Testing hypotheses on shape and distribution of ecological response curves

Niche theory with hypotheses on shape and distribution of ecological response curves is used in the studies of resource sharing of competing plant species. Predictions based on theory should be applicable when, e.g., effects of competing species on the ecological tolerances are assessed or species’ diversity along a resource gradient is evaluated. We studied the ecological response curves of competing plant species along a resource gradient in boreal forests. The study was based on nation-wide soil and vegetation data collected from 455 sample plots on boreal forests in Finland. Species response curves along a soil fertility gradient (in terms of C/N ratio) were estimated using generalized additive models. Distribution of species optima and the relationship of niche width and skewness to the location of the optimum were analyzed with new bootstrap tests. The developed tests can account for the effects of truncation observed in the response curves of several species and for the uneven distribution of observations on the gradient.The estimated response curves of the major field layer species of boreal forests were not evenly distributed along soil C/N gradient. The density of optima peaked with relatively high nitrogen availability. Species with optima at low nitrogen availability had relatively broad realized niches. Niche width was negatively correlated with the density of optima. Species optima were packed and niches were narrow at high resource levels. This result suggests that a greater number of more specialized species can occur and interspecific competition decreases niche widths at high resource levels. Species were packed in the gradient where the C/N ratio was lower than 25, i.e., in conditions where nitrification can take place. This indicates that the majority of the vascular plants of boreal forests are favoured by the availability of NO₃⁻. Those few species thriving at high C/N ratios have broader realized niches.     

Using multistate mark-recapture methods to model adult salmonid migration in an industrialized river

A multistate mark-recapture (MSMR) model of the adult salmonid migration through the lower Columbia River and into the Snake River was developed, designed for radiotelemetry detections at dams and tributary mouths. The model focuses on upstream-directed travel, with states determined from observed fish movement patterns indicating directed upstream travel, downstream travel (fallback), and use of non-natal tributaries. The model was used to analyze telemetry data from 846 migrating adult spring-summer Chinook salmon (Oncorhynchus tshawytscha) tagged in 1996 at Bonneville Dam on the Columbia River. We used the model to test competing hypotheses regarding delayed effects of fallback at dams and visits to tributaries, and to define and estimate migration summary measures. Tagged fish had an average probability of 0.755 () of ending migration at a tributary or upstream of Lower Granite Dam on the Snake River, and a probability of 0.245 () of unaccountable loss (i.e., mortality or mainstem spawning) between the release site downstream of Bonneville Dam and Lower Granite Dam. The highest probability of unaccountable loss (0.092; ) was in the reach between Bonneville Dam and The Dalles Dam. Study fish used the tributaries primarily as exits from the hydrosystem, and visits to non-natal tributaries had no significant effect on subsequent movement upriver (P = 0.4245). However, fallback behavior had a small effect on subsequent tributary entry and exit (P = 0.0530), with fish using tributaries as resting areas after reascending Bonneville Dam after fallback. The spatial MSMR model developed here can be adapted to address additional questions about the interaction of migrating organisms with their environment, or for the study of migrations in other river systems.     

Fish population dynamics in a seasonally varying wetland

Small fishes in seasonally flooded environments such as the Everglades are capable of spreading into newly flooded areas and building up substantial biomass. Passive drift cannot account for the rapidity of observed population expansions. To test the ‘reaction-diffusion’ mechanism for spread of the fish, we estimated their diffusion coefficient and applied a reaction-diffusion model. This mechanism was also too weak to account for the spatial dynamics. Two other hypotheses were tested through modeling. The first—the ‘refuge mechanism’—hypothesizes that small remnant populations of small fishes survive the dry season in small permanent bodies of water (refugia), sites where the water level is otherwise below the surface. The second mechanism, which we call the ‘dynamic ideal free distribution mechanism’ is that consumption by the fish creates a prey density gradient and that fish taxis along this gradient can lead to rapid population expansion in space. We examined the two alternatives and concluded that although refugia may play an important role in recolonization by the fish population during reflooding, only the second, taxis in the direction of the flooding front, seems capable of matching empirical observations. This study has important implications for management of wetlands, as fish biomass is an essential support of higher trophic levels.     

