Alternatives to statistical hypothesis testing in ecology: a guide to self teaching

Effects of pasture intensification and sugarcane cultivation on non-target species: A realistic evaluation in pesticide-contaminated mesocosms

Conventional soil management in agricultural areas may expose non-target organisms living nearby to several types of contaminants. In this study, the effects of soil management in extensive pasture (EP), intensive pasture (IP), and sugarcane crops (C) were evaluated in a realistic-field-scale study. Thirteen aquatic mesocosms embedded in EP, IP, and C treatments were monitored over 392 days. The recommended management for each of the areas was simulated, such as tillage, fertilizer, pesticides (i.e. 2,4-D, fipronil) and vinasse application, and cattle pasture. To access the potential toxic effects that the different steps of soil management in these areas may cause, the cladoceran Ceriophania silvestrii was used as aquatic bioindicator, the dicot Eruca sativa as phytotoxicity bioindicator in water, and the dipteran Chironomus sancticaroli as sediment bioindicator. Generalized linear mixed models were used to identify differences between the treatments. Low concentrations of 2,4-D (<97 μg L-1) and fipronil (<0.21 μg L-1) in water were able to alter fecundity, female survival, and the intrinsic rate of population increase of C. silvestrii in IP and C treatments. Similarly, the dicot E. sativa had germination, shoot and root growth affected mainly by 2,4-D concentrations in the water. For C. sancticarolli, larval development was affected by the presence of fipronil (<402.6 ng g-1). The acidic pH (below 5) reduced the fecundity and female survival of C. silvestrii and affected the germination and growth of E. sativa. Fecundity and female survival of C. silvestrii decrease in the presence of phosphorus-containing elements. The outcomes of this study may improve our understanding of the consequences of exposure of freshwater biota to complex stressors in an environment that is rapidly and constantly changing.

Artificial shelters and marine infectious disease: no detectable effect of the use of casitas to enhance juvenile Panulirus argus in shelter-poor habitats on a viral disease dynamics

Casitas, low-lying artificial shelters that mimic large crevices, are used in some fisheries for Caribbean spiny lobsters (Panulirus argus). These lobsters are highly gregarious and express communal defense of the shelter. Scaled-down casitas have been shown to increase survival, persistence, and foraging ranges of juveniles. Therefore, the use of casitas has been suggested to help enhance local populations of juvenile P. argus in Caribbean seagrass habitats, poor in natural crevice shelters, in marine protected areas. Following the emergence of Panulirus argus virus 1 (PaV1), which is lethal to juveniles of P. argus, concern was raised about the potential increase in PaV1 transmission with the use of casitas. It was then discovered that lobsters tend to avoid shelters harboring diseased conspecifics, a behavior which, alone or in conjunction with predatory culling of diseased lobsters, has been proposed as a mechanism reducing the spread of PaV1. However, this behavior may depend on the ecological context (i.e., availability of alternative shelter and immediacy of predation risk). We conducted an experiment in a lobster nursery area to examine the effect of the use of casitas on the dynamics of the PaV1 disease. We deployed 10 scaled-down casitas per site on five 1-ha sites over a reef lagoon (casita sites) and left five additional sites with no casitas (control sites). All sites were sampled 10 times every 3-4 months. Within each site, all lobsters found were counted, measured, and examined for clinical signs of the PaV1 disease. Mean density and size of lobsters significantly increased on casita sites relative to control sites, but overall prevalence levels remained similar. There was no relationship between lobster density and disease prevalence. Dispersion parameters (m and k of the negative binomial distribution) revealed that lobsters tended to avoid sharing natural crevices, but not casitas, with diseased conspecifics. These results confirm that casitas provide much needed shelter in seagrass habitats and that their large refuge area may allow distancing between healthy and diseased lobsters. On eight additional sampling times over two years, we culled all diseased lobsters observed on casita sites. During this period, disease prevalence did not decrease but rather increased and varied with site, suggesting that other factors (e.g., environmental) may be influencing the disease dynamics. Using scaled-down casitas in shelter-poor habitats may help efforts to enhance juvenile lobsters for conservation purposes, but monitoring PaV1 prevalence at least once a year during the first few years would be advisable.

Monitoring Plant Diversity to Support Agri-Environmental Schemes: Evaluating Statistical Models Informed by Satellite and Local Factors in Southern European Mountain Pastoral Systems

