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At least 523 records · Page 29Linked to original sources
USGS2018

Behavioral differences following ingestion of large meals and consequences for management of a harmful invasive snake: A field experiment

Many snakes are uniquely adapted to ingest large prey at infrequent intervals. Digestion of large prey is metabolically and aerobically costly, and large prey boluses can impair snake locomotion, increasing vulnerability to predation. Cessation of foraging and use of refugia with microclimates facilitating digestion are expected to be strategies employed by free‐ranging snakes to cope with the demands of digestion while minimizing risk of predation. However, empirical observations of such submergent behavior from field experiments are limited. The brown treesnake (Serpentes: Colubridae: Boiga irregularis ) is a nocturnal, arboreal, colubrid snake that was accidentally introduced to the island of Guam, with ecologically and economically costly consequences. Because tools for brown treesnake damage prevention generally rely on snakes being visible or responding to lures or baits while foraging, cessation of foraging activities after feeding would complicate management. We sought to characterize differences in brown treesnake activity, movement, habitat use, and detectability following feeding of large meals (rodents 33% of the snake's unfed body mass) via radio telemetry, trapping, and visual surveys. Compared to unfed snakes, snakes in the feeding treatment group showed drastic decreases in hourly and nightly activity rates, differences in refuge height and microhabitat type, and a marked decrease in detectability by trapping and visual surveys. Depression of activity lasted approximately 5–7 days, a period that corresponds to previous studies of brown treesnake digestion and cycles of detectability. Our results indicate that management strategies for invasive brown treesnakes need to account for cycles of unavailability and underscore the importance of preventing spread of brown treesnakes to new environments where large prey are abundant and periods of cryptic behavior are likely to be frequent. Characterization of postfeeding behavior changes provides a richer understanding of snake ecology and foraging models for species that consume large prey.

Ecology and Evolution↗
USGS2013

The Hyper-Envelope Modeling Interface (HEMI): A Novel Approach Illustrated Through Predicting Tamarisk ( Tamarix spp.) Habitat in the Western USA

Habitat suitability maps are commonly created by modeling a species’ environmental niche from occurrences and environmental characteristics. Here, we introduce the hyper-envelope modeling interface (HEMI), providing a new method for creating habitat suitability models using Bezier surfaces to model a species niche in environmental space. HEMI allows modeled surfaces to be visualized and edited in environmental space based on expert knowledge and does not require absence points for model development. The modeled surfaces require relatively few parameters compared to similar modeling approaches and may produce models that better match ecological niche theory. As a case study, we modeled the invasive species tamarisk ( Tamarix spp.) in the western USA. We compare results from HEMI with those from existing similar modeling approaches (including BioClim, BioMapper, and Maxent). We used synthetic surfaces to create visualizations of the various models in environmental space and used modified area under the curve (AUC) statistic and akaike information criterion (AIC) as measures of model performance. We show that HEMI produced slightly better AUC values, except for Maxent and better AIC values overall. HEMI created a model with only ten parameters while Maxent produced a model with over 100 and BioClim used only eight. Additionally, HEMI allowed visualization and editing of the model in environmental space to develop alternative potential habitat scenarios. The use of Bezier surfaces can provide simple models that match our expectations of biological niche models and, at least in some cases, out-perform more complex approaches.

Environmental Management↗
USGS2020

Simulation modeling of complex climate, wildfire, and vegetation dynamics to address wicked problems in land management

