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Ecological Models in Support of Regulatory Risk Assessments of Pesticides: Developing a Strategy for the Future [Short Communication]
Abstract
This brief communication reports on the main findings of the LEMTOX workshop, held from 9 to 12 September 2007, at the Helmholtz Centre for Environmental Research (UFZ) in Leipzig, Germany. The workshop brought together a diverse group of stakeholders from academia, regulatory authorities, contract research organizations, and industry, representing Europe, the United States, and Asia, to discuss the role of ecological modeling in risk assessments of pesticides, particularly under the European regulatory framework. The following questions were addressed: What are the potential benefits of using ecological models in pesticide registration and risk assessment? What obstacles prevent ecological modeling from being used routinely in regulatory submissions? What actions are needed to overcome the identified obstacles? What recommendations should be made to ensure good modeling practice in this context? The workshop focused exclusively on population models, and discussion was focused on those categories of population models that link effects on individuals (e.g., survival, growth, reproduction, behavior) to effects on population dynamics. The workshop participants concluded that the overall benefits of ecological modeling are that it could bring more ecology into ecological risk assessment, and it could provide an excellent tool for exploring the importance of, and interactions among, ecological complexities. However, there are a number of challenges that need to be overcome before such models will receive wide acceptance for pesticide risk assessment, despite having been used extensively in other contexts (e.g., conservation biology). The need for guidance on Good Modeling Practice (on model development, analysis, interpretation, evaluation, documentation, and communication), as well as the need for case studies that can be used to explore the added value of ecological models for risk assessment, were identified as top priorities. Assessing recovery potential of exposed nontarget species and clarifying the ecological relevance of standard laboratory test results are two areas for which ecological modeling may be able to provide considerable benefits.
... For effects assessments to best reflect the available data they must be put in the context of life history [26][27][28][29][30][31][32][33]. Raimondo et al. [33] used simulations to demonstrate how populations of species with different life histories deterministically respond to proportional reductions in survival and reproduction caused by chemical stressors (Figure 2). ...
... Since that time, several books, e.g., [29,50], and numerous scientific review articles [51] have been published on this topic. A number of international workshops sponsored by the Society of Environmental Toxicology and Chemistry have been held, including in Roskilde, Denmark, in 2004 [50], Leipzig, Germany, in 2007 [32], Le Croisic, France, in 2012, and Monschau, Germany, in 2013 [52]. A USEPA Risk Assessment Forum Technical Workshop on Population-Level Ecological Risk Assessment was held in Washington (DC, USA) in 2008 to develop guidance for the USEPA programs [53]. ...
... The research supporting the use of population models (or mechanistic, demographic models) as the best available science for ERAs is extensive, comprehensive, and exhaustive [8][9][10][11][12][13][14][15][26][27][28][29][30][31][32][33][34][35][41][42][43][44][45][46][47][50][51][52][53][54][60][61][62][63][64][65][66]. Meanwhile, the continued use of deterministic RQs-LOCs is side-tracked by discourse over the interpretation and relevance of NOAECs and ECxs. ...
Under standard guidance for conducting Ecological Risk Assessments (ERAs), the risks of chemical exposure to diverse organisms are most often based on deterministic point estimates evaluated against safety-factor-based levels of concern (LOCs). While the science and guidance for mechanistic effect models (e.g., demographic, population, and agent-based) have long been demonstrated to provide more ecologically relevant effect endpoints upon which risk can be evaluated, their application in ERAs has been limited, particularly in the US. This special issue highlights the state of the science in effect modeling for ERAs through demonstrated application of the recently published Population modeling Guidance, Use, Interpretation, and Development for ERA (Pop-GUIDE). We introduce this issue with a perspective on why it is critical to move past the current application of deterministic endpoints and LOCs. We demonstrate how the current, widely used approaches contain extensive uncertainty that could be reduced considerably by applying models that account for species life histories and other important endogenous and exogenous factors critical to species sustainability. We emphasize that it is long past time to incorporate better, more robust, and ecologically relevant effect models into ERAs, particularly for chronic risk determination. The papers in this special issue demonstrate how mechanistic models that follow Pop-GUIDE better inform ERAs compared to the current standard practice.
... PVA models have long supported protection and management for the recovery of vulnerable, threatened, and endangered species [1]. In contrast, regulatory acceptance of PLRA models has been slow [2][3][4], though demonstrations and reviews of PLRA models have been available for decades [5][6][7][8][9][10][11][12][13][14]. ...
... As the principles of ecological risk assessment (ERA) developed to embrace a tiered evaluation strategy, population models were recognized as a valuable tool for higher-tier risk assessment when screening assessments suggested potential risk [16]. PopGUIDE [3] and associated works [4,12] have provided a roadmap for the development of population models for PLRA that considers the regulatory framework under which the risk assessment is conducted, the availability of organismal, toxicological, and exposure data, and the resources available for model development [3][4][5][6][7][8][9][10][11][12][13][14]17]. ...
... The efficiency principle articulated above may conflict with generally accepted practices for the development and deployment of ecological models, which will be referred to collectively as "best practices" [3][4][5][6][7][8][9][10][11][12][13][14]17,21]. Under best practices, parsimony is applied to optimize the complexity of a particular model given the available data and the objectives of the risk assessment. ...
Recent research has provided valuable momentum for the development and use of population models for ecological risk assessment (ERA). In general, ERA proceeds along a tiered strategy, with conservative assumptions deployed at lower tiers that are relaxed at higher tiers with ever more realistic models. As the tier increases, so do the levels of time and effort required by the assessor. When faced with many stressors, species, and habitats, risk assessors need to find efficiencies. Conservative lower-tier approaches are well established, but higher-tier models often prioritize accuracy, and conservative approaches are relatively unexplored at higher tiers. A principle of efficiency for ecological modeling for population-level ecological risk assessment is articulated and evaluated against a conceptual model and an existing set of avian models for chemical risk assessment. Here, four published avian models are reviewed in increasing order of realism (risk quotient → Markov chain nest productivity model → endogenous lifecycle model → spatially explicit population model). Models are compared in a pairwise fashion according to increasing realism and evaluated as to whether conservatism increases or decreases with each step. The principle of efficiency is shown to be a challenging ideal, though some cause for optimism is identified. Strategies are suggested for studying efficiency in tiered ecological model deployment.
... Current ERA often lacks the means to realistically extrapolate effects from standard protocols to relevant effects of chemical mixtures in real-life environments. There is thus a need to increase ecological realism in risk assessment (Forbes et al., 2009;Forbes and Calow, 2013;Goussen et al., 2016;Goussen et al., 2020). ...
... There is thus an increased need for prospective risk assessment, to help prevent potential risk of substances and (un)intended mixtures (Goussen et al., 2020). But as many have argued, current risk assessment regulations are designed to be protective rather than predictive (Forbes et al., 2009;Ashauer et al., 2011;Forbes and Callow, 2013;Goussen et al., 2016;Goussen et al., 2020). ERA methods simplify many of the relevant ecological processes, to generate a standardized approach that is widely applicable. ...
... ERA methods simplify many of the relevant ecological processes, to generate a standardized approach that is widely applicable. These approaches often lack the inclusion of ecological realism (Forbes et al., 2009;Goussen et al., 2020), generalize mixture toxicity interactions (Kortenkamp et al., 2009), and simplify combined effects of multiple stressors (i.e., combinations of chemical, biotic, and abiotic stressors) (Goussen et al., 2016). Mechanistic population models, such as DEB-IBMs, could help increase ecological realism and relevancy of prospective risk assessment methods in ERA. ...
