Mechanistic home range models are important tools in modeling animal dynamics in spatially complex environments. We introduce a class of stochastic models for animal movement in a habitat of varying preference. Such models interpolate between spatially implicit resource selection analysis (RSA) and advection-diffusion models, possessing these two models as limiting cases. We find a closed-form solution for the steady-state (equilibrium) probability distribution u* using a factorization of the redistribution operator into symmetric and diagonal parts. How space use is controlled by the habitat preference function w depends on the characteristic width of the animals' redistribution kernel: when the redistribution kernel is wide relative to variation in w, u*. w, whereas when it is narrow relative to variation in w, u* alpha w(2). In addition, we analyze the behavior at discontinuities in w which occur at habitat type boundaries, and simulate the dynamics of space use given two-dimensional prey-availability data, exploring the effect of the redistribution kernel width. Our factorization allows such numerical simulations to be done extremely fast; we expect this to aid the computationally intensive task of model parameter fitting and inverse modeling.
Historically, northern peatlands have functioned as a carbon sink, sequestering large amounts of soil organic carbon, mainly due to low decomposition in cold, largely waterlogged soils(1,2). The water table, an essential determinant of soil-organic-carbon dynamics(3-10), interacts with soil organic carbon. Because of the high water-holding capacity of peat and its low hydraulic conductivity, accumulation of soil organic carbon raises the water table, which lowers decomposition rates of soil organic carbon in a positive feedback loop. This two-way interaction between hydrology and biogeochemistry has been noted(3,5-8), but is not reproduced in process-based simulations(9). Here we present simulations with a coupled physical-biogeochemical soil model with peat depths that are continuously updated from the dynamic balance of soil organic carbon. Our model reproduces dynamics of shallow and deep peatlands in northern Manitoba, Canada, on both short and longer timescales. We find that the feedback between the water table and peat depth increases the sensitivity of peat decomposition to temperature, and intensifies the loss of soil organic carbon in a changing climate. In our long-term simulation, an experimental warming of 4 degrees C causes a 40% loss of soil organic carbon from the shallow peat and 86% from the deep peat. We conclude that peatlands will quickly respond to the expected warming in this century by losing labile soil organic carbon during dry periods.
In the three decades since its introduction, resource selection analysis (RSA) has become a widespread method for analyzing spatial patterns of animal relocations obtained from telemetry studies. Recently, mechanistic home range models have been proposed as an alternative framework for studying patterns of animal space-use. In contrast to RSA models, mechanistic home range models are derived from underlying mechanistic descriptions of individual movement behavior and yield spatially explicit predictions for patterns of animal space-use. In addition, their mechanistic underpinning means that, unlike RSA, mechanistic home range models can also be used to predict changes in space-use following perturbation. In this paper, we develop a formal reconciliation between these two methods of home range analysis, showing how differences in the habitat preferences of individuals give rise to spatially explicit patterns of space-use. The resulting unified framework combines the simplicity of resource selection analysis with the spatially explicit and predictive capabilities of mechanistic home range models.