Complex, reciprocal interactions among climate, disturbance, and vegetation dramatically alter spatial landscape patterns and influence ecosystem dynamics. As climate and disturbance regimes shift, historical analogs and past empirical studies may not be entirely appropriate as templates for future management. The need for a better understanding of the potential impacts of climate changes on ecosystems is reaching a new level of urgency, especially in highly perturbed or vulnerable ecological systems. Simulation models are extremely useful tools for guiding management decisions in an era of rapid change, thus providing potential solutions for wicked problems in land management—those that are difficult to solve and inherently resistant to easily definable solutions. We identify three experimental approaches for landscape modeling that address management challenges in the context of uncertain climate futures and complex ecological interactions: (1) an historical comparative approach, (2) a future comparative approach, and (3) threshold detection. We provide examples of each approach from previously published studies of simulated climate, disturbance, and landscape dynamics in forested landscapes of the western United States, modeled with the FireBGCv2 ecosystem process model. Cumulatively, model outcomes indicate that typical land management strategies will likely not be sufficient to counteract the impacts of rapid climate change and altered disturbance regimes that threaten the stability of ecosystems. Without implementation of new, adaptive management strategies, future landscapes are very likely to be different than historical or contemporary ones, with significant and sometimes persistent changes triggered by interactions of climate and wildfire.

Montana, New Mexico, Wyoming↗
USGS2007

Climate change and disturbance interactions: Workshop on climate change and disturbance interactions in western North America, Tucson, Ariz., 12-15 February 2007

Warming temperatures across western North America, coupled with increased drought, are expected to exacerbate disturbance regimes, particularly wildfires, insect outbreaks, and invasions of exotic species. Many ecologists and resource managers expect ecosystems to change more rapidly from disturbance effects than from the effects of a changing climate by itself. A particular challenge is to understand the interactions among disturbance regimes; for example, how will massive outbreaks of bark beetles, which kill drought-stressed trees by feeding on cambial tissues, increase the potential for large severe wildfires in a warming climate? Researchers in climatology, ecosystem science, fire and insect ecology, and landscape modeling from across western North America convened in Tucson, Ariz., for a 2 and a half day intensive workshop to identify new research directions in climate change and disturbance ecology. Four work groups focused on different aspects of the response of disturbance regimes to climate change: (1) extreme events and climatic variability (2) the effects of changing disturbance regimes on ecosystems, (3) disturbance interactions and cumulative effects, and (4) developing new landscape disturbance models. The workshop was structured with the analytic hierarchy process, a decision support method for achieving consensus from diverse groups of experts without sacrificing individual contributions.

Eos, Earth and Space Science News↗
USGS2021

Evaluating the impact of watershed development and climate change on stream ecosystems: A Bayesian network modeling approach

A continuous-variable Bayesian network (cBN) model is used to link watershed development and climate change to stream ecosystem indicators. A graphical model, reflecting our understanding of the connections between climate change, weather condition, loss of natural land cover, stream flow characteristics , and stream ecosystem indicators is used as the basis for selecting flow metrics for predicting macroinvertebrate-based indicators. Selected flow metrics were then linked to variables representing watershed development and climate change. We fit the model to data from two river basins in southeast US and the resulting model was used to simulate future stream ecological conditions using projected future climate and development scenarios. The three climate models predicted varying ecological condition trajectories, but similar worst-case ecological conditions. The established modeling approach couples mechanistic understanding with field data to develop predictions of management-relevant variables across a heterogeneous landscape. We discussed the transferability of the modeling approach.

North Carolina, South Carolina, Virginia↗
USGS2014

Users' guide to system dynamics model describing Coho salmon survival in Olema Creek, Point Reyes National Seashore, Marin County, California