Anthropogenic chemicals are essential for modern society, but many of these man-made chemicals enter the environment one way or another. For the last 10 years, the chemical industry in Europe has doubled in production and is expected to double again in the next 10 years. Ecological risk assessment aims to estimate the concentration of harmful substances in the environment and the associated effects on the ecosystem. Based on the estimated concentrations and the predicted effects, potential risks for adverse effects to the environment are identified. Effect assessment nowadays is performed using standardized toxicity tests, with individual organisms, exposed to single substances, in strictly controlled laboratory conditions. However, the environment is complex, as we have individuals living together in populations, exposed to mixtures of chemical substances, under varying environmental conditions. Mechanistic effect models have gained increasing interest from the scientific community, as they can extrapolate effects across biological levels, i.e., from sub-organismal to the population or community level. Yet, applications of mechanistic effect models for mixture toxicity in a population context are limited. The aim of the current thesis is to demonstrate the use of mechanistic models to predict population-level effects of mixtures. Focus is on the freshwater crustacean Daphnia magna (the water flea), two metals (copper and zinc), and four organic priority substances listed under the Water Framework Directive (pyrene, dicofol, alfa-hexachlorocyclohexane, and endosulfan). A generic individual-based model (IBM) implementation of the dynamic energy budget (DEB) theory was used to predict mixture toxicity effects to D. magna populations. Toxic stress was predicted using DEB-TKTD (extension of DEB including toxicokinetic-toxicodynamic processes) for sub-lethal effects and GUTS-RED-SD (reduced version of the general unified threshold model for survival assuming stochastic death) for lethal effects. A mixture toxicity implementation was developed, based on the general statistical models for mixture toxicity used in risk assessment. Two mixture toxicity approaches in mechanistic effect models can be considered: independent action or damage addition. In a first case (Chapter 2), we extrapolate effects observed at the individual level to relevant population-level effects of mixtures. The model was applied for mixtures of copper and zinc. The DEB- TKTD and GUTS sub-models were calibrated based on data from a standard 21-day chronic reproduction test (endpoints: growth, reproduction, and survival over time). A population experiment with mixtures of copper and zinc was performed. The DEB-IBM, assuming independent action for mixture toxicity, was able to reproduce the effects observed in the population experiment. Using the DEB-IBM, the observed trends were explained. The absence of zinc effects was explained through population-level compensation mechanisms. The increased mortality due to zinc is compensated by a decrease in starvation-related mortality. For copper, the switch from copper-induced mortality to starvation-related mortality explained the recovery over time observed in the experiment. Based on standard toxicity data at the individual level, mixture toxicity effects at the population were predicted. Based on the DEB-TKTD theoretical model, we hypothesize that combinations of physiological modes of action (PMoAs) in DEB-TKTD can lead to diverging effects at the population level. As a matter of fact, the PMoA will determine how the energy from food is redistributed within the population under chemical stress. We used DEB-IBM to design a population experiment, testing specific combinations of substances based on their inferred PMoAs (Chapter 3). We tested combinations of four organic substances: pyrene, dicofol, alfa-hexachlorocyclohexane (α-HCH), and endosulfan. An independent validation of mixture toxicity effects at the population level was performed with blind predictions, calibrated on individual-level effects of single substances only. Strong correlation was found between data and predictions during the constant exposed phase, the recovery phase after, and the pulsed acute phase. However, the recovery after the acute phase was not well predicted, meaning the model is unreliable in situations with high lethality. Overall, the independent action approach correctly predicted the observed mixture effects in the population experiment. The damage addition model was tested for the HCH-endosulfan mixture, but overpredicted the effects. Interestingly, synergisms (compared to statistical independent action) were observed in the population experiment that were correctly predicted by the DEB-IBM. We initially hypothesized that increased or decreased effects can occur due to the linking of DEB energy flows within the population. Overall, DEB-IBM was better in predicting mixture toxicity at the population level than current statistical models used in risk assessment. The two cases have shown the validity and relevance of mechanistic population models for mixture toxicity risk assessment. Application of these models for regulatory risk assessment is currently limited. We envision applications of mechanistic population models in current European regulations that encompass the risk assessment of chemicals, such as REACH, PPP, and BPR (Chapter 4). In this chapter, three example applications are highlighted. In a first example, mechanistic population models are used as predictive tools for the risk assessment of chemicals. Look-up tables and flowcharts were developed. A second example discusses the use of DEB-IBM as refinement tool for laboratory-to-field extrapolations. The effect of food density in combination with lethal and and sub-lethal effects to D. magna populations was investigated. As final example, DEB-IBM was linked to FOCUS (a dedicated exposure model that predicts the fate of pesticides in the environment) to predicted realistic effects of pesticide mixtures to D. magna populations. A realistic example was developed with a water body contaminated with endosulfan and funguren (a copper pesticide) due to pesticide application on nearby fields. The predicted surface water concentrations from FOCUS were linked with DEB-IBM. In addition, the DEB-IBM predictions were compared to a traditional dose-response curve analysis and predictions with a TKTD model. Good model documentation and accessibility is required to increase model transparency and reliability. An extensive description of the model, following the TRACE (transparent and comprehensive model ‘evaludation’) documentation, is provided (Appendix E). We conclude that mechanistic population models can be used for prospective and predictive risk assessment of chemical mixtures. More so than predicting effects, mechanistic population models can also give information and understanding of the driving forces of mixture toxicity within a population context. However, there is still a lack of guidance on the ‘standardized’ use of these models. More applications and communication of results would help increase acceptance of mechanistic population models for regulatory risk assessment. With this thesis we have shown that mechanistic population models can bridge multiple uncertainty gaps that were previously unaddressed in ecological risk assessment: the divide between individuals and populations, between single substances and mixtures of substances, and between constant controlled exposure conditions and dynamic exposure conditions.
... A survey of EPA ecological risk assessors at that time ranked effects at higher levels of biological organization, along with assessment endpoints and measures of effect, as having the highest priority for development of additional guidance. The call for guidance has been repeated in recent international efforts addressing population-level ecological risk assessment (e.g., Barnthouse et al. 2008;Forbes et al. 2009). In particular, Barnthouse et al. (2008) recommended development of guidance to assist risk assessors, risk managers and stakeholders in selecting, applying, interpreting and communicating population-level ecological risk assessment procedures and analysis tools to cover a range of environmental management contexts. ...
... Also at that time, a survey of EPA ecological risk assessors ranked effects at higher levels of biological organization, along with assessment endpoints and measures of effect, as having the highest priority for development of additional guidance. The call for guidance has been repeated in recent international efforts addressing population-level ecological risk assessment (e.g., Barnthouse et al. 2008, Forbes et al. 2009). In particular, Barnthouse et al. (2008) recommend development of guidance to assist risk assessors, risk managers and stakeholders in selecting, applying, interpreting and communicating population-level ecological risk assessment procedures and analysis tools to cover a range of environmental management contexts. ...
... The conclusions drawn from the workshop included: 1) the science is sufficiently mature to develop guidance; 2) specific guidance should be developed for use of models and data within a tiered assessment format; 3) training programs should be developed; and 4) acceptable levels of population risk in different management contexts should be articulated. The third activity, the SETAC "LEMTOX Workshop on Ecological Models in Support of Regulatory Risk Assessments of Pesticides," focused on the role of population models to support pesticide registration (primarily) in the European Union (Forbes et al. 2009). The conclusions identified in this 2007 workshop in Germany included the need to develop guidance for good modeling practice. ...
In June 2008, the Environmental Protection Agency’s (EPA) Risk Assessment Forum convened a technical workshop on population-level ecological risk assessment to consider whether the current state of knowledge about this subject was sufficiently mature to develop guidance, and if so, to help to identify key actions needed to produce such guidance. The purpose of this document is to communicate the findings of that workshop.
... However, despite the usefulness of toxicokinetictoxicodynamic for this purpose, and despite the routine use of population models in ecology, conservation biology, and natural resource management, the application of such models in environmental risk assessment is extremely limited (Schmolke et al. 2010;Galic et al. 2014;Hommen et al. 2016). Although guidance for the use of a toxicokinetic-toxicodynamic population model (based on dynamic energy budget theory) has been included in the European Union's risk-assessment guidance for the evaluation of ecotoxicological data (Organisation for Economic Co-operation and Development 2006), its use (or the use of any other population model) in formal risk assessments is uncommon (Forbes et al. 2009;Ducrot et al. 2016;Raimondo et al. 2018). The use of dynamic energy budget theory in populationlevel risk assessments is one of the more sophisticated modeling efforts. ...
... These are routinely used in pesticide risk evaluations in the European Union (Forum for the Co-ordination of Pesticide Fate Models and their Use [FOCUS] 2001) and the United States (Pesticide in Water Calculator; US Environmental Protection Agency 2016b). Despite the acceptance and use of exposure models, obstacles facing the adoption of population effect models in ecological risk assessment remain-not the least of which is convincing risk assessors and other stakeholders that effects models can result in more accurate and realistic risk characterizations (Forbes et al. 2009). Current risk assessment relies heavily on exposure model predictions compared with empirical toxicity test results and assumes the latter are not amenable to predictive modeling such as toxicokinetictoxicodynamic. ...