Effects of temperature variation on TSD in turtle (C. picta) populations

We formulate a two-sex model of temperature-dependent sex determination (TSD) for a freshwater turtle (C. picta) population. The aim is to understand how environmental temperature variations and nest heat conduction properties affect the long term dynamics of the population. This is a key to understanding how global temperature changes may affect their survival. With stochastic inputs of ambient temperature and solar radiation, the model uses the heat equation to determine the temperature in the egg layer in the nest; in turn, this determines the sex ratio in the egg clutch using a variable degree-day model. Finally, a nonlinear Leslie type, stage-based, two-sex model, is used to determine the long term male and female populations. A two-sex model is required because of different development rates for males and females. The model is flexible enough to enable other researchers to examine the effects of temperature variation variations on other species with TSD, e.g., crocodilians, reptilians, as well as other turtle species. It can be adapted to study effects of nest location, soil type, rain events, different incubation periods, and density effects, for example, the dependence of the mating function on the ratio of males to females and each’s contribution to the sex of hatchlings. Modifications can be easily made to fit a specific life history traits. The model is a beginning step in understanding the long term, high fitness shown by many reptile species with TSD, and it may suggest to experimentalists what data may be relevant to these issues; it can also be useful to wildlife managers in developing strategies for intervention if needed. Among the principal findings are that temperature variability and detailed nest heat conduction properties may buffer projected negative effects on a population.     

A heuristic approach to predicting water beetle diversity in temporary and fluctuating waters

An understanding of the causal mechanisms and processes that shape macroinvertebrate communities at a local scale has important implications for the management and conservation of freshwater biodiversity. Here we compare the performance of linear and non-linear statistics to explore diversity-environment relationships using data from 76 temporary and fluctuating ponds in two regions of southern England. We focus on aquatic beetle assemblages, which have been shown to be excellent surrogates of wider freshwater macroinvertebrate diversity. Ponds in the region contained a rich coleopteran fauna, totaling 68 species, which provided an excellent model system with which to compare the performance of two non-linear procedures (artificial neural networks—ANNs and generalised additive models—GAMs) and one more traditional linear approach (Multiple linear regression—MLR) to modelling diversity-environment relationships. Of all approaches employed, the best fit was obtained using an ANN model with only four input variables (conductivity, turbidity, magnesium concentration and depth). This model accounted for 82% of the observed variability in Shannon diversity index across ponds. In contrast, the best GAM and MLR models only explained 50% and 14% of this variation, respectively. Contribution profile analysis of conductivity, turbidity, magnesium concentration and depth, obtained from the best fit ANN through a hierarchical cluster analysis, allowed the identification of direct and proxy effects in relation to the environmental variables measured in this study. In each case, distinct clusters of ponds were identified in contribution profile analysis, suggesting that ponds across the two regions fall into a number of discrete groups, whose beetle faunas respond in subtly yet significantly different ways to key environmental variables. Aquatic coleopteran diversity in ponds in the two regions appears to be driven at a local scale by changes in relatively few physicochemical gradients, which are related to diversity in a clearly non-linear manner.     

Biogeochemical model (BGC-ES) and its basin-level application for evaluating ecosystem services under forest management practices

It is important for humans to live in harmony with ecosystems. Evaluation of ecosystem services (ES) may be helpful in achieving this objective. In Japan, forest ecosystems need to be re-evaluated to prevent their degradation due to lack of forest management.In order to evaluate the effects of forest management on forest ES, we developed a process-based biogeochemical model to estimate water, carbon, and nitrogen cycles in forest ecosystems (BGC-ES). This model consists of four submodels: biomass, water cycle, carbon-nitrogen (CN) cycles, and forest management. The biomass submodel can calculate growth of forest biomass under forest managements.Several parameters of the model were calibrated using data from observations of evapotranspiration flux and quality of stream flow in forests. The model results were compared with observations of runoff water from a dam catchment site and with carbon flux observations.Our model was coupled with a basin-level GIS database of forests. Evaluations under various forest management scenarios were carried out for forests in a basin contained in the Ise Bay basin (Chubu region, Japan), where plantations (artificial forests) seemed to have degraded from poor forest management.Comparing our simulation results with those of forests without management in the basin, we found that the amounts of absorbed carbon and runoff were larger in managed forests. In addition, the volume of harvested timber was larger and its quality (diameter) was better in managed forests. Changes of ES within the various scenarios were estimated for their economic value and were compared with the cost of forest management.     