The system dynamics model described in this report is the result of a collaboration between U.S. Geological Survey (USGS) scientists and National Park Service (NPS) San Francisco Bay Area Network (SFAN) staff, whose goal was to develop a methodology to integrate inventory and monitoring data to better understand ecosystem dynamics and trends using salmon in Olema Creek, Marin County, California, as an example case. The SFAN began monitoring multiple life stages of coho salmon (Oncorhynchus kisutch) in Olema Creek during 2003 (Carlisle and others, 2013), building on previous monitoring of spawning fish and redds. They initiated water-quality and habitat monitoring, and had access to flow and weather data from other sources. This system dynamics model of the freshwater portion of the coho salmon life cycle in Olema Creek integrated 8 years of existing monitoring data, literature values, and expert opinion to investigate potential factors limiting survival and production, identify data gaps, and improve monitoring and restoration prescriptions. A system dynamics model is particularly effective when (1) data are insufficient in time series length and/or measured parameters for a statistical or mechanistic model, and (2) the model must be easily accessible by users who are not modelers. These characteristics helped us meet the following overarching goals for this model: Summarize and synthesize NPS monitoring data with data and information from other sources to describe factors and processes affecting freshwater survival of coho salmon in Olema Creek. Provide a model that can be easily manipulated to experiment with alternative values of model parameters and novel scenarios of environmental drivers. Although the model describes the ecological dynamics of Olema Creek, these dynamics are structurally similar to numerous other coastal streams along the California coast that also contain anadromous fish populations. The model developed for Olema can be used, at least as a starting point, for other watersheds. This report describes each of the model elements with sufficient detail to guide the primary target audience, the NPS resource specialist, to run the model, interpret the results, change the input data to explore hypotheses, and ultimately modify and improve the model. Running the model and interpreting the results does not require modeling expertise on the part of the user. Additional companion publications will highlight other aspects of the model, such as its development, the rationale behind the methodological approach, scenario testing, and discussions of its use. System dynamics models consist of three basic elements: stocks , flows , and converters . Stocks are measurable quantities that can change over time, such as animal populations. Flows are any processes or conditions that change the quantity in a stock over time (Ford, 1999), are expressed in the model as a rate of change, and are diagrammed as arrows to or from stocks. Converters are processes or conditions that change the rate of flows. A converter is connected to a flow with an arrow indicating that it alters the rate of change. Anything that influences the rate of change (such as different environmental conditions, other external factors, or feedbacks from other stocks or flows) is modeled as a converter. For example, the number of fish in a population is appropriately modeled as a stock. Mortality is modeled as a flow because it is a rate of change over time used to determine the number of fish in the population. The density-dependent effect on mortality is modeled as a converter because it influences the rate of morality. Together, the flow and converter change the number, or stock, of juvenile coho. The instructions embedded in the stocks, flows, converters, and the sequence in which they are linked are processed by the simulation software with each completed sequence composing a model run. At each modeled time step within the model run, the stock counts will go up, down, or stay the same based on the modeled flows and the influence of converters on those flows. The model includes a user-friendly interface to change model parameters, which allows park staff and others to conduct sensitivity analyses, incorporate future knowledge, and implement scenarios for various future conditions. The model structure incorporates place holders for relationships that we hypothesize are significant but data are currently lacking. Future climate scenarios project stream temperatures higher than any that have ever been recorded at Olema Creek. Exploring climate change impacts on coho survival is a high priority for park staff, therefore the model provides the user with the option to experiment with hypothesized effects and to incorporate effects based on future observations.

California↗
USGS2025

Mapping predicted ecological states at landscape scales using remote sensing data and machine learning

Dryland ecosystems, covering 45% of the Earth's land and supporting over one-third of the global population, face significant threats from land degradation and ecological state change. Managing these ecosystems is complex, and science-based frameworks like Ecological Site Descriptions and state-and-transition models are essential tools for guiding decisions to support ecological health while maintaining stakeholder values such as grazing, wildlife, and recreation. However, alignment of these frameworks with smaller scale soil survey maps limits their applicability to broader ecological processes. Here, we extend these frameworks to larger landscapes with a machine learning approach that integrates large-scale, high-resolution vegetation data with identified ecological states from a data-driven state-and-transition model developed for a landscape-scale Ecological Site Group. A “global” model, which used combined inputs from multiple remotely sensed datasets, outperformed individual dataset models based on evaluation with independent data. Ecological state maps generated through this approach broaden the utility of state-and-transition models across Ecological Site Groups, providing a more spatially robust tool for land management at watershed and larger landscape scales. These methods, and the associated ecological state maps, can help meet critical needs for improved land condition assessments that support development of resource management plans and help identify priority areas for restoration and conservation.