Growth rings of a tree are simultaneously affected by various environmental constraints, including regional factors such as climate fluctuations and also local, gap-scale dynamics such as competition and stochastic mortality of neighbor trees. Although these local effects are often discarded by dendroclimatologists as random variation, the dendroecological trends may provide valuable information on past forest dynamics. Since dendroecological trends arising from local stand dynamics often have medium-term frequencies with persistence of several years to a few decades, it is Usually difficult to separate local, gap-scale forcings from regional, medium-frequency forcings such as El Nino Southern Oscillation or North Atlantic Oscillation. Moreover, conventional dendroecological practices have failed to analyze the continuously changing medium frequency trends. In this study, a continuous index of medium-frequency dendrochronological trends was developed, by generalizing previous analytical methods that evaluate relative changes using moving averages. This method was then tested against a tree ring dataset from a site with a known history of release and suppression due to a hurricane disturbance. To quantify the effects of local gap dynamics against the regional, often climatic effects, increments cores of black spruce (Picea mariana) were sampled from boreal forests in Saskatchewan, Canada, using a stratified sampling design. Assuming that regional forcings affect trees in the given stand homogeneously, the relative effect of stochastic heterogeneity within stand was quantified. The results closely agreed with conventional dendrochronological observations. In closed-canopy stands, stochastic local effects explained 12.9-35.4% of the variation in tree ring widths, because interactions between neighbor trees were likely to be intense. In open-canopy stands, on the other hand, the proportion of explained variance was 1.4-10.2%, reflecting the less-intense local tree interactions in low-density stands. These advancements in statistical analysis and study design will help ecologists and paleoclimatologists to objectively evaluate the effects of climate fluctuations, relative to the effects of local, ecological interactions. Moreover, forest managers can apply concepts of filtering medium-frequency trends to assess release and suppression caused by forest management practices, such as selective cutting and forest thinning. (C) 2008 Elsevier B.V. All rights reserved.
In this paper, we extend traditional methods of spatial statistics to study spatiotemporal correlations between two different point processes. After introducing the methodology, we apply this analysis to a particular case study of interest in ecology, the interaction between damage by a particular forest pest (western spruce budworm (Choristoneura occidentalis)) and forest fires. Our analysis, which covers parts of British Columbia in the 26-year period from 1970 to 1995, indicates that areas affected by budworm infestation have a significantly decreased risk of forest fire for the 7 years following the infestation. Conversely, forest fires decrease the risk of infestation for at least 6 years after the fire. These temporal correlations extend over a spatial range of at least 25 km. Our study rejects the common assumption that insect infestation necessarily results in increased fire risk. This case study illustrates the utility of point process modeling and spatial statistics to understanding ecosystem dynamics extending over both space and time.
The Ecosystem Demography (ED) model was parameterized with ecological, forest inventory, and historical land use observations in an intensively managed, wetland-rich forested landscape in the upper midwest United States. Model results were evaluated against a regional network of eddy covariance flux towers and analyzed about the roles of disturbance, forest management, and CO2 fertilization. The model captured modern regional vegetation structure with worst comparison in wetlands. Model net ecosystem exchange of CO2 ( NEE) was highly correlated on monthly (r(2) = 0.65) and annual (r(2) = 0.53) timescales to 7 years of NEE observed at a 396-m-tall eddy covariance (EC) tower and to 2 years of growing season NEE from 13 regional stand-scale EC sites of varying cover and age (r(2) = 0.64). Model summer NEE had higher than observed net uptake for the tall tower and mature hardwood sites, and correlation to growing season ecosystem respiration at these sites was poor (r(2) = 0.09). Exclusion of forestry led to overestimation of aboveground living plant biomass accumulation by 109% between two forest inventory cycles (1996-2004). On the long-term ( 200 years), forestry significantly altered ecosystem cover and age, and increased NEE by 32%. CO2 fertilization over that time period increased NEE by 93% owing to a doubling of plant density. While the model showed that harvest and afforestation had smaller impacts on NEE than CO2 increase, the former were still significant and require consideration when making future NEE predictions or scaling plot-level data to regional and global flux estimates.
