric wildlife. We used GPS locations of female burros (72 animals across both study areas) to delineate annual and seasonal ranges and resource selection patterns. We evaluated effects of mean and CV of habitat covariates, including forage, distance to water, and topography, representing availability and heterogeneity of resources, on seasonal and annual range size of burros. Moreover, we explored how burro seasonal and annual resource selection patterns were affected by availability and heterogeneity of resources. In the Sonoran Desert study area, burros had smaller seasonal and annual ranges and constant resource selection patterns across a year, likely due to a freshwater lake in the area, making water a nonlimiting resource. Human presence was the greatest factor affecting range size and resource selection in the Sonoran Desert, where burros selected for areas near roads and human recreation. In the Colorado Plateau study area, where resources were more seasonal, we found larger range sizes and fluctuating resource selection patterns compared to the Sonoran Desert population. Spatial variation in forage, water, and topography significantly affected range size of burros inhabiting the Colorado Plateau study area. Productive habitats with available water support smaller ranges and a more consistent pattern of resource selection. Our results highlight the positive effect of habitat heterogeneity and the negative effect of habitat productivity on range size of animals. Our findings contribute to an improved understanding of habitat requirements for free-roaming burros that currently live under various climate and habitat conditions globally.

Arizona, Utah↗
USGS2024

Evaluating the effects of nest management on a recovering raptor using integrated population modeling

Evaluating population responses to management is a crucial component of successful conservation programs. Models predicting population growth under different management scenarios can provide key insights into the efficacy of specific management actions both in reversing population decline and in maintaining recovered populations. Bald eagle ( Haliaeetus leucocephalus ) conservation in the United States has seen many successes over the last 50 years, yet the extent to which the bald eagle population has recovered in Arizona, an important population within the Southwest region, remains an area of debate. Estimates of the species' population trend and an evaluation of ongoing nest-level management practices are needed to inform management decisions. We developed a Bayesian integrated population model (IPM) and population viability analysis (PVA) using a 36-year dataset to assess Arizona bald eagle population dynamics and their underlying demographic rates under current and possible future management practices. We estimated that the population grew from 77 females in 1993 to 180 females in 2022, an average yearly increase of 3%. Breeding sites that had trained personnel (i.e., nestwatchers) stationed at active nests to mitigate human disturbance had a 28% higher reproductive output than nests without this protection. Uncertainty around population trends was high, but scenarios that continued the nestwatcher program were less likely to predict abundance declines than scenarios without nestwatchers. Here, the IPM-PVA framework provides a useful tool both for estimating the effectiveness of past management actions and for exploring the management needs of a delisted population, highlighting that continued management action may be necessary to maintain population viability even after meeting certain recovery criteria.

Arizona↗
USGS2024

Hookworm prevalence in ocelots in Costa Rica is inconsistent with spillover from domestic dogs despite high overlap

Spatial overlap between wildlife and related domestic animals can lead to disease transmission, with substantial evidence for viral and bacterial spillover. Domestic and wild animals can also share potentially harmful helminth parasites, many of which have environmental transmission stages that do not require direct contact between hosts. We used camera traps, fecal sampling, and mathematical modeling to evaluate the potential for hookworm parasites to spillover from domestic dogs to wild cats in the Osa Peninsula, Costa Rica. Traditional microscopy was found to be more sensitive than DNA-based diagnostics for parasites, though the methods were complementary. We found high hookworm ( Ancylostoma spp.) prevalence in domestic dogs (74.2%, 95% CI: 67.0%–80.7%, N = 155), and considerable spatial overlap with ocelots ( Leopardus pardalis ) and pumas ( Puma concolor ), particularly on trails and dirt roads. Pumas had hookworm prevalence of 36.4% (18.6%–57.2%, N = 22), and ocelots had 27.3% (7.6%–56.5%, N = 11); however, molecular identification of these parasites was inconclusive. We developed a macroparasite transmission model to infer the likelihood of spillover, compared with separate parasite cycles, or different parasite species in each host. According to the model, spillover of hookworm from dogs would lead to a prevalence of less than 10% in wild hosts. Low presumed compatibility between wild hosts and parasites adapted to domestic species limits the prevalence that could be reached in wild species, even under potentially higher overlap. The prevalence observed was more consistent with a model that assumes hookworms in wild cats in the Osa are a cat-specific parasite. The combination of parasitology, molecular diagnostics, and mathematical modeling used here could complement wildlife disease monitoring programs worldwide to shed light on understudied helminth–host dynamics at the domestic–wild animal interface.