Arizona, Colorado, New Mexico, Utah, Wyoming↗
USGS2024

Applying local and global sensitivity analysis to inform bigheaded carp management

Natural resource managers commonly use population-level models to aid in understanding the status of target populations or the potential implications of management actions. Sensitivity analyses, specifically, local sensitivity analysis (LSA) and global sensitivity analysis (GSA), exist as tools to improve understanding of these models, the importance of specific parameters to model outcomes, and the assumptions inherent to population models. However, applied ecology and closely related fields such as fisheries and wildlife management rarely use LSA or GSA. Here, we apply LSA and GSA to the Spatially Explicit Invasive Carp Population model, a population-level model used to simulate and compare management actions on the invasive silver carp Hypophthalmichthys molitrix population in the Illinois River. The spatial structure within the model arises from the navigation “pools” that exist between navigation lock-and-dam in the river and are also the focus of fisheries management in the river. We completed LSA and GSA for two reasons: (1) to compare the importance of model input parameters on simulated model populations and (2) to demonstrate the use of LSA and GSA for population-level models. In our system, the most important sensitivity values generally were detected in Alton Pool. This pool is the farthest downstream and includes a direct connection with the Mississippi River. These results indicate that the population dynamics in Alton Pool are an important driver of the population dynamics throughout the Illinois River. Management outcomes in this system will, therefore, be strongly dependent on the dynamics of Alton Pool.

Illinois↗
USGS2023

Combining resilience and resistance with threat-based approaches for prioritizing management actions in sagebrush ecosystems

The sagebrush biome is a dryland region in the western United States experiencing rapid transformations to novel ecological states. Threat-based approaches for managing anthropogenic and ecosystem threats have recently become prominent, but successfully mitigating threats depends on the ecological resilience of ecosystems. We used a spatially explicit approach for prioritizing management actions that combined a threat-based model with models of resilience to disturbance and resistance to annual grass invasion. The threat-based model assessed geographic patterns in sagebrush ecological integrity (SEI) to identify core sagebrush, growth opportunity, and other rangeland areas. The resilience and resistance model identified ecologically relevant climate and soil water availability indicators from process-based ecohydrological models. The SEI areas and resilience and resistance indicators were consistent—the resilience and resistance indicators showed generally positive relationships with the SEI areas. They also were complementary—SEI areas provided information on intact sagebrush areas and threats, while resilience and resistance provided information on responses to disturbances and management actions. The SEI index and resilience and resistance indicators provide the basis for prioritizing conservation and restoration actions and determining appropriate strategies. The difficulty and time required to conserve or restore SEI areas increase as threats increases and resilience and resistance decrease.

Conservation Science and Practice↗
USGS2024

Evaluating the sagebrush conservation design through the lens of a sagebrush indicator species

Sagebrush ecosystems support a suite of unique species such as the emblematic greater sage-grouse ( Centrocercus urophasianus ; sage-grouse) but are under increasing pressure from anthropogenic stressors such as annual grass invasion, conifer encroachment, altered wildfire regimes, and land use change. We examined the ability of an ecosystem-based framework for sagebrush conservation, the sagebrush conservation design (SCD) strategy, and the associated model of sagebrush ecological integrity (SEI), to identify and rank priority habitats for sage-grouse, a sagebrush indicator species. We compared sage-grouse population trends from 1996–2021 across the three ranked SEI categories. We then modeled those trends directly as a function of the same landcover predictors underlying SEI, used the median trend estimates to recategorize the sage-grouse’s range, and used spatial correlation methods to compare our sage-grouse performance categories with those of SEI. Finally, we compared the sage-grouse condition categories, predicted by our landcover-based model, to empirical trends derived from population count data. We found that the SCD and SEI were effective tools for identifying and ranking priority habitats for sage-grouse. Population trends were stable in the core areas identified by SEI but declining in the lower (i.e., growth and other) condition categories. As a result, core areas encompassed an increasingly larger share of the total sage-grouse population in a disproportionately smaller area. Our model supports the general functional relationships between landcover and sage-grouse performance suggested by SEI. We found strong spatial congruence between our categories of predicted sage-grouse population performance, the condition categories of SEI, and empirical trends derived from population count data. Our analysis demonstrates that proactive ecosystem-based approaches to the conservation of the sagebrush biome can help optimize the return on limited conservation resources and benefits for sagebrush obligate species and help reduce some of the real and perceived conflicts inherent in single-species management.

