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At least 91 records · Page 5Linked to original sources
USGS2021

Potential for carbon and nitrogen sequestration by restoring tidal connectivity and enhancing soil surface elevations in denuded and degraded south Florida mangrove ecosystems

Mangroves are tidally dependent wetlands that are influenced often by alterations in hydrology associated with coastal developments that impact their distribution, health, and function. Alteration in frequency, depth, duration, and seasonality of tidal inundation can lead to changes in forest condition, although these stress-adapted ecosystems may persist for many years before succumbing to mortality. However, arresting this decline through hydrological restoration can significantly improve ecosystem condition and the provision of ecosystem services. Much of the mangrove resource on Marco Island, Florida, USA, is unhealthy if not already dead or dying due to soil structural shifts, permanent flooding, and peat compression resulting from road construction, tidal restriction, and delays in restoration actions. In order to determine the impact of restricted hydrology on these mangrove forests, we examined soil surface elevation change and soil carbon (C) and nitrogen (N) content along a degradation gradient and within a small-scale, community-driven restoration area. Using a space-for-time substitution approach, we found that the restoration of regular tidal inundation to Marco Island mangroves has the potential to increase C sequestration in surface soils alone from 0 to 360 g C/m 2 /yr (3.60 Mg C/ha/yr) and increase N sequestration from 0 to 24 g N/m2/yr (0.24 Mg N/ha/yr). Additional sequestration benefits would be realized with aboveground forest recovery. Successful mangrove restoration trials and small community-based projects such as those on Marco Island could serve as a model for larger efforts and empower stakeholders and policy makers to restore other wetlands and better manage coastal carbon.

Florida↗
USGS2021

Preface to book: Wetland carbon and environmental management

The idea for this book, including its organization and contents, has its origin in the latest environmental and climate policy requirements in the United States, as well as science advances. In 2007, the U.S. Congress passed the Energy Independence and Security Act (EISA), from which Section 712 required U.S. Federal agencies to provide a better understanding of carbon and greenhouse gas fluxes across the United States. As a result, largescale and coordinated efforts were launched to assess carbon storage, carbon fluxes, and greenhouse gas fluxes including CO2, CH4, and N2O from all major terrestrial and freshwater aquatic ecosystems, including forest, grassland/shrub, agricultural lands, wetlands, and rivers, streams, lakes, and impoundments. The EISA assessment produced major results (Selmants et al., 2017; Zhu, 2011; Zhu & McGuire, 2016; Zhu & Reed, 2012, 2014), but recognized that wetlands remained a significant source of uncertainty, especially for those wetlands that were being actively managed. The more recent Second State of the Carbon Cycle Report by the U.S. Global Change Research Program (USGCRP), which devoted two separate chapters to inland and coastal wetlands, respectively, noted that large knowledge gaps still remain, ranging from inadequate analysis of restored and managed wetlands, and consequences of management decisions, to future wetland responses to climate change (USGCRP, 2018). In recent literature, wetland management is suggested as a potential natural solution to mitigate climate change (Fargione et al., 2018, Kroeger et al., 2017) and help offset direct losses of wetlands from sea level rise, subsidence, and coastal erosion (Wang et al., 2017). The recognition that a synthesis of wetland carbon management was urgently needed was the genesis of Wetland Carbon and Environmental Management; discerning the relationships between wetland management and carbon flux (loss or gain) is an international goal.