Population modeling evaluations of pesticide exposure time series were compared to aspects of a currently used risk assessment process. The US Environmental Protection Agency's Office of Pesticide Programs models daily aquatic pesticide exposure values for 30 years in its risk assessments, but does not routinely make full use of the information in those time series. We used mysid shrimp Americamysis bahia toxicity and demographic data to demonstrate the value of a toxicokinetic‐toxicodynamic model coupled with a series of matrix population models in risk assessment refinements. This species is a small epibenthic marine crustacean routinely used in regulatory toxicity tests. We demonstrate how the model coupling can refine current risk assessments using only existing standard regulatory toxicity test results. Several exposure scenarios (each with the same initial risk characterization as determined by a more traditional organismal‐based approach) were created within which population modeling documented different risks than assessments based on the traditional approach. We also present different acute and chronic toxicity data scenarios where TK‐TD coupled with population modeling can distinguish different responses; responses that tradition risk evaluations are not designed to detect. Our results reinforce the benefits of this type of modeling in risk evaluations, especially related to time‐varying exposure concentrations. This article is protected by copyright. All rights reserved
... The value of models that link organism-level impacts of chemicals to the responses of a population in ecological risk assessments (ERAs) has been described extensively over the past few decades (Pastorok et al. 2002;Munns et al. 2008;Forbes et al. 2009Forbes et al. , 2015Schmolke, Thorbek, Chapman et al. 2010;Schmolke, Thorbek, DeAngelis et al. 2010;Grimm and Thorbek 2014;Galic and Forbes 2014;Forbes et al. 2016). ...
... Numerous reviews of population models in ERA have highlighted the technical basis, added value, and best practices in developing models, and all agree on the need for more comprehensive guidance to assist risk assessors in developing models for ERAs (Pastorok et al. 2002;Forbes et al. 2009Forbes et al. , 2016Schmolke, Thorbek, Chapman et al. 2010). We develop a framework that will help risk assessors identify appropriate population model complexity for an ERA objective as it pertains to the trade-offs among generality, realism, and precision of both the assessment and the model. ...
The value of models that link organism-level impacts to the responses of a population in ecological risk assessments (ERA) has been demonstrated extensively over the past few decades. There is little debate about the utility of these models to translate multiple organism-level endpoints into a holistic interpretation of effect to the population; however, there continues to be a struggle for actual application of these models as a common practice in ERA. While general frameworks for developing models for ERA have been proposed, there is limited guidance on when models should be used, in what form, and how to interpret model output to inform the risk manager's decision. We propose a framework for developing and applying population models in regulatory decision making that focuses on tradeoffs of generality, realism, and precision for both ERAs and models. We approach the framework development from the perspective of regulators aimed at defining the needs of specific models commensurate with the assessment objective. We explore why models are not widely used by comparing their requirements and limitations with the needs of regulators. Using a series of case studies under specific regulatory frameworks, we classify ERA objectives by tradeoffs of generality, realism, and precision and demonstrate how the output of population models developed with these same tradeoffs informs the ERA objective. We examine attributes for both assessments and models that aid in the discussion of these trade-offs. The proposed framework will assist risk assessors and managers to identify models of appropriate complexity and to understand the utility and limitations of a model's output and associated uncertainty in the context of their assessment goals. This article is protected by copyright. All rights reserved.
... Further, risk projections also differed among species exposed to the same insecticide. Results from the TIM/ MCnest model offer additional insight, compared to RQs, when information about the magnitude of risk or the probability of adverse effects [17,42,43] is needed for higher tier risk assessments. Another important advantage of the model we describe here is that it allows risk Relative risk of pesticides assessors to rationally combine both acute and chronic effects into a single unified measure of risk (percent reduction in annual fecundity) that gives appropriate weights to acute versus chronic effects. ...
... Another advantage of the integrated TIM/MCnest model is that it provides results (fledglings per female per breeding season) that can be used directly as an input for population models to predict population-level response to insecticide exposure in birds. This methodological need has long been identified [45][46][47], but has been difficult to implement [43], especially given the limitations to standard toxicity data [16,17,42]. The model does so by translating standard toxicity data into endpoints that are relevant to life cycles of birds. ...
Insecticide usage in the United States is ubiquitous in urban, suburban, and rural environments. There is accumulating evidence that insecticides adversely affect non-target wildlife species, including birds, causing mortality, reproductive impairment, and indirect effects through loss of prey base, and the type and magnitude of such effects differs by chemical class, or mode of action. In evaluating data for an insecticide registration application and for registration review, scientists at the United States Environmental Protection Agency (USEPA) assess the fate of the insecticide and the risk the insecticide poses to the environment and non-target wildlife. Current USEPA risk assessments for pesticides generally rely on endpoints from laboratory based toxicity studies focused on groups of individuals and do not directly assess population-level endpoints. In this paper, we present a mechanistic model, which allows risk assessors to estimate the effects of insecticide exposure on the survival and seasonal productivity of birds known to forage in agricultural fields during their breeding season. This model relies on individual-based toxicity data and translates effects into endpoints meaningful at the population level (i.e., magnitude of mortality and reproductive impairment). The model was created from two existing USEPA avian risk assessment models, the Terrestrial Investigation Model (TIM v.3.0) and the Markov Chain Nest Productivity model (MCnest). The integrated TIM/MCnest model was used to assess the relative risk of 12 insecticides applied via aerial spray to control corn pests on a suite of 31 avian species known to forage in cornfields in agroecosystems of the Midwest, USA. We found extensive differences in risk to birds among insecticides, with chlorpyrifos and malathion (organophosphates) generally posing the greatest risk, and bifenthrin and λ-cyhalothrin (pyrethroids) posing the least risk. Comparative sensitivity analysis across the 31 species showed that ecological trait parameters related to the timing of breeding and reproductive output per nest attempt offered the greatest explanatory power for predicting the magnitude of risk. An important advantage of TIM/MCnest is that it allows risk assessors to rationally combine both acute (lethal) and chronic (reproductive) effects into a single unified measure of risk.
... Fortunately, Individual-based modeling approaches (IBMs), where population level characteristics emerge from heterogeneous individuals interacting with their biotic and abiotic environments, are designed to facilitate this process. Therefore, there has recently been considerable growth in leveraging IBMs approaches across ecological risk assessment [2] [3][4] [5]. This increase stems from the improved predictive power of such modeling approaches in recent years and also recently issued appeals for more realistic evaluation of potential risks of contaminants [3] [6]. ...
... Therefore, there has recently been considerable growth in leveraging IBMs approaches across ecological risk assessment [2] [3][4] [5]. This increase stems from the improved predictive power of such modeling approaches in recent years and also recently issued appeals for more realistic evaluation of potential risks of contaminants [3] [6]. By including a detailed consideration of spatial variation, demographic stochasticity, and population dynamics emerging from unique individuals interacting with each other and with their environment, IBMs pave the way for ecological realism and thereby improving their corresponding predictabilities [7][8] [9]. ...
Polychlorinated biphenyls (PCBs) are classified as one the most extremely regulated
anthropogenic contaminants and they have been deeply probed in aquatic ecosystems.
However, there is very limited understanding of the population level effects of exposure to
PCBs on terrestrial animal species and this has been unanimously indicated as a critical gap in
ecological risk assessment. To bridge this information gap, we integrated an individual-based
model (IBM) framework into toxicokinetics resulting in a deeper ecological insight to simulate
the accumulation of a hypothetical PCB in a terrestrial three-level food chain at the population
level. We then validated our simulated system utilizing the observed field bioaccumulation
factors in a well-studied terrestrial prey-predator, caribou-wolf.
Key findings of the present study indicate that in a PCB-contaminated environment, where all
food sources contain some amount of contaminants, producing more offspring results in lower
toxic concentration in herbivores (prey) and higher concentration in carnivores (predator). Our
novel contribution in this work is that we have achieved a validated system that enables us to
investigate toxicokinetics in any animal species involved in a prey-predation interaction by
providing lipid, non-lipid, and water fractions in their bodies. Additionally, we demonstrated
how using IBM modelling approach could facilitate ecological risk assessment by offering
detailed information of generations spanning as many years as required.
Keywords: Individual-based model, Toxicokinetics, Ecological risk, Population-level
assessment, and Polychlorinated biphenyls.
... Finally, the obstacle degree model was used to calculate the obstacle degree of each evaluation index in the ecological health assessment of the Yinchuan Plain. In the research process, it was found that the entropy weight method [26], comprehensive evaluation index model [52,53], and obstacle degree model [54,55] were widely used in international wetland ecological health assessment and other related fields [56][57][58], indicating that the data processing and application methods of are reliable and effective and can systematically reflect wetland ecological health status, which is suitable for the health assessment of wetland ecological restoration and protection in the world's major river basins and also applicable to the related fields of ecological health assessment. ...