Arizona, California, Colorado, Idaho, New Mexico, ↗
USGS2017

Identifying western yellow-billed cuckoo breeding habitat with a dual modelling approach

The western population of the yellow-billed cuckoo ( Coccyzus americanus ) was recently listed as threatened under the federal Endangered Species Act. Yellow-billed cuckoo conservation efforts require the identification of features and area requirements associated with high quality, riparian forest habitat at spatial scales that range from nest microhabitat to landscape, as well as lower-suitability areas that can be enhanced or restored. Spatially explicit models inform conservation efforts by increasing ecological understanding of a target species, especially at landscape scales. Previous yellow-billed cuckoo modelling efforts derived plant-community maps from aerial photography, an expensive and oftentimes inconsistent approach. Satellite models can remotely map vegetation features (e.g., vegetation density, heterogeneity in vegetation density or structure) across large areas with near perfect repeatability, but they usually cannot identify plant communities. We used aerial photos and satellite imagery, and a hierarchical spatial scale approach, to identify yellow-billed cuckoo breeding habitat along the Lower Colorado River and its tributaries. Aerial-photo and satellite models identified several key features associated with yellow-billed cuckoo breeding locations: (1) a 4.5 ha core area of dense cottonwood-willow vegetation, (2) a large native, heterogeneously dense forest (72 ha) around the core area, and (3) moderately rough topography. The odds of yellow-billed cuckoo occurrence decreased rapidly as the amount of tamarisk cover increased or when cottonwood-willow vegetation was limited. We achieved model accuracies of 75–80% in the project area the following year after updating the imagery and location data. The two model types had very similar probability maps, largely predicting the same areas as high quality habitat. While each model provided unique information, a dual-modelling approach provided a more complete picture of yellow-billed cuckoo habitat requirements and will be useful for management and conservation activities.

Ecological Modelling↗
USGS2003

Normality of raw data in general linear models: The most widespread myth in statistics

In years of statistical consulting for ecologists and wildlife biologists, by far the most common misconception we have come across has been the one about normality in general linear models. These comprise a very large part of the statistical models used in ecology and include t tests, simple and multiple linear regression, polynomial regression, and analysis of variance (ANOVA) and covariance (ANCOVA). There is a widely held belief that the normality assumption pertains to the raw data rather than to the model residuals. We suspect that this error may also occur in countless published studies, whenever the normality assumption is tested prior to analysis. This may lead to the use of nonparametric alternatives (if there are any), when parametric tests would indeed be appropriate, or to use of transformations of raw data, which may introduce hidden assumptions such as multiplicative effects on the natural scale in the case of log-transformed data. Our aim here is to dispel this myth. We very briefly describe relevant theory for two cases of general linear models to show that the residuals need to be normally distributed if tests requiring normality are to be used, such as t and F tests. We then give two examples demonstrating that the distribution of the response variable may be nonnormal, and yet the residuals are well behaved. We do not go into the issue of how to test normality; instead we display the distributions of response variables and residuals graphically.