The spatial monitoring of plant diversity in the endangered species-rich grasslands of European mountain pastoral systems is an important step for fairer and more efficient Agri-Environmental policy schemes supporting conservation. This study assessed the underlying support for a spatially explicit monitoring of plant species richness at parcel level (policy making scale) in Southern European mountain grasslands, with statistical models informed by Sentinel-2 satellite and environmental factors. Twenty-four grassland parcels were surveyed for species richness in the Peneda-Gerês National Park, northern Portugal. Using a multi-model inference approach, three competing hypotheses guided by the species-scaling theoretical framework were established: species–area (P1), species–energy (P2) and species–spectral heterogeneity (P3), each representing a candidate spatial pathway to predict species richness. To evaluate the statistical support of each spatial pathway, generalized linear models were fitted and model selection based on Akaike information criterion (AIC) was conducted. Later, the performance of the most supported spatial pathway(s) was assessed using a leave-one-out cross validation. A model guided by the species–energy hypothesis (P2) was the most parsimonious spatial pathway to monitor plant species richness in mountain grassland parcels (P2, AICc = 137.6, ∆AIC = 0.0, wi = 0.97). Species–area and species–spectral heterogeneity pathways (P1 and P3) were less statistically supported (ΔAICc values in the range 5.7–10.0). The underlying support of the species–energy spatial pathway was based on Sentinel-2 satellite data, namely on the near-infrared (NIR) green ratio in the spring season (NIR/Greenspring) and on its ratio of change between spring and summer (NIR/Greenchange). Both predictor variables related negatively to species richness. Grassland parcels with lower values of near-infrared (NIR) green ratio and lower seasonal amplitude presented higher species richness records. The leave-one-out cross validation indicated a moderate performance of the species–energy spatial pathway in predicting species richness in the grassland parcels covered by the dataset (R2 = 0.44, RMSE = 4.3 species, MAE = 3.5 species). Overall, a species–energy framework based on Sentinel 2 data resulted in a promising spatial pathway for the monitoring of species richness in mountain grassland parcels and for informing decision making on Agri-Environmental policy schemes. The near-infrared (NIR) green ratio and its change in time seems a relevant variable to deliver predictions for plant species richness and further research should be conducted on that.

Towards a pragmatic use of statistics in ecology

Although null hypothesis testing (NHT) is the primary method for analyzing data in many natural sciences, it has been increasingly criticized. Recently, approaches based on information theory (IT) have become popular and were held by many to be superior because it enables researchers to properly assess the strength of the evidence that data provide for competing hypotheses. Many studies have compared IT and NHT in the context of model selection and stepwise regression, but a systematic comparison of the most basic uses of statistics by ecologists is still lacking. We used computer simulations to compare how both approaches perform in four basic test designs (t-test, ANOVA, correlation tests, and multiple linear regression). Performance was measured by the proportion of simulated samples for which each method provided the correct conclusion (power), the proportion of detected effects with a wrong sign (S-error), and the mean ratio of the estimated effect to the true effect (M-error). We also checked if the p-value from significance tests correlated to a measure of strength of evidence, the Akaike weight. In general both methods performed equally well. The concordance is explained by the monotonic relationship between p-values and evidence weights in simple designs, which agree with analytic results. Our results show that researchers can agree on the conclusions drawn from a data set even when they are using different statistical approaches. By focusing on the practical consequences of inferences, such a pragmatic view of statistics can promote insightful dialogue among researchers on how to find a common ground from different pieces of evidence. A less dogmatic view of statistical inference can also help to broaden the debate about the role of statistics in science to the entire path that leads from a research hypothesis to a statistical hypothesis.

Species' traits drive amphibian tolerance to anthropogenic habitat modification

Anthropogenic habitat modification is accelerating, threatening the world's biodiversity. Understanding species' responses to anthropogenic modification is vital for halting species' declines. However, this information is lacking for globally threatened amphibians, informed primarily by small community-level studies. We integrated >126,000 verified citizen science observations of frogs, with a global continuous measure of anthropogenic habitat modification for a continental scale analysis of the effects of habitat modification on frogs. We derived a modification tolerance index-accounting for anthropogenic stressors such as human habitation, agriculture, transport and energy production-for 87 species (36% of all Australian frog species). We used this index to quantify and rank each species' tolerance of anthropogenic habitat modification, then compiled traits of all the frog species and assessed how well these equipped species to tolerate modified habitats. Most of Australia's frog species examined were adversely affected by habitat modification. Habitat specialists and species with large geographic range sizes were the least tolerant of habitat modification. Call dominant frequency, body size, clutch type and calling position (i.e. from vegetation) were also related to tolerance of habitat modification. There is an urgent need for improved consideration of anthropogenic impacts and improved conservation measures to ensure the long-term persistence of frog populations, particularly focused on specialists and species identified as intolerant of modified habitats.

Limited evidence for a positive relationship between hybridization and diversification across seed plant families

Hybridization has experimental and observational ties to evolutionary processes and outcomes such as adaptation, speciation, and radiation. Although it has been hypothesized that hybridization and diversification are positively correlated, this idea remains largely untested empirically, and hybridization can also potentially reduce diversity. Here, we use a hybridization database on 170 seed plant families, life history information, and a time-calibrated phylogeny to test for phylogenetically-corrected associations between hybridization and diversification rates, while also taking into account life-history traits that may be correlated with both processes. We use three methods to estimate diversification rates and two metrics of hybridization. Although hybridization explains only a small amount of overall variation in diversification rates, we show that diversification and hybridization are sometimes positively correlated, although the effect sizes are very small. Moreover, the relationship remains detectable when incorporating the correlations between diversification and two other life history characteristics, perenniality and woodiness. We discuss potential mechanisms for this association under four different scenarios: hybridization may drive diversification, diversification may drive hybridization, both hybridization and diversification may jointly be driven by other factors, or, as an alternative, that there is in fact no relationship between the two. We suggest future studies to disentangle the causal structure.

