Influence of host migration between woodland and pasture on the population dynamics of the tick Ixodes ricinus: A modelling approach

Ticks act as vectors of pathogens that can be harmful to animals and/or humans. Epidemiological models can be useful tools to investigate the potential effects of control strategies on diseases such as tick-borne diseases. The modelling of tick population dynamics is a prerequisite to simulating tick-borne diseases and the corresponding spread of the pathogen. We have developed a dynamic model to simulate changes in tick density at different stages (egg, larva, nymph and adult) under the influence of temperature. We have focused on the tick Ixodes ricinus, which is widespread in Europe. The main processes governing the biological cycles of ticks were taken into account: egg laying, hatching, development, host (small, mainly rodents, or large, like deer and cattle, mammals) questing, feeding and mortality. This model was first applied to a homogeneous habitat, where simulations showed the ability of the model to reproduce the general patterns of tick population dynamics. We considered thereafter a multi-habitat model, where three different habitats (woodland, ecotone and meadow) were connected through host migration. Based on this second application, it appears that migration from woodland, via the ecotone, is necessary to sustain the presence of ticks in the meadow. Woodland can therefore be considered as a source of ticks for the meadow, which in turn can be regarded as a sink. The influence of woodland on surrounding tick densities increases in line with the area of this habitat before reaching a plateau. A sensitivity analysis to parameter values was carried out and demonstrated that demographic parameters (sex ratio, development, mortality during feeding and questing, host finding) played a crucial role in the determination of questing nymph densities. This type of modelling approach provides insight into the influence of spatial heterogeneity on tick population dynamics.     

Productivity alters the scale dependence of the diversity-invasibility relationship

At small scales, areas with high native diversity are often resistant to invasion, while at large scales, areas with more native species harbor more exotic species, suggesting that different processes control the relationship between native and exotic species diversity at different spatial scales. Although the small-scale negative relationship between native and exotic diversity has a satisfactory explanation, we lack a mechanistic explanation for the change in relationship to positive at large scales. We investigated the native-exotic diversity relationship at three scales (range: 1-4000 km2) in California serpentine, a system with a wide range in the productivity of sites from harsh to lush. Native and exotic diversity were positively correlated at all three scales; it is rarer to detect a positive relationship at the small scales within which interactions between individuals occur. However, although positively correlated on average, the small-scale relationship between native and exotic diversity was positive at low-productivity sites and negative at high-productivity sites. Thus, the change in the relationship between native and exotic diversity does not depend on spatial scale per se, but occurs whenever environmental conditions change to promote species coexistence rather than competitive exclusion. This occurred within a single spatial scale when the environment shifted from being locally unproductive to productive.     

Variation at local scales need not impede tests for broader scale patterns

Ecologists are not always mindful of the constraints imposed by their scale of observation and sometimes prematurely attempt broad generalisations or become mesmerised by local details depending on their predilections. We specifically chose a character that is known for its local and unpredictable variation (morphology of kelp) to test the effect of scale on our ability to determine spatial patterns. We compared the morphology of Ecklonia radiata between monospecific and mixed stands of canopy-forming algae across temperate Australia (>5,100 km coastal distance) within a hierarchy of several spatial scales. While E. radiata specimens were generally larger in monospecific than in mixed stands, we failed to observe differences in morphology between stands at many sites and locations. Despite substantial local variation, differences between stands became increasingly clear at broader scales. The frequency of inconsistent differences between stands was greatest at local scales (sites separated by kms), intermediate at intermediate scales (locations separated by 100s of kms) and least at regional scales (regions separated by 1,000s of kms). These observations support the idea that large scale patterns can emerge from apparent stochasticity at small scales, and that unaccountable variation at local scales need not impede tests for similar patterns at broader scales. Most ecologists work at scales where complexity tends to be greatest (i.e. local) and is likely to be explained by special and unique events. It is encouraging, therefore, to observe that patterns can emerge from complexity at local scales to provide new opportunities to answer some of the more interesting questions about the relative importance of processes across the vast parts of the worlds coast.Communicated by M.S. Johnson, Crawley     

