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Pollinator decline worldwide is well-documented; globally, chemical pesticides (especially the class of pesticides known as neonicotinoids) have been implicated in hymenopteran decline, but the mechanics and drivers of population trends and dynamics of wild bees is poorly understood. Declines and shifts in community composition of bumble bees (Bombusspp.) have been documented in North America and Europe, with a suite of lethal and sub-lethal effects of pesticides on bumble bee populations documented. We employ a mathematical model parameterized with values taken from the literature that uses differential equations to track bumble bee populations through time in order to attain a better understanding of toxicant effects on a developing colony of bumble bees. We use a delay differential equation (DDE) model, which requires fewer parameter estimations than agent-based models while affording us the ability to explicitly describe the effect of larval incubation and colony history on population outcomes. We explore how both lethal and sublethal effects such as reduced foraging ability may combine to affect population outcomes, and discuss the implications for the protection and conservation of ecosystem services.

The glassy-winged sharpshooter, Homalodisca vitripennis (Germar), is an invasive pest which presents a major economic threat to grape industries in California, because it spreads a disease-causing bacterium, Xylella fastidiosa. In this note we develop a time and temperature dependent mathematical model to analyze aggregate population data for H. vitripennis from a 10-year study consisting of biweekly monitoring of H. vitripennis populations on unsprayed citrus, during which H. vitripennis decreased significantly. This model was fitted to the aggregate H. vitripennis time series data using iterative reweighted weighted least squares (IRWLS) with assumed probability distributions for certain parameter values. Results indicate that the H. vitripennis model fits the phenological and temperature data reasonably well, but the observed population decrease may possibly be attributed to factors other than the abiotic effect of temperature. A key factor responsible for this decline but not analyzed here could be biotic, for example, potentially parasitism of H. vitripennis eggs by Cosmocomoidea ashmeadi. A biological control program targeting H. vitripennis utilizing the mymarid egg parasitoid Cosmocomoidea (formerly Gonatocerus) ashmeadi (Girault) is described.

The use of indicator species to test for environmental stability and functioning is a widespread practice. In aquatic systems, several daphniids (Cladocera: Daphniidae) are commonly used as indicator species; registration of new pesticides are mandated by the Environmental Protection Agency to be accompanied by daphniid toxicity data. This reliance upon a few species to infer ecosystem health and function assumes similar responses to toxicants across species with potentially very different life histories and susceptibility. Incorporating lab-derived life-history data into a simple mathematical model, we explore the reliability of three different daphniid species as surrogates for each other by comparing their responses to reductions in survivorship and fecundity after simulated exposure to toxicants. Our results demonstrate that daphniid species' responses to toxicant exposure render them poor surrogates for one another, highlighting that caution should be exercised in using a surrogate approach to the use of indicator species in risk assessment.

The risk that two closely related insecticides, spinetoram and spinosad, posed to three Cladoceran species, Ceriodaphnia dubia, Daphnia pulex, and D. magna was determined using two approaches, the USEPA Risk Quotient method and the Delay in Population Growth Index (DPGI). Results of the RQ method showed that spinetoram posed a risk to all three species, but spinosad posed a risk only to C. dubia. The DPGI analysis showed that exposure to spinetoram resulted in populations of all three species being delayed > 3 generation times. Exposure to the LC50 and the lower 95% CL resulted in delayed populations while exposure to the upper 95% CL concentration of spinetoram resulted in no recovery of any of the three species over the course of the modeling exercise (88 d). Exposure to the lower and upper 95% Cl and the LC50 of spinosad resulted in C. dubia populations being delayed > 3 generations. D. pulex populations were not negatively affected after exposure to spinosad. D. magna populations were delayed > 3 generations, but only after exposure to the upper 95% Cl of spinosad. These results illustrate that although the EPA risk quotient method indicated that spinetoram posed a risk to all three species and that spinosad only posed a risk to C. dubia, the DPGI showed that D. magna would be negatively affected by spinosad and none of the three species would recover after exposure to the upper 95% CL of spinetoram. Because the DPGI uses the 95% Cl as well as the LC50 in its calculation and produces a measure of population growth and recovery or lack thereof, it provides more detailed information in terms of the potential risk of pesticides to populations than the RQ method.