Geophysical Monograph Series↗
USGS2017

Climatic controls on the global distribution, abundance, and species richness of mangrove forests

Mangrove forests are highly productive tidal saline wetland ecosystems found along sheltered tropical and subtropical coasts. Ecologists have long assumed that climatic drivers (i.e., temperature and rainfall regimes) govern the global distribution, structure, and function of mangrove forests. However, data constraints have hindered the quantification of direct climate-mangrove linkages in many parts of the world. Recently, the quality and availability of global-scale climate and mangrove data have been improving. Here, we used these data to better understand the influence of air temperature and rainfall regimes upon the distribution, abundance, and species richness of mangrove forests. Although our analyses identify global-scale relationships and thresholds, we show that the influence of climatic drivers is best characterized via regional range limit-specific analyses. We quantified climatic controls across targeted gradients in temperature and/or rainfall within 14 mangrove distributional range limits. Climatic thresholds for mangrove presence, abundance, and species richness differed among the 14 studied range limits. We identified minimum temperature-based thresholds for range limits in eastern North America, eastern Australia, New Zealand, eastern Asia, eastern South America, and southeast Africa. We identified rainfall-based thresholds for range limits in western North America, western Gulf of Mexico, western South America, western Australia, Middle East, northwest Africa, east central Africa, and west central Africa. Our results show that in certain range limits (e.g., eastern North America, western Gulf of Mexico, eastern Asia), winter air temperature extremes play an especially important role. We conclude that rainfall and temperature regimes are both important in western North America, western Gulf of Mexico, and western Australia. With climate change, alterations in temperature and rainfall regimes will affect the global distribution, abundance, and diversity of mangrove forests. In general, warmer winter temperatures are expected to allow mangroves to expand poleward at the expense of salt marshes. However, dispersal and habitat availability constraints may hinder expansion near certain range limits. Along arid and semi-arid coasts, decreases or increases in rainfall are expected to lead to mangrove contraction or expansion, respectively. Collectively, our analyses quantify climate-mangrove linkages and improve our understanding of the expected global- and regional-scale effects of climate change upon mangrove forests.

Ecological Monographs↗
USGS2017

Altitudinal migration and the future of an iconic Hawaiian honeycreeper in response to climate change and management

Altitudinal movement by tropical birds to track seasonally variable resources can move them from protected areas to areas of increased vulnerability. In Hawaiʻi, historical reports suggest that many Hawaiian honeycreepers such as the ‘I‘iwi ( Drepanis coccinea ) once undertook seasonal migrations, but the existence of such movements today is unclear. Because Hawaiian honeycreepers are highly susceptible to avian malaria, currently minimal in high-elevation forests, understanding the degree to which honeycreepers visit lower elevation forests may be critical to predict the current impact of malaria on population dynamics and how susceptible bird populations may respond to climate change and mitigation scenarios. Using radio telemetry data, we demonstrate for the first time that a large fraction of breeding adult and juvenile ‘I‘iwi originating from an upper-elevation (1,920 m) population at Hakalau Forest National Wildlife Refuge exhibit post-breeding movements well below the upper elevational limit for mosquitoes. Bloom data suggest seasonal variation in floral resources is the primary driver of seasonal movement for ‘I‘iwi. To understand the demographic implications of such movement, we developed a spatial individual-based model calibrated using previously published and original data. ʻI‘iwi dynamics were simulated backward in time, to estimate population levels in the absence of avian malaria, and forward in time, to assess the impact of climate warming as well as two potential mitigation actions. Even in disease-free ‘refuge’ populations, we found that breeding densities failed to reach the estimated carrying capacity, suggesting the existence of a seasonal “migration load” as a result of travel to disease-prevalent areas. We predict that ‘I‘iwi may be on the verge of extinction in 2100, with the total number of pairs reaching only ~ 0.2–12.3% of the estimated pre-malaria density, based on an optimistic climate change scenario. The probability of extinction of ‘I‘iwi populations, as measured by population estimates for 2100, is strongly related to their estimated migration propensity. Long-term conservation strategies likely will require a multi-pronged response including a reduction of malaria threats, habitat restoration and continued landscape-level access to seasonally variable nectar resources.