Simulating the temporal pattern of waste production in farmed gilthead seabream (Sparus aurata), European seabass (Dicentrarchus labrax) and Atlantic bluefin tuna (Thunnus thynnus)

Most fish farming waste output models provide gross waste rates as a function of stocked or produced biomass for a year or total culture cycle, but without contemplating the temporality of the discharges. This work aims to ascertain the temporal pattern of waste loads by coupling available growth and waste production models and developing simulation under real production rearing conditions, considering the overlapping of batches and management of stocks for three widely cultured species in the Mediterranean Sea: gilthead seabream (Sparus aurata), European seabass (Dicentrarchus labrax) and Atlantic bluefin tuna (Thunnus thynnus). For a similar annual biomass production, the simulations showed that waste output and temporal dumping patterns differ between the three species as a result of the disparities in growth velocity, nutrient digestibility, maintenance metabolic budget and husbandry. The simulations allowed the temporal patterns including the periods of maximum discharge and the dissolved and particulate nitrogen and phosphorus content in the wastes released to be determined, both of which were seen to be species-specific.     

Wildfire, landscape diversity and the Drossel-Schwabl model

How simple can a model be that still captures essential aspects of wildfire ecosystems at large spatial and temporal scales? The Drossel-Schwabl model (DSM) is a metaphorical forest-fire model developed to reproduce only one pattern of real systems: a frequency distribution of fire sizes resembling a power law. Consequently, and because it appears oversimplified, it remains unclear what bearings the DSM has in reality. Here, we test whether the DSM is capable of reproducing a pattern that was not considered in its design, the hump-shaped relation between the diversity of succession stages and average annual area burnt. We found that the model, once reformulated to represent succession, produces realistic landscape diversity patterns. We investigated four succession scenarios of forest-fire ecosystems in the USA and Canada. In all scenarios, landscape diversity is highest at an intermediate average annual area burnt as predicted by the intermediate disturbance hypothesis. These results show that a model based solely on the dynamics of the fuel mosaic has surprisingly high predictive power with regard to observed statistical properties of wildfire systems at large spatial scales. Parsimonious models, such as the DSM can be used as starting points for systematic development of more structurally realistic but tractable wildfire models. Due to their simplicity they allow analytical approaches that further our understanding under increasing complexity.     

Modelling habitat overlap among sympatric mesocarnivores in southern Illinois,USA

Few researchers have developed large-scale habitat models for sympatric carnivore species. We created habitat models for red foxes (Vulpes vulpes), coyotes (Canis latrans) and bobcats (Lynx rufus) in southern Illinois, USA, using the Penrose distance statistic, remotely sensed landscape data, and sighting location data within a GIS. Our objectives were to quantify and spatially model potential habitat differences among species. Habitat variables were quantified for 1-km² buffered areas around mesocarnivore sighting locations. Following variable reduction procedures, five habitat variables (percentage of grassland patches, interspersion–juxtaposition of forest patches, mean fractal dimension of wetland patches and the landscape, and road density) were used for analysis. Only one variable differed (P < 0.05) between red fox and coyote sighting areas (road density) and bobcat and coyote sighting areas (mean fractal dimension of the landscape). However, all five variables differed between red fox and bobcat sighting areas, indicating considerable differences in habitat affiliation between this pair-group. Compared to bobcats, red fox sightings were affiliated with more grassland cover and larger grassland patches, higher road densities, lower interspersion and juxtaposition of forest patches, and lower mean fractal dimension of wetland patches. These differences can be explained by different life history requirements relative to specific cover types. We then used the Penrose distance statistic to create habitat models for red foxes and bobcats, respectively, based on the five-variable dataset. An independent set of sighting locations were used to validate these models; model fit was good with 65% of mesocarnivore locations within the top 50% of Penrose distance values. In general, red foxes were affiliated with mixtures of agricultural and grassland cover, whereas bobcats were associated with a combination of grassland, wetland, and forest cover. The greatest habitat overlap between red foxes and bobcats was found at the interface between forested areas and more open cover types. Our study provides insight into habitat overlap among sympatric mesocarnivores, and the distance-based modelling approach we used has numerous applications for modelling wildlife–habitat relationships over large scales.     