Atmospheric measurements and land-based inventories imply that terrestrial ecosystems in the northern hemisphere are taking up significant amounts of anthropogenic cabon dioxide (CO2) emissions; however, there is considerable disagreement about the causes of this uptake, and its expected future trajectory. In this paper, we use the ecosystem demography (ED) model to quantify the contributions of disturbance history, CO2 fertilization and climate variability to the past, current, and future terrestrial carbon fluxes in the Eastern United States. The simulations indicate that forest regrowth following agricultural abandonment accounts for uptake of 0.11 Pg C yr(-1) in the 1980s and 0.15 Pg C yr(-1) in the 1990s, and regrowth following forest harvesting accounts for an additional 0.1 Pg C yr(-1) of uptake during both these decades. The addition of CO2 fertilization into the model simulations increases carbon uptake rates to 0.38 Pg C yr(-1) in the 1980s and 0.47 Pg C yr(-1) in the 1990s. Comparisons of predicted aboveground carbon uptake to regional-scale forest inventory measurements indicate that the model's predictions in the absence of CO2 fertilization are 14% lower than observed, while in the presence of CO2 fertilization, predicted uptake rates are 28% larger than observed. Comparable results are obtained from comparisons of predicted total Net Ecosystem Productivity to the carbon fluxes observed at the Harvard Forest flux tower site and in model simulations free-air CO2 enrichment (FACE) experiments. These results imply that disturbance history is the principal mechanism responsible for current carbon uptake in the Eastern United States, and that conventional biogeochemical formulations of plant growth overestimate the response of plants to rising CO2 levels. Model projections out to 2100 imply that the carbon uptake arising from forest regrowth will increasingly be dominated by forest regrowth following harvesting. Consequently, actual carbon storage declines to near zero by the end of the 21st century as the forest regrowth that has occurred since agricultural abandonment comes into equilibrium with the landscape's new disturbance regime. Incorporating interannual climate variability into the model simulations gives rise to large interannual variation in regional carbon fluxes, indicating that long-term measurements are necessary to detect the signature of processes that give rise to long-term uptake and storage.
Since the decomposition rate of soil organic carbon (SOC) varies as a function of environmental conditions, global climate change is expected to alter SOC decomposition dynamics, and the resulting changes in the amount of CO2 emitted from soils will feedback onto the rate at which climate change occurs. While this soil feedback is expected to be significant because the amount of SOC is substantially more than the amount of carbon in the atmosphere, the environmental dependencies of decomposition at global scales that determine the magnitude of the soil feedback have remained poorly characterized. In this study, we address this issue by fitting a mechanistic decomposition model to a global dataset of SOC, optimizing the model's temperature and moisture dependencies to best match the observed global distribution of SOC. The results of the analysis indicate that the temperature sensitivity of decomposition at global scales (Q(10)=1.37) is significantly less than is assumed by many terrestrial ecosystem models that directly apply temperature sensitivity from small-scale studies, and that the maximal rate of decomposition occurs at higher moisture values than is assumed by many models. These findings imply that the magnitude of the soil decomposition feedback onto rate of global climate change will be less sensitive to increases in temperature, and modeling of temperature and moisture dependencies of SOC decomposition in global-scale models should consider effects of scale.
In just 20 years, the field of biosphere-atmosphere interactions has gone from a nascent discipline to a central area of modern climate change research. The development of terrestrial biosphere models that predict the responses of ecosystems to climate and increasing CO2 levels has highlighted several mechanisms by which changes in ecosystem composition and function might alter regional and global climate. However, results from empirical studies suggest that ecosystem responses can differ markedly from the predictions of terrestrial biosphere models. As I discuss here, the challenge now is to connect terrestrial biosphere models to empirical ecosystem measurements. Only by systematically evaluating the predictions of terrestrial biosphere models against suites of ecosystem observations and experiments measurements will a true predictive science of the biosphere be achieved.
We examined the historical record of mountain pine beetle (Dendroctonus ponderosae Hopkins) activity within Yellowstone National Park, Wyoming, for the 25-years period leading up to the 1988 Yellowstone fires (1963-86) to determine how prior beetle activity and the resulting tree mortality affected the spatial pattern of the 1988 Yellowstone fires. To obtain accurate estimates of our model parameters, we used a Markov chain Monte Carlo method to account for the high degree of spatial autocorrelation inherent to forest fires. Our final model included three statistically significant variables: drought, aspect, and sustained mountain pine beetle activity in the period 1972-75. Of the two major mountain pine beetle outbreaks that preceded the 1988 fires, the earlier outbreak (1972-75) was significantly correlated with the burn pattern, whereas the more recent one (1980-83) was not. Although regional drought and high winds were responsible for the large scale of this event, the analysis indicates that mountain pine beetle activity in the mid-1970s increased the odds of burning in 1988 by 11% over unaffected areas. Although relatively small in magnitude, this effect, combined with the effects of aspect and spatial variation in drought, had a dramatic impact on the spatial pattern of burned and unburned areas in 1988.