Ecological Monographs↗
USGS2017

Carbon dynamics of river corridors and the effects of human alterations

Research in stream metabolism, gas exchange, and sediment dynamics indicates that rivers are an active component of the global carbon cycle and that river form and process can influence partitioning of terrestrially derived carbon among the atmosphere, geosphere, and ocean. Here we develop a conceptual model of carbon dynamics (inputs, outputs, and storage of organic carbon) within a river corridor, which includes the active channel and the riparian zone. The exchange of carbon from the channel to the riparian zone represents potential for storage of transported carbon not included in the “active pipe” model of organic carbon (OC) dynamics in freshwater systems. The active pipe model recognizes that river processes influence carbon dynamics, but focuses on CO 2 emissions from the channel and eventual delivery to the ocean. We also review how human activities directly and indirectly alter carbon dynamics within river corridors. We propose that dams create the most significant alteration of carbon dynamics within a channel, but that alteration of riparian zones, including the reduction of lateral connectivity between the channel and riparian zone, constitutes the most substantial change of carbon dynamics in river corridors. We argue that the morphology and processes of a river corridor regulate the ability to store, transform, and transport OC, and that people are pervasive modifiers of river morphology and processes. The net effect of most human activities, with the notable exception of reservoir construction, appears to be that of reducing the ability of river corridors to store OC within biota and sediment, which effectively converts river corridors to OC sources rather than OC sinks. We conclude by summarizing knowledge gaps in OC dynamics and the implications of our findings for managing OC dynamics within river corridors.

Ecological Monographs↗
USGS2017

Coastal habitat and biological community response to dam removal on the Elwha River

Habitat diversity and heterogeneity play a fundamental role in structuring ecological communities. Dam emplacement and removal can fundamentally alter habitat characteristics, which in turn can affect associated biological communities. Beginning in the early 1900s, the Elwha and Glines Canyon dams in Washington, USA, withheld an estimated 30 million tonnes of sediment from river, coastal, and nearshore habitats. During the staged removal of these dams—the largest dam removal project in history—over 14 million tonnes of sediment were released from the former reservoirs. Our interdisciplinary study in coastal habitats—the first of its kind—shows how the physical changes to the river delta and estuary habitats during dam removal were linked to responses in biological communities. Sediment released during dam removal resulted in over a meter of sedimentation in the estuary and over 400 m of expansion of the river mouth delta landform. These changes increased the amount of supratidal and intertidal habitat, but also reduced the influx of seawater into the pre-removal estuary complex. The effects of these geomorphic and hydrologic changes cascaded to biological systems, reducing the abundance of macroinvertebrates and fish in the estuary and shifting community composition from brackish to freshwater-dominated species. Vegetation did not significantly change on the delta, but pioneer vegetation increased during dam removal, coinciding with the addition of newly available habitat. Understanding how coastal habitats respond to large-scale human stressors—and in some cases the removal of those stressors—is increasingly important as human uses and restoration activities increase in these habitats.

Washington↗
USGS2018

Spatial autoregressive models for statistical inference from ecological data

Ecological data often exhibit spatial pattern, which can be modeled as autocorrelation. Conditional autoregressive (CAR) and simultaneous autoregressive (SAR) models are network‐based models (also known as graphical models) specifically designed to model spatially autocorrelated data based on neighborhood relationships. We identify and discuss six different types of practical ecological inference using CAR and SAR models, including: (1) model selection, (2) spatial regression, (3) estimation of autocorrelation, (4) estimation of other connectivity parameters, (5) spatial prediction, and (6) spatial smoothing. We compare CAR and SAR models, showing their development and connection to partial correlations. Special cases, such as the intrinsic autoregressive model (IAR), are described. Conditional autoregressive and SAR models depend on weight matrices, whose practical development uses neighborhood definition and row‐standardization. Weight matrices can also include ecological covariates and connectivity structures, which we emphasize, but have been rarely used. Trends in harbor seals ( Phoca vitulina ) in southeastern Alaska from 463 polygons, some with missing data, are used to illustrate the six inference types. We develop a variety of weight matrices and CAR and SAR spatial regression models are fit using maximum likelihood and Bayesian methods. Profile likelihood graphs illustrate inference for covariance parameters. The same data set is used for both prediction and smoothing, and the relative merits of each are discussed. We show the nonstationary variances and correlations of a CAR model and demonstrate the effect of row‐standardization. We include several take‐home messages for CAR and SAR models, including (1) choosing between CAR and IAR models, (2) modeling ecological effects in the covariance matrix, (3) the appeal of spatial smoothing, and (4) how to handle isolated neighbors. We highlight several reasons why ecologists will want to make use of autoregressive models, both directly and in hierarchical models, and not only in explicit spatial settings, but also for more general connectivity models.

