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Results for “Modeling and Using Context”

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USGS2023

Biotic and abiotic factors shaping bat activity in Maryland soybean fields

Bats are important pest control agents in agriculture. Yet, the underlying fine-scale biotic and abiotic mechanisms that drive their foraging behaviors and responses to insect outbreaks are unclear. Herbivore-induced plant volatiles (HIPVs) can attract both invertebrate and vertebrate natural enemies that use the chemical plant cues to locate insect prey. The ability of HIPVs to attract multiple species raises the question of whether they may also be a biotic factor influencing insectivorous bat activity. Additionally, abiotic factors, such as weather conditions, can affect bat activity in agricultural settings, but little is known about how bats respond to shifting environmental conditions on short timescales in this landscape context. Using a model crop system, soybean ( Glycine max ), our study asked three questions: (1) Which bat species are active in eastern Maryland soybean fields? (2) Is insectivorous bat activity affected by naturally occurring soybean HIPVs and/or synthetic soybean HIPVs (indole or farnesene)? (3) How is insectivorous bat activity affected by hourly weather conditions in this landscape? In soybean fields in eastern Maryland, we created paired treatment plots: HIPV plots (damaged plants or synthetic HIPV dispensers) and control plots (undamaged plants or empty dispensers). We measured bat activity using ultrasonic recorders, summarizing hourly and nightly activity, and detected 10 total species. The most abundant species were big brown/silver-haired bats ( Eptesicus fuscus / Lasionycteris noctivagans ). Bat activity did not significantly differ between control and HIPV plots in any of the three experiments. Thus, our results do not support our expectation that bats in eastern Maryland use soybean HIPVs to locate insect prey. However, bat activity did increase with increasing average hourly temperature and wind speed. This initial study of bats and HIPVs, as well as the fine-scale examination of weather conditions on bat activity, may serve as a guide for future research on bat–plant interactions that can support the development of new strategies for sustainable pest management.

Maryland↗

USGS2018

Examining speed versus selection in connectivity models using elk migration as an example

Context Landscape resistance is vital to connectivity modeling and frequently derived from resource selection functions (RSFs). RSFs estimate relative probability of use and tend to focus on understanding habitat preferences during slow, routine animal movements (e.g., foraging). Dispersal and migration, however, can produce rarer, faster movements, in which case models of movement speed rather than resource selection may be more realistic for identifying habitats that facilitate connectivity. Objective To compare two connectivity modeling approaches applied to resistance estimated from models of movement rate and resource selection. Methods Using movement data from migrating elk, we evaluated continuous time Markov chain (CTMC) and movement-based RSF models (i.e., step selection functions [SSFs]). We applied circuit theory and shortest random path (SRP) algorithms to CTMC, SSF and null (i.e., flat) resistance surfaces to predict corridors between elk seasonal ranges. We evaluated prediction accuracy by comparing model predictions to empirical elk movements. Results All connectivity models predicted elk movements well, but models applied to CTMC resistance were more accurate than models applied to SSF and null resistance. Circuit theory models were more accurate on average than SRP models. Conclusions CTMC can be more realistic than SSFs for estimating resistance for fast movements, though SSFs may demonstrate some predictive ability when animals also move slowly through corridors (e.g., stopover use during migration). High null model accuracy suggests seasonal range data may also be critical for predicting direct migration routes. For animals that migrate or disperse across large landscapes, we recommend incorporating CTMC into the connectivity modeling toolkit.