Drawing heavily upon the Sustainable Development Goals (SDGs), an SDG–pressure–state–response (PSR)–ecological–economic–social (EES) model and an index system for wetland ecosystem health assessment were constructed from the three dimensions of environment, economy, and society. By using the Yinchuan Plain urban wetlands in the Yellow River Basin of China as a case study, their ecological health status from 2000 to 2020 was systematically evaluated by integrating information from remote sensing technology, geographic information technology, field sampling, information entropy (IE), a landscape index, and a Comprehensive Evaluation Index. The results show that the restoration and protection of wetland ecosystems have achieved remarkable results in the Yinchuan Plain. The wetland ecological health index has significantly increased from 0.26 to 0.67, which is an increase of 157.7%, and the health level increased from poor (II) to sub-healthy (IV). Factors restricting the healthy development of wetland ecology in the Yinchuan Plain include wetland construction, investment, population density, the number of tourists, and fertilizer use. The research results show that the wetland restoration and protection have achieved specific environmental, economic, and social results in the Yinchuan Plain. However, we also need to pay attention to increasing the investment in wetland environmental governance, strictly controlling the intensity of land use and the total amount of chemical fertilizer applied in various regions, scientifically carrying out wetland restoration and protection, reasonably coordinating the relationship between environment and society, and providing technical and decision-making support for wetland management and protection. This study provides a reference for the ecological governance and sustainable development of wetlands in large river basins worldwide.
... Risk assessment (RA) of the pesticides before registration is an approach under-action in many regulatory bodies around the world, especially in the EU countries (Forbes et al. 2009;Galic et al. 2010). Model based risk assessment of plant protection products before registration helps the evaluation process before allowing it to be applied. ...
Pesticide registration is an activity often not supported with proper risk assessment procedures in developing countries like Ethiopia. In this study, we tried the PRIMET (Pesticide Risks in the Tropics for Man, Environment and Trade), a tool developed to assess the risks to non-target protection goals believed to assist the pesticide registration and monitoring activities in Ethiopia. For this study, seven pesticides ( imdacloprid, difenoconazole, metalaxyl, dimethoate, thiamethoxam, nicosulfuron and bupirimate) were selected randomly and their data of physico-chemical characteristics, toxicological information and pesticide use were mined from either the information given on the dossier or PPDB (Pesticides Properties Database). Results indicated that imidacloprid , dimethoate and thiamethoxam are highly risky to bees when bee hives are present inside the field of the sprayed crop, while Thiamethoxam is highly risky at when hives are present in and off field crop situation. Another outcome was that imidacloprid and dimethoate are expected to have high acute risk for birds, while difenoconazole, metalaxyl, dimethoate showed high acute and chronic risks to the aquatic ecosystem, respectively. Future studies should give emphasis on how the results of risk assessment can be practical to help the registration processes and how the results are compared with actual measurement values.
... Several BSM control measures have been reported for the last three decades, including cultural control practices such as adjustment of planting date (Muleke et al., 2013), crop rotation, and associated cropping (Abate and Ampofo, 1996;Amoabeng et al., 2014), earthing or hilling up soil around the stem of seedlings (Forbes et al., 2009), mulching and fertilizer applications (Gogo et al., 2012), cultivation of resistant crop varieties (Tsedeke, 1990;Ampofo, 1993;Ampofo and Massomo, 1998;Kiptoo et al., 2016) and use of seed dressing insecticides (Abate, 1991;Williamson et al., 2008;Uburyo, 2016). In this regard, the inclination of the year after year production of common bean in Ethiopia asked for a BSM control strategy mainly towards the use of insecticides either as a seed treatment or foliar application since cultural approaches could merely control BSM to a limited extent. ...
Bean stem maggot (BSM) is one of the main threatening insect pests that cause significant bean plant mortalities and associated grain yield reductions. The field research work was conducted for three successive years (2018 - 2020) in Burji, southern Ethiopia, to decide the effects of insecticide seed treatment in reducing bean plant mortality and severity/damage caused by BSM and enhancing the grain yield of common bean. The research contained seven treatments and was arrayed in a randomized complete block design with three replicas. In 2018, the lowest seedling mortality (SM) (11.78%) and matured plant mortality (MPM) (21.89%) were registered from Diazinone-treated plots. However, it was not statistically varied from Thiram + Carbofuran (13.33% for SM and 22.22% for MPM). Bean seeds treated with Diazinon considerably reduced initial percent severity index (PSIi) by 79.79% and final percent severity index (PSIf) by 79.98%, followed by Thiram + Carbofuran with PSIi by 55.67% and PSIf by 76.98% over untreated plots. Lowest total number of larvae (TNL) (15.00 and 22.67) and pupae (TNP) (11.00 and 13.67) were noted from Diazinone and Thiram + Carbofu-ran, in that order. Comparable fashions for SM, MPM, PSIi, PSIf, TNL, and TNP were encoun-tered for these insecticides in 2019 and 2020. Grain yields of 2229.37 and 2213.39 kg ha-1 (in 2018) and 2648.29 and 2503.20 kg ha-1 (in 2020) were attained from Diazinone and Thiram + Carbofuran, respectively. Monetary analysis also affirmed that Diazinone ($126,429.52 ha-1) and Thiram + Carbofuran ($122,241.67 ha-1) led to a higher monitory advantage over untreat-ed control and other insecticides. Therefore, Diazinon and Thiram + Carbofuran, one of them as an alternative option, could be advised as a seed treatment to the growers for efficient control of BSM and optimization of grain yield.
... Optimum to higher plant density has positive effects into decreased 10% -20% common bean virus incidence transmitted by aphids [69]. Forbes et al. [33] described the use of straw and mustard mulches which can reduce bean stem maggot up to 80%. Since these methods are safe and cheap, there is a need of conducting detailed research to understand mechanism involved in providing protection and hence advocating well their usage. ...
Common bean (Phaseolus vulgaris L.) production in Ethiopia is injured by several insect pest and diseases. The major insect pests which attack common bean in pre and post harvests are the bean maggot (Ophiomyia phaseoli), bruchids, Z. subfasciatus, C. maculates, ootheca (Ootheca bennigseni) and aphids (Aphis fabae). These pests affected yield and yield components of common bean through direct and indirect of the total production. Some insects like Aphid is used as the way of transmitting other diseases like mosaic virus from plant to plants, in addition to direct reducing common bean production. The second constraints of common bean production under biotic is diseases virulence of fungus, bacteria and viruses. The important diseases are angular leaf spot, anthracnose, rust, bacterial blight, and mosaic, halo blight and ascochyta blight causes significant yield losses. Completely free of pests production is very difficult in the world of agriculture, while reducing effects as level of under economic importances. Minimizing the losses caused from insect pests and diseases possible through several managements such as; cultural, biological, botanical, and chemical, and integration control methods developed. Further, to assure ecofriendly management techniques; integrated management, botanic and biological control are prioritized than chemical control methods. The main targets of current and future protection plan should be more balances to the natural system balancing than yield improvements. Thus, more encourages to control though ingratiation and economic friendly ways. Therefore, this review was revised the major common bean insect pests and diseases in the pre and post harvest, their hosts, biological and recommended management system were portrayed.
... The Table 1. Methods refers to general methodological papers not necessarily related to PPP dynamics recorded from 2010 onwards correlates with the structuring of a community of European and North American researchers, both academic and industrial, on ecological modeling for regulatory chemical risk assessments (LEM-TOX workshop 2007Forbes et al. 2009 For instance, the European CREAM project (https://creamitn.eu/) was responsible for a strong increase in papers on TKTD and population models in PPP effect modeling in this period. The agrochemical industry has invested heavily in this dynamics, signing nearly 40% of the publications on PPP population models since 2011, whereas before this date it was practically absent from the authorship (less than 10%). ...
A wide diversity of plant protection products (PPP) is used for crop protection leading to the contamination of soil, water, and air, which can have ecotoxicological impacts on living organisms. It is inconceivable to study the effects of each compound on each species from each compartment, experimental studies being time consuming and cost prohibitive, and animal testing having to be avoided. Therefore, numerous models are developed to assess PPP ecotoxicological effects. Our objective was to provide an overview of the modeling approaches enabling the assessment of PPP effects (including biopesticides) on the biota. Six categories of models were inventoried: (Q)SAR, DR and TKTD, population, multi-species, landscape, and mixture models. They were developed for various species (terrestrial and aquatic vertebrates and invertebrates, primary producers, micro-organisms) belonging to diverse environmental compartments, to address different goals (e.g., species sensitivity or PPP bioaccumulation assessment, ecosystem services protection). Among them, mechanistic models are increasingly recognized by EFSA for PPP regulatory risk assessment but, to date, remain not considered in notified guidance documents. The strengths and limits of the reviewed models are discussed together with improvement avenues (multigenerational effects, multiple biotic and abiotic stressors). This review also underlines a lack of model testing by means of field data and of sensitivity and uncertainty analyses. Accurate and robust modeling of PPP effects and other stressors on living organisms, from their application in the field to their functional consequences on the ecosystems at different scales of time and space, would help going toward a more sustainable management of the environment. [Figure not available: see fulltext.]. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
... Mechanistic effects models (MEMs) aim to simulate the mechanisms by which chemicals affect individuals, populations and communities (Grimm and Martin, 2013). This is an appealing prospect with great potential for use in ecological risk assessment (ERA) of chemicals such as pesticides (Forbes et al., 2009;Forbes and Calow, 2012). Simulating underlying processes confers several advantages over traditional analysis of data from laboratory-based toxicity studies and extrapolations to field scenarios based on summary statistics. ...