The effects of toxicants, such as pesticides, may be more severe for some life stages of an organism than others. However, in most toxicity studies, data is developed for only one life stage, which may lead to misleading interpretations. Furthermore, population stage-structure may interact with differential susceptibility, especially when populations consist of higher proportions of individuals in more susceptible stages at the time of toxicant exposure. We explore the interaction of differential stage susceptibility and stage distribution using a stage-structured Lefkovitch matrix model. We incorporate lab-derived toxicity data for a common parasitoid, the braconid Diaeretiella rapae (M’Intosh), a common natural enemy of the cabbage aphid (Brevicoryne brassicae L.), exposed to the pesticide imidacloprid. We compare population outcomes of simulations in which we vary both the population stage structure along with the susceptibility of each stage to toxicants. Our results illustrate an interaction between differential susceptibility and initial stage distribution, highlighting the fact that both of these demographic features should be considered in interpreting toxicity data and the development of ecological risk assessments.

We consider a population dynamics model in investigating data from controlled experiments with aphids in broccoli patches surrounded by different margin types (bare or weedy ground) and three levels of insecticide spray (no, light, or heavy spray). The experimental data is clearly aggregate in nature. In previous efforts [1], the aggregate nature of the data was ignored. In this paper, we embrace this aspect of the experiment and correctly model the data as aggregate data, comparing the results to the previous approach. We discuss cases in which the approach may provide similar results as well as cases in which there is a clear difference in the resulting fit to the data.

Most ecosystem functions and related services involve species interactions across trophic levels, for example, pollination and biological pest control. Despite this, our understanding of ecosystem function in multitrophic communities is poor, and research has been limited to either manipulation in small communities or statistical descriptions in larger ones. Recent advances in food web ecology may allow us to overcome the trade-off between mechanistic insight and ecological realism. Molecular tools now simplify the detection of feeding interactions, and trait-based approaches allow the application of dynamic food web models to real ecosystems. We performed the first test of an allometric food web model's ability to replicate temporally nonaggregated abundance data from the field and to provide mechanistic insight into the function of predation. We aimed to reproduce and explore the drivers of the population dynamics of the aphid herbivore Rhopalosiphum padi observed in ten Swedish barley fields. We used a dynamic food web model, taking observed interactions and abundances of predators and alternative prey as input data, allowing us to examine the role of predation in aphid population control. The inverse problem methods were used for simultaneous model fit optimization and model parameterization. The model captured70% of the variation in aphid abundance in five of ten fields, supporting the model-embodied hypothesis that body size can be an important determinant of predation in the arthropod community. We further demonstrate how in-depth model analysis can disentangle the likely drivers of function, such as the community's abundance and trait composition. Analysing the variability in model performance revealed knowledge gaps, such as the source of episodic aphid mortality, and general method development needs that, if addressed, would further increase model success and enable stronger inference about ecosystem function. The results demonstrate that confronting dynamic food web models with abundance data from the field is a viable approach to evaluate ecological theory and to aid our understanding of function in real ecosystems. However, to realize the full potential of food web models, in ecosystem function research and beyond, trait-based parameterization must be refined and extended to include more traits than body size.

Prolonged exposure to a disturbance such as a toxicant has the potential to result in rapid evolution to toxicant resistance in many short-lived species such as daphniids. This evolution may allow a population to persist at higher levels of the toxicant than is possible without evolution. Here we apply evolutionary game theory to a Leslie matrix model for a daphniid population to obtain a Darwinian model that couples population dynamics with the dynamics of an evolving trait. We use the Darwinian model to consider how the evolution of resistance to the lethal or sublethal effects of a disturbance may change the population dynamics. In particular, we determine the conditions under which a daphniid population can persist by evolving toxicant resistance. We then consider the implications of this evolution in terms of the use of daphniids as surrogate species. We show for three species of daphniids that evolution of toxicant resistance means that one species may persist while another does not. These results suggest that toxicant studies that do not consider the potential of a species (or its surrogate) to develop toxicant resistance may not accurately predict the long term persistence of the species.