Landscape Ecology↗

USGS2026

Ecological diffusion models are still useful in ecology

Context Connecting animal movement across scales—both among spatial scales and between individual and population level processes—is a central theme in ecology, including the sub-fields of movement and landscape ecology. However, most modeling frameworks operate at either the individual (Lagrangian) or population (Eulerian) level. Objectives We aimed to review how a partial differential equation (PDE) known as the ecological diffusion equation (EDE) offers connections among the scales of animal movement and how it can be embedded in commonly used statistical frameworks (e.g., hierarchical models) and fit to multiple types of data, such as count, presence-only, presence-absence, and telemetry data. In doing so, we also highlight emerging and promising avenues of research related to statistical EDE models. Methods We present how the EDE is derived from a simple set of first principles, can be used to represent both individual animal movement and spatiotemporal population dynamics, and can be fit to data by being applied in a hierarchical statistical framework. Finally, we review literature on recent applications and advances related to EDE models. Results In presenting the derivation of the EDE and reviewing recent advances and applications of EDE models, we established that the EDE can enable a mechanistic understanding of animal movement across scales, as well as the integration of multiple types of data. We also identified a number of topics where EDE models can be advanced to gain additional ecological insight. Conclusions Ecological diffusion is a process that emerges naturally from first principles governing animal movement and can be used to model both individual and population-level spatiotemporal dynamics. Statistical EDE models are related to other modeling frameworks, such as species distribution models, occupancy models, and step-selection analysis. The flexibility of hierarchical statistical modeling allows combining different types of data collected on movement and population dynamics, and the theoretical underpinnings of the EDE allow it to connect ecological processes occurring at fine and coarse spatial scales, as well as individual animal movement and population dynamics.

Landscape Ecology↗

USGS2017

Building a state-space life cycle model for naturally produced Snake River fall Chinook salmon

In 1992, Snake River basin fall Chinook salmon (Oncorhynchus tshawytscha) were listed for protection under the U.S. Endangered Species Act (NMFS 1992) and the population remained below 1000 individuals until 2000. Since then, returns from natural production has rebounded to over 20,000 spawners owing to a host of factors including reduced harvest (Peters et al. 2001), stable minimum spawning flows (Groves and Chandler 1999), summer flow augmentation (Connor et al. 2003), predator control (Beamesderfer et al. 1996), hatchery supplementation (Rosenberger et al. 2017), improved juvenile passage structures (Adams et al. 2014), summer spill operations (Perry et al. 2006; Adams et al. 2008), and periods of favorable ocean conditions and food availability (Logerwell et al. 2003; Peterson et al. 2014). Given this change in abundance coincident with numerous management actions and fluctuation in environmental drivers, quantifying which factors contributed to the observed rebound in natural production can provide critical insights into future management actions for this at-risk population. Multistage life cycle models provide a powerful analytical framework for understating how each life stage of a population contributes to population growth rate (Moussalli and Hilborn 1986; Greene and Beechie 2004). Multistage models may also be used as an analytical framework to explicitly estimate demographic parameters of a population model. This approach has an advantage over single-stage stock-recruitment models by allowing population growth rates to be partitioned among life stages rather than aggregated over an entire life cycle. Such partitioning allows for estimating 1) stage-specific density dependence, and 2) stage-specific effects of environmental factors or management actions. For example, Zabel et al. (2006) estimated parameters of a multistage model used in the context of a population viability analysis for spring/summer Chinook salmon in the Snake River, but such an approach has yet to be applied to fall Chinook salmon in the Snake River basin. Typically, data informing estimates of abundance at particular “check points” in the life cycle determines the complexity of the multistage model that can be fit to the data. For fall Chinook salmon, we are developing a two-stage model that encompasses: 1) upstream passage of spawners at Lower Granite Dam (LGR) to the subsequent downstream passage of their progeny at the dam, and 2) downstream passage of juveniles at LGR to their subsequent return from the ocean and passage at the Dam 2‒6 years later. This approach partitions the life cycle of fall Chinook salmon both spatially and temporally, which allows us to fit and compare alternative models with covariates specific to each stage. Our previous report to the ISAB (Zabel et al. 2013) detailed methods for estimating abundance of naturally produced adults and juveniles passing Lower Granite Dam, which provides the requisite data for fitting a two-stage model. The intent of this report is to describe the structure of the two-stage life cycle model, present preliminary results from fitting the model to data, and outline future directions and developments. As is clear from the diversity of models presented in this report, “life cycle models” range from very simple theoretically based population models (e.g., the Beverton-Holt stock- recruitment model) to very complex spatially explicit simulation models linked to hydrosystem hydrodynamic models (e.g., the COMPASS model for a single transition in a life cycle model, Zabel et al. 2008). We chose to develop a model of intermediate complexity that casts the two- stage life cycle model in a state-space framework (Newman et al. 2014). We chose to use a state-space framework implemented in a Bayesian framework because: • It provides both a statistical estimation framework for retrospective statistical analysis and a stochastic simulation framework for prospective analysis to evaluate alternative management actions. • Abundance estimates are uncertain. A state-space framework accounts for observation uncertainty in the abundance estimates and other data (e.g., age structure) while simultaneously estimating process uncertainty. • It allows for missing data. By drawing missing data from an appropriate probability model, uncertainty owing to missing data can be propagated without having to omit data or assume fixed values for missing data. Thus, a two-stage state-space life cycle model for fall Chinook salmon strikes an appropriate balance between model complexity, tractability, and applicability given the goals of performing both retrospective and prospective analysis to guide future management of this population.