Patterns of space-use by individuals are fundamental to the ecology of animal populations influencing their social organization, mating systems, demography and the spatial distribution of prey and competitors. To date, the principal method used to analyse the underlying determinants of animal home range patterns has been resource selection analysis (RSA), a spatially implicit approach that examines the relative frequencies of animal relocations in relation to landscape attributes. In this analysis, we adopt an alternative approach, using a series of mechanistic home range models to analyse observed patterns of territorial space-use by coyote packs in the heterogeneous landscape of Yellowstone National Park. Unlike RSAs, mechanistic home range models are derived from underlying correlated random walk models of individual movement behaviour, and yield spatially explicit predictions for patterns of space-use by individuals. As we show here, mechanistic home range models can be used to determine the underlying determinants of animal home range patterns, incorporating both movement responses to underlying landscape heterogeneities and the effects of behavioural interactions between individuals. Our analysis indicates that the spatial arrangement of coyote territories in Yellowstone is determined by the spatial distribution of prey resources and an avoidance response to the presence of neighbouring packs. We then show how the fitted mechanistic home range model can be used to correctly predict observed shifts in the patterns of coyote space-use in response to perturbation.
Recent investigations have shown how chance, long-range dispersal events can allow tree populations to migrate rapidly in response to changes in climate. However, this apparent solution to Reid's paradox applies solely within the context of single species models, while the rapid migration rates seen in pollen records occurred within multispecies communities. Ecologists are therefore presented with a new challenge: reconciling the macroscopic dynamics of spread seen in the pollen record with the rules and interactions governing plant community assembly. A case that highlights this issue is the rapid spread of Beech during the Holocene into a landscape already dominated by a close competitor, Hemlock. In this study, we analyse a simple model of plant community assembly incorporating competition for space and dispersal dynamics, showing how, even when a species is capable of rapid migration into an empty landscape, the presence of an ecologically similar competitor causes Reid's paradox to re-emerge because of the dramatic slowing effect of competitive interactions on a species' rate of spread. We then show how the answer to the question of how tree species dispersed rapidly into occupied landscapes may lie in secondary interactions with host-specific pathogens and parasites. Inclusion of host-specific pathogens into the simple community assembly model illustrates how tree species undergoing range expansions can temporarily outstrip specialist predators, giving rise to a transient Jansen-Connell effect, in which the invader acts as temporary 'super-species' that spreads rapidly into communities already occupied by competitors at rates consistent with those observed in the paleo-record.
The atmosphere and terrestrial ecosystems are fundamentally coupled on a variety of time-scales. On short time-scales, this bi-directional interaction is dominated by the rapid exchange of CO(2), water and energy between the atmosphere and the land surface; on long time-scales, the interaction involves changes in ecosystem structure and composition in response to changes in climate that feed back through biophysical and biogeochemical mechanisms to influence climate over decades and centuries. After briefly describing some early pioneering work, I focus this review on recent advances in understanding long-term ecosystem-atmosphere interactions through a discussion of three case studies. I then examine how efforts to assess the stability and resilience of ecosystem-atmosphere interactions over these long time-scales using Dynamic Global Vegetation Models are hampered by the presence of important functional diversity and heterogeneity within plant communities. Recent work illustrates how this issue can be addressed through the use of Structured Ecosystem Models that more accurately scale between the short-term physiological responses of individual plants and the long-term, large-scale dynamics of heterogeneous, functionally diverse ecosystems.