Integrated ecosystem models (soil-water) to analyze pesticide toxicity to aquatic organisms at two different temperature conditions

In order to increase the knowledge about pesticides considering the soil-water interaction, ecosystem models (mesoscosms) were used to analyze the of leachate on the immobility and feeding rate of the cladocerans, Ceriodaphnia silvestrii and D. similis and algae Raphidocelis subcapitata, at two different temperatures. Mesocosm were filled with natural soil (latosolo) that were contaminated with insecticide/acaricide Kraft 36 EC® and fungicide Score 250 EC®, using the recommended concentration for strawberry crops (10.8 g abamectin/ha and 20 g difenoconazole/ha). Pesticides were applied once (hand sprayers) and the precipitation was simulated twice a week (Days 1, 4, 8, 11, 15 and 18). The mesocosm were kept in a room with a controlled temperature (23 and 33 °C) and photoperiod (12h light/12h dark). The Kraft 36 EC® insecticide showed toxicity for both species of cladocerans tested, with effects on immobility and feeding rate, both at 23 and 33 °C. Score 250 EC® showed to be toxic only for the experiments that analyzed the immobility of C. silvestrii at 23 °C and the feeding of D. smilis at 33 °C, demonstrating that the effects are species-specific and related to the temperature at which they are tested. While for species R. subcapitata there was an effect only for mixture treatments of the pesticides analyzed at both temperatures. Thereby, zooplanktonic organisms may be at risk when exposed to this compound even after percolating in a soil column, which could lead to effects on the entire aquatic trophic chain and that temperature can influence the organism response to the contaminant.

Influence of sex and maturity state on trace elements content in liver and muscle of the Sciaenidae Totoaba macdonaldi

BACKGROUND

The fish Totoaba macdonaldi is endemic to the Upper Gulf of California. Its migratory movements involve sites with high levels of trace elements in the environment that can accumulate in tissues. In this study, lead (Pb), copper (Cu), cadmium (Cd), zinc (Zn) and iron (Fe) concentrations in male and female totoaba liver and muscle were quantified at various sexual maturity stages along the species' geographic distribution.

METHODS

Generalized linear models were used to explore associations between trace element concentrations and season of the year, sex/maturity stage, and total fish length.

RESULTS

No detectable Pb concentrations were recorded in liver or muscle; Cu, Cd, Zn and Fe contents in totoaba liver and muscle were typical of fish inhabiting areas with no contamination issues and are within international maximum permissible levels for human consumption. Variations in the content of Cd, Cu, Zn and Fe in liver of totoaba seem to be more related to the feeding and reproductive physiology of this species than as result of environmental exposure. Results suggest that consumption of totoaba muscle does not pose a public health risk. Furthermore, depending on the sex/maturity stage of totoaba, this fish's muscle may provide approximately 70% Cu, 60% Zn and 100% Fe of the recommended dietary reference intake.

Resampling-based methods for biologists

Ecological data often violate common assumptions of traditional parametric statistics (e.g., that residuals are Normally distributed, have constant variance, and cases are independent). Modern statistical methods are well equipped to handle these complications, but they can be challenging for non-statisticians to understand and implement. Rather than default to increasingly complex statistical methods, resampling-based methods can sometimes provide an alternative method for performing statistical inference, while also facilitating a deeper understanding of foundational concepts in frequentist statistics (e.g., sampling distributions, confidence intervals, p-values). Using simple examples and case studies, we demonstrate how resampling-based methods can help elucidate core statistical concepts and provide alternative methods for tackling challenging problems across a broad range of ecological applications.

Photodegradation influences litter decomposition rate in a humid tropical ecosystem, Brazil

Solar radiation in general and UV radiation in particular have been recognized to stimulate plant litter decomposition through photochemical mineralization of organic molecules such as lignin and through facilitation of microbial decomposition in dryland ecosystems. However, little is known about how photodegradation may influence decomposition in other ecosystems not subject to moisture limitations and under what conditions photodegradation may be favored. Decomposition in humid tropical ecosystems is a complex process, and it can be influenced by a number of environmental factors that are distinct from arid and semi-arid ecosystems. To assess the mechanisms underlying photodegradation by ultraviolet B (UV-B) radiation in a humid tropical ecosystem, we designed a 300-day field experiment in a tropical site in Brazil with high levels of annual precipitation, compared to arid ecosystems, and exposed litter to three levels of radiation (full sun, UV-B removed, and shade) combined with a biocide treatment. Results show that after nearly one year of exposure, the microbial biomass was not affected by UV-B incidence, and this effect has not yet been fully understood for tropical ecosystems. Modeled using an exponential deceleration equation, the removal of UV-B radiation decelerated the plant litter decomposition rate for the control conditions by 21% compared to litter exposed to full sun. Interestingly, shaded litter exhibited similar mass loss compared to litter exposed to full sun. Furthermore, differences in the decay constant among radiation treatments due to the UV-B effect were independent of lignin loss. Overall, our study suggests that UV-B radiation contributed to plant litter decomposition through carbon losses but had no discernible effect on nitrogen, lignin, or cellulose loss specifically. Importantly, our results demonstrate that photodegradation occurs under humid tropical conditions, and further studies are necessary to examine the mechanisms of carbon loss.

Forecasting Zoonotic Infectious Disease Response to Climate Change: Mosquito Vectors and a Changing Environment

Infectious diseases are changing due to the environment and altered interactions among hosts, reservoirs, vectors, and pathogens. This is particularly true for zoonotic diseases that infect humans, agricultural animals, and wildlife. Within the subset of zoonoses, vector-borne pathogens are changing more rapidly with climate change, and have a complex epidemiology, which may allow them to take advantage of a changing environment. Most mosquito-borne infectious diseases are transmitted by mosquitoes in three genera: Aedes, Anopheles, and Culex, and the expansion of these genera is well documented. There is an urgent need to study vector-borne diseases in response to climate change and to produce a generalizable approach capable of generating risk maps and forecasting outbreaks. Here, we provide a strategy for coupling climate and epidemiological models for zoonotic infectious diseases. We discuss the complexity and challenges of data and model fusion, baseline requirements for data, and animal and human population movement. Disease forecasting needs significant investment to build the infrastructure necessary to collect data about the environment, vectors, and hosts at all spatial and temporal resolutions. These investments can contribute to building a modeling community around the globe to support public health officials so as to reduce disease burden through forecasts with quantified uncertainty.