Idaho, Oregon, Washington, Wyoming↗

USGS2023

Forecasting natural regeneration of sagebrush after wildfires using population models and spatial matching

Context Addressing ecosystem degradation in the Anthropocene will require ecological restoration across large spatial extents. Identifying areas where natural regeneration will occur without direct resource investment will improve scalability of restoration actions. Objectives An ecoregion in need of large scale restoration is the Great Basin of the Western US, where increasingly large and frequent wildfires threaten ecosystem integrity and its foundational shrub species. We develop a framework to forecast where post-wildfire regeneration of sagebrush cover ( Artemisia spp.) is likely to occur within the burnt areas across the region (> 900,000 km 2 ). Methods First, we parameterized population models using Landsat satellite-derived time series of sagebrush cover. Second, we evaluated the out-of-sample performance by predicting natural regeneration in wildfires not used for model training. This model assessment reproduces a management-oriented scenario: making restoration decisions shortly after wildfires with minimal local information. Third, we asked how accounting for increasingly fine-scale spatial heterogeneity could improve model forecasting accuracy. Results Regional-level models revealed that sagebrush post-fire recovery is slow, estimating > 80-year time horizon to reach an average cover at equilibrium of 16.6% (CI95% 9–25). Accounting for wildfire and within-wildfire spatial heterogeneity improved out-of-sample forecasts, resulting in a mean absolute error of 3.5 ± 4.3% cover, compared to the regional model with an error of 7.2 ± 5.1% cover. Conclusions We demonstrate that combining population models and non-parametric spatial matching provides a flexible framework for forecasting plant population recovery. Models for population recovery applied to Landsat-derived time series will assist restoration decision-making, including identifying priority targets for restoration.

Great Basin↗

USGS2020

A non-intrusive approach for efficient stochastic emulation and optimization of model-based nitrate-loading management decision support

Use of physically-motivated numerical models like groundwater flow-and-transport models for probabilistic impact assessments and optimization under uncertainty (OUU) typically incurs such a computational burdensome that these tools cannot be used during decision making. The computational challenges associated with these models can be addressed through emulation. In the land-use/water-quality context, the linear relation between nitrate loading and surface-water/groundwater nitrate concentrations presents an opportunity for employing an efficient model emulator through the application of impulse-response matrices. When paired with first-order second-moment techniques, the emulation strategy gives rise to the “stochastic impulse-response emulator” (SIRE). SIRE is shown to facilitate non-intrusive, near-real time, and risk-based evaluation of nitrate-loading change scenarios, as well as nitrate-loading OUU subject to surface-water/groundwater concentration constraints in high decision variable and parameter dimensions. Two case studies are used to demonstrate SIRE in the nitrate-loading context.

Environmental Modelling and Software↗

USGS2014

The response of stream periphyton to Pacific salmon: using a model to understand the role of environmental context