Atmospheric and ground-based methods agree on the presence of a carbon sink in the coterminous United States (the United States minus Alaska and Hawaii), and the primary causes for the sink recently have been identified. Projecting the future behavior of the sink is necessary for projecting future net emissions. Here we use two models, the Ecosystem Demography model and a second simpler empirically based model (Miami Land Use History), to estimate the spatio-temporal patterns of ecosystem carbon stocks and fluxes resulting from land-use changes and fire suppression from 1700 to 2100. Our results are compared with other historical reconstructions of ecosystem carbon fluxes and to a detailed carbon budget for the 1980s. Our projections indicate that the ecosystem recovery processes that are primarily responsible for the contemporary U.S. carbon sink will slow over the next century, resulting in a significant reduction of the sink. The projected rate of decrease depends strongly on scenarios of future land use and the long-term effectiveness of fire suppression.
Carbon accumulation in forests has been attributed to historical changes in land use and the enhancement of tree growth by CO2 fertilization, N deposition, and climate change. The relative contribution of land use and growth enhancement is estimated by using inventory data from five states spanning a latitudinal gradient in the eastern United States. Land use is the dominant factor governing the rate of carbon accumulation in these states, with growth enhancement contributing far less than previously reported. The estimated fraction of aboveground net ecosystem production due to growth enhancement is 2.0 +/- 4.4%, with the remainder due to land use.
The traditional models used to characterize animal home ranges have no mechanistic basis underlying their descriptions of space use, and as a result, the analysis of animal home ranges has primarily been a descriptive endeavor. In this paper, we characterize coyote (Canis latrans) home range patterns using partial differential equations for expected space use that are formally derived from underlying descriptions of individual movement behavior. To our knowledge, this is the first time that mechanistic models have been used to characterize animal home ranges. The results provide empirical support for a model formulation of movement response to scent marks, and suggest that having relocation data for individuals in adjacent groups is necessary to capture the spatial arrangement of home range boundaries. We then show how the model fits can be used to obtain predictions for individual movement and scent marking behavior and to predict changes in home range patterns. More generally, ourfindings illustrate how mechanistic models permit the development of a predictive theory for the relationship between movement behavior and animal spatial distribution.
Despite considerable theoretical interest no direct examples of density-dependent natural selection acting on simple polymorphic variation have been documented in a natural population. Here we show that the magnitude of selective differences in survival between phenotypes in two conspicuous polymorphisms of coat colour and horn type in Soay sheep Ovis aries living on St Kilda, Scotland are associated with marked changes in population density. Selection is strongest in years of high density but weak in years of low density. In addition to direct observations of density-dependent 'soft' selection in a natural population, the analysis revealed that the level of overcompensatory mortality (responsible for promoting population instability) was higher after accounting for genetic variation in the coat and horn morph traits. The results emphasize the importance of understanding the interaction between selection and population demography for both genetic and ecological studies of natural populations.
An unmanaged population of Soay sheep living on Hirta, St Kilda, Scotland is persistently unstable, fluctuating between about 600 and 1600 individuals. Population crashes occurring approximately every 3 years are primarily due to winter food shortage. In this paper we show that sheep experimentally relieved of their gastrointestinal nematodes (predominantly Teladorsagia spp.) survived a crash better than matched controls, showing that nematode parasites contribute to the probability that a sheep dies in a crash. We also show that over three successive crashes mortality was significantly different between individuals of the three different genotypes at the diallelic adenosine deaminase locus (Ada). FF animals were most likely to die, SS animals had an intermediate probability of dying, and FS animals were least likely to die. Finally, three independent lines of evidence suggest that nematode burdens differ between the three Ada genotypes. First, in August, heterozygous females are less likely to have nematode eggs in their faeces than homozygous females. Second, at lambing, the periparturient rise in faecal egg count was highest in homozygous FF individuals. Finally, during the Autumn mating season, heterozygous males has lower faecal egg counts than homozgyotes, although this relation was complicated by interactions with year and age of male. These results are consistent with the idea that Ada allele frequencies are maintained in the sheep population by parasite-associated selection.