Modeling seed dispersal distances: implications for transgenic Pinus taeda.

Predicting forest-tree seed dispersal across a landscape is useful for estimating gene flow from genetically engineered (GE) or transgenic trees. The question of biocontainment has yet to be resolved, although field-trial permits for transgenic forest trees are on the rise. Most current field trials in the United States occur in the Southeast where Pinus taeda L., an indigenous species, is the major timber commodity. Seed dispersal distances were simulated using a model where the major determinants were: (1) forest canopy height at seed release, (2) terminal velocity of the seeds, (3) absolute seed release, and (4) turbulent-flow statistics, all of which were measured or determined within a P. taeda plantation established from seeds collected from wild forest-tree stands at the Duke Forest near Durham, North Carolina, USA. In plantations aged 16 and 25 years our model results showed that most of the seeds fell within local-neighborhood dispersal distances, with estimates ranging from 0.05 to 0.14 km from the source. A fraction of seeds was uplifted above the forest canopy and moved via the long-distance dispersal (LDD) process as far as 11.9-33.7 km. Out of 10(5) seeds produced per hectare per year, roughly 440 seeds were predicted to be uplifted by vertical eddies above the forest canopy and transported via LDD. Of these, 70 seeds/ha traveled distances in excess of 1 km from the source, a distance too great to serve as a biocontainment zone. The probability of LDD occurrence of transgenic conifer seeds at distances exceeding 1 km approached 100%.     

Incorporating behaviour into simple models of dispersal using the biological control agent Dicyphus hesperus

We explored the utility of incorporating easily measured, biologically realistic movement rules into simple models of dispersal. We depart from traditional random walk models by designing an individual-based simulation model where we decompose animal movement into three separate processes: emigration, between-patch movement, and immigration behaviour. These processes were quantified using experiments on the omnivorous insect Dicyphus hesperus moving through a tomato greenhouse. We compare the predictions of the individual-based model, along with a series of biased random walk models, against an independent experimental release of D. hesperus. We find that in this system, the short-term dispersal of these insects is described well by our individual-based model, but can also be described by a 2D grid-based biased random walk model when mortality is accounted for.     

Incorporating evolutionary processes into population viability models

We examined how ecological and evolutionary (eco‐evo) processes in population dynamics could be better integrated into population viability analysis (PVA). Complementary advances in computation and population genomics can be combined into an eco‐evo PVA to offer powerful new approaches to understand the influence of evolutionary processes on population persistence. We developed the mechanistic basis of an eco‐evo PVA using individual‐based models with individual‐level genotype tracking and dynamic genotype–phenotype mapping to model emergent population‐level effects, such as local adaptation and genetic rescue. We then outline how genomics can allow or improve parameter estimation for PVA models by providing genotypic information at large numbers of loci for neutral and functional genome regions. As climate change and other threatening processes increase in rate and scale, eco‐evo PVAs will become essential research tools to evaluate the effects of adaptive potential, evolutionary rescue, and locally adapted traits on persistence.     

Nonrandom movement behavior at habitat boundaries in two butterfly species: implications for dispersal

We observed meadow brown (Maniola jurtina) and gatekeeper (Pyronia tithonus) butterflies at habitat boundaries and observed spontaneous movements out of suitable habitat in order to investigate such movements in relation to dispersal. We found that butterflies of both species were aware of the position of a highly permeable habitat boundary without needing to cross it. Nevertheless, a considerable proportion of butterflies close to the boundary left their habitat (25-43%). Butterflies that crossed the boundary, and moved substantial distances into unsuitable habitat (up to 350 m in M. jurtina and 70 m in P. tithonus), usually returned to their original habitat patch (98-100%). Movement trajectories, at least in M. jurtina, were significantly different from, and more directed and systematic than, a correlated random walk. Approximately 70-80% of spontaneous movements into unsuitable habitat in both species were "foray" loops comparable to those described in mammals and birds. We conclude that, since migrants seemed to have considerable control over leaving their patch and over their subsequent movement trajectories, chance encounter rates with habitat boundaries, and indeed habitat leaving rates, might be less crucial in determining dispersal rates than is usually assumed. In addition, random dispersal trajectories should not be taken for granted in population or evolution models.     