Ecological Monographs↗
USGS2018

The epidemiology of avian pox and interaction with avian malaria in Hawaiian forest birds

Despite the purported role of avian pox ( Avipoxvirus spp.) in the decline of endemic Hawaiian birds, few studies have been conducted on the dynamics of this disease, its impact on free‐living avian populations, or its interactions with avian malaria ( Plasmodium relictum ). We conducted four longitudinal studies of 3–7 yr in length and used generalized linear models to evaluate cross‐sectional prevalence of active pox infection and individuals with healed deformities that had recovered from pox. Our goal was to understand how species, season, elevation, malaria infection, and other biological characteristics influenced pox infection in ʻApapane, Hawaiʻi ʻAmakihi, ʻIʻiwi, and Japanese White‐eye across low‐, mid‐, and high‐elevation forests on the island of Hawaiʻi. We also used multi‐state capture‐recapture (longitudinal) models to estimate pox infection rates, recovery rates, and potential pox‐related mortality. Pox infection rates were typically highest in low‐elevation forests, followed by mid‐elevation forests, and lowest in high‐elevation forests. We also found seasonal changes in pox prevalence throughout the annual cycle; typically increasing from spring through summer, peaking in fall, and declining in winter. These seasonal changes occurred in low‐ and mid‐elevation forests, but not in high elevations where pox infection was low. Seasonal and elevation patterns of pox infection are like those for avian malaria, strongly implicating mosquito vectors, rather than other biting arthropods or contact transmission, as the primary source of transmitting both diseases. Most native Hawaiian birds recovered from pox infection within 6 months; frequently without permanent lesions. Contrary to our expectations, we found no direct evidence that pox is a substantial mortality factor in any of the three native bird species we studied. Birds with chronic malaria infection were more likely to have both active pox infection and healed pox lesions suggesting a synergistic interaction that may influence the evolution of pox virulence. Because pox infection can be assessed visually, and birds have a high recovery rate, this disease may be a sensitive indicator of the seasonal and annual risk of transmission of malaria in Hawaiʻi.

Ecological Monographs↗
USGS2018

Diel predator activity drives a dynamic landscape of fear

A “landscape of fear” (LOF) is a map that describes continuous spatial variation in an animal's perception of predation risk. The relief on this map reflects, for example, places that an animal avoids to minimize risk. Although the LOF concept is a potentially unifying theme in ecology that is often invoked to explain the ecological and conservation significance of fear, little is known about the daily dynamics of an LOF. Despite theory and data to the contrary, investigators often assume, implicitly or explicitly, that an LOF is a static consequence of a predator's mere presence within an ecosystem. We tested the prediction that an LOF in a large‐scale, free‐living system is a highly dynamic map with “peaks” and “valleys” that alternate across the diel (24‐h) cycle in response to daily lulls in predator activity. We did so with extensive data from the case study of Yellowstone elk ( Cervus elaphus ) and wolves ( Canis lupus ) that was the original basis for the LOF concept. We quantified the elk LOF, defined here as spatial allocation of time away from risky places and times, across nearly 1,000‐km 2 of northern Yellowstone National Park and found that it fluctuated with the crepuscular activity pattern of wolves, enabling elk to use risky places during wolf downtimes. This may help explain evidence that wolf predation risk has no effect on elk stress levels, body condition, pregnancy, or herbivory. The ability of free‐living animals to adaptively allocate habitat use across periods of high and low predator activity within the diel cycle is an underappreciated aspect of animal behavior that helps explain why strong antipredator responses may trigger weak ecological effects, and why an LOF may have less conceptual and practical importance than direct killing.