Development and optimization of an Agro-BGC ecosystem model for C4 perennial grasses

Extrapolating simulations of bioenergy crop agro-ecosystems beyond data-rich sites requires biophysically accurate ecosystem models and careful estimation of model parameters not available in the literature. To increase biophysical accuracy we added C₄ perennial grass functionality and agricultural practices to the Biome-BGC (BioGeochemical Cycles) ecosystem model. This new model, Agro-BGC, includes enzyme-driven C₄ photosynthesis, individual live and dead leaf, stem, and root carbon and nitrogen pools, separate senescence and litter fall processes, fruit growth, optional annual seeding, flood irrigation, a growing degree day phenology with a killing frost option, and a disturbance handler that simulates nitrogen fertilization, harvest, fire, and incremental irrigation. To obtain spatially generalizable vegetation parameters we used a numerical method to optimize five unavailable parameters for Panicum virgatum (switchgrass) using biomass yield data from three sites: Mead, Nebraska, Rockspring, Pennsylvania, and Mandan, North Dakota. We then verified simulated switchgrass yields at three independent sites in Illinois (IL). Agro-BGC is more accurate than Biome-BGC in representing the physiology and dynamics of C₄ grass and management practices associated with agro-ecosystems. The simulated two-year average mature yields with single-site Rockspring optimization have Root Mean Square Errors (RMSE) of 70, 152, and 162 and biases of 43, −87, 156 g carbon m⁻² for Shabbona, Urbana, and Simpson IL, respectively. The simulated annual yields in June, August, October, December, and February have RMSEs of 114, 390, and 185 and biases of −19, −258, and 147 g carbon m⁻² for Shabbona, Urbana, and Simpson IL, respectively. These RMSE and bias values are all within the largest 90% confidence interval around respective IL site measurements. Twenty-four of twenty-six simulated annual yields with Rockspring optimization are within 95% confidence intervals of Illinois site measurements during the mature fourth and fifth years of growth. Ten of eleven simulated two-year average mature yields with Rockspring optimization are within 65% confidence intervals of Illinois site measurements and the eleventh is within the 95% confidence interval. Rockspring optimized Agro-BGC achieves accuracies comparable to those of two previously published models: Agricultural Land Management Alternatives with Numerical Assessment Criteria (ALMANAC) and Integrated Farm System Model (IFSM). Agro-BGC suffers from static vegetation parameters that can change seasonally and as plants age. Using mature plant data for optimization mitigates this deficiency. Our results suggest that a multi-site optimization scheme using mature plant data from more sites would be adequate for generating spatially generalizable vegetation parameters for simulating mature bioenergy crop agro-ecosystems with Agro-BGC.     

Modeling high-frequency position data of large herbivores with a phase-state model

Understanding the rules and factors that drive the foraging behavior of large herbivores is important to describe their interaction with the landscape at various spatial scales. Some unresolved questions refer to landscape-behavioral interactions that result in oriented or random search in seasonally changing landscapes. Remotely sensed position data indicate that herbivores select local patches of heterogeneous landscapes depending on a complex host of dynamically varying animal and environmental conditions. Since foraging paths consist in successions of relatively short steps, increasing the frequency at which position information is acquired would contribute to entangle the mechanisms resulting in herbivores’ foraging paths. We addressed the question whether herbivores would obtain information at a patch scale that would modify their distribution at a landscape scale based on directed movement or navigation ability. We considered a set of 100,000 high-frequency (1 min intervals) position data of several free-ranging sheep (Ovis aries) at a seasonal-varying range (Patagonian Monte, Argentina) and observed their movements at landscape and at single vegetation patch scales. At a landscape scale, we inspected the spatial co-variation of seasonally varying forage offer and ewes’ movement speeds. At a patch scale, we developed a phase-state (P-S) model of movement cycles based on the occurrence of behavioral phases along foraging paths, and fitted it to the observed daily time series of ewes’ movement speeds. Ewes were preferentially distributed in areas with high forage offer during periods of low forage availability and the reverse occurred during the season of high forage availability. Parameters of the model of activity cycles amenable to control by ewes (duration of speed phases, time elapsed between speed cycles) did not covariate with forage offer, but varied significantly among ewes. The shape (kurtosis) parameter of the model of movement cycles, one which is unlikely under ewes’ control, co-varied significantly with spatial forage offer but did not differ among ewes. We conclude that ewes allocated foraging time along a series of similar movement efforts irrespective of forage availability at small patches. Average forage scarcity at multi-patch level increases the ratio of searching to feeding time. This results in apparent selective time allocation to richer forage areas but does not imply evidence for oriented movement at a landscape scale. We advance a behavioral-based definition of forage patches and discuss its implications in developing foraging theory and models. The P-S model applied to high-frequency position data of large herbivores substantially improves the interpretation of the factors controlling their time allocation in space with respect to previous models of herbivore spatial behavior by discriminating among behavioral-based and environmentally induced components of their movements.     