Bumble bees are ubiquitous creatures and crucial pollinators to a vast assortment of crops worldwide. Bumble bee populations have been decreasing in recent decades, with demise of flower resources and pesticide exposure being two of several suggested pressures causing declines. Many empirical investigations have been performed on bumble bees and their natural history is well documented, but the understanding of their population dynamics over time, causes for observed declines, and potential benefits of management actions is poor. To provide a tool for projecting and testing sensitivity of growth of populations under contrasting and combined pressures, we propose a delay differential equation model that describes multi-colony bumble bee population dynamics. We explain the usefulness of delay equations as a natural modeling formulation, particularly for bumble bee modeling. We then introduce a particular numerical method that approximates the solution of the delay model. Next, we provide simulations of seasonal population dynamics in the absence of pressures. We conclude by describing ways in which resource limitation, pesticide exposure and other pressures can be reflected in the model.

The glassy-winged sharpshooter, Homalodisca vit-ripennis, is an invasive pest which presents a major economic threat to grape industries in California, as well as Texas [24] and other wine growing regions, because it spreads a disease-causing bacterium, Xylella fastidiosa. We continue an earlier investigation [1] into a long-term phenological decline of H. vitripennisdensities by studying a system of delayed differential equations (DDEs). We analyze aggregate population data for H. vitripennisfrom a 10-year study in which bi-weekly monitoring of H. vitripennispopulation numbers significantly decreased. These data present several challenges for modelers. First, they involve truly aggregate population level sampling and hence cannot properly be treated as ordinary longitudinal time series data corresponding to individual level models. The appropriate modeling involves estimation of probability distributions for parameters rather than estimation of the dynamic parameters themselves. Moreover, our analysis reveals that the correct corresponding statistical models involve errors that are observation size dependent (e.g., relative errors should be employed in statistical models). We use these data to test whether DDEs are useful in modeling the observed H. vitripennispopulation decline. To do this, we perform an analysis of variance (ANOVA) type test comparing the glassy-winged sharpshooter (GWSS) model with delay to a model without delay. The model is fit to the aggregate H. vitripennisdata using iterative reweighted weighted least squares (IRWLS) by estimating probability densities over the delay and one of the egg developmental rate parameters. Results indicate that a positive delay provides improvement with a significance level of $P •

How animal populations respond to habitat manipulations is a central theme in ecology. In recent years, the role that vegetation heterogeneity plays in regulating arthropod populations has received particular attention in both conservation science and agricultural ecology. Numerous observational studies have demonstrated that herbivores and their natural enemies are sensitive to vegetation heterogeneity, but the individual effects of percentage land cover, degree of fragmentation and patch size remain little understood. We present here the results of a manipulative field experiment that explicitly incorporates both habitat heterogeneity and the degree to which that heterogeneity is fragmented in order to determine the effects of each factor on parasitism in an agroecosystem. We deployed combinations of broccoli (crop) and weedy vegetation (non-crop) in linear arrays that varied in their percentage devoted to crop and in the degree at which crop patches were fragmented with weeds, and recorded parasitism rates on two aphid species multiple times during two years. Parasitoids responded to the percentage of crop in plots, but not to the spatial scale at which they were fragmented. Our results suggest that vegetation heterogeneity may be more important than fragmentation scale in biological control by parasitoids.

We develop statistical and mathematical based methodologies for determining (as the experiment progresses) the amount of information required to complete the estimation of stable population parameters with pre-specified levels of confidence. We do this in the context of life table models and data for growth/death for three species of Daphniids as investigated by J. Stark and J. Banks [17]. The ideas developed here also have wide application in the health and social sciences where experimental data are often expensive as well as difficult to obtain.