A major limitation of dietary toxicity studies on rodents is that food consumption often differs between treatments. The control treatment serves as a reference of how animals would have grown if not for the toxicant in their diet, but this comparison unavoidably conflates the effects of toxicity and feeding rate on body weight over time. A key advantage of toxicity models based on dynamic energy budget theory (DEB) is that chemical stress and food consumption are separate model inputs, so their effects on growth rate can be separated. To reduce data requirements, DEB convention is to derive a simplified feeding input, f, from food availability; its value ranges from zero (starvation) to one (food available ad libitum). Observed food consumption in dietary toxicity studies shows that, even in the control treatment, rats limit their food consumption, contradicting DEB assumptions regarding feeding rate. Relatively little work has focused on addressing this mismatch, but accurately modelling the effects of food intake on growth rate is essential for the effects of toxicity to be isolated. This can provide greater insight into the results of chronic toxicity studies and allows accurate extrapolation of toxic effects from laboratory data. Here we trial a new method for calculating f, based on the observed relationships between food consumption and body size in laboratory rats. We compare model results with those of the conventional DEB method and a previous effort to calculate f using observed food consumption data. Our results showed that the new method improved model accuracy while modelled reserve dynamics closely followed observed body fat percentage over time. The new method assumes that digestive efficiency increases with body size. Verifying this relationship through data collection would strengthen the basis of DEB theory and support the case for its use in ecological risk assessment.
... Several BSM control measures have been reported for the last three decades, including cultural control practices such as adjustment of planting date (Muleke et al., 2013), crop rotation, and associated cropping (Abate and Ampofo, 1996;Amoabeng et al., 2014), earthing or hilling up soil around the stem of seedlings (Forbes et al., 2009), mulching and fertilizer applications (Gogo et al., 2012), cultivation of resistant crop varieties (Tsedeke, 1990;Ampofo, 1993;Ampofo and Massomo, 1998;Kiptoo et al., 2016) and use of seed dressing insecticides (Abate, 1991;Williamson et al., 2008;Uburyo, 2016). In this regard, the inclination of the year after year production of common bean in Ethiopia asked for a BSM control strategy mainly towards the use of insecticides either as a seed treatment or foliar application since cultural approaches could merely control BSM to a limited extent. ...
... Appendix A. 4 ...
Environmental risk assessment is a key process for the authorization of pesticides, and is subjected to continuous challenges and updates. Current approaches are based on standard scenarios and independent substance-crop assessments. This arrangement does not address the complexity of agricultural ecosystems with mammals feeding on different crops. This work presents a simplified model for regulatory use addressing landscape variability, co-exposure to several pesticides, and predicting the effect on population abundance. The focus is on terrestrial vertebrates and the aim is the identification of the key risk drivers impacting on mid-term population dynamics. The model is parameterized for EU assessments according to the European Food Safety Authority (EFSA) Guidance Document, but can be adapted to other regulatory schemes. The conceptual approach includes two modules: (a) the species population dynamics, and (b) the population impact of pesticide exposure. Population dynamics is modelled through daily survival and seasonal reproductions rates; which are modified in case of pesticide exposure. All variables, parameters, and functions can be modified. The model has been calibrated with ecological data for wild rabbits and brown hares and tested for two herbicides, glyphosate and bromoxynil, using validated toxicity data extracted from EFSA assessments. Results demonstrate that the information available for a regulatory assessment, according to current EU information requirements, is sufficient for predicting the impact and possible consequences at population dynamic levels. The model confirms that agroecological parameters play a key role when assessing the effect of pesticide exposure on population abundance. The integration of laboratory toxicity studies with this simplified landscape model allows for the identification of conditions leading to population vulnerability or resilience. An Annex includes a detailed assessment of the model characteristics according to the EFSA scheme on Good Modelling Practice.
... In recent years it became apparent that the protection goals and environmental risk assessment (ERA) schemes must be shifted from individual toxicity testing and single compound regulation towards a population-level approach and ecosystem services (Forbes et al., 2009;Landis, 2003;Nienstedt et al., 2012). This is because in real agricultural landscapes the anthropogenic stressors mentioned above co-vary, and their effects on NTAs depend on particular landscape configuration and complex spatial and temporal population dynamics, including 'action at a distance' or 'source-sink' phenomenon (Focks et al., 2014;Topping et al., 2014;Topping and Lagisz, 2012;Topping et al., 2020). ...
Intensification of agricultural practices is one of the most important drivers of the dramatic decline of arthropod species. We do not know, however, the relative contribution to decline of different anthropogenic stressors that are part of this process. We used high-resolution dynamic landscape models and advanced spatially-explicit population modelling to estimate the relative importance of insecticide use and landscape structure for population dynamics of a widespread carabid beetle Bembidion lampros. The effects of in-crop mitigation measures through the application of insecticides with reduced lethality, and off-crop mitigation measures by increasing abundance of grassy field margins, were evaluated for the beetle along the gradient of landscape heterogeneity. Reducing the insecticide-driven lethality (from 90 to 10%) had larger positive impacts on beetle density and occupancy than increasing the abundance of field margins in a landscape. The effects of increasing field margins depended on their width and overall abundance in the landscape, but only field margins 4 m wide, applied to at least 40% of fields, resulted in an increase in beetle population density comparable to the scenario with the smallest reduction of insecticide-driven lethality we considered. Our findings suggest the importance of field margins rather as a supporting not stand-alone mitigation measure, as they generally improved effects of reduction of insecticide-driven lethality. Therefore, adding sufficiently broad off-field habitats should help to maintain viable beetle populations in agricultural landscapes even with moderate use of insecticides. In general, the less persistent the insecticides are in the environment, the larger positive impacts of applied mitigation measures on beetle populations were found. We also showed that the effectiveness of applied mitigation measures strongly depends on landscape and farmland heterogeneity. Thus, to achieve the same management or mitigation target in different landscapes might require different strategies.
... In the last two decades, there have been multiple initiatives to increase the use of mechanistic models in ERA Thorbek et al., 2010). Chipps and Wahl (2008) recommended focusing on model evaluation, fostering interactions between model developers and model users, and reducing uncertainty in modeling applications for guiding management. ...
Population models can provide valuable tools for Ecological Risk Assessment (ERA). A growing amount of work on model development and documentation is now available to guide modelers and risk assessors to address different ERA questions. However, there remain misconceptions about population models for ERA, and communication between regulators and modelers can still be hindered by a lack of clarity in the underlying formalism, implementation, and complexity of different model types. In particular, there is confusion about differences among types of models and the implications of including or ignoring interactions of organisms with each other and their environment. In this review, we provide an overview of the key features represented in population models of relevance for ERA, which include density dependence, spatial heterogeneity, external drivers, stochasticity, life-history traits, behavior, energetics, and how exposure and effects are integrated in the models. We differentiate three broadly defined population model types (unstructured, structured, and agent-based) and explain how they can represent these key features. Depending on the ERA context, some model features will be more important than others, and this can inform model-type choice, how features are implemented, and, possibly the collection of additional data. We show that nearly all features can be included irrespective of formalization, but some features are more or less easily incorporated in certain model types. We also analyze how the key features have been used in published population models implemented as unstructured, structured, and agent-based models. The overall aim of this review is to increase confidence and understanding by model users and evaluators when considering the potential and adequacy of population models for use in ERA.
... 3 This is an appealing prospect with great potential for use in ERA of pesticides. 8,9 By focusing on the underlying processes, modeling techniques can add ecological realism to extrapolations and even reduce animal testing requirements. 10 Accounting for the mismatch in exposure between laboratory and field 11 was identified as one of five key obstacles to long-term risk assessment of pesticides for mammals (along with selection of suitable toxicity end points, extrapolation of toxicity between species, exposure assessment, and evaluation of population level effects 12 ). ...