Modelling coupled peach tree-aphid population dynamics and their control by winter pruning and nitrogen fertilization

Nitrogen fertilization and winter pruning are commonly used to control crop production in peach [Prunus persica (L.) Batsch] orchards. They are also known to affect the dynamics of Myzus persicae (Sulzer) (Homoptera: Aphididae) aphid populations via bottom-up regulation processes. Interactions between crops and pests can cause complex system behaviour in response to management practices. An integrated approach will therefore improve the understanding of the effects of these two cultural practices on aphid and peach performances.We developed a simulation model that describes the cultural control of interacting peach tree and aphid population dynamics. It uses the principles of common trophic models while gathering available knowledge and explicit assumptions on peach and aphid functioning and the effects of cultural practices.The model was able to qualitatively reproduce the system behaviour observed in the field. It accounted for actions and feedback such as stimulation of foliar growth by winter pruning, consecutive aphid population increase, subsequent damage to foliage, and partial compensatory growth of foliage. The model also reproduced low losses in fruit production due to aphid infestations. However, it called for further integration of ‘long-term’ effects. Analysis of the model showed the complexity of peach tree and aphid responses to leaf N × winter pruning interactions. Simulations indicated that fruit production losses remained low within a range of realistic values of leaf N and pruning intensity, whereas manipulating peach and aphid dynamics, their interactions and their relationships to practices could result in higher losses.The model is useful to evaluate the relevance of cultural practices for a bottom-up regulation of aphid dynamics in crop-pest management. After considering other control methods and fruit quality, it can be used to find a combination of practices that optimises trade-offs between fruit production and environmental conservation goals. A modelling approach that links crop growth and pest population dynamics and integrates management practice effects has strong potential for improving crop-pest management in an integrated crop production context.     

How humans shape wolf behavior in Banff and Kootenay National Parks, Canada

This paper describes the conceptualization and implementation of an agent-based model to investigate how varying levels of human presence could affect elements of wolf behavior, including highway crossings; use of areas in proximity to roads and trails; size of home ranges; activities, such as hunting, patrolling, resting, and feeding pups; and survival of individuals in Banff and Kootenay National Parks, Canada. The model consists of a wolf module as the primary component with five packs represented as cognitive agents, and grizzly bear, elk, and human modules that represent dynamic components of the environment. A set of environmental data layers was used to develop a friction model that serves as a base map representing the landscape over which wolves moved. A decision model was built to simulate the sequence of wolf activities. The model was implemented in a Java Programming Language using RePast, an agent-based modeling library. Six months of wolf activities were simulated from April 16 to October 15 (i.e., a season coherent with regard to known wolf behaviors), and calibrated with GPS data from wolf radiocollars (n = 15) deployed from 2002 to 2004. Results showed that the simulated trajectories of wolf movements were correlated with the observed trajectories (Spearman's rho 0.566, P < 0.001); other critical behaviors, such as time spent at the den and not traveling were also correlated. The simulations revealed that wolf movements and behaviors were noticeably affected by the intensity of human presence. The packs’ home ranges shrank and wolves crossed highways less frequently with increased human presence. In an extreme example, a wolf pack whose home range is traversed by a high-traffic-volume highway was extirpated due to inability to hunt successfully under a scenario wherein human presence levels were increased 10-fold. The modeling prototype developed in this study may serve as a tool to test hypotheses about human effects on wolves and on other mammals, and guide decision-makers in designing management strategies that minimize impacts on wolves and on other species functionally related to wolves in the ecosystem.