We apply SE-optimal design methodology to investigate optimal data collection procedures as a first step in investigating information content in ecoinformatics data sets. To illustrate ideas we use a simple phenomenological citrus red mite population model for pest dynamics. First the optimal sampling distributions for a varying number of data points are determined. We then analyze these optimal distributions by comparing the standard errors of parameter estimates corresponding to each distribution. This allows us to investigate how many data are required to have confidence in model parameter estimates in order to employ dynamical modeling to infer population dynamics. Our results suggest that a field researcher should collect at least 12 data points at the optimal times. Data collected according to this procedure along with dynamical modeling will allow us to estimate population dynamics from presence/absence-based data sets through the development of a scaling relationship. These Likert-type data sets are commonly collected by agricultural pest management consultants and are increasingly being used in ecoinformatics studies. By applying mathematical modeling with the relationship scale from the new data, we can then explore important integrated pest management questions using past and future presence/absence data sets.

Successfully applying theoretical models to natural communities and predicting ecosystem behavior under changing conditions is the backbone of predictive ecology. However, the experiments required to test these models are dictated by practical constraints, and models are often opportunistically validated against data for which they were never intended. Alternatively, we can inform and improve experimental design by an in-depth pre-experimental analysis of the model, generating experiments better targeted at testing the validity of a theory. Here, we describe this process for a specific experiment. Starting from food web ecological theory, we formulate a model and design an experiment to optimally test the validity of the theory, supplementing traditional design considerations with model analysis. The experiment itself will be run and described in a separate paper. The theory we test is that trophic population dynamics are dictated by species traits, and we study this in a community of terrestrial arthropods. We depart from the Allometric Trophic Network (ATN) model and hypothesize that including habitat use, in addition to body mass, is necessary to better model trophic interactions. We therefore formulate new terms which account for micro-habitat use as well as intra-and interspecific interference in the ATN model. We design an experiment and an effective sampling regime to test this model and the underlying assumptions about the traits dominating trophic interactions. We arrive at a detailed sampling protocol to maximize information content in the empirical data obtained from the experiment and, relying on theoretical analysis of the proposed model, explore potential shortcomings of our design. Consequently, since this is a "pre-experimental" exercise aimed at improving the links between hypothesis formulation, model construction, experimental design and data collection, we hasten to publish our findings before analyzing data from the actual experiment, thus setting the stage for strong inference. CC BY 4.0

Remaining fragments of East African coastal dry forests contain very high levels of endemic species and are in critical need of conservation and restoration. Little is known about natural regeneration dynamics of these forests, or the potential for human action to aid recovery of lost structures and functions after deforestation/degradation. Here, data and analyses are presented from long-term monitoring plots in a 20 year-old forest restoration project in Gede, Kenya, in a fragment of Zanzibar-Inhambane (ZI) regional forest mosaic. Study results provided previously unavailable indigenous tree species growth rates and human-assisted forest regeneration rates for ZI forests and highlighted issues relevant to conserving and regenerating remnants of coastal dry forest throughout East Africa. Enrichment plantings accelerated recovery of indigenous tree species diversity and increased species density above natural levels. A strategy of inter-planting within existing natural regeneration, including leaving large relic trees, accelerated regrowth of the forest, but the main beneficiary of the strategy was exotic Azadirachta indica, which came to dominate significant areas. Analyses indicated that A. indica, which produces insecticidal compounds, was significantly altering the structure of arthropod communities; flying to ground-dwelling arthropod ratios were higher where A. indica made up a higher proportion of above-ground woody biomass. Management strategies appear to be mostly restoring indigenous forest structures, despite continued casual illegal tree cutting and invasion by A. indica. Analysis of illegally harvested trees highlighted the important role of indigenous tree species as a source of ecosystems services to local people; an important consideration for forest conservation planning worldwide.