Ecosphere↗
USGS2024

Correcting for measurement errors in a long-term aerial survey with auxiliary photographic data

Long-term, large-scale monitoring of wildlife populations is an integral part of conservation research and management. However, some traditional monitoring protocols lack the information needed to account for sources of measurement error in data analyses. Ignoring measurement error, such as partial availability, imperfect detection, and species misidentification, can lead to mischaracterizations of population states and processes. Accounting for measurement error is key to robust monitoring of populations, which can inform a wide variety of decisions, including harvest, habitat restoration, and determination of the legal status of species. We undertook an effort to retroactively minimize bias in a large-scale, long-term monitoring program for marine birds in the Salish Sea, Washington, USA, by conducting an auxiliary study to jointly estimate components of measurement error. We built a novel model in a Bayesian framework that simultaneously harnessed human observer and photographic data types to produce estimates necessary to correct for the effects of partial availability, imperfect detection, and species misidentification. Across all 31 species identified in photographs, both observers had instances of undercounting and overcounting birds but tended to undercount (observers undercounted totals across all species on 69.3%–78.9% of transects). We estimated species-specific correction factors that can be used to correct both historical and future counts from the Salish Sea survey, which has been running since 1992. Our novel modeling framework can be applied in other multispecies monitoring contexts where minimal photographic data can be collected for the purposes of correcting for measurement error in large-scale, long-term datasets.

Washington↗
USGS2024

Estimating ungulate migration corridors from sparse movement data

Many ungulates migrate between distinct summer and winter ranges, and identifying, mapping, and conserving these migration corridors have become a focus of local, regional, and global conservation efforts. Brownian bridge movement models (BBMMs) are commonly used to empirically identify these seasonal migration corridors; however, they require location data sampled at relatively frequent intervals to obtain a robust estimate of an animal’s movement path. Fitting BBMMs to sparse location data violates the assumption of conditional random movement between successive locations, overestimating the area (and width) of a migration corridor when creating individual and population-level occurrence distributions, and precluding the use of low-frequency, or sparse, data in mapping migration corridors. In an effort to expand the utility of BBMMs to include sparse global positioning system (GPS) data, we propose an alternative approach to model migration corridors from sparse GPS data. We demonstrate this method using GPS data collected every 2 hours from four mule deer (Odocoileus hemionus) and four elk (Cervus canadensis) herds within Wyoming and Idaho. First, we used BBMMs to estimate a baseline corridor for the 2-hour data. We then subsampled the 2-hour data to one location every 12 hours (a proxy for sparse data) and fitted BBMMs to the 12-hour data using a fixed motion variance (FMV) value, instead of estimating the Brownian motion variance empirically. A range of FMV values was tested to identify the value that best approximated the baseline migration corridor. FMV values within a species-specific range (mule deer: 400–1,200 m2; elk: 600–1,600 m2) successfully delineated migration corridors similar to the 2-hour baseline corridors; overall, lower values delineated narrower corridors and higher values delineated wider corridors. Optimal FMV values of 800 m2 (mule deer) and 1,000 m2 (elk) decreased the inflation of the 12-hour corridors relative to the 2-hour corridors from traditional BBMMs. This FMV approach thus enables using sparse movement data to approximate realistic migration corridor dimensions, providing an important alternative when movement data are collected infrequently. This approach greatly expands the number of datasets that can be used for migration corridor mapping—a useful tool for management and conservation across the globe.

Idaho, Wyoming↗
USGS2024

Phenology forecasting models for detection and management of invasive annual grasses

Non-native annual grasses can dramatically alter fire frequency and reduce forage quality and biodiversity in the ecosystems they invade. Effective management techniques are needed to reduce these undesirable invasive species and maintain ecosystem services. Well-timed management strategies, such as grazing, that are applied when invasive grasses are active prior to native plants can control invasive species spread and reduce their impact; however, anticipating the timing of key phenological stages that are susceptible to management over vast landscapes is difficult, as the phenology of these species can vary greatly over time and space. To address this challenge, we created range-wide phenology forecasts for two problematic invasive annual grasses: cheatgrass ( Bromus tectorum ), and red brome ( Bromus rubens ). We tested a suite of 18 mechanistic phenology models using observations from monitoring experiments, volunteer science, herbarium records, timelapse camera imagery, and downscaled gridded climate data to identify the models that best predicted the dates of flowering and senescence of the two invasive grass species. We found that the timing of flowering and senescence of cheatgrass and red brome were best predicted by photothermal time models that had been adjusted for topography using gridded continuous heat-insolation load index values. Phenology forecasts based on these models can help managers make decisions about when to schedule management actions such as grazing to reduce undesirable invasive grasses and promote forage production, quality, and biodiversity in grasslands; to predict the timing of greatest fire risk after annual grasses dry out; and to select remote sensing imagery to accurately map invasive grasses across topographic and latitudinal gradients. These phenology models also have the potential to be operationalized for within-season or within-year decision support.