Integrating river hydromorphology and water quality into ecological status modelling by artificial neural networks

The aim of the study was to develop predictive models of the ecological status of rivers by using artificial neural networks. The relationships between five macrophyte indices and the combined impact of water pollution as well as hydromorphological degradation were examined. The dataset consisted of hydromorphologically modified rivers representing a wide water quality gradient. Three ecological status indices, namely the Macrophyte Index for Rivers (MIR), the Macrophyte Biological Index for Rivers (IBMR) and the River Macrophyte Nutrient Index (RMNI), were tested. Moreover two diversity indices, species richness (N) and the Simpson index (D) were tested. Physico-chemical parameters reflecting both water quality and hydromorphological status were utilized as explanatory variables for the artificial neural networks. The best modelling quality in terms of high values of coefficients of determination and low values of the normalized root mean square error was obtained for the RMNI and the MIR networks. The networks constructed for IBMR, species richness and the Simpson index showed a lower degree of fit. In all cases, modelling quality improved by adding two hydromorphological indices to the pool of explanatory variables. The significant effect of these indices in the models was confirmed by sensitivity analysis. The research showed that ecological assessment of rivers based on macrophyte metrics does not only reflect the water quality but also the hydromorphological status.

Within-clutch variability in gamete size arises from the size variation in gametangia in the marine green alga Monostroma angicava

Within-clutch gamete size variability in Monostroma angicava. In many organisms, it is unclear how the size variation in gametes is generated in each clutch (i.e., total gametes produced by a gametophyte for a single spawning) or how gamete size is adjusted. Within-clutch variation in gamete size has been explained as a result of either physiological/developmental constraints or bet hedging during gametogenesis. These two explanations have been assumed to be mutually exclusive, and related observations are conflicting. The slightly anisogamous dioecious green alga Monostroma angicava employs a simple mechanism to produce gametes of each sex: each vegetative cell becomes a single gametangium cell, which synchronously divides to form equally sized gametes. The number of such cell divisions has several variations, which might vary gamete size. We measured the volume of gametangia in each clutch, counted the number of cell divisions in each gametangium and estimated the size of the gametes. We found that larger gametangia divided more times than smaller gametangia in both sexes, although male gametangia were smaller than female gametangia when they underwent the same number of cell divisions. Therefore, the variation in the number of cell divisions during gametogenesis serves to adjust gamete size in each sex rather than to vary it. Within-clutch gamete size variability originates in within-clutch variation in gametangium size: any factors that increase the variation in the size of gametangia can increase the within-clutch variation in gamete size.

Modeling the temporal periodicity of growth increments based on harmonic functions

Age estimation methods based on hard structures require a process of validation to confirm the periodical pattern of growth marks. Among such processes, one of the most used is the marginal increment ratio (MIR), which was stated to follow a sinusoidal cycle in a population. Despite its utility, in most cases, its implementation has lacked robust statistical analysis. Accordingly, we propose a modeling approach for the temporal periodicity of growth increments based on single and second order harmonic functions. For illustrative purposes, the MIR periodicities for two geoduck species (Panopea generosa and Panopea globosa) were modeled to identify the periodical pattern of growth increments in the shell. This model identified an annual periodicity for both species but described different temporal patterns. The proposed procedure can be broadly used to objectively define the timing of the peak, the degree of symmetry, and therefore, the synchrony of band deposition of different species on the basis of MIR data.

Identifying sources, pathways and risk drivers in ecosystems of Japanese Encephalitis in an epidemic-prone north Indian district

Japanese Encephalitis (JE) has caused repeated outbreaks in endemic pockets of India. This study was conducted in Kushinagar, a highly endemic district, to understand the human-animal-ecosystem interactions, and the drivers that influence disease transmission. Utilizing the ecosystems approach, a cross-sectional, descriptive study, employing mixed methods design was employed. Four villages (two with pig-rearing and two without) were randomly selected from a high, a medium and a low burden (based on case counts) block of Kushinagar. Children, pigs and vectors were sampled from these villages. A qualitative arm was incorporated to explain the findings from the quantitative surveys. All human serum samples were screened for JE-specific IgM using MAC ELISA and negative samples for JE RNA by rRT-PCR in peripheral blood mononuclear cells. In pigs, IgG ELISA and rRT-PCR for viral RNA were used. Of the 242 children tested, 24 tested positive by either rRT-PCR or MAC ELISA; in pigs, 38 out of the 51 pigs were positive. Of the known vectors, Culex vishnui was most commonly isolated across all biotopes. Analysis of 15 blood meals revealed human blood in 10 samples. Univariable analysis showed that gender, religion, lack of indoor residual spraying of insecticides in the past year, indoor vector density (all species), and not being vaccinated against JE in children were significantly associated with JE positivity. In multivariate analysis, only male gender remained as a significant risk factor. Based on previous estimates of symptomatic: asymptomatic cases of JE, we estimate that there should have been 618 cases from Kushinagar, although only 139 were reported. Vaccination of children and vector control measures emerged as major control activities; they had very poor coverage in the studied villages. In addition, lack of awareness about the cause of JE, lack of faith in the conventional medical healthcare system and multiple referral levels causing delay in diagnosis and treatment emerged as factors likely to result in adverse clinical outcomes.