Incorporating the perceptual range of animals into connectivity models

The perceptual range of an animal towards different landscape elements affects its movements through heterogeneous landscapes. However, empirical knowledge and modeling tools are lacking to assess the consequences of variation in the perceptual range for movement patterns and connectivity. In this study we tested how changes in the assumed perception of different landscape elements affect the outcomes of a connectivity model. We used an existing individual-based, spatially explicit model for the dispersal of Eurasian lynx (Lynx lynx). We systematically altered the perceptual range in which animals recognize forest fragments, water bodies or cities, as well as the probability that they respond to these landscape elements. Overall, increasing the perceptual range of the animals enhanced connectivity substantially, both qualitatively and quantitatively. An enhanced range of attraction to forests had the strongest impact, doubling immigration success; an enhanced range of attraction to rivers had a slightly lower impact; and an enhanced range of avoidance of cities had the lowest impact. Correcting the enhancement in connectivity by the abundance of each of the landscape elements in question reversed the results, indicating the potential sensitivity of connectivity models to rare landscape elements (in our case barriers such as cities). Qualitatively, the enhanced perception resulted in strong changes in movement patterns and connectivity. Furthermore, model results were highly parameter-specific and patch-specific. These results emphasize the need for further empirical research on the perceptual capabilities of different animals in different landscapes and conditions. They further indicate the usefulness of spatially explicit individual-based simulation models for recognizing consistent patterns that emerge, despite uncertainty regarding animals’ movement behavior. Altogether, this study demonstrates the need to extend the concept of ‘perceptual ranges’ beyond patch detection processes, to encompass the wide range of elements that can direct animal movements during dispersal through heterogeneous landscapes.     

Incorporating multidimensional behavior into a risk management tool for a critically endangered and migratory species

Conservation of migratory species exhibiting wide-ranging and multidimensional behaviors is challenged by management efforts that only utilize horizontal movements or produce static spatial–temporal products. For the deep-diving, critically endangered eastern Pacific leatherback turtle, tools that predict where turtles have high risks of fisheries interactions are urgently needed to prevent further population decline. We incorporated horizontal–vertical movement model results with spatial–temporal kernel density estimates and threat data (gear-specific fishing) to develop monthly maps of spatial risk. Specifically, we applied multistate hidden Markov models to a biotelemetry data set (n = 28 leatherback tracks, 2004–2007). Tracks with dive information were used to characterize turtle behavior as belonging to 1 of 3 states (transiting, residential with mixed diving, and residential with deep diving). Recent fishing effort data from Global Fishing Watch were integrated with predicted behaviors and monthly space-use estimates to create maps of relative risk of turtle–fisheries interactions. Drifting (pelagic) longline fishing gear had the highest average monthly fishing effort in the study region, and risk indices showed this gear to also have the greatest potential for high-risk interactions with turtles in a residential, deep-diving behavioral state. Monthly relative risk surfaces for all gears and behaviors were added to South Pacific TurtleWatch (SPTW) ( https://www.upwell.org/sptw ), a dynamic management tool for this leatherback population. These modifications will refine SPTW's capability to provide important predictions of potential high-risk bycatch areas for turtles undertaking specific behaviors. Our results demonstrate how multidimensional movement data, spatial–temporal density estimates, and threat data can be used to create a unique conservation tool. These methods serve as a framework for incorporating behavior into similar tools for other aquatic, aerial, and terrestrial taxa with multidimensional movement behaviors.     

Incorporating covariates into fisheries stock assessment models with application to Pacific herring.