Yellowstone National Park↗
USGS2018

A guide to Bayesian model checking for ecologists

Checking that models adequately represent data is an essential component of applied statistical inference. Ecologists increasingly use hierarchical Bayesian statistical models in their research. The appeal of this modeling paradigm is undeniable, as researchers can build and fit models that embody complex ecological processes while simultaneously accounting for observation error. However, ecologists tend to be less focused on checking model assumptions and assessing potential lack of fit when applying Bayesian methods than when applying more traditional modes of inference such as maximum likelihood. There are also multiple ways of assessing the fit of Bayesian models, each of which has strengths and weaknesses. For instance, Bayesian P values are relatively easy to compute, but are well known to be conservative, producing P values biased toward 0.5. Alternatively, lesser known approaches to model checking, such as prior predictive checks, cross‐validation probability integral transforms, and pivot discrepancy measures may produce more accurate characterizations of goodness‐of‐fit but are not as well known to ecologists. In addition, a suite of visual and targeted diagnostics can be used to examine violations of different model assumptions and lack of fit at different levels of the modeling hierarchy, and to check for residual temporal or spatial autocorrelation. In this review, we synthesize existing literature to guide ecologists through the many available options for Bayesian model checking. We illustrate methods and procedures with several ecological case studies including (1) analysis of simulated spatiotemporal count data, (2) N‐mixture models for estimating abundance of sea otters from an aircraft, and (3) hidden Markov modeling to describe attendance patterns of California sea lion mothers on a rookery. We find that commonly used procedures based on posterior predictive P values detect extreme model inadequacy, but often do not detect more subtle cases of lack of fit. Tests based on cross‐validation and pivot discrepancy measures (including the “sampled predictive P value”) appear to be better suited to model checking and to have better overall statistical performance. We conclude that model checking is necessary to ensure that scientific inference is well founded. As an essential component of scientific discovery, it should accompany most Bayesian analyses presented in the literature.

Ecological Monographs↗
USGS2018

A multistate open robust design: population dynamics, reproductive effort, and phenology of sea turtles from tagging data

Understanding population dynamics, and how it is influenced by exogenous and endogenous factors, is important to the study and conservation of species. Moreover, for migratory species, the phenology and duration of use of a given location can also influence population structure and dynamics. For many species, breeding abundance, survival, and reproductive performance, as well as phenology of nesting, are often the most accessible, and therefore practical, elements of their life history to study. For a population of hawksbill sea turtles (Eretmochelys imbricata), we modeled population change for nesters and total adult females, survival, and breeding probability, from 25 years of intensive tagging data. We modeled breeding probability as a function of the number of years since last breeding, and tested for differences between neophyte and experienced nesters. For each year, we also estimated the number of clutches deposited per female, and phenology of use, for neophytes and experienced nesters. In order to implement the analysis we developed a novel generalized multistate open robust design mark-recapture modeling framework, with parameters for survival and transition probabilities, and for each primary period, state structure and arrival, persistence, and detection probabilities. Derived parameters included abundance of observable and unobservable components of the population, residence time, expected arrival and departure periods, and per-period intensity of study area use. Abundance of nesters increased over most of the time series. Survival probability was 0.935 (se = 0.01). All hawksbills skipped at least one year of nesting. Breeding probability increased by skipping a second year, but then decreased thereafter. Subsequent breeding probability was lower for neophyte nesters than for experienced nesters, but the effect was weaker than the effect of years since breeding. Clutch frequency varied by year, with no discernable pattern of differences between neophytes and experienced nesters. Mean arrival and departure dates also varied, with a slight shift of nesting activity to earlier in the season. The multistate open robust design model developed here provides a flexible framework for modeling the dynamics of structured migratory populations, and the phenology and duration of their seasonal use of study areas.