Spatial scale of local breeding habitat quality and adjustment of breeding decisions

Experimental studies provide evidence that, in spatially and temporally heterogeneous environments, individuals track variation in breeding habitat quality to adjust breeding decisions to local conditions. However, most experiments consider environmental variation at one spatial scale only, while the ability to detect the influence of a factor depends on the scale of analysis. We show that different breeding decisions by adults are based on information about habitat quality at different spatial scales. We manipulated (increased or decreased) local breeding habitat quality through food availability and parasite prevalence at a small (territory) and a large (patch) scale simultaneously in a wild population of Great Tits (Parus major). Females laid earlier in high-quality large-scale patches, but laying date did not depend on small-scale territory quality. Conversely, offspring sex ratio was higher (i.e., biased toward males) in high-quality, small-scale territories but did not depend on large-scale patch quality. Clutch size and territory occupancy probability did not depend on our experimental manipulation of habitat quality, but territories located at the edge of patches were more likely to be occupied than central territories. These results suggest that integrating different decisions taken by breeders according to environmental variation at different spatial scales is required to understand patterns of breeding strategy adjustment.     

Patterns in reproductive dynamics of burrowing ghost shrimp Trypaea australiensis from small to intermediate scales

Many studies have examined latitudinal differences in reproduction of marine invertebrates, but few have measured variation at small to intermediate scales (kilometres to hundreds of kilometres), which may confound comparisons across broader geographic regions. Here, we examined variation in the reproductive biology of a little-studied species of burrowing ghost shrimp (Trypaea australiensis) at spatial scales ranging from km (between sites within estuaries) to 100s of km (among estuaries), over a 2-year period in south-eastern Australia. Sex ratios of populations were consistently biased towards females through time and space. Although reproduction started in summer months across all spatial scales, there was a pattern of earlier spawning from southern to northern estuaries. Integration of results from previous studies of T. australiensis supported a similar pattern of earlier breeding from high to low latitudes. Fecundity of shrimp increased linearly with female size, but the relationship varied inconsistently across the different spatial scales. Similarly, sizes at maturity varied from small to intermediate scales and observed patterns were not consistent with general predictions e.g. shrimp were smaller and ovigerous at smaller sizes at sites in the southern-most estuary, compared to estuaries further north. We found no differences in the sizes of embryos across the different spatial scales, but confirm that T. australiensis employs a strategy of high fecundity and small embryo size compared to other thalassinidean shrimp. Our results suggest that factors at smaller scales (e.g. food availability) may be important in affecting reproductive dynamics of T. australiensis, but further research is needed in testing hypotheses about patterns observed here. A lack of similar studies on other marine organisms remains an impediment to understanding life-history strategies and the sustainable management and conservation of populations.     

Comparison of sensitivity analysis techniques: A case study with the rice model WARM

The considerable complexity often included in biophysical models leads to the need of specifying a large number of parameters and inputs, which are available with various levels of uncertainty. Also, models may behave counter-intuitively, particularly when there are nonlinearities in multiple input-output relationships. Quantitative knowledge of the sensitivity of models to changes in their parameters is hence a prerequisite for operational use of models. This can be achieved using sensitivity analysis (SA) via methods which differ for specific characteristics, including computational resources required to perform the analysis. Running SA on biophysical models across several contexts requires flexible and computationally efficient SA approaches, which must be able to account also for possible interactions among parameters. A number of SA experiments were performed on a crop model for the simulation of rice growth (Water Accounting Rice Model, WARM) in Northern Italy. SAs were carried out using the Morris method, three regression-based methods (Latin hypercube sampling, random and Quasi-Random, LpTau), and two methods based on variance decomposition: Extended Fourier Amplitude Sensitivity Test (E-FAST) and Sobol’, with the latter adopted as benchmark. Aboveground biomass at physiological maturity was selected as reference output to facilitate the comparison of alternative SA methods. Rankings of crop parameters (from the most to the least relevant) were generated according to sensitivity experiments using different SA methods and alternate parameterizations for each method, and calculating the top-down coefficient of concordance (TDCC) as measure of agreement between rankings. With few exceptions, significant TDCC values were obtained both for different parameterizations within each method and for the comparison of each method to the Sobol’ one. The substantial stability observed in the rankings seem to indicate that, for a crop model of average complexity such as WARM, resource intensive SA methods could not be needed to identify most relevant parameters. In fact, the simplest among the SA methods used (i.e., Morris method) produced results comparable to those obtained by methods more computationally expensive.     