We present a framework for evaluating the cause of fishery declines by integrating covariates into a fisheries stock assessment model. This allows the evaluation of fisheries' effects vs. natural and other human impacts. The analyses presented are based on integrating ecological science and statistics and form the basis for environmental decision-making advice. Hypothesis tests are described to rank hypotheses and determine the size of a multiple covariate model. We extend recent developments in integrated analysis and use novel methods to produce effect size estimates that are relevant to policy makers and include estimates of uncertainty. Results can be directly applied to evaluate trade-offs among alternative management decisions. The methods and results are also broadly applicable outside fisheries stock assessment. We show that multiple factors influence populations and that analysis of factors in isolation can be misleading. We illustrate the framework by applying it to Pacific herring of Prince William Sound, Alaska (USA). The Pacific herring stock that spawns in Prince William Sound is a stock that has collapsed, but there are several competing or alternative hypotheses to account for the initial collapse and subsequent lack of recovery. Factors failing the initial screening tests for statistical significance included indicators of the 1989 Exxon Valdez oil spill, coho salmon predation, sea lion predation, Pacific Decadal Oscillation, Northern Oscillation Index, and effects of containment in the herring egg-on-kelp pound fishery. The overall results indicate that the most statistically significant factors related to the lack of recovery of the herring stock involve competition or predation by juvenile hatchery pink salmon on herring juveniles. Secondary factors identified in the analysis were poor nutrition in the winter, ocean (Gulf of Alaska) temperature in the winter, the viral hemorrhagic septicemia virus, and the pathogen Ichthyophonus hoferi. The implication of this result to fisheries management in Prince William Sound is that it may well be difficult to simultaneously increase the production of pink salmon and maintain a viable Pacific herring fishery. The impact can be extended to other commercially important fisheries, and a whole ecosystem approach may be needed to evaluate the costs and benefits of salmon hatcheries.     

Incorporating Uncertainty into Management Models for Marine Mammals

Abstract: Good management models and good models for understanding biology differ in basic philosophy. Management models must facilitate management decisions despite large amounts of uncertainty about the managed populations. Such models must be based on parameters that can be estimated readily, must explicitly account for uncertainty, and should be simple to understand and implement. In contrast, biological models are designed to elucidate the workings of biology and should not be constrained by management concerns. We illustrate the need to incorporate uncertainty in management models by reviewing the inadequacy of using standard biological models to manage marine mammals in the United States. Past management was based on a simple model that, although it may have represented population dynamics adequately, failed as a management tool because the parameter that triggered management action was extremely difficult to estimate for the majority of populations. Uncertainty in parameter estimation resulted in few conservation actions. We describe a recently adopted management scheme that incorporates uncertainty and its resulting implementation. The approach used in this simple management scheme, which was tested by using simulation models, incorporates uncertainty and mandates monitoring abundance and human-caused mortality. Although the entire scheme may be suitable for application to some terrestrial and marine problems, two features are broadly applicable: the incorporation of uncertainty through simulations of management and the use of quantitative management criteria to translate verbal objectives into levels of acceptable risk.     

Animal dispersal modelling: Handling landscape features and related animal choices

Animal dispersal in a fragmented landscape depends on the complex interaction between landscape structure and animal behavior. To better understand how individuals disperse, it is important to explicitly represent the properties of organisms and the landscape in which they move. A common approach to modelling dispersal includes representing the landscape as a grid of equal sized cells and then simulating individual movement as a correlated random walk. This approach uses a priori scale of resolution, which limits the representation of all landscape features and how different dispersal abilities are modelled.We develop a vector-based landscape model coupled with an object-oriented model for animal dispersal. In this spatially explicit dispersal model, landscape features are defined based on their geographic and thematic properties and dispersal is modelled through consideration of an organism's behavior, movement rules and searching strategies (such as visual cues). We present the model's underlying concepts, its ability to adequately represent landscape features and provide simulation of dispersal according to different dispersal abilities. We demonstrate the potential of the model by simulating two virtual species in a real Swiss landscape. This illustrates the model's ability to simulate complex dispersal processes and provides information about dispersal such as colonization probability and spatial distribution of the organism's path.     