Caribbean, Jumby Bay Leeward Islands, Long Island↗
USGS2019

An analysis of autocorrelation and bias in home range estimation

Home range estimation is routine practice in ecological research. While advances in animal tracking technology have increased our capacity to collect data to support home range analysis, these same advances have also resulted in increasingly autocorrelated data. Consequently, the question of which home range estimator to use on modern, highly autocorrelated tracking data remains open. This question is particularly relevant given that most estimators assume independently sampled data. Here, we provide a comprehensive evaluation of the effects of autocorrelation on home range estimation. We base our study on an extensive data set of GPS locations from 369 individuals representing 27 species distributed across five continents. We first assemble a broad array of home range estimators, including Kernel Density Estimation (KDE) with four bandwidth optimizers (Gaussian reference function, autocorrelated-Gaussian reference function AKDE, Silvermans rule of thumb, and least squares cross-validation), Minimum Convex Polygon, and Local Convex Hull methods. Notably, all of these estimators except AKDE assume independent and identically distributed (IID) data. We then employ half-sample cross-validation to objectively quantify estimator performance, and the recently introduced effective sample size for home range area estimation ( N̂ area ) to quantify the information content of each data set. We found that AKDE 95% area estimates were larger than conventional IID-based estimates by a mean factor of 2. The median number of cross-validated locations included in the hold-out sets by AKDE 95% (or 50%) estimates was 95.3% (or 50.1%), confirming the larger AKDE ranges were appropriately selective at the specified quantile. Conversely, conventional estimates exhibited negative bias that increased with decreasing N̂ area . To contextualize our empirical results, we performed a detailed simulation study to tease apart how sampling frequency, sampling duration, and the focal animals movement conspire to affect range estimates. Paralleling our empirical results, the simulation study demonstrated that AKDE was generally more accurate than conventional methods, particularly for small N̂ area . While 72% of the 369 empirical data sets had >1,000 total observations, only 4% had an N̂ area >1,000, where 30% had an N̂ area <30. In this frequently encountered scenario of small N̂ area , AKDE was the only estimator capable of producing an accurate home range estimate on autocorrelated data.

Ecological Monographs↗
USGS2019

Spatially-structured statistical network models for landscape genetics

A basic understanding of how the landscape impedes, or creates resistance to, the dispersal of organisms and hence gene flow is paramount for successful conservation science and management. Spatially structured ecological networks are often used to represent spatial landscape‐genetic relationships, where nodes represent individuals or populations and resistance to movement is represented using non‐binary edge weights. Weights are typically assigned or estimated by the user, rather than observed, and validating such weights is challenging. We provide a synthesis of current methods used to estimate edge weights and an overview of common model types, stressing the advantages and disadvantages of each approach and their ability to model landscape‐genetic data. We further explore a set of spatial‐statistical methods that provide ecologists with alternative approaches for modeling spatially explicit processes that may affect genetic structure. This includes an overview of spatial autoregressive models, with a particular focus on how correlation and partial correlation are used to represent neighborhood structure with the inverse of the covariance matrix (i.e., precision matrix). We then demonstrate how to model resistance by specifying an appropriate statistical model on the nodes, conditioned on the edge weights, through the precision matrix. This integration of network ecology and spatial statistics provides a practical analytical framework for landscape‐genetic studies. The results can be used to make statistical inferences about the relative importance of individual landscape characteristics, such as the vegetative cover, hillslope, or the presence of roads or rivers, on gene flow. In addition, the R code we include allows readers to explore landscape‐genetic structure in their own datasets, which will potentially provide new insights into the evolutionary processes that generated ecological networks, as well as valuable information about the optimal characteristics of conservation corridors.