1. In stream ecosystems, Pacific salmon deliver subsidies of marine-derived nutrients and disturb the stream bed during spawning. The net effect of this nutrient subsidy and physical disturbance on biological communities can be hard to predict and is likely to be mediated by environmental conditions. For periphyton, empirical studies have revealed that the magnitude and direction of the response to salmon varies from one location to the next. Salmon appear to increase periphyton biomass and/or production in some contexts (a positive response), but decrease them in others (a negative response). 2. To reconcile these seemingly conflicting results, we constructed a system dynamics model that links periphyton biomass and production to salmon spawning. We used this model to explore how environmental conditions influence the periphyton response to salmon. 3. Our simulations suggest that the periphyton response to salmon is strongly mediated by both background nutrient concentrations and the proportion of the stream bed suitable for spawning. Positive periphyton responses occurred when both background nutrient concentrations were low (nutrient limiting conditions) and when little of the stream bed was suitable for spawning (because the substratum is too coarse). In contrast, negative responses occurred when nutrient concentrations were higher or a larger proportion of the bed was suitable for spawning. 4. Although periphyton biomass generally remained above or below background conditions for several months following spawning, periphyton production returned quickly to background values shortly afterwards. As a result, based upon our simulations, salmon did not greatly increase or decrease overall annual periphyton production. This suggests that any increase in production by fish or invertebrates in response to returning salmon is more likely to occur via direct consumption of salmon carcasses and/or eggs, rather than the indirect effects of greater periphyton production. 5. Overall, our simulations suggest that environmental factors need to be taken into account when considering the effects of spawning salmon on aquatic ecosystems. Our model offers researchers a framework for testing periphyton response to salmon across a range of conditions, which can be used to generate hypotheses, plan field experiments and guide data collection.

Freshwater Biology↗

USGS2023

Merging machine learning and geostatistical approaches for spatial modeling of geoenergy resources

Geostatistics is the most commonly used probabilistic approach for modeling earth systems, including quality parameters of various geoenergy resources. In geostatistics, estimates, either on a point or block support, are generated as a spatially-weighted average of surrounding samples. The optimal weights are determined through the stationary variogram model which accounts for the spatial structure of the samples. Recently, efficient modeling workflows using various machine learning algorithms (MLAs) have been expanded to the spatial context for modeling geological heterogeneity. The flexible use of MLAs as a spatial estimation tool stems mainly from the fact that unlike kriging, they do not require any variogram, nor do they depend strongly on a prior stationarity assumption (i.e., second order stationarity). This study evaluates the performance of two MLAs (ensemble super learner and elliptical radial basis neural network), ordinary kriging, and hybrid spatial modeling approaches using ordinary intrinsic collocated cokriging. The aforementioned modeling techniques are compared for estimating resources for four coal variables (wash yield, ash yield, calorific value and thickness) as an example. The results suggest that MLAs, when implemented alone, do not outperform ordinary kriging, but the estimation accuracy of the final model, measured by the root mean squared error tends to subtly improve (

Virginia↗

USGS2022

Balancing model generality and specificity in management-focused habitat selection models for Gunnison sage-grouse

Identifying, protecting, and restoring habitats for declining wildlife populations is foundational to conservation and recovery planning for any species at risk of decline. Resource selection analysis is a key tool to assess habitat and prescribe management actions. Yet, it can be challenging to map suitable resource conditions across a wide range of ecological contexts and use the resulting models to identify effective and universal habitat improvement actions. We developed a management-centric modeling approach that sought to balance the need to evaluate the consistency of key habitat conditions and improvement actions across multiple, distinct populations, while allowing context-specific environmental variables and spatial scales to nuance selection responses that form the basis of location-specific management prescriptions. To demonstrate this approach, we developed a set of habitat selection models for Gunnison sage-grouse ( Centrocercus minimus ), a threatened species under the U.S. Endangered Species Act. Conservation, species recovery, and habitat management efforts are needed in six isolated satellite populations (San Miguel, Crawford, Piñon Mesa, Dove Creek, Cerro Summit-Cimarron-Sims, and Poncha Pass) where environmental conditions differ, and the already small number of birds are declining. We used multi-scale and seasonal resource selection analyses to quantify relationships between environmental conditions and sites used by animals. All models included key habitat variables often altered through management actions to assess their differential influences across models. We found important similarities and differences among satellites, indicating that, although some rules of thumb are generally well-grounded, the consideration of population-specific environmental differences could increase the efficiency of local habitat improvement actions. Sage-grouse also had diverse responses to resource conditions at different scales, indicating that regional spatial (e.g., landscape) and local patch scale can differentially influence expected habitat improvements associated with where such management actions are implemented. Although context variables such as topography cannot be manipulated, sage-grouse associations revealed information that could guide the siting of improvement actions. This approach to balancing management objectives associated with habitat assessment may benefit spatially-structured populations with different environmental contexts and species with complex habitat needs and associations.