Ecosphere↗
USGS2024

Dryland fungi are spatially heterogeneous and resistant to global change drivers

Fungi are considered particularly important in regulating the structure and function of dryland ecosystems, yet the response of dryland fungal communities to global change remains notably understudied. Without a clear understanding of how fungi respond to global change drivers, mitigation plans—required for biodiversity and ecosystem service conservation and restoration—are impossible to develop. In this study, we asked the following: (1) How does the fungal community respond to the individual and interactive effects of physical disturbance and drought in a heterogeneous dryland landscape comprised of drought-adapted shrubs separated by adjacent open areas of soil? (2) What are the larger scale impacts of this response? We assessed fungal communities (using fungal-specific DNA metabarcoding analyses) of surface soil samples in an in situ global change experiment that included disturbance and drought in a full factorial design in the northern extent of the Chihuahuan Desert. We found that the fungal community was spatially heterogenous and remarkably resistant to disturbance and drought. We also show that dryland soils harbor high shares of facultative pathogenic and obligately pathogenic fungal taxa, with several concerning taxa reaching high relative abundances under drought. Our results suggest that the fungal community is highly influenced by microclimatic conditions associated with the presence or absence of vegetation. Moreover, our results imply that the fungal community in our experiment was already adapted to the magnitude of stress imposed by two years of experimental disturbance and drought treatments. Overall, our study shows that the fungal community is spatially heterogeneous and resistant to global change drivers and houses many fungal species known for being stress tolerant and pathogenic.

New Mexico↗
USGS2024

Reproductive ecology and egg parasitism of the Samoan swallowtail butterfly

We investigated the reproductive ecology and effects of egg parasitism on the Samoan swallowtail butterfly ( Papilio godeffroyi ), which survives only on Tutuila Island, American Samoa, after having disappeared from the much larger islands of Upolu and Savai‘i in independent Samoa. During monthly surveys of its only known host plant, Micromelum minutum , across eight sites in 2013 and 2014, we collected eggs, eggshells, larvae, pupae, and pupal exuviae. Live specimens were reared under laboratory conditions to determine reproductive outcomes, developmental rates, and sex ratios, as well as parasitoid attack frequencies, brood sizes, and sex ratios. Sixty-six of 448 (14.7%) eggs produced larvae, 47 of which became adults. The sex ratio was approximately even overall and within each developmental stage. Eggs were slightly larger on individual host trees and in host tree stands that yielded more eggs per unit of foliage, indicating that ovipositing females responded to some features of host trees and stands. Eggs hatching female or male larvae were similar in size, and the sexes developed at similar rates. A newly described species of parasitoid wasp, Ooencyrtus pitosina (Encyrtidae), emerged from 73.6% of 382 butterfly eggs that failed to hatch in the laboratory (62.7% of 448 eggs overall). Forty-one other eggs contained dead parasitoid larvae. An additional, unidentified Ooencyrtus wasp species emerged from a single P. godeffroyi egg. No parasitoids were reared from P. godeffroyi larvae or pupae. Of 656 P. godeffroyi eggshells collected in the field and examined in the laboratory, 62.2% showed signs of having been parasitized by O. pitosina. There was no evidence that parasitism rates were density-dependent. O. pitosina brood sizes ranged from 1 to 5, with the sex ratio skewed toward females (2.40 F:1.00 M). Larger parasitoid broods were associated with slightly larger host eggs, indicating that female wasps may adjust brood size according to host egg size or that fewer wasp larvae are able to complete development in smaller eggs. Techniques used to rear both P. godeffroyi and O. pitosina in the laboratory could be applied to a captive-rear, wild-release program, which may facilitate reestablishment of the species in Samoa.

Tutuila Island↗
USGS2024

Partly cloudy with a chance of mosquitoes: Developing a flexible approach to forecasting mosquito populations

Climate-induced shifts in mosquito phenology and population structure have important implications for the health of humans and wildlife. The timing and intensity of mosquito interactions with infected and susceptible hosts are a primary determinant of vector-borne disease dynamics. Like most ectotherms, rates of mosquito development and corresponding phenological patterns are expected to change under shifting climates. However, developing accurate forecast of mosquito phenology under climate change that can be used to inform management programs remains challenging despite an abundance of available data. As climate change will have variable effects on mosquito demography and phenology across species it is vital that we identify associated traits which may explain the observed variation. Here, we review a suite of modeling approaches that could be applied to generate forecasts of mosquito activity under climate change and evaluate the strengths and weaknesses of the different approaches. We describe four primary life-history and physiological traits that can be used to constrain models and demonstrate how this prior information can be harnessed to develop a more general understanding of how mosquito activity will shift under changing climates. Combining a trait-based approach with appropriate modeling techniques can allow for the development of actionable, flexible, and multi-scale forecasts of mosquito population dynamics and phenology for diverse stakeholders.

Michigan, Wisconsin↗