Ecological Monographs↗
USGS2019

Geographic variation in the intensity of warming and phenological mismatch between Arctic shorebirds and invertebrates

Responses to climate change can vary across functional groups and trophic levels, leading to a temporal decoupling of trophic interactions or ‘phenological mismatches.’ Despite a growing number of single-species studies that identified phenological mismatches as a nearly universal consequence of climate change, we have a limited understanding of the spatial variation in the intensity of this phenomenon nor what influences this variation. In this study, we tested for geographic patterns in phenological mismatches between six species of shorebirds and their invertebrate prey at ten sites spread across ~13º latitude and ~84º longitude in the Arctic over three years. At each site, we quantified the phenological mismatch between shorebirds and their invertebrate prey at: 1) an individual nest level, as the difference in days between the seasonal peak in food and the peak demand by chicks, and 2) a population level, as the overlapped area under fitted curves for total daily biomass of invertebrates and dates of the peak demand by chicks. We tested whether the intensity of past climatic change observed at each site corresponded with the extent of phenological mismatch and used Structural Equation Modeling to test for causal relationships among: 1) environmental factors, including geographic location and current climatic conditions, 2) the timing of invertebrate emergence and the breeding phenology of shorebirds, and 3) the phenological mismatch between the two trophic levels. The extent of phenological mismatch varied more among different sites than among different species within each site. A greater extent of phenological mismatch at both the individual-nest and population-levels coincided with changes in the timing of snowmelt as well as the potential dissociation of long-term snow phenology from changes in temperature. The timing of snowmelt also affected the shape of the food and demand curves, which determined the extent of phenological mismatch at the population level. Finally, we found larger mismatches at more easterly longitudes, which may be affecting the population dynamics of shorebirds, as two of our study species show regional population declines in only the eastern part of their range. This suggests that phenological mismatches may be resulting in demographic consequences for arctic-nesting birds.

Alaska↗
USGS2020

Changes in prey, turbidity, and competition reduce somatic growth and cause the collapse of a fish population

Somatic growth exerts strong control on patterns in the abundance of animal populations via effects on maturation, fecundity, and survival rates of juveniles and adults. In this paper, we quantify abiotic and biotic drivers of rainbow trout growth in the Colorado River, AZ, and the resulting impact on spatial and temporal variation in abundance. Inferences are based on approximately 10,000 observations of individual growth grates obtained through an intensive mark‐recapture effort conducted over five years (2012‐2016) in a 130 km‐long study segment downstream of Glen Canyon Dam. Prey availability, turbidity‐driven feeding efficiency, and intra‐specific competition were the dominant drivers of rainbow trout growth. Discharge, water temperature, and solar insulation were also evaluated but had a smaller influence. Mixed‐effect models explained 79‐82% of the variability in observed growth rates, with fixed covariate effects explaining 79‐87% of the total variation in growth parameters across five reaches and 18 quarterly sampling intervals. Reductions in growth owing in part to a phosphorous‐driven decline in prey availability, led to substantive weight loss and poor fish condition. This in turn lowered survival rates and delayed maturation, which led to a rapid decline in abundance and later recruitments. Reductions in feeding efficiency, due to episodic inputs of fine sediment from tributaries, and warmer water temperatures, contributed to reduced growth in downstream reaches, which led to more severe declines in abundance. Somatic growth rates increased following the population collapse due to reduced competition, and in the absence of substantive increases in prey availability. Our study elucidates important linkages between abiotic and biotic factors, somatic growth, and vital rates, and demonstrates how variation in somatic growth influences temporal and spatial patterns in abundance.

Arizona↗
USGS2021

Sea otter population collapse in southwest Alaska: Assessing ecological covariates, consequences, and causal factors