Grizzly bear habitat selection is scale dependent.

The purpose of our study is to show how ecologists' interpretation of habitat selection by grizzly bears (Ursus arctos) is altered by the scale of observation and also how management questions would be best addressed using predetermined scales of analysis. Using resource selection functions (RSF) we examined how variation in the spatial extent of availability affected our interpretation of habitat selection by grizzly bears inhabiting mountain and plateau landscapes. We estimated separate models for females and males using three spatial extents: within the study area, within the home range, and within predetermined movement buffers. We employed two methods for evaluating the effects of scale on our RSF designs. First, we chose a priori six candidate models, estimated at each scale, and ranked them using Akaike Information Criteria. Using this method, results changed among scales for males but not for females. For female bears, models that included the full suite of covariates predicted habitat use best at each scale. For male bears that resided in the mountains, models based on forest successional stages ranked highest at the study-wide and home range extents, whereas models containing covariates based on terrain features ranked highest at the buffer extent. For male bears on the plateau, each scale estimated a different highest-ranked model. Second, we examined differences among model coefficients across the three scales for one candidate model. We found that both the magnitude and direction of coefficients were dependent upon the scale examined; results varied between landscapes, scales, and sexes. Greenness, reflecting lush green vegetation, was a strong predictor of the presence of female bears in both landscapes and males that resided in the mountains. Male bears on the plateau were the only animals to select areas that exposed them to a high risk of mortality by humans. Our results show that grizzly bear habitat selection is scale dependent. Further, the selection of resources can be dependent upon the availability of a particular vegetation type on the landscape. From a management perspective, decisions should be based on a hierarchical process of habitat selection, recognizing that selection patterns vary across scales.     

Aquatic plant community invasibility and scale-dependent patterns in native and invasive species richness

Invasive species richness often is negatively correlated with native species richness at the small spatial scale of sampling plots, but positively correlated in larger areas. The pattern at small scales has been interpreted as evidence that native plants can competitively exclude invasive species. Large-scale patterns have been understood to result from environmental heterogeneity, among other causes. We investigated species richness patterns among submerged and floating-leaved aquatic plants (87 native species and eight invasives) in 103 temperate lakes in Connecticut (northeastern USA) and found neither a consistently negative relationship at small (3-m2) scales, nor a positive relationship at large scales. Native species richness at sampling locations was uncorrelated with invasive species richness in 37 of the 60 lakes where invasive plants occurred; richness was negatively correlated in 16 lakes and positively correlated in seven. No correlation between native and invasive species richness was found at larger spatial scales (whole lakes and counties). Increases in richness with area were uncorrelated with abiotic heterogeneity. Logistic regression showed that the probability of occurrence of five invasive species increased in sampling locations (3 m2, n = 2980 samples) where native plants occurred, indicating that native plant species richness provided no resistance against invasion. However, the probability of three invasive species' occurrence declined as native plant density increased, indicating that density, if not species richness, provided some resistance with these species. Density had no effect on occurrence of three other invasive species. Based on these results, native species may resist invasion at small spatial scales only in communities where density is high (i.e., in communities where competition among individuals contributes to community structure). Most hydrophyte communities, however, appear to be maintained in a nonequilibrial condition by stress and/or disturbance. Therefore, most aquatic plant communities in temperate lakes are likely to be vulnerable to invasion.     

Succession patterns of phytoplankton blooms: directionality and influence of algal cell size

Using four replicate microcosms in the laboratory, we induced a phytoplankton bloom by enclosing a natural community sampled from Masnou Harbor (N.E. Spain) in November 1987, and examined the pattern of algal succession during the bloom. Good replicability of the temporal patterns of the community biomass and the abundance of most species demonstrated that succession was a directional, non-random process. The successional pathway observed (small flagellates » small centric diatoms » small flagellates) resembled that observed by other authors studying phytoplankton blooms. This pattern differed from previous models of algal succession in that dinoflagellates never comprised a substantial fraction of the community biomass, and in that algal cell size did not tend to increase along the successional sequence. Algal cell size, however, was an important determinant of phytoplankton community structure, since it constrained the density, but not the biomass, achievable by the different species. We suggest that there is not a single, general pattern of phytoplankton succession, but that distinction should be made, at least between seasonal and bloom patterns of phytoplankton succession.     