Opposing effects of different soil organic matter fractions on crop yields

Soil organic matter is critical to sustainable agriculture because it provides nutrients to crops as it decomposes and increases nutrient- and water-holding capacity when built up. Fast- and slow-cycling fractions of soil organic matter can have different impacts on crop production because fast-cycling fractions rapidly release nutrients for short-term plant growth and slow-cycling fractions bind nutrients that mineralize slowly and build up water-holding capacity. We explored the controls on these fractions in a tropical agroecosystem and their relationship to crop yields. We performed physical fractionation of soil organic matter from 48 farms and plots in western Kenya. We found that fast-cycling, particulate organic matter was positively related to crop yields, but did not have a strong effect, while slower-cycling, mineral-associated organic matter was negatively related to yields. Our finding that slower-cycling organic matter was negatively related to yield points to a need to revise the view that stabilization of organic matter positively impacts food security. Our results support a new paradigm that different soil organic matter fractions are controlled by different mechanisms, potentially leading to different relationships with management outcomes, like crop yield. Effectively managing soils for sustainable agriculture requires quantifying the effects of specific organic matter fractions on these outcomes.

From Metaphors to Formalism: A Heuristic Approach to Holistic Assessments of Ecosystem Health

Environmental policies employ metaphoric objectives such as ecosystem health, resilience and sustainable provision of ecosystem services, which influence corresponding sustainability assessments by means of normative settings such as assumptions on system description, indicator selection, aggregation of information and target setting. A heuristic approach is developed for sustainability assessments to avoid ambiguity and applications to the EU Marine Strategy Framework Directive (MSFD) and OSPAR assessments are presented. For MSFD, nineteen different assessment procedures have been proposed, but at present no agreed assessment procedure is available. The heuristic assessment framework is a functional-holistic approach comprising an ex-ante/ex-post assessment framework with specifically defined normative and systemic dimensions (EAEPNS). The outer normative dimension defines the ex-ante/ex-post framework, of which the latter branch delivers one measure of ecosystem health based on indicators and the former allows to account for the multi-dimensional nature of sustainability (social, economic, ecological) in terms of modeling approaches. For MSFD, the ex-ante/ex-post framework replaces the current distinction between assessments based on pressure and state descriptors. The ex-ante and the ex-post branch each comprise an inner normative and a systemic dimension. The inner normative dimension in the ex-post branch considers additive utility models and likelihood functions to standardize variables normalized with Bayesian modeling. Likelihood functions allow precautionary target setting. The ex-post systemic dimension considers a posteriori indicator selection by means of analysis of indicator space to avoid redundant indicator information as opposed to a priori indicator selection in deconstructive-structural approaches. Indicator information is expressed in terms of ecosystem variability by means of multivariate analysis procedures. The application to the OSPAR assessment for the southern North Sea showed, that with the selected 36 indicators 48% of ecosystem variability could be explained. Tools for the ex-ante branch are risk and ecosystem models with the capability to analyze trade-offs, generating model output for each of the pressure chains to allow for a phasing-out of human pressures. The Bayesian measure of ecosystem health is sensitive to trends in environmental features, but robust to ecosystem variability in line with state space models. The combination of the ex-ante and ex-post branch is essential to evaluate ecosystem resilience and to adopt adaptive management. Based on requirements of the heuristic approach, three possible developments of this concept can be envisioned, i.e. a governance driven approach built upon participatory processes, a science driven functional-holistic approach requiring extensive monitoring to analyze complete ecosystem variability, and an approach with emphasis on ex-ante modeling and ex-post assessment of well-studied subsystems.

Strong Inference in Mathematical Modeling: A Method for Robust Science in the Twenty-First Century

While there are many opinions on what mathematical modeling in biology is, in essence, modeling is a mathematical tool, like a microscope, which allows consequences to logically follow from a set of assumptions. Only when this tool is applied appropriately, as microscope is used to look at small items, it may allow to understand importance of specific mechanisms/assumptions in biological processes. Mathematical modeling can be less useful or even misleading if used inappropriately, for example, when a microscope is used to study stars. According to some philosophers (Oreskes et al., 1994), the best use of mathematical models is not when a model is used to confirm a hypothesis but rather when a model shows inconsistency of the model (defined by a specific set of assumptions) and data. Following the principle of strong inference for experimental sciences proposed by Platt (1964), I suggest “strong inference in mathematical modeling” as an effective and robust way of using mathematical modeling to understand mechanisms driving dynamics of biological systems. The major steps of strong inference in mathematical modeling are (1) to develop multiple alternative models for the phenomenon in question; (2) to compare the models with available experimental data and to determine which of the models are not consistent with the data; (3) to determine reasons why rejected models failed to explain the data, and (4) to suggest experiments which would allow to discriminate between remaining alternative models. The use of strong inference is likely to provide better robustness of predictions of mathematical models and it should be strongly encouraged in mathematical modeling-based publications in the Twenty-First century.