Ecological risk assessment is carried out for chemicals such as pesticides before they are released into the environment. Such risk assessment currently relies on summary statistics gathered in standardized laboratory studies. However, these statistics extract only limited information and depend on duration of exposure. Their extrapolation to realistic ecological scenarios is inherently limited. Mechanistic effect models simulate the processes underlying toxicity and so have the potential to overcome these issues. Toxicokinetic–toxicodynamic (TK–TD) models operate at the individual level, predicting the internal concentration of a chemical over time and the stress it places on an organism. TK–TD models are particularly suited to addressing the difference in exposure patterns between laboratory (constant) and field (variable) scenarios. So far, few studies have sought to predict sublethal effects of pesticide exposure to wild mammals in the field, even though such effects are of particular interest with respect to longer term exposure. We developed a TK–TD model based on the dynamic energy budget (DEB) theory, which can be parametrized and tested solely using standard regulatory studies. We demonstrate that this approach can be used effectively to predict toxic effects on the body weight of rats over time. Model predictions separate the impacts of feeding avoidance and toxic action, highlighting which was the primary driver of effects on growth. Such information is relevant to the ecological risk posed by a compound because in the environment alternative food sources may or may not be available to focal species. While this study focused on a single end point, growth, this approach could be expanded to include reproductive output. The framework developed is simple to use and could be of great utility for ecological and toxicological research as well as to risk assessors in industry and regulatory agencies.
... Mechanistic effects models are increasingly recognized as valuable tools to assess risks of pesticides to nontarget organisms and communities because they can combine information on the biology of the modeled system with exposure and effects, and judge risks on the level of organization of interest (Forbes et al. 2009;Raimondo et al. 2018). For the gauging of risks of pesticides to honey bees, potential effects on colonies are of interest; however, the mechanistic link among exposures, toxicological findings in laboratory tests on larvae and adult bees, and long-term effects on colonies are not well understood (Henry et al. 2017;Sponsler and Johnson 2017). ...
Discerning potential effects of insecticides on honey bee colonies in field studies conducted under realistic conditions can be challenging because of concurrent interactions with other environmental conditions. Honey bee colony models can control exposures and other environmental factors, as well as assess links among pollen and nectar residues in the landscape, their influx into the colony, and the resulting exposures and effects on bees at different developmental stages. We extended the colony model BEEHAVE to represent exposure to the insecticide clothianidin via residues in pollen from treated cornfields set in real agricultural landscapes in the US Midwest. We assessed their potential risks to honey bee colonies over a 1-yr cycle. Clothianidin effects on colony strength were only observed if unrealistically high residue levels in the pollen were simulated. The landscape composition significantly impacted the collection of pollen (residue exposure) fromthe cornfields, resulting in higher colony-level effects in landscapes with lower proportions of semi-natural land. The application of the extended BEEHAVE model with a pollen exposure-effects module provides a case study for the application of a mechanistic honey bee colony model in pesticide risk assessment integrating the impact of a range of landscape compositions.
... Our findings further demonstrate the importance of understanding life-history traits, including reproductive strategies and behaviours, and their density-dependence, when assessing the potential population-level risks of EDCs. obtained from traditional regulatory testing, and population effects (Forbes et al., 2009;Hommen et al., 2010;Thorbek et al., 2010) and to support more realistic ERAs. Matrix models are currently the most common method for analysing the effects of toxicant exposure on fish populations (e.g. Brown et al., 2014;Ibrahim et al., 2014;Miller and Ankley, 2004) due to their minimal data requirements, but they have limited ability to incorporate complex behaviours and density-dependent regulation (Caswell, 2001). ...
The effects of toxicant exposure on individuals captured in standard environmental risk assessments (ERA) do not necessarily translate proportionally into effects at the population-level. Population models can incorporate population resilience, physiological susceptibility, and likelihood of exposure, and can therefore be employed to extrapolate from individual-to population-level effects in ERA. Here, we present the development of an individual based model (IBM) for the three-spined stickleback (Gasterosteus aculeatus) and its application in assessing population-level effects of disrupted male breeding behaviour after exposure to the anti-androgenic pesticide, fenitrothion. The stickleback is abundant in marine, brackish, and freshwater systems throughout Europe and their complex breeding strategy makes wild populations potentially vulnerable to the effects of endocrine disrupting chemicals (EDCs). Modelled population dynamics matched those of a UK field population and the IBM is therefore considered to be representative of a natural population. Literature derived dose-response relationships of fenitrothion-induced disruption of male breeding behaviours were applied in the IBM to assess population-level impacts. The modelled population was exposed to fenitrothion under both continuous (worst-case) and intermittent (realistic) exposure patterns and population recovery was assessed. The results suggest that disruption of male breeding behaviours at the individual-level cause impacts on population abundance under both fenitrothion exposure regimes; however, density-dependent processes can compensate for some of these effects, particularly for an intermittent exposure scenario. Our findings further demonstrate the importance of understanding life-history traits, including reproductive strategies and behaviours, and their density-dependence, when assessing the potential population-level risks of EDCs.
... The subregions involving bistability in panels (b)-(c) are highlighted of view. The development of ecotoxicological models over the past decades has significantly contributed to understanding and evaluating the effects of toxin exposure (Freedman and Shukla 1991;Luna and Hallam 1987;Thieme 2003;Thomas et al. 1996;Luna 1984, 1990;Forbes et al. 2001Forbes et al. , 2009. Many current population models in ecotoxicology that use data from individual-based studies do not consider the interactions between different species. ...
When two competing species are simultaneously exposed in a polluted environment, one species may be more vulnerable to toxins than the other. To study the impact of environmental toxins on competition dynamics of two species, we develop a toxin-dependent competition model that incorporates both direct and indirect toxic effects on the species. The direct effects of toxins typically reduce population abundance by increasing mortality and reducing reproduction. However, the indirect effects, which are mediated through competitive interactions, may lead to counterintuitive effects. We investigate the toxin-dependent competition model and explore the impact of the interplay between environmental toxins and distinct toxic tolerance of two species on the competition outcomes. The results of theoretical analysis and numerical studies reveal that while high level of toxins is harmful to both species, possibly leading to extirpation of both species, intermediate level of toxins, plus different vulnerabilities of two species to toxins, affect competition outcomes in many counterintuitive ways. It turns out that sublethal toxins may boost coexistence of two species (hence keep species diversity in ecosystems) by reducing the abundance of the predominant species; sublethal toxins may overturn and exchange roles of winner and loser in competition; sublethal toxins may also induce different types of bistability of the competition dynamics, where the competition outcome is doomed to exclusion or coexistence, depending on initial population densities. The theory developed here provides a sound foundation for understanding competitive interactions between two species in a polluted aquatic environment.
... However, none of these methods can provide predictions on how chemical exposure may impact whole populations and are therefore limited as tools when used on their own. Population models, on the other hand, provide tools for extrapolating from individual-to population-level effects, including exploring the importance of interactions between individuals and between individuals and their surrounding environments (Forbes et al. 2009). The choice of model within chemical assessment is dependent upon the specific questions addressed in the risk or hazard assessment schemes and on the level of speciesspecific detail required, how broad an application or ecological scenario is desired, and the amount of empirical data available ( Figure 1). ...
Endocrine active chemicals (EACs) are widespread in freshwater environments and both laboratory and field based studies have shown reproductive effects in fish at environmentally relevant exposures. Environmental risk assessment (ERA) seeks to protect wildlife populations and prospective assessments rely on extrapolation from individual-level effects established for laboratory fish species to populations of wild fish using arbitrary safety factors. Population susceptibility to chemical effects, however, depends on exposure risk, physiological susceptibility, and population resilience, each of which can differ widely between fish species. Population models have significant potential to address these shortfalls and to include individual variability relating to life-history traits, demographic and density-dependent vital rates, and behaviors which arise from inter-organism and organism-environment interactions. Confidence in population models has recently resulted in the EU Commission stating that results derived from reliable models may be considered when assessing the relevance of adverse effects of EACs at the population level. This review critically assesses the potential risks posed by EACs for fish populations, considers the ecological factors influencing these risks and explores the benefits and challenges of applying population modeling (including individual-based modeling) in ERA for EACs in fish. We conclude that population modeling offers a way forward for incorporating greater environmental relevance in assessing the risks of EACs for fishes and for identifying key risk factors through sensitivity analysis. Individual-based models (IBMs) allow for the incorporation of physiological and behavioral endpoints relevant to EAC exposure effects, thus capturing both direct and indirect population-level effects.
... These practices enable the host plant to escape the peak period of pest population. Other methods such as earthing or hilling up soil around the stem of seedlings encourage the development of adventitious roots above the damaged stem resulting in the recovery of plants from the BSM damage (Forbes et al., 2009). Use of chemicals especially seed dressing has also been found effective against BSM (Abate, 1991;Williamson et al., 2008;Uburyo, 2016). ...