Colorado↗

USGS2010

Complementary models of tree species-soil relationships in old-growth temperate forests

Ecosystem-level studies identify plant–soil feedbacks as important controls on soil nutrient availability, particularly for nitrogen and phosphorus. Although site- and species-specific studies of tree species–soil relationships are relatively common, comparatively fewer studies consider multiple co-existing species in old-growth forests across a range of sites that vary in underlying soil fertility. We characterized patterns in forest floor and mineral soil nutrients associated with four common tree species across eight undisturbed old-growth forests in Oregon, USA, and used two complementary conceptual models to assess tree species–soil relationships. Plant–soil feedbacks that could reinforce site-level differences in nutrient availability were assessed using the context-dependent relationships model, whereby relative species-based differences in each soil nutrient diverged or converged as nutrient status changed across sites. Tree species–soil relationships that did not reflect strong feedbacks were evaluated using a site-independent relationships model, whereby forest floor and surface mineral soil nutrient pools differed consistently by tree species across sites, without variation in deeper mineral soils. We found that the organically cycled elements carbon, nitrogen, and phosphorus exhibited context-dependent differences among species in both forest floor and mineral soil, and most often followed a divergence model, whereby species differences were greatest at high-nutrient sites. These patterns are consistent with theory emphasizing biotic control of these elements through plant–soil feedback mechanisms. Site-independent species differences were strongest for pools of the weatherable cations calcium, magnesium, potassium, as well as phosphorus, in mineral soils. Site-independent species differences in forest floor nutrients were attributable to one species that displayed significantly greater forest floor mass accumulation. Our findings confirm that site-independent and context-dependent tree species-soil relationships occur simultaneously in old-growth temperate forests, with context-dependent relationships strongest for organically cycled elements, and site-independent relationships strongest for weatherable elements with inorganic cycling phases. These models provide complementary explanations for patterns of nutrient accumulation and cycling in mixed-species old-growth temperate forests.

Oregon↗

USGS1999

Assessing the effects of fire disturbances on ecosystems: A scientific agenda for research and management

A team of fire scientists and resource managers convened 17-19 April 1996 in Seattle, Washington, to assess the effects of fire disturbance on ecosystems. Objectives of this workshop were to develop scientific recommendations for future fire research and management activities. These recommendations included a series of numerically ranked scientific and managerial questions and responses focusing on (1) links among fire effects, fuels, and climate; (2) fire as a large-scale disturbance; (3) fire-effects modeling structures; and (4) managerial concerns, applications, and decision support. At the present time, understanding of fire effects and the ability to extrapolate fire effects knowledge to large spatial scales are limited, because most data have been collected at small spatial scales for specific applications. Although we clearly need more large-scale fire-effects data, it will be more expedient to concentrate efforts on improving and linking existing models that simulate fire effects in a georeferenced format while integrating empirical data as they become available. A significant component of this effort should be improved communication between modelers and managers to develop modeling tools to use in a planning context. Another component of this modeling effort should improve our ability to predict the interactions of fire and potential climatic change at very large spatial scales. The priority issues and approaches described here provide a template for fire science and fire management programs in the next decade and beyond.

General Technical Report↗

USGS2021

Decision context as an essential component of population viability analysis

Population viability analysis (PVA) is a widely used tool that applies demographic data in simulation frameworks to assess extinction risk for species or populations. It is used in diverse conservation applications, including evaluating management effectiveness, relative risk of threats, and potential changes to protective status (Beissinger & McCullough, 2002 ), and can be a critical tool for making decisions with imperfect knowledge of the system state, often on limited timelines (Meine et al., 2006 ). Chaudhary and Oli ( 2020 ) recently developed a framework to appraise the quality of PVAs based on the presence of essential background, model, and analysis components. They evaluated 160 published PVAs and reported a decline in the quality of PVAs over time (1990−2017). We agree PVA studies should report unambiguous descriptions of their essential components (Table 1 in Chaudhary and Oli) and explicitly state the model's biological and statistical assumptions. The need for increased transparency in PVAs is evident. Morrison et al. ( 2016 ) reported that only 50% of PVAs published in peer-reviewed and gray literature were both reproducible and repeatable. Further, in an examination of 67 studies that used matrix population models (widely used in PVAs), Kendall et al. ( 2019 ) reported that models frequently contained misspecification errors. Given the rapid advancement of simulation techniques, updated guidance for PVA construction is warranted. However, we believe the essential PVA components identified by Chaudhary and Oli contain a critical omission: the decision context in which the PVA was created and its usefulness in that context. Quality and utility are not mutually exclusive; however, some models that do not meet idealized quality standards might still be valuable because they are useful and represent the best available science for a given decision context (hereafter, decision-support models). The definition of quality for decision-support models should be different than models developed for the purpose of learning (hereafter, heuristic models) and should incorporate how useful the model was, despite information gaps. We further argue that assessment questions should be used prospectively to guide modeling projects, rather than for retrospective comparison of model quality.