Effects of forests, roads and mistletoe on bird diversity in monoculture rubber plantations

Rising global demand for natural rubber is expanding monoculture rubber (Hevea brasilensis) at the expense of natural forests in the Old World tropics. Conversion of forests into rubber plantations has a devastating impact on biodiversity and we have yet to identify management strategies that can mitigate this. We determined the life-history traits that best predict bird species occurrence in rubber plantations in SW China and investigated the effects of surrounding forest cover and distance to roads on bird diversity. Mistletoes provide nectar and fruit resources in rubber so we examined mistletoe densities and the relationship with forest cover and rubber tree diameter. In rubber plantations, we recorded less than half of all bird species extant in the surrounding area. Birds with wider habitat breadths and low conservation value had a higher probability of occurrence. Species richness and diversity increased logarithmically with surrounding forest cover, but roads had little effect. Mistletoe density increased exponentially with rubber tree diameters, but was unrelated to forest cover. To maximize bird diversity in rubber-dominated landscapes it is therefore necessary to preserve as much forest as possible, construct roads through plantations and not forest, and retain some large rubber trees with mistletoes during crop rotations.

Model averaging and muddled multimodel inferences

Three flawed practices associated with model averaging coefficients for predictor variables in regression models commonly occur when making multimodel inferences in analyses of ecological data. Model-averaged regression coefficients based on Akaike information criterion (AIC) weights have been recommended for addressing model uncertainty but they are not valid, interpretable estimates of partial effects for individual predictors when there is multicollinearity among the predictor variables. Multicollinearity implies that the scaling of units in the denominators of the regression coefficients may change across models such that neither the parameters nor their estimates have common scales, therefore averaging them makes no sense. The associated sums of AIC model weights recommended to assess relative importance of individual predictors are really a measure of relative importance of models, with little information about contributions by individual predictors compared to other measures of relative importance based on effects size or variance reduction. Sometimes the model-averaged regression coefficients for predictor variables are incorrectly used to make model-averaged predictions of the response variable when the models are not linear in the parameters. I demonstrate the issues with the first two practices using the college grade point average example extensively analyzed by Burnham and Anderson. I show how partial standard deviations of the predictor variables can be used to detect changing scales of their estimates with multicollinearity. Standardizing estimates based on partial standard deviations for their variables can be used to make the scaling of the estimates commensurate across models, a necessary but not sufficient condition for model averaging of the estimates to be sensible. A unimodal distribution of estimates and valid interpretation of individual parameters are additional requisite conditions. The standardized estimates or equivalently the t statistics on unstandardized estimates also can be used to provide more informative measures of relative importance than sums of AIC weights. Finally, I illustrate how seriously compromised statistical interpretations and predictions can be for all three of these flawed practices by critiquing their use in a recent species distribution modeling technique developed for predicting Greater Sage-Grouse (Centrocercus urophasianus) distribution in Colorado, USA. These model averaging issues are common in other ecological literature and ought to be discontinued if we are to make effective scientific contributions to ecological knowledge and conservation of natural resources.

Patterns and Variation in Benthic Biodiversity in a Large Marine Ecosystem

While there is a persistent inverse relationship between latitude and species diversity across many taxa and ecosystems, deviations from this norm offer an opportunity to understand the conditions that contribute to large-scale diversity patterns. Marine systems, in particular, provide such an opportunity, as marine diversity does not always follow a strict latitudinal gradient, perhaps because several hypothesized drivers of the latitudinal diversity gradient are uncorrelated in marine systems. We used a large scale public monitoring dataset collected over an eight year period to examine benthic marine faunal biodiversity patterns for the continental shelf (55-183 m depth) and slope habitats (184-1280 m depth) off the US West Coast (47°20'N-32°40'N). We specifically asked whether marine biodiversity followed a strict latitudinal gradient, and if these latitudinal patterns varied across depth, in different benthic substrates, and over ecological time scales. Further, we subdivided our study area into three smaller regions to test whether coast-wide patterns of biodiversity held at regional scales, where local oceanographic processes tend to influence community structure and function. Overall, we found complex patterns of biodiversity on both the coast-wide and regional scales that differed by taxonomic group. Importantly, marine biodiversity was not always highest at low latitudes. We found that latitude, depth, substrate, and year were all important descriptors of fish and invertebrate diversity. Invertebrate richness and taxonomic diversity were highest at high latitudes and in deeper waters. Fish richness also increased with latitude, but exhibited a hump-shaped relationship with depth, increasing with depth up to the continental shelf break, ~200 m depth, and then decreasing in deeper waters. We found relationships between fish taxonomic and functional diversity and latitude, depth, substrate, and time at the regional scale, but not at the coast-wide scale, suggesting that coast-wide patterns can obscure important correlates at smaller scales. Our study provides insight into complex diversity patterns of the deep water soft substrate benthic ecosystems off the US West Coast.