A systematic survey of the integration of animal behavior into conservation

The role of behavioral ecology in improving wildlife conservation and management has been the subject of much recent debate. We sought to answer 2 foundational questions about the current use of behavioral knowledge in conservation: To what extent is behavioral knowledge used in wildlife conservation and management, and how does the use of animal behavior differ among conservation fields in both frequency and types of use? We searched the literature for intersections between key fields of animal behavior and conservation and created a systematic heat map (i.e., graphical representation of data where values are represented as colors) to visualize relative efforts. Some behaviors, such as dispersal and foraging, were commonly considered (mean [SE] of 1147.38 [353.11] and 439.44 [108.85] papers per cell, respectively). In contrast, other behaviors, such as learning, social, and antipredatory behaviors were rarely considered (mean [SE] of 33.88 [7.62], 44.81 [10.65], and 22.69 [6.37] papers per cell, respectively). In many cases, awareness of the importance of behavior did not translate into applicable management tools. Our results challenge previous suggestions that there is little association between the fields of behavioral ecology and conservation and reveals tremendous variation in the use of different behaviors in conservation. We recommend that researchers focus on examining underutilized intersections of behavior and conservation themes for which preliminary work shows a potential for improving conservation and management, translating behavioral theory into applicable and testable predictions, and creating systematic reviews to summarize the behavioral evidence within the behavior‐conservation intersections for which many studies exist.     

Estimating dispersal potential for marine larvae: dynamic models applied to scleractinian corals

Dispersal influences ecological dynamics, evolution, biogeography, and biodiversity conservation, but models of larval dispersal in marine organisms make simplifying assumptions that are likely to approximate poorly the temporal dynamics of larval survival and capacity for settlement. In particular, larval mortality rates are typically assumed to be constant throughout larval life; and all larvae are frequently assumed to acquire and lose competence at the same time. To improve upon these assumptions, we here develop simple models of dispersal potential that incorporate rates of mortality, and acquisition and loss of settlement competence. We fit these models to empirical competence and survival data for five scleractinian coral species, to test the models' ability to characterize empirical survival and competence patterns, and to estimate the dispersal potential implied by those patterns. The models fit the data well, incorporating qualitative features of competence and survival that traditional approaches to modeling dispersal do not, with important implications for dispersal potential. Most notably, there was high within-cohort variation in the duration of the competent period in all species, and this variation increases both self-recruitment and long-distance dispersal compared with models assuming a fixed competent period. These findings help to explain the seeming paradox of high genetic population structure, coupled with large geographic range size, observed in many coral species. More broadly, our approach offers a way to parsimoniously account for variation in competence dynamics in dispersal models, a phenomenon that our results suggest has important effects on patterns of connectivity in marine metapopulations.     

Geographic variations in Helleborus foetidus elaiosome lipid composition: implications for dispersal by ants

Summary. Geographic variations in the correspondence between diaspore phenotypes and disperser behavior are thought to determine the evolution of plant-animal dispersal mutualisms. Helleborus foetidus is a widely distributed plant in Western Europe, which seeds bear a lipid rich elaiosome attracting ant dispersers. Laboratory cross-tests were conducted to check the correspondence between diaspore phenotypes and ant preference in two localities of the Iberian Peninsula, Caurel and Cazorla, separated by 750 km. Diaspores from Caurel were systematically preferred to those from Cazorla by Formica lugubris (the major disperser at Caurel), and Aphaenogatser iberica and Camponotus cruentatus (both major dispersers at Cazorla). Further bioassays conducted on A. iberica only showed that differences in elaiosome traits were sufficient to explain ant preference. Separation of the lipid fractions composing the elaiosome revealed that triglycerides, diglycerides and free fatty acids were all dominated by oleic acid. The elaiosomes from Caurel contained relatively more free oleic acid but were less concentrated in linoleyl-containing triglycerides, free palmitic acid and free linoleic acid than those from Cazorla. The three lipid fractions were attractive to ants but dummies soaked with the free fatty acids extracted from Caurel were preferred to those from Cazorla. Taken together, these results reinforce the idea that oleic acid is a major releaser of seed collection by ants and suggest that geographic variations in free fatty acid composition affect the probability of diaspore removal by ants which in turn potentially determine plant demography.     