Historically, point estimates such as the median lethal concentration (LC50) have been instrumental in assessing risks associated with toxicants to rare or economically important species. In recent years, growing awareness of the shortcomings of this approach has led to an increased focus on analyses using population endpoints. However, risk assessment of pesticides still relies heavily on large amounts of LC50 data amassed over decades in the laboratory. Despite the fact that these data are generally well replicated, little or no attention has been given to the sometime high levels of variability associated with the generation of point estimates. This is especially important in agroecosystems where arthropod predator–prey interactions are often disrupted by the use of pesticides. Using laboratory derived data of 4 economically important species (2 fruit fly pest species and 2 braconid parasitoid species) and matrix based population models, the authors demonstrate in the present study a method for bridging traditional point estimate risk assessments with population outcomes. The results illustrate that even closely related species can show strikingly divergent responses to the same exposures to pesticides. Furthermore, the authors show that using different values within the 95% confidence intervals of LC50 values can result in very different population outcomes, ranging from quick recovery to extinction for both pest and parasitoid species. The authors discuss the implications of these results and emphasize the need to incorporate variability and uncertainty in point estimates for use in risk assessment. Environ Toxicol Chem 2015;34:1683–1688. © 2015 SETAC

Conservation of threatened or endangered species relies critically on accurate population counts over time. In practice, many population censuses are conducted by non-governmental organizations or volunteer citizen scientists who are constrained by fiscal and temporal resources. Less than optimal sampling regimens (characterized by infrequent and/or irregular schedules) for conducting population censuses can result in woefully misleading population estimates – and thus have dire consequences for management and conservation. We illustrate this using an East African case study in which 14 years of bird data was collected in the Arabuko-Sokoke Forest in coastal Kenya. We first estimate life history parameters in a discrete matrix model. Desiring a data collection protocol which would lessen observation error and lend to a deeper understanding of population projections and dynamics of a threatened species, we carry out mathematical and statistical modeling efforts with an adaptation of a Leslie model for simulated population estimates stemming from different population sampling schemes. We illustrate how resource managers might take a strategic approach, using simple quantitative models, to develop an optimal sampling scheme that considers important species traits, such as breeding season, and balances the tradeoff between resources and accuracy.

In the context of inverse or parameter estimation problems we demonstrate the use of statistically based model comparison tests in several examples of practical interest. In these examples we are interested in questions related to information content of a particular given data set and whether the data will support a more complicated model to describe it. In the first example we compare fits for several different models to describe simple decay in a size histogram for aggregates in amyloid fibril formation. In a second example we investigate whether the information content in data sets for the pest Lygus hesperus in cotton fields as it is currently collected is sufficient to support a model in which one distinguishes between nymphs and adults. Finally in a third example with data for patients having undergone an organ transplant, we question whether the data content is sufficient to estimate more than 5 of the fundamental parameters in a particular dynamic model.

The application of mechanistic models to natural systems is of interest to ecological researchers. We use the mechanistic Allometric Trophic Network (ATN) model, whichis well-studied for controlled and theoretical systems, to describe the dynamics of the aphidRhopalosiphum padi in an agricultural field. We diagnose problems that arise in a first attemptat a least squares parameter estimation on this system, including formulation of the modelfor the inverse problem and information content present in the data. We seek to establishwhether the field data, as it is currently collected, can support parameter estimation for theATN model. CC BY 4.0

The management of natural vegetation and forested habitat within agricultural areas can be important for bolstering ecosystem services. In coffee agro-ecosystems, proximate forested habitat can provide resources for Hymenoptera that contribute to crop pollination and biological control of pests. We conducted a field study on 12 coffee farms in the highlands of Tarrazu, Costa Rica over a period of 3 years during both dry (flowering) and rainy (nonflowering) seasons. We compared the abundance of Hymenoptera observed around coffee plants on farms that were either isolated from or immediately adjacent to substantial forest habitat. Our results demonstrated a seasonally dependent response, with higher numbers of Hymenoptera observed in adjacent farms than in isolated farms during dry seasons, but no difference in the numbers in isolated farms and adjacent farms during rainy seasons. We discuss the implications of these findings with respect to the potential pollination and biological control benefits associate...