Scaling-up spatially-explicit ecological models using graphics processors

How the properties of ecosystems relate to spatial scale is a prominent topic in current ecosystem research. Despite this, spatially explicit models typically include only a limited range of spatial scales, mostly because of computing limitations. Here, we describe the use of graphics processors to efficiently solve spatially explicit ecological models at large spatial scale using the CUDA language extension. We explain this technique by implementing three classical models of spatial self-organization in ecology: a spiral-wave forming predator-prey model, a model of pattern formation in arid vegetation, and a model of disturbance in mussel beds on rocky shores. Using these models, we show that the solutions of models on large spatial grids can be obtained on graphics processors with up to two orders of magnitude reduction in simulation time relative to normal pc processors. This allows for efficient simulation of very large spatial grids, which is crucial for, for instance, the study of the effect of spatial heterogeneity on the formation of self-organized spatial patterns, thereby facilitating the comparison between theoretical results and empirical data. Finally, we show that large-scale spatial simulations are preferable over repetitions at smaller spatial scales in identifying the presence of scaling relations in spatially self-organized ecosystems. Hence, the study of scaling laws in ecology may benefit significantly from implementation of ecological models on graphics processors.     

Modelling N mineralization from green manure and farmyard manure from a laboratory incubation study

Predicting N mineralization from organic manures like farmyard manure (FYM) is more difficult than from fresh organic materials like crop residues, as the manures vary greatly in composition. A laboratory incubation experiment was carried out for 98 days at 30 °C under aerobic conditions to study the effects on N dynamics of Gliricidia (Gliricidia sepium, Jacquin) and FYM application to soil at 5 and 10 g kg⁻¹. Application of Gliricidia induced N mineralization from the start of incubation period, with the amount of N mineralized increasing with rate of application. In contrast, application of FYM resulted in immobilization of mineral N in soil, irrespective of the rate of application. The initial net immobilization from FYM was limited by availability of N in the soil for the higher rate of application.We used the APSIM SoilN module to simulate these contrasting patterns of mineralization of N from Gliricidia and from FYM. The prediction of N mineralized from Gliricidia was better than FYM. The default model parameters specify that the fresh organic matter pools (FPOOL1, FPOOL2 and FPOOL3) have the same C:N ratio and this assumption was ineffective in predicting N mineralized from FYM. The predictive ability of the model improved when this default assumption was modified based on the size of the individual pools (FPOOL1, FPOOL2 and FPOOL3), and the pool's C:N ratios. The modelling efficiency, a measure of goodness of fit between the simulated and observed data, improved markedly for the modified model. The discrepancy between the modelled and observed data was a tendency for the model to underestimate the rate of re-mineralization at the lower rate of application of FYM in the later part of incubation. Unfortunately the appropriate modification to the size and C:N ratios of the FPOOLs could not be determined on the basis of chemical analysis alone. Thus, a true predictive application of the model to a new FYM material is not yet possible.     

Windows of change: temporal scale of analysis is decisive to detect ecosystem responses to climate change

Long-term ecological research has become a cornerstone of the scientific endeavour to better understand ecosystem responses to environmental change. This paper provides a perspective on how such research could be advanced. It emphasizes that a profound understanding of the mechanisms underlying these responses requires that records of ecologic processes be not only sufficiently long, but also collected at an appropriate temporal resolution. We base our argument on an overview of studies of climate impacts in limnic and marine ecosystems, suggesting that lakes and oceans respond to (short-term) weather conditions during critical time windows in the year. The observed response patterns are often time-lagged or driven by the crossing of thresholds in weather-related variables (such as water temperature and thermal stratification intensity). It becomes clear from the previous studies that average annual, seasonal or monthly climate data often fall short of characterizing the thermal dynamics that most organisms respond to. To illustrate such literature-based evidence using a concrete example, we compare 2?years of water temperature data from Müggelsee (Berlin, Germany) at multiple temporal scales (from hours to years). This comparison underlines the pitfalls of analysing data at resolutions not high enough to detect critical differences in environmental forcing. Current science initiatives that aim at improving the temporal resolution of long-term observatory data in aquatic systems will help to identify adequate timescales of analysis necessary for the understanding of ecosystem responses to climate change.     