Sea otter ( Enhydra lutris ) populations in southwest Alaska declined substantially between about 1990 and the most recent set of surveys in 2015. Here we report changes in the distribution and abundance of sea otters, and covarying patterns in reproduction, mortality, body size and condition, diet and foraging behavior, food availability, health profiles, and exposure to environmental contaminants over this 25-yr period. The population decline, which resulted in densities on the order of 5% of environmental carrying capacity, ranged from Attu Island in the west to about Castle Cape (on the south side of the Alaska Peninsula) in the east. Remaining sea otters moved closer to shore and into shallow, protected habitats. Reproductive rates appeared unchanged with the decline. Although the demographic cause of the decline was clearly elevated mortality, stranded carcasses were rare or absent. The net rate of energy gain by foraging sea otters, body length and condition, and prey biomass density, all increased after the decline and varied inversely with sea otter population density beyond the area of decline. Sea otters within the area of decline showed no increases in health anomalies, disease, contaminant exposure, or abnormal gene transcription patterns as compared to animals outside the area of decline. These collective findings are inconsistent with nutritional limitation, disease, or environmental contaminants, and consistent with predation (or possibly some other density-independent factor) as the reason for the sea otter population decline. Our approach and analyses provide a broad conceptual template for thinking about and assessing the causes of wildlife population declines.

Alaska↗
USGS2021

Upwelling and the persistence of coral-reef frameworks in the eastern tropical Pacific

In an era of global change, the fate and form of reef habitats will depend on shifting assemblages of organisms and their responses to multiple stressors. Multiphyletic assemblages of calcifying and bioeroding species contribute to a dynamic balance between constructive and erosive processes, and reef-framework growth occurs only when calcium-carbonate deposition exceeds erosion. Each contributing species exhibits a unique combination of environmental sensitivities, trophic needs, and competitive abilities, making the net outcome of their habitat-altering behavior difficult to predict. In this study, standardized blocks of clean, massive Porites were placed at six reef sites in the eastern tropical Pacific, in the strongly and more-weakly upwelling Gulfs of Panamá (GoP) and Chiriquí (GoC), respectively. Sites were chosen to characterize the unique thermal and carbonate-chemistry conditions of each gulf. Satellite products were used to examine differences in sea-surface productivity, and surveys were conducted to quantify the abundance of important grazing taxa. After two years in situ, the Porites blocks were collected and scanned using high-resolution computed tomography to volumetrically quantify both endolithic and epilithic habitat alteration. Scan-volumes were further classified into functional groups according to morphology to quantify external bioerosion by fish and sea urchins, as well as the calcifying and bioeroding activity of crustose coralline algae, scleractinian corals, mollusks, annelids, and barnacles. The GoP, which has higher productivity, cooler temperatures, and periodically lower pH conditions, had higher rates of macroboring, but also higher rates of calcification. These unexpectedly higher rates of calcification in the GoP were a result of high recruitment of suspension-feeding taxa, particularly barnacles and vermiform fauna that have poor reef-forming potential. External bioerosion by grazers was the dominant process influencing these dead coral substrates across both gulfs, contributing to higher rates of net erosion in the GoC and underscoring the important roles that urchins and fish play in not just removing algae on reefs, but also eroding reef habitat. Ultimately these findings reveal that the trophic requirements of habitat-altering taxa are closely tied to reef-framework stability, and that environmental conditions conducive to carbonate precipitation are not necessarily those that will lead to habitat persistence.

Ecological Monographs↗
USGS2022

Parasites in kelp-forest food webs increase food-chain length, complexity, and specialization, but reduce connectance

We explored whether parasites are important in kelp forests by examining their effects on a high-quality, high-resolution kelp-forest food web. After controlling for generic effects of network size, parasites affected kelp-forest food web structure in some ways consistent with other systems. Parasites increased the trophic span of the web, increasing top predator vulnerability and the longest chain length. Unique links associated with parasites, such as concomitant predation (consumption of parasites along with their hosts by predators) increased the frequency of network motifs involving mutual consumption and decreased niche contiguity of free-living species. However, parasites also affected kelp-forest food web structure in ways not seen in other systems. Kelp-forest parasites are richer and more specialized than other systems. As a result, parasites reduced diet generality and decreased connectance in the kelp forest. Although mutual consumption motifs increased in frequency, this motif type was still a small fraction of all possible motifs, so their increase in frequency was not enough to compensate for the decrease in connectance caused by adding many specialist parasite species.

California↗