Destructive sampling schemes are the most direct and accurate methods to estimate yields in agro-ecosystem studies. However, in many situations these resource-intensive schemes are not feasible and/or sustainable. The objective of this research was to develop and compare non-destructive visual censuses and analytical methods for estimating fruit loads on Coffea arabica var. Caturra and Catuai trees using different components of yield. Fruit load data were collected in coffee farms found in the Los Santos Region of Costa Rica. Two components of yield where estimated: number of productive lateral branches per tree and fruit load per lateral branch. OLS regression was used to develop empirical models relating these components of yield with total fruit load per plant. Productive laterals at medium relative distance from the apical meristem had higher fruit loads than those found at the top or bottom of the orthotropic stem in C. arabica plants. In addition, by sampling eight to nine productive laterals per plant, the maximum observed error of the estimated fruit load per lateral was reduced by half. Regression coefficients of the empirical models relating total fruit load with yield components ranged between 0.73 and 0.92. Sampling schemes which grant equal probability of selection to productive laterals at different relative distances from the apical meristem should be chosen when estimating fruit load per lateral in commonly cultivated varieties of C. arabica plants. Furthermore, a non-destructive sampling protocol of the key components of yield provides accurate estimates of total fruit load per tree. Additional research is required to relate fruit loads with total biomass of fresh fruit and dry biomass in this perennial crop.

Many farmers rely on regular pesticide applications to avoid losses from arthropod pests and the diseases they vector. However, widespread and injudicious use of pesticides is detrimental to the environment, poses a health risk, and undermines biocontrol services. Researchers are increasingly required to develop techniques to quantify the trade-offs and risks associated with pesticides. Laboratory studies, though useful for assessing short-term impacts (e.g., mortality), cannot detect longer-term or indirect effects that can potentially be assessed using semifield studies. Here we review the range and scope of studies that have used semifield methods for regulatory testing and risk assessment of pesticides and for understanding the community-level effects of pesticide use in agricultural landscapes. We include studies on target and nontarget species, with an emphasis on quantifying effects when the target species is highly mobile. We suggest improvements in the design and analysis of semifield studies to more effectively assess effects on highly mobile species.

The incorporation of natural, non-crop vegetation in and around farmlands is a common practice in many agroecosystems, and may provide benefits both for production and conservation of biological diversity. In coffee agroecosystems, non-crop vegetation is routinely incorporated in the form of shade trees or forest habitat in and around coffee plants. In order to assess the importance of adjacent forest habitat to Hymenoptera associated with coffee farms, we sampled Hymenoptera on twelve coffee farms in the Tarrazu region in the central highlands of Costa Rica. Six of the farms were adjacent to forest habitat, and six were spatially isolated from forest; farms were sampled in both the dry (flowering) and the rainy (non-flowering) seasons. On adjacent farms, Hymenoptera samples taken in coffee and forest habitat were also compared. Finally, we also conducted pollinator observations in each type of farm, comparing Hymenoptera visitors to coffee plants in adjacent vs. isolated farms. Our results revealed an interaction in hymenopteran diversity between farm isolation and season, suggesting that both forest and coffee habitats play important roles at different times for Hymenoptera, especially bees.

Understanding the population dynamics of herbivorous insects is critical to developing and implementing effective pest control protocols. In the context of inverse problems, we explore the dynamic effects of pesticide treatments on Lygus hesperus, a common pest of cotton in the western United States. Fitting models to field data, we explore the topic of model selection for an appropriate mathematical model and corresponding statistical models, and use techniques including ANOVA-based model comparison tests and residual plot analysis to make the best selections. In addition we explore the topic of data information content: in this example, we are testing the question of whether data, as it is currently collected, can support time-dependent parameter estimation. Furthermore, we investigate the statistical assumptions often haphazardly made in the process of parameter estimation and consider the implications of unfounded assumptions.