Haricot beans are the most important food and cash crop for most of the Ethiopian farmers. However, bean stem maggot (BSM) (Ophiomyia species) is limiting the production of the crop particularly in dry areas. Three species are involved in the infestation: Ophiomyia phaseoli, Ophiomyia spencerlla and Ophiomyia centrosematis. Two decades ago, two management options of BSM were developed and used for the last twenty years. However, there is a tendency of decline in effectiveness. Hence, the current experiment was conducted to see the age effect on the efficacy of BSM resistant varieties (Beshbesh and Melkae) and high bean plant population density (400,000 plants ha-1) (narrow spacing). The experiments were conducted at Shalla (southern Ethiopia) and Omonada (Jimma). Randomized Complete Block Design (RCBD) in three replications was used as the experimental design. The plot size was 10 m × 10 m. The experiment was conducted in 2014 and 2015. The treatments were BSM resistant varieties (Beshbesh and Melkae), high bean plant population, standard check (Awash-1) and Imdalem (seed dressing chemical). For plant population treatment and Imdalem 70% WS seed treatment a bean variety Awash-1 was used as a planting material. Data collected include stand count, seedling percent mortality by BSM, vigorously score, number of larva per plant and grain yield. The data were analyzed using SAS software and mean separation was done by Tukey Standardized Range Test (p<0.05). In all parameters measured, Melkae bean variety become susceptible to BSM and cannot be used any more as a resistant variety. From the result of the experiment, it can be concluded that Beshebeshe bean variety, high plant population and Imdalem seed dressing can be used as integrated management of BSM in Ethiopia in general and study sites in particular.
Key words: Bean stem maggot, Imdalem 70% WS (Imdachloprid), seed dressing, plant population, resistant variety, two decades, proven technology.
... Ecological sensitivity or population resilience can be assessed by relating effects measured on individuals, such as for example a reduction in offspring size, to the population of a species while accounting for the species' life history and dispersal characteristics. Population models provide a way to compare the resilience of populations of different species ( Forbes et al. 2009Forbes et al. , 2010. ...
http://publications.rwth-aachen.de/record/483931
... IBMs have been used in ecology for 40 years (DeAngelis and and are increasingly being used as practical tools in contexts such as wildlife conservation (McLane et al., 2011), ecosystem restoration (Darby et al., 2015;Fitz, 2015;Orem et al., 2014), agro-chemical risk assessment (Forbes et al., 2009;Topping et al., 2015), fisheries management (Rose, 2000) and assessing the wildlife impact of renewable energy developments (Nabe-Nielsen et al., 2014 Stillman andGoss-Custard, 2010). They have several advantages over phenomenological models in such contexts (Table 1), including the ability to predict the consequences of different management scenarios, so that decision-makers can visualise the outcomes of alternative courses of action. ...
Complex computer models are used to predict how ecological systems respond to changing environmental conditions or management actions. Communicating these complex models to non-scientists is challenging, but necessary, because decision-makers and other end users need to understand, accept, and use the models and their predictions. Despite the importance of communicating effectively with end users, there is little guidance available as to how this may be achieved. Here, we review the challenges typically encountered by modellers attempting to communicate complex models and their outputs to managers and other non-scientist end users. We discuss the implications of failing to communicate effectively in each case. We then suggest a general approach for communicating with non-scientist end users. We detail the specific elements to be communicated using the example of individual-based models, which are widely used in ecology. We demonstrate that despite their complexity, individual-based models have characteristics that can facilitate communication with non-scientists. The approach we propose is based on our experiences and methods used in other fields, but which until now have not been synthesised or made broadly available to ecologists. Our aim is to facilitate the process of communicating with end users of complex models and encourage more modellers to engage in it by providing a structured approach to the communication process. We argue that developing measures of the effectiveness of communication with end users will help increase the impact of complex models in ecology.
... Difficulties have to be overcome to arrive at the point which more advanced countries have reached at present. Model-based risk assessment has its own general limitations and strengths, and is widely applied for assessing the fate and increasingly also to assess the effects of pesticides for registration purposes in Europe (FOCUS, 2001;Forbes et al., 2009;Galic et al., 2010;Dohmen et al., 2015;De Laender et al., 2014). The development of the integrated fate, effects, and risk assessment model called PRIMET_Registration_Ethiopia_1.1 is believed to take the current registration system in Ethiopia a big step forward. ...
... It also needs to quantify service provision and possible adverse effects from human activities (Galic et al., 2012;Xu et al., 2014). Improved modeling approaches are therefore particularly needed to overcome the limitations of current approaches to ecological risk assessment (Chen et al., 2012;Forbes et al., 2009). ...
... A broad range of ecological models have been applied to chemical risk assessment in the scientific literature, and the use of ecological models in risk assessments is compatible with current regulations of pesticides in Europe [3] and the United States. Furthermore, ecological models have the potential to reduce animal testing [2,7]. One would therefore expect ample motivation for using ecological modeling in support of risk assessments of pesticides. ...
Ecological risk assessments of pesticides usually focus on risk at the level of individuals, and are carried out by comparing exposure and toxicological endpoints. However, in most cases the protection goal is populations rather than individuals. On the population level, effects of pesticides depend not only on exposure and toxicity, but also on factors such as life history characteristics, population structure, timing of application, presence of refuges in time and space, and landscape structure. Ecological models can integrate such factors and have the potential to become important tools for the prediction of population-level effects of exposure to pesticides, thus allowing extrapolations, for example, from laboratory to field. Indeed, a broad range of ecological models have been applied to chemical risk assessment in the scientific literature, but so far such models have only rarely been used to support regulatory risk assessments of pesticides. To better understand the reasons for this situation, the current modeling practice in this field was assessed in the present study. The scientific literature was searched for relevant models and assessed according to nine characteristics: model type, model complexity, toxicity measure, exposure pattern, other factors, taxonomic group, risk assessment endpoint, parameterization, and model evaluation. The present study found that, although most models were of a high scientific standard, many of them would need modification before they are suitable for regulatory risk assessments. The main shortcomings of currently available models in the context of regulatory pesticide risk assessments were identified. When ecological models are applied to regulatory risk assessments, we recommend reviewing these models according to the nine characteristics evaluated here.
... Despite this broad coverage provided by experimental protocols, not every situation can be tested experimentally. Mechanistic effect models (MEMs) have therefore been suggested as useful tools to complement experimentation in higher-tier risk assessments (e.g., Brock et al. 2009;Forbes et al. 2009;Galic et al. 2010;Hommen et al. 2010;Schmolke et al. 2010;EFSA 2010;DG SANCO 2012;EFSA 2013a;EFSA 2013b;Fischer and Moriarty 2014). The MEMs comprise ecological models, such as population models, and organism-level effect models, such as toxicokinetictoxicodynamic (TK-TD) models. ...
Mechanistic effect models (MEMs) are useful tools for ecological risk assessment of chemicals to complement experimentation. However, there are currently no recommendations for how to use them in risk assessments. Therefore, the SETAC MODELINK workshop aimed at providing guidance for when and how to apply MEMs in regulatory risk assessments. The workshop focused on risk assessment of plant protection products under Regulation (EC) No 1107/2009 using MEMs at the organism- and population levels. Realistic applications of MEMs were demonstrated in six case studies covering assessments for plants, invertebrates and vertebrates in aquatic and terrestrial habitats. From the case studies and their evaluation, 12 recommendations on the future use of MEMs were formulated, addressing the issues of how to translate specific protection goals into workable questions, how to select species and scenarios to be modelled, and where and how to fit MEMs into current and future risk assessment schemes. The most important recommendations are: protection goals should be made more quantitative; the species to be modelled must be vulnerable not only regarding toxic effects but also regarding their life history and dispersal traits; the models should be as realistic as possible for a specific risk assessment question, and the level of conservatism required for a specific risk assessment should be reached by designing appropriately conservative environmental and exposure scenarios; scenarios should include different regions of the EU and different crops; in the long run, generic MEMs covering relevant species based on representative scenarios should be developed, which will require EU-level joint initiatives of all stakeholders involved. The main conclusion from the MODELINK workshop is that the considerable effort required for making MEMs an integral part of environmental risk assessment of pesticides is worthwhile because it will make risk assessments not only more ecologically relevant and less uncertain but also more comprehensive, coherent, and cost effective. This article is protected by copyright. All rights reserved.
... A number of articles address the more general limitations and strengths of models in the risk assessment (Forbes et al. 2009;Galic et al. 2010). We refer to these publications and refrain from repeating such aspects within this publication. ...