Bulletin of the Ecological Society of America↗
USGS2006

Effects of sample survey design on the accuracy of classification tree models in species distribution models

We evaluated the effects of probabilistic (hereafter DESIGN) and non-probabilistic (PURPOSIVE) sample surveys on resultant classification tree models for predicting the presence of four lichen species in the Pacific Northwest, USA. Models derived from both survey forms were assessed using an independent data set (EVALUATION). Measures of accuracy as gauged by resubstitution rates were similar for each lichen species irrespective of the underlying sample survey form. Cross-validation estimates of prediction accuracies were lower than resubstitution accuracies for all species and both design types, and in all cases were closer to the true prediction accuracies based on the EVALUATION data set. We argue that greater emphasis should be placed on calculating and reporting cross-validation accuracy rates rather than simple resubstitution accuracy rates. Evaluation of the DESIGN and PURPOSIVE tree models on the EVALUATION data set shows significantly lower prediction accuracy for the PURPOSIVE tree models relative to the DESIGN models, indicating that non-probabilistic sample surveys may generate models with limited predictive capability. These differences were consistent across all four lichen species, with 11 of the 12 possible species and sample survey type comparisons having significantly lower accuracy rates. Some differences in accuracy were as large as 50%. The classification tree structures also differed considerably both among and within the modelled species, depending on the sample survey form. Overlap in the predictor variables selected by the DESIGN and PURPOSIVE tree models ranged from only 20% to 38%, indicating the classification trees fit the two evaluated survey forms on different sets of predictor variables. The magnitude of these differences in predictor variables throws doubt on ecological interpretation derived from prediction models based on non-probabilistic sample surveys. ?? 2006 Elsevier B.V. All rights reserved.

Ecological Modelling↗
USGS2016

How will predicted land-use change affect waterfowl spring stopover ecology? Inferences from an individual-based model

Habitat loss, habitat fragmentation, overexploitation and climate change pose familiar and new challenges to conserving natural populations throughout the world. One approach conservation planners may use to evaluate the effects of these challenges on wildlife populations is scenario planning. We developed an individual-based model to evaluate the effects of future land use and land cover changes on spring-migrating dabbling ducks in North America. We assessed the effects of three Intergovernmental Panel on Climate Change emission scenarios (A1B, A2 and B1) on dabbling duck stopover duration, movement distances and mortality. We specifically focused on migration stopover duration because previous research has demonstrated that individuals arriving earlier on the nesting grounds exhibit increased reproductive fitness. Compared to present conditions, all three scenarios increased stopover duration and movement distances of agent ducks. Although all three scenarios presented migrating ducks with increased amounts of wetland habitat, scenarios also contained substantially less cropland, which decreased overall carrying capacity of the study area. Synthesis and applications . Land-use change may increase waterfowl spring migration stopover duration in the midcontinent region of North America due to reduced landscape energetic carrying capacity. Climate change will alter spatial patterns of crop distributions with corn and rice production areas shifting to different regions. Thus, conservation planners will have to address population-level energetic implications of shifting agricultural food resources and increased uncertainty in yearly precipitation patterns within the next 50 years.

Journal of Applied Ecology↗
USGS2008

Biomorphodynamics: Physical-biological feedbacks that shape landscapes

Plants and animals affect morphological evolution in many environments. The term "ecogeomorphology" describes studies that address such effects. In this opinion article we use the term "biomorphodynamics" to characterize a subset of ecogeomorphologic studies: those that investigate not only the effects of organisms on physical processes and morphology but also how the biological processes depend on morphology and physical forcing. The two-way coupling precipitates feedbacks, leading to interesting modes of behavior, much like the coupling between flow/sediment transport and morphology leads to rich morphodynamic behaviors. Select examples illustrate how even the basic aspects of some systems cannot be understood without considering biomorphodynamic coupling. Prominent examples include the dynamic interactions between vegetation and flow/sediment transport that can determine river channel patterns and the multifaceted biomorphodynamic feedbacks shaping tidal marshes and channel networks. These examples suggest that the effects of morphology and physical processes on biology tend to operate over the timescale of the evolution of the morphological pattern. Thus, in field studies, which represent a snapshot in the pattern evolution, these effects are often not as obvious as the effects of biology on physical processes. However, numerical modeling indicates that the influences on biology from physical processes can play a key role in shaping landscapes and that even local and temporary vegetation disturbances can steer large-scale, long-term landscape evolution. The prevalence of biomorphodynamic research is burgeoning in recent years, driven by societal need and a confluence of complex systems-inspired modeling approaches in ecology and geomorphology. To make fundamental progress in understanding the dynamics of many landscapes, our community needs to increasingly learn to look for two-way, biomorphodynamic feedbacks and to collect new types of data to support the modeling of such emergent interactions. Copyright 2008 by the American Geophysical Union.