Incorporating fragmentation and non‐native species into distribution models to inform fluvial fish conservation

Fluvial fishes face increased imperilment from anthropogenic activities, but the specific factors contributing most to range declines are often poorly understood. For example, the range of the fluvial‐specialist shoal bass (Micropterus cataractae) continues to decrease, yet how perceived threats have contributed to range loss is largely unknown. We used species distribution models to determine which factors contributed most to shoal bass range loss. We estimated a potential distribution based on natural abiotic factors and a series of currently occupied distributions that incorporated variables characterizing land cover, non‐native species, and river fragmentation intensity (no fragmentation, dams only, and dams and large impoundments). We allowed interspecific relationships between non‐native congeners and shoal bass to vary across fragmentation intensities. Results from the potential distribution model estimated shoal bass presence throughout much of their native basin, whereas models of currently occupied distribution showed that range loss increased as fragmentation intensified. Response curves from models of currently occupied distribution indicated a potential interaction between fragmentation intensity and the relationship between shoal bass and non‐native congeners, wherein non‐natives may be favored at the highest fragmentation intensity. Response curves also suggested that >100 km of interconnected, free‐flowing stream fragments were necessary to support shoal bass presence. Model evaluation, including an independent validation, suggested that models had favorable predictive and discriminative abilities. Similar approaches that use readily available, diverse, geospatial data sets may deliver insights into the biology and conservation needs of other fluvial species facing similar threats.     

Consequences of ignoring dispersal variation in network models for landscape connectivity

Habitat loss and fragmentation can negatively influence population persistence and biodiversity, but the effects can be mitigated if species successfully disperse between isolated habitat patches. Network models are the primary tool for quantifying landscape connectivity, yet in practice, an overly simplistic view of species dispersal is applied. These models often ignore individual variation in dispersal ability under the assumption that all individuals move the same fixed distance with equal probability. We developed a modeling approach to address this problem. We incorporated dispersal kernels into network models to determine how individual variation in dispersal alters understanding of landscape-level connectivity and implemented our approach on a fragmented grassland landscape in Minnesota. Ignoring dispersal variation consistently overestimated a population's robustness to local extinctions and underestimated its robustness to local habitat loss. Furthermore, a simplified view of dispersal underestimated the amount of habitat substructure for small populations but overestimated habitat substructure for large populations. Our results demonstrate that considering biologically realistic dispersal alters understanding of landscape connectivity in ecological theory and conservation practice.     

Bayesian multi-species N-mixture models for unmarked animal communities

Environmental and Ecological Statistics - We propose an extension of the N-mixture model that enables the estimation of abundances of multiple species as well as the correlations between them. Our...     

Hidden process models for animal population dynamics.

Hidden process models are a conceptually useful and practical way to simultaneously account for process variation in animal population dynamics and measurement errors in observations and estimates made on the population. Process variation, which can be both demographic and environmental, is modeled by linking a series of stochastic and deterministic subprocesses that characterize processes such as birth, survival, maturation, and movement. Observations of the population can be modeled as functions of true abundance with realistic probability distributions to describe observation or estimation error. Computer-intensive procedures, such as sequential Monte Carlo methods or Markov chain Monte Carlo, condition on the observed data to yield estimates of both the underlying true population abundances and the unknown population dynamics parameters. Formulation and fitting of a hidden process model are demonstrated for Sacramento River winter-run chinook salmon (Oncorhynchus tshawytsha).     

A mathematical model for dispersal of an annual plant population with a seed bank

In order to describe the spatial dispersion of a population of annual plants with a seed bank, we have formulated a delay integrodifference equation model. The plant population growth is considered as being dependent on the density of seedlings that spring from seeds of the two previous years. We have analyzed the effects of the seed bank on the total population size, on the population spatial distribution as well as on the population velocity of invasion. It is showed that the seed bank can significantly alter the dynamics of annual plants, since it can have both a stabilizing and a destabilizing effect on the total population and can also homogenize the spatial distribution of the plants.