Annual dynamics of wild bee densities: attractiveness and productivity effects of oilseed rape

Mass-flowering crops may affect long-term population dynamics, but effects on pollinators have never been studied across several years. We monitored wild bees in oilseed rape fields in 16 landscapes in Germany in two consecutive years. Effects on bee densities of landscape oilseed rape cover in the years of monitoring and in the previous years were evaluated with landscape data from three consecutive years. We fit empirical data to a mechanistic model to provide estimates for oilseed rape attractiveness and its effect on bee productivity in comparison to the rest of the landscape, and we evaluated consequences for pollinator densities in consecutive years. Our results show that high oilseed rape cover in the previous year enhances current densities of wild bees (except for bumble bees). Moreover, we show a strong attractiveness of and dilution on (i.e., decreasing bee densities with increasing landscape oilseed rape cover) oilseed rape for bees during flowering in the current year, modifying the effect of the previous year's oilseed rape cover in the case of wild bees (excluding Bombus). As long as other factors such as nesting sites or natural enemies do not limit bee reproduction, our findings suggest long-term positive effects of mass-flowering crops on bee populations, at least for non-Bombus generalists, which possibly help to maintain crop pollination services even when crop area increases. Similar effects are conceivable for other organisms providing ecosystem services in annual crops and should be considered in future studies.

Association of blood products administration during cardiopulmonary bypass and excessive post-operative bleeding in pediatric cardiac surgery

Our objectives were to study risk factors and post-operative outcomes associated with excessive post-operative bleeding in pediatric cardiac surgeries performed using cardiopulmonary bypass (CPB) support. A retrospective observational study was undertaken, and all consecutive pediatric heart surgeries over 1 year period were studied. Excessive post-operative bleeding was defined as 10 ml/kg/h of chest tube output for 1 h or 5 ml/kg/h for three consecutive hours in the first 12 h of pediatric cardiac intensive care unit (PCICU) stay. Risk factors including demographics, complexity of cardiac defect, CPB parameters, hematological studies, and post-operative morbidity and mortality were evaluated for excessive bleeding. 253 patients were studied, and 107 (42 %) met the criteria for excessive bleeding. Bayesian model averaging revealed that greater volume of blood products transfusion during CPB was significantly associated with excessive bleeding. Multiple logistic regression analysis of blood products transfusion revealed that increased volume of packed red blood cells (PRBCs) administration for CPB prime and during CPB was significantly associated with excessive bleeding (p = 0.028 and p = 0.0012, respectively). Proportional odds logistic regression revealed that excessive bleeding was associated with greater time to achieve negative fluid balance, prolonged mechanical ventilation, and duration of PCICU stay (p < 0.001) after adjusting for multiple parameters. A greater volume of blood products administration, especially PRBCs transfusion for CPB prime, and during the CPB period is associated with excessive post-operative bleeding. Excessive bleeding is associated with worse post-operative outcomes.

Climate change and species interactions: ways forward

With ongoing and rapid climate change, ecologists are being challenged to predict how individual species will change in abundance and distribution, how biotic communities will change in structure and function, and the consequences of these climate-induced changes for ecosystem functioning. It is now well documented that indirect effects of climate change on species abundances and distributions, via climatic effects on interspecific interactions, can outweigh and even reverse the direct effects of climate. However, a clear framework for incorporating species interactions into projections of biological change remains elusive. To move forward, we suggest three priorities for the research community: (1) utilize tractable study systems as case studies to illustrate possible outcomes, test processes highlighted by theory, and feed back into modeling efforts; (2) develop a robust analytical framework that allows for better cross-scale linkages; and (3) determine over what time scales and for which systems prediction of biological responses to climate change is a useful and feasible goal. We end with a list of research questions that can guide future research to help understand, and hopefully mitigate, the negative effects of climate change on biota and the ecosystem services they provide.

Benefit of shading by nurse plant does not change along a stress gradient in a coastal dune

The proximity of adult neighbors often increases the performance of woody seedlings under harsh environmental conditions but this nurse plant effect becomes less intense when abiotic stress is alleviated, as predicted by the stress gradient hypothesis (SGH). Although some studies have tested how the net nurse effect is changed by stress, few studies have tested how the mechanism that drives the facilitative effect of nurse responds to changes in stress. We conducted field experiments in a subtropical coastal dune to test if shading drives the known nurse effect of adults of the tree Guapira opposita on seedling performance of another tree species, Ternstroemia brasiliensis. We transplanted T. brasiliensis seedlings to three neighbor environments: under a G. opposita crown, under artificial shade and without neighbor as a control. Furthermore, assuming that proximity to the seashore correlates with stress intensity, we tested if the potential shade-driven facilitation became less intense as stress decreased. Regardless of the proximity to the seashore, after a year, the survival of T. brasiliensis seedlings was twice as high when the seedlings were under G. opposita or under artificial shade compared to the control, indicating that the nurse effect is driven by shade and that this facilitation mechanism is constant along the stress gradient. However, G. opposita and artificial shade had a negative effect on seedlings growth. Overall, our results showed that the facilitation mechanism behind the nurse effect did not wane as the stress was reduced. Furthermore, in spite of the potential costs in terms of biomass production, our study highlights the potential of nurse plants and artificial shade as techniques to improve the survival of transplanted seedlings used in the restoration of degraded shrubland coastal dunes.