Variations in mortality of a coral-reef fish: links with predator abundance

The mortality rates of a pomacentrid Acanthochromis polyacanthus were examined in relation to the abundance of large predatory fish (>200 mm total length, TL) at two spatial scales. Survivorship was negatively related to patterns of predator abundance at a large spatial scale (hundreds of metres) over 3 yr, but not at a small spatial scale (tens of metres) over 2 yr. On the large scale, mortality was consistently greater (14 to 30%) in locations where there were greater numbers of predators, and lower in locations where predators occurred in smaller numbers. Among these locations, spatial differences in rank abundance of surviving juveniles were primarily due to mortality, whereas temporal differences in rank abundance were primarily due to initial juvenile abundance. These data suggest that impacts of large predatory fish were likely to have been greater in space than time and at the large spatial scale than the small spatial scale.     

Predicting modes of spatial change from state-and-transition models

State-and-transition models (STMs) can represent many different types of landscape change, from simple gradient-driven transitions to complex, (pseudo-) random patterns. While previous applications of STMs have focused on individual states and transitions, this study addresses broader-scale modes of spatial change based on the entire network of states and transitions. STMs are treated as mathematical graphs, and several metrics from algebraic graph theory are applied—spectral radius, algebraic connectivity, and the S-metric. These indicate, respectively, the amplification of environmental change by state transitions, the relative rate of propagation of state changes through the landscape, and the degree of system structural constraints on the spatial propagation of state transitions. The analysis is illustrated by application to the Gualalupe/San Antonio River delta, Texas, with soil types as representations of system states. Concepts of change in deltaic environments are typically based on successional patterns in response to forcings such as sea level change or river inflows. However, results indicate more complex modes of change associated with amplification of changes in system states, relatively rapid spatial propagation of state transitions, and some structural constraints within the system. The implications are that complex, spatially variable state transitions are likely, constrained by local (within-delta) environmental gradients and initial conditions. As in most applications, the STM used in this study is a representation of observed state transitions. While the usual predictive application of STMs is identification of local state changes associated with, e.g., management strategies, the methods presented here show how STMs can be used at a broader scale to identify landscape scale modes of spatial change.     

Linking chronic wasting disease to mule deer movement scales: a hierarchical Bayesian approach.

Observed spatial patterns in natural systems may result from processes acting across multiple spatial and temporal scales. Although spatially explicit data on processes that generate ecological patterns, such as the distribution of disease over a landscape, are frequently unavailable, information about the scales over which processes operate can be used to understand the link between pattern and process. Our goal was to identify scales of mule deer (Odocoileus hemionus) movement and mixing that exerted the greatest influence on the spatial pattern of chronic wasting disease (CWD) in northcentral Colorado, USA. We hypothesized that three scales of mixing (individual, winter subpopulation, or summer subpopulation) might control spatial variation in disease prevalence. We developed a fully Bayesian hierarchical model to compare the strength of evidence for each mixing scale. We found strong evidence that the finest mixing scale corresponded best to the spatial distribution of CWD infection. There was also evidence that land ownership and habitat use play a role in exacerbating the disease, along with the known effects of sex and age. Our analysis demonstrates how information on the scales of spatial processes that generate observed patterns can be used to gain insight when process data are sparse or unavailable.     