Conservation Biology↗

USGS2021

Context-dependent deep learning

Explicitly representing an agent’s context has been shown to have many benefits, which should also apply to machine learning. In this paper, we describe an approach to do this called context-dependent deep learning (CDDL), which is based on earlier work in context-mediated behavior (CMB) that uses contextual schemas (c-schemas) to represent clas-ses of situations along with knowledge useful in them. These are then recalled, and they guide reasoning in the corre-sponding contexts. CDDL stores knowledge about deep neural network structure and weights in c-schemas, which al-lows context-specific learning. Our work is being developed in the domain of seabird detection in aerial images of islands for use by biologists.

Modeling and Using Context↗

USGS2011

Influence of harvesting pressure on demographic tactics: Implications for wildlife management

1. Demographic tactics within animal populations are shaped by selective pressures. Exploitation exerts additional pressures so that differing demographic tactics might be expected among populations with differences in levels of exploitation. Yet little has been done so far to assess the possible consequences of exploitation on the demographic tactics of mammals, even though such information could influence the choice of effective management strategies. 2. Compared with similar‐sized ungulate species, wild boar Sus scrofa has high reproductive capabilities, which complicates population management. Using a perturbation analysis, we investigated how population growth rates (λ) and critical life‐history stages differed between two wild boar populations monitored for several years, one of which was heavily harvested and the other lightly harvested. 3. Asymptotic λ was 1·242 in the lightly hunted population and 1·115 in the heavily hunted population, while the ratio between the elasticity of adult survival and juvenile survival was 2·63 and 1·27, respectively. A comparative analysis including 21 other ungulate species showed that the elasticity ratio in the heavily hunted population was the lowest ever observed. 4. Compared with expected generation times of similar‐sized ungulates (more than 6 years), wild boar has a fast life‐history speed, especially when facing high hunting pressure. This is well illustrated by our results, where generation times were 3·6 years in the lightly hunted population and only 2·3 years in the heavily hunted population. High human‐induced mortality combined with non‐limiting food resources accounted for the accelerated life history of the hunted population because of earlier reproduction. 5. Synthesis and applications . For wild boar, we show that when a population is facing a high hunting pressure, increasing the mortality in only one age‐class (e.g. adults or juveniles) may not allow managers to limit population growth. We suggest that simulations of management strategies based on context‐specific demographic models are useful for selecting interventions for population control. This type of approach allows the assessment of population response to exploitation by considering a range of plausible scenarios, improving the chance of selecting appropriate management actions.

Journal of Applied Ecology↗

USGS2018

A tale of two wildfires; testing detection and prediction of invasive species distributions using models fit with topographic and spectral indices