Analyzing human-landscape interactions: tools that integrate

Humans have transformed much of Earth's land surface, giving rise to loss of biodiversity, climate change, and a host of other environmental issues that are affecting human and biophysical systems in unexpected ways. To confront these problems, environmental managers must consider human and landscape systems in integrated ways. This means making use of data obtained from a broad range of methods (e.g., sensors, surveys), while taking into account new findings from the social and biophysical science literatures. New integrative methods (including data fusion, simulation modeling, and participatory approaches) have emerged in recent years to address these challenges, and to allow analysts to provide information that links qualitative and quantitative elements for policymakers. This paper brings attention to these emergent tools while providing an overview of the tools currently in use for analysis of human-landscape interactions. Analysts are now faced with a staggering array of approaches in the human-landscape literature--in an attempt to bring increased clarity to the field, we identify the relative strengths of each tool, and provide guidance to analysts on the areas to which each tool is best applied. We discuss four broad categories of tools: statistical methods (including survival analysis, multi-level modeling, and Bayesian approaches), GIS and spatial analysis methods, simulation approaches (including cellular automata, agent-based modeling, and participatory modeling), and mixed-method techniques (such as alternative futures modeling and integrated assessment). For each tool, we offer an example from the literature of its application in human-landscape research. Among these tools, participatory approaches are gaining prominence for analysts to make the broadest possible array of information available to researchers, environmental managers, and policymakers. Further development of new approaches of data fusion and integration across sites or disciplines pose an important challenge for future work in integrating human and landscape components.

Species or local environment, what determines the infection of rodents by Toxoplasma gondii?

Toxoplasmosis is largely present in rural areas but its spatial distribution in this environment remains poorly known. In particular, it is unclear if areas of high density of cats, the only hosts excreting Toxoplasma gondii, constitute foci of high prevalence. To improve our understanding of the spatial distribution of T. gondii in rural areas, we performed a serological survey in rodents from two villages in France. We trapped 710 rodents including commensal rats and meadow or forest voles and mice. The presence of T. gondii was examined using PCR, mice inoculation and modified agglutination test for antibodies (MAT). We conducted multivariate and discriminant analyses to identify biological, ecological or spatial variables that could explain T. gondii serology in rodents. We then used a logistic regression to assess the relative influence of each explanatory variable. Overall seroprevalence was 4·1%. Commensal-rats were more infected (12·5%) than non-commensal species (3·7%). However, the major determinant of the risk of infection was the distance to the nearest farm (OR = 0·75 for 100 m), which explained the risk in all species or non-commensal species only. We contrast the role of species characteristics and that of the local environment, and discuss the risk of environmental contamination for humans.

Examining change over time in habitat attributes using Bayesian reinterpretation of categorical assessments

Prospects for evaluating effects of vegetation restoration have long been limited by availability of appropriately sensitive baseline data. Data that are typically collected to justify investment in restoration are rarely suitable for estimating subsequent change over time, but given how commonly such data are collected, can they contribute something to learning about ecological change over time? We compared vegetation and habitat data from a quantitative reassessment of 25 habitat restoration sites seven years after they were initially assessed using a semiquantitative, categorical scoring system. Our aim was to estimate the change at sites between the first, semiquantitative survey and a second, quantitative survey. We treated the initial values as effectively unknown and used Bayesian models to infer plausible values using three different informative prior distributions, variously comprising the initial site assessments and modeled values from a statewide data set. We successfully constructed models of change over time between the two surveys, and regardless of which prior model was implemented, our data analysis suggested that cover of exotic species was reduced, but canopy cover, the cover of organic litter, and the length of fallen logs were all increased after the seven-year period. A small increase in the mean number of large-diameter trees was likely due to initial measurement error. Site fertility and canopy cover were important covariates in explaining the magnitude of change in total log length. Sites with higher canopy cover decreased more in weed cover and increased more in litter cover. Our approach could be used to retrospectively analyze any ordinal data set where there is a scoring logic that can be interpreted quantitatively. Data sets where treatment contrasts and untreated controls exist will be particularly valuable for testing the utility of our approach. While this novel approach should prove a useful analytical complement to genuine longitudinal monitoring and space-for-time surveys, it is no substitute for initiation of learning about management effectiveness using data from purposefully designed and measured surveys.

Complex carbon cycle responses to multi-level warming and supplemental summer rain in the high Arctic

The Arctic has experienced rapid warming and, although there are uncertainties, increases in precipitation are projected to accompany future warming. Climate changes are expected to affect magnitudes of gross ecosystem photosynthesis (GEP), ecosystem respiration (ER) and the net ecosystem exchange of CO2 (NEE). Furthermore, ecosystem responses to climate change are likely to be characterized by nonlinearities, thresholds and interactions among system components and the driving variables. These complex interactions increase the difficulty of predicting responses to climate change and necessitate the use of manipulative experiments. In 2003, we established a long-term, multi-level and multi-factor climate change experiment in a polar semidesert in northwest Greenland. Two levels of heating (30 and 60 W m(-2) ) were applied and the higher level was combined with supplemental summer rain. We made plot-level measurements of CO2 exchange, plant community composition, foliar nitrogen concentrations, leaf δ(13) C and NDVI to examine responses to our treatments at ecosystem- and leaf-levels. We confronted simple models of GEP and ER with our data to test hypotheses regarding key drivers of CO2 exchange and to estimate growing season CO2 -C budgets. Low-level warming increased the magnitude of the ecosystem C sink. Meanwhile, high-level warming made the ecosystem a source of C to the atmosphere. When high-level warming was combined with increased summer rain, the ecosystem became a C sink of magnitude similar to that observed under low-level warming. Competition among our ER models revealed the importance of soil moisture as a driving variable, likely through its effects on microbial activity and nutrient cycling. Measurements of community composition and proxies for leaf-level physiology suggest GEP responses largely reflect changes in leaf area of Salix arctica, rather than changes in leaf-level physiology. Our findings indicate that the sign and magnitude of the future High Arctic C budget may depend upon changes in summer rain.