Water Resources Research↗
USGS2005

Modelling occurrence and abundance of species when detection is imperfect

Relationships between species abundance and occupancy are of considerable interest in metapopulation biology and in macroecology. Such relationships may be described concisely using probability models that characterize variation in abundance of a species. However, estimation of the parameters of these models in most ecological problems is impaired by imperfect detection. When organisms are detected imperfectly, observed counts are biased estimates of true abundance, and this induces bias in stated occupancy or occurrence probability. In this paper we consider a class of models that enable estimation of abundance/occupancy relationships from counts of organisms that result from surveys in which detection is imperfect. Under such models, parameter estimation and inference are based on conventional likelihood methods. We provide an application of these models to geographically extensive breeding bird survey data in which alternative models of abundance are considered that include factors that influence variation in abundance and detectability. Using these models, we produce estimates of abundance and occupancy maps that honor important sources of spatial variation in avian abundance and provide clearly interpretable characterizations of abundance and occupancy adjusted for imperfect detection.

Oikos↗
USGS2005

Landscape characteristics of Rhizophora mangle forests and propagule deposition in coastal environments of Florida (USA)

Field dispersal studies are seldom conducted at regional scales even though reliable information on mid-range dispersal distance is essential for models of colonization. The purpose of this study was to examine the potential distance of dispersal of Rhizophora mangle propagules by comparing deposition density with landscape characteristics of mangrove forests. Propagule density was estimated at various distances to mangrove sources (R. mangle) on beaches in southwestern Florida in both high-and low-energy environments, either facing open gulf waters vs. sheltered, respectively. Remote sensing and Geographic Information Systems were used to identify source forests and to determine their landscape characteristics (forest size and distance to deposition area) for the regression analyses. Our results indicated that increasing density of propagules stranded on beaches was related negatively to the distance of the deposition sites from the nearest stands of R. mangle and that deposition was greatly diminished 2 km or more from the source. Measures of fragmentation such as the area of the R. mangle forests were related to propagule deposition but only in low-energy environments. Our results suggest that geographic models involving the colonization of coastal mangrove systems should include dispersal dynamics at mid-range scales, i.e., for our purposes here, beyond the local scale of the forest and up to 5 km distant. Studies of mangrove propagule deposition at various spatial scales are key to understanding regeneration limitations in natural gaps and restoration areas. Therefore, our study of mid-range propagule dispersal has broad application to plant ecology, restoration, and modeling. ?? Springer 2005.

Landscape Ecology↗
USGS2025

Bridging social and ecological science to create spatially-explicit models of human-caused mortality of carnivores

Research indicates that human-caused mortality (HCM) is a key factor limiting numerous large carnivore populations. However, efforts to represent HCM in spatially explicit models have generally been limited in scope—often relying on proxies, such as road or human density. Yet such efforts fail to distinguish different sources of HCM, which can arise from different antecedent processes. We offer a systems-based conceptual framework for understanding the antecedents of HCMs that is grounded in theory from the social and behavioral sciences. Specifically, we first explain how HCMs are usefully distinguished into four types (e.g., accidental, harvest, illicit, control actions), then discuss how these different types tend to be driven by different sets of psychological and sociopolitical processes. We contend that improvements in understanding the spatial variation in HCMs would rise from more explicit attention to the various antecedent processes that precede each mortality type.

Ambio↗