Context Developing species distribution models (SDMs) to detect invasive species cover and evaluate habitat suitability are high priorities for land managers. Objectives We tested SDMs fit with different variable combinations to provide guidelines for future invasive species model development based on transferability between landscapes. Methods Generalized linear model, boosted regression trees, multivariate adaptive regression splines, and Random Forests were fit with location data for high cheatgrass ( Bromus tectorum ) cover in situ for two post-burn sites independently using topographic indices, spectral indices derived from multiple dates of Landsat 8 satellite imagery, or both. Models developed for one site were applied to the other, using independent cheatgrass cover data from the respective ex situ site to test model transferability. Results Fitted models were statistically robust and comparable when fit with at least 200 cover plots in situ and transferred to the ex situ site. Only the Random Forests models were robust when fit with a small number of cover plots in situ. Conclusions Our study indicated spectral indices can be used in SDMs to estimate species cover across landscapes (e.g., both within the same Landsat scene and in an adjacent Landsat scene). Important considerations for transferability include the model employed, quantity of cover data used to train/test the models, and phenology of the species coupled with the timing of imagery. The results also suggest that when cover data are limited, SDMs fit with topographic indices are sufficient for evaluating cheatgrass habitat suitability in new post-disturbance landscapes; however, spectral indices can provide a more robust estimate for detection based on local phenology.

Wyoming↗

USGS2010

Nutrient fluxes at the landscape level and the R* rule

Nutrient cycling in terrestrial ecosystems involves not only the vertical recycling of nutrients at specific locations in space, but also biologically driven horizontal fluxes between different areas of the landscape. This latter process can result in net accumulation of nutrients in some places and net losses in others. We examined the effects of such nutrient-concentrating fluxes on the R * rule, which predicts that the species that can survive in steady state at the lowest level of limiting resource, R *, can exclude all competing species. To study the R * rule in this context, we used a literature model of plant growth and nutrient cycling in which both nutrients and light may limit growth, with plants allocating carbon and nutrients between foliage and roots according to different strategies. We incorporated the assumption that biological processes may concentrate nutrients in some parts of the landscape. We assumed further that these processes draw nutrients from outside the zone of local recycling at a rate proportional to the local biomass density. Analysis showed that at sites where there is a sufficient biomass-dependent accumulation of nutrients, the plant species with the highest biomass production rates (roughly corresponding to the best competitors) do not reduce locally available nutrients to a minimum concentration level (that is, minimum R *), as expected from the R * rule, but instead maximize local nutrient concentration. These new results require broadening of our understanding of the relationships between nutrients and vegetation competition on the landscape level. The R * rule is replaced by a more complex criterion that varies across a landscape and reduces to the R * rule only under certain limiting conditions.

Ecological Modelling↗

USGS2016

Updating movement estimates for American black ducks (Anas rubripes)

Understanding migratory connectivity for species of concern is of great importance if we are to implement management aimed at conserving them. New methods are improving our understanding of migration; however, banding (ringing) data is by far the most widely available and accessible movement data for researchers. Here, we use band recovery data for American black ducks ( Anas rubripes ) from 1951–2011 and analyze their movement among seven management regions using a hierarchical Bayesian framework. We showed that black ducks generally exhibit flyway fidelity, and that many black ducks, regardless of breeding region, stopover or overwinter on the Atlantic coast of the United States. We also show that a non-trivial portion of the continental black duck population either does not move at all or moves to the north during the fall migration (they typically move to the south). The results of this analysis will be used in a projection modeling context to evaluate how habitat or harvest management actions in one region would propagate throughout the continental population of black ducks. This analysis may provide a guide for future research and help inform management efforts for black ducks as well as other migratory species.

PeerJ↗

USGS2019

Using social-context matching to improve spatial function-transfer performance for cultural ecosystem service models

Recreational and aesthetic enjoyment of public lands is increasing across a wide range of activities, highlighting the need to assess and adapt management to accommodate these uses. Despite a growing number of studies on mapping cultural ecosystem services, most are local-scale assessments that rely on costly and time-consuming primary data collection. As a result, the availability of spatial information on non-market values associated with cultural ecosystem services (social values) remains limited. Spatial function transfer, if it could be justified for social-value models, would expedite the development of social-value information and promote its more regular inclusion in ecosystem service assessments. We used survey data from six national forests in Colorado and Wyoming to explore the potential for transferring cultural ecosystem service models between forests and specifically to test the hypothesis that transfer performance increases with social-context similarity between transferring and receiving areas. Results confirm this relationship but fall just short of being able to predict with certainty when transferred models will meet the minimum performance criterion needed for defensible use by managers. Social values are highly variable and can be difficult to predict, but our results suggest that with the right combination of indicators spatial function transfer can become a defensible means of generating social-value information when primary data collection is not feasible.

Colorado, Wyoming↗