Evaluating weather effects on interannual variation in net ecosystem productivity of a coastal temperate forest landscape: A model intercomparison

Forest productivity is strongly affected by seasonal weather patterns and by natural or anthropogenic disturbances. However weather effects on forest productivity are not currently represented in inventory-based models such as CBM-CFS3 used in national forest C accounting programs. To evaluate different approaches to modelling these effects, a model intercomparison was conducted among CBM-CFS3 and four process models (ecosys, CN-CLASS, Can-IBIS and 3PG) over a 2500 ha landscape in the Oyster River (OR) area of British Columbia, Canada. The process models used local weather data to simulate net primary productivity (NPP), net ecosystem productivity (NEP) and net biome productivity (NBP) from 1920 to 2005. Other inputs used by the process and inventory models were generated from soil, land cover and disturbance records. During a period of intense disturbance from 1928 to 1943, simulated NBP diverged considerably among the models. This divergence was attributed to differences among models in the sizes of detrital and humus C stocks in different soil layers to which a uniform set of soil C transformation coefficients was applied during disturbances. After the disturbance period, divergence in modelled NBP among models was much smaller, and attributed mainly to differences in simulated NPP caused by different approaches to modelling weather effects on productivity. In spite of these differences, age-detrended variation in annual NPP and NEP of closed canopy forest stands was negatively correlated with mean daily maximum air temperature during July-September (T_amax) in all process models (R² = 0.4-0.6), indicating that these correlations were robust. The negative correlation between T_amax and NEP was attributed to different processes in different models, which were tested by comparing CO₂ fluxes from these models with those measured by eddy covariance (EC) under contrasting air temperatures (T_a). The general agreement in sensitivity of annual NPP to T_amax among the process models led to the development of a generalized algorithm for weather effects on NPP of coastal temperate coniferous forests for use in inventory-based models such as CBM-CFS3: NPP′ = NPP − 57.1 (T_amax − 18.6), where NPP and NPP′ are the current and temperature-adjusted annual NPP estimates from the inventory-based model, 18.6 is the long-term mean daily maximum air temperature during July-September, and T_amax is the mean value for the current year. Our analysis indicated that the sensitivity of NPP to T_amax was nonlinear, so that this algorithm should not be extrapolated beyond the conditions of this study. However the process-based methodology to estimate weather effects on NPP and NEP developed in this study is widely applicable to other forest types and may be adopted for other inventory based forest carbon cycle models.     

Microclimate influence in a physiological model of cattle-fever tick (Boophilus spp.) population dynamics

Since their official eradication from the US in 1943, the cattle-tick species Boophilus microplus and Boophilus annulatus, vectors of bovine babesiosis, frequently have penetrated a quarantine zone established along the Texas–Mexico border designed to exclude them. Inspection and quarantine procedures have eradicated reinfestations successfully within the US, but increasing acaricide resistance in Mexican B. microplus populations poses a threat to future eradication efforts. Better understanding of interrelationships among Boophilus populations, their hosts, and vegetation communities in south Texas could improve prediction of the behavior of reintroduced Boophilus populations and increase management options. To this end, we constructed a simulation model to evaluate how microclimate, habitat (i.e. vegetation) heterogeneity, and within-pasture cattle movement may influence dynamics of Boophilus ticks in south Texas. Unlike previous Boophilus tick models, this model simulates dynamics at an hourly time-step, calculates all off-host dynamics as functions of temperature and relative humidity, and runs with ground-level microclimate data collected bi-hourly in three different habitat types. Sensitivity analysis of the model showed that temperatures and relative humidities created by habitat type, as well as engorged female mass, influenced tick population dynamics most strongly. Host habitat selection, initial number of larvae per cow, and the number of cells into which the simulated pasture was divided also had a strong influence. Population dynamics appeared moderately sensitive to the proportion of Bos indicus in cattle genotypes and the larval attachment rate, while appearing relatively insensitive to factors such as mortality rate of engorged females. When used to simulate laboratory experiments from the literature, the model predicted most observed life-history characteristics fairly well; however, it tended to underestimate oviposition duration, incubation duration, and egg mortality and overestimate larval longevity, especially at low temperatures and high humidities. Use of the model to predict Boophilus population dynamics in hypothetical south Texas pastures showed that it reasonably generated qualitative patterns of stage-wise abundances but tended to overestimate on-host tick burdens. Collection and incorporation of data that appear not to exist for Boophilus ticks, such as larval lipid content and lipid-use rates, may improve model accuracy. Though this model needs refinements such as a smaller spatial resolution, it provides insight into responses of B. microplus or B. annulatus populations to specific weather patterns, habitat heterogeneity, and host movement.