Parasitoid wasps are important control agents for a suite of economically important arthropod pests. However, it is well known that hymenoptera in general are highly vulnerable to the effects of chemical pesticides, and so represent an opportunity to better understand the compatibility of biological and chemical control of pests. Closely related species are often used in trials aimed at assessing the effects of different chemical toxicants on hymenoptera. Most of these tests focus on acute toxicity, with few exploring longer-term population outcomes. Here, using a simple mathematical framework, we quantify the relative vulnerability of a suite of parasitoid species to the sublethal effects of a toxicant acting on fecundity. Using an equation that describes a critical extinction threshold, we calculate levels of reduction in fecundity above which parasitoid species important for biological control are driven to local extinction. We apply this framework to four economically important braconid biological control agents (Diachasmimorpha longicaudata, Psyttalia fletcheri, Fopius arisanus, and Diaeretiella rapae), and find that threshold levels vary widely among them. In particular, we find that D. rapae is far more robust to pesticide exposure than the other three species, suggesting that these four species are not interchangeable in terms of risk assessment. Our findings imply that caution should be exercised in assuming species even within the same functional guild may be used as surrogate species for one another.

We explored the relationships between synthetic fertilizer use, yield, and ecosystem services in a coffee agroecosystem in the Tarrazu region in the central highlands of Costa Rica. Working in nine farms ranging from 0.3 to 2.7 ha in the CoopeTarrazu farmers' cooperative, we focused on two important indicators of ecosystem services: biological control agents and mycorrhizal fungi. Biological control agents (especially parasitoid wasps) are essential for population regulation of herbivorous arthropods that reside in coffee agroecosystems. Mycorrhizal fungi contribute to plant nutrition and disease resistance and produce a highly stable protein, glomalin, which is critical for soil aggregation. Soil aggregates hedge against erosion, particularly in sloping soils, by improving porosity. Slopes in this study ranged from 4% to 66%. We manipulated synthetic fertilizer levels in experimental plots ranging from 6.9% increase to 53.3% decrease in total N relative to control plots that received a standard amount of...

Although studies have explored how habitat structure and disturbance affect arthropod communities, few have explicitly tested the effects of both structure and disturbance level across trophic levels and phyla. We present here the results of a study conducted in the Arabuko-Sokoke Forest (ASF) of coastal Kenya, in which abundance of arthropods and one of their avian predators, the East Coast Akalat Sheppardia gunning sokokensis was compared in relatively undisturbed habitat (outside elephant roaming areas) and in disturbed habitat (inside elephant roaming areas). Vegetation structure in both areas was measured using several metrics, including leaf litter depth, understory vegetation density, animal disturbance and fallen log counts. Leaf litter and coleopteran abundance were higher outside the elephant roaming areas, whereas understory visibility, animal disturbance and dipteran diversity were much higher inside the elephant areas. Species composition of several arthropod taxa (e.g. Hymenoptera, Coleoptera, Diptera, Hemiptera and Araneae) was also influenced by degree of disturbance, whereas akalat abundance was inversely related to understory visibility. Our results suggest that differences in species sensitivity to habitat disturbance and vegetation structure across trophic levels should be incorporated into the management and conservation of rare and endangered species.

The use of surrogate species is an important tool in predicting the effects of management decisions or the establishment of protective measures for endangered/threatened species. While relying on a handful of model species to predict the fate of scores of distantly related target species has been criticized, a quantitative measure linking life history traits and population predictions has been sorely missing. We derive here a closed-form expression aimed at determining conditions under which sublethal effects of a toxicant on surrogate species population outcomes will reliably predict outcomes of target (listed) species. We develop a critical threshold in fecundity reduction above which the surrogate species outcomes indicate positive population growth, while the listed species is driven to extinction. Thus we have established a means of determining conditions under which we are prone to making a "Type II" error in assessing ecological risk using surrogate species. Finally, we use the derived expression and life history data to compare outcomes from four different commonly used fish surrogate species (round goby, fathead minnow, smallmouth bass, cutthroat trout) and their target listed species (Chinook and Coho salmon). We illustrate that all four surrogate species fail to predict population outcomes for the listed species in cases of as little as 15% fecundity reduction due to toxicant exposure. Furthermore, surrogate species reliability is a function of toxicant level, so that some species are reliable at some levels but not at others. We discuss the implications of these findings, and outline further analyses that occur as a natural extension of the criteria developed here.