Standard risk assessment of plant protection products (PPP) combines 'worst-case' exposure scenarios with effect thresholds using assessment (safety) factors to account for uncertainties. If needed, risks can be addressed applying more realistic conditions at higher tiers, which refine exposure and/or effect assessments using additional data. However, it is not possible to investigate the wide range of potential scenarios experimentally. In contrast, ecotoxicological mechanistic effect models do allow addressing a multitude of scenarios. Furthermore, they may aid the interpretation of experiments such as mesocosm studies, allowing extrapolation to conditions not covered in experiments. Here we explore how to use mechanistic effect models in the aquatic risk assessment of a model insecticide (Modelmethrin), applied several times per season, but rapidly dissipating between applications. The case study focuses on potential effects on aquatic arthropods, the most sensitive group for this substance. The models provide information on the impact of a number of short exposure pulses on sensitive/vulnerable populations and, when impacted, assess recovery. The species to model were selected based on their sensitivity as in laboratory and field (mesocosm) studies. The 'GUTS' model, which describes the toxicokinetics and toxicodynamics of chemicals in individuals, was linked to three >individual >based >models (IBM), translating individual survival of sensitive organisms into population level effects. The impact of pulsed insecticide exposures on populations were modeled using the spatially explicit IBM 'MASTEP' for Gammarus pulex, the Chaoborus IBM for populations of Chaoborus crystallinus and the 'IdamP' model for populations of Daphnia magna. The different models were able to predict the potential effects of Modelmethrin applications to key arthropod species inhabiting different aquatic ecosystems; the most sensitive species were significantly impacted unless respective mitigation measures were implemented (buffer zones resulting in reduced exposure). As expected the impact was stronger in shallow ditches as compared to deeper pond scenarios. Furthermore, as expected, recovery depended on factors such as temperature (affecting population growth rate and number of generations) and the frequency of non-impacted systems, respectively the connectivity of aquatic ecosystems. These model predictions were largely in line with field observations and/or the results of a mesocosm study, providing additional evidence on the suitability and reliability of the models for risk assessment purposes. Due to their flexibility, models may predict the likelihood of unacceptable effects - based on previously defined protection goals - for a range of insecticide exposure scenarios and freshwater habitats. This article is protected by copyright. All rights reserved.
This article is protected by copyright. All rights reserved.
... Also [28] demonstrated that planting cereal border around faba bean field reduces the spread of non-persistently bean transmitted virus. The use of straw and mustard mulches can reduce bean stem maggot and other bean insect pest population by up to 80% and 75% [39]. Since these methods are safe and cheap, there is a need of conducting detailed research to understand mechanism involved in providing protection and hence advocating well their usage. ...
Common bean production in Africa suffers from different constrains. The main damage is caused by insect pest infestations in the field. The most common insects pests which attack common bean in the field are the bean stem maggot (Ophiomyia phaseoli), ootheca (Ootheca bennigseni) and aphids (Aphis fabae). Currently, few farmers in Africa are using commercial pesticides for the control of these insect pests. Due to the negative side effects of commercial pesticides to human health and the environment, there is a need for developing and recommending alternative methods such as those involving agronomic and botanical/biological measures in controlling common bean insect pests. This review aim to report the most common insects pests which attack common bean (Phaseolus vulgaris L.) in the field and explore the potential of agronomic, biological and botanical methods as a low-cost, safe and environmentally friendly means of controlling insect pests in legumes.
... Ecological risk assessment focused on the population level gains 1) in robustness by considering consequences of combined effects on several individual endpoints and integrating life history properties of the different species, and 2) in ecological relevancy compared to approaches based on the organism level (Forbes and Calow, 2002;Stark et al., 2004;De Mott et al., 2005;Raimondo et al., 2006). Matrix population models have previously been recognized as valuable tools to predict toxic effects on population dynamics (Forbes et al., 2009;Salice and Miller, 2003;Chandler et al., 2004;Bin-Le and Yaobin, 2009;Charles et al., 2009). The method is promising under the condition that detailed descriptions of life histories and robust datasets on biological effects of ionising radiation are available for reliability of model predictions (Klok and de Roos, 1996). ...
This report describes in details the basic concepts, needs and data treatment for the population modeling approaches that have been implemented under WP-5 dedicated to “ecologically-relevant low doses effects to non-human species” as part of Task 5.1 devoted to the derivation of population-level protection criteria. Two modeling approaches are presented for extrapolating radiation dose effects from individuals to populations of non-human biota.
The first approach, developed as part of the STAR programme, is inspired from methods which are increasingly used in ecotoxicology to address population effects of chemical contaminants. The approach applies Leslie matrix techniques to the case of chronic external gamma irradiation on a range of wildlife species, based on effect data available in the FREDERICA database and interpreted as dose rate response curves. Considered species cover 14 species representing four taxonomic groups (aquatic and soil invertebrates, fish and terrestrial mammals). The strength of the method is its suitability to integrate outcomes of DEBTox applications that will be conducted under Task 5.3 as consequences for population dynamics (limited to the few experimentally tested species).
The second approach evolves from a model specifically developed to address radiation effects at the population level in the European lobster and generalised to some mammalian species during the IAEA programme EMRAS II from 2009 to 2011. The model, based on a set of differential equations describing a simplified life history (with two life stages) with logistic functions for reproduction and mortality and a radiation repair mechanism, is reported in this deliverable with its application to fish and mouse.
Results of simulations are compared and discussed underlying the following conclusions:
(i) Population consequences vary depending on impaired individual endpoints and life history characteristics of exposed species;
(ii) Populations can be more radiosensitive than the most sensitive individual endpoint, as a result of combined slight effects on several individual endpoints;
(iii) The scarcity of data for acute and chronic exposure often makes it necessary to rely on highly speculative extrapolations (e.g. among species, acute to chronic exposures, among radiation types). This point underlines the need to improve our understanding of the mechanisms underlying radiation toxicity, in order to make better use and interpretation of all the available effect data.
(iv) One major limit of the present approaches resides in their incapacity to integrate all the molecular, cellular or histological damages described in exposed organisms. This limit is the cause for one main discrepancy between population-level results and those based on the most sensitive individual endpoints taking account of all sub-individual levels of biological organisation.
Future directions include analysing effects using mechanistic concepts in order to make the best possible use of all available data and defining adequate threshold levels assumed to protect species and/or taxonomic groups according to their life history characteristics.
Ecological risk assessment (ERA) is charged with assessing the likelihood a chemical will have adverse environmental or ecological effects. When assessing the risk of a potential contaminant to biological organisms, ecologists are most concerned with the sustainability of populations of organisms, rather than protecting every individual. However, ERA most commonly relies on data on the effect of a potential contaminant on individuals because these experiments are more feasible than costly population-level exposures. In this work, we address the challenge of extrapolating these individual-level results to predict population-level effects. Previous per-capita population growth rate estimates calculated from individual-level exposures of Daphnia pulicaria to silver nanoparticles (AgNPs) at different food rations predict a critical daily food requirement for daphnid populations exposed to 200 μg/L AgNPs to avoid extinction. To test this, we exposed daphnid populations to the same AgNP concentration at three different food inputs, with the lowest ration close to the extinction threshold predicted from data on individuals. The two populations with the higher food inputs persisted, and the population with the lowest food input went extinct after 50 days but did persist through two generations. We demonstrate that we can extrapolate between these levels of biological organization by parameterizing an individual-level biomass model with data on individuals’ response to AgNPs and using these parameters to predict the outcome for control and AgNP-exposed populations. Key to successful extrapolation is careful modeling of temporal changes in resource density, driven by both the experimental protocols and feedback from the consumer. The implication for ecotoxicology is that estimates of extinction thresholds based on studies of individuals may be reliable predictors of population outcomes, but only with careful treatment of resource dynamics.
A wide diversity of plant protection products (PPP) is used for crop protection leading to the contamination of soil, water, and air, which can have ecotoxicological impacts on living organisms. It is inconceivable to study the effects of each compound on each species from each compartment, experimental studies being time consuming and cost prohibitive, and animal testing having to be avoided. Therefore, numerous models are developed to assess PPP ecotoxicological effects. Our objective was to provide an overview of the modeling approaches enabling the assessment of PPP effects (including biopesticides) on the biota. Six categories ofmodels were inventoried: (Q)SAR, DR and TKTD, population, multi-species, landscape, and mixture models. They were developed for various species (terrestrial and aquatic vertebrates and invertebrates, primary producers, micro-organisms) belonging to diverse environmental compartments, to address different goals (e.g., species sensitivity or PPP bioaccumulation assessment, ecosystem services protection). Among them, mechanistic models are increasingly recognized by EFSA for PPP regulatory risk assessment but, to date, remain not considered in notified guidance documents. The strengths and limits of the reviewed models are discussed together with improvement avenues (multigenerational effects, multiple biotic and abiotic stressors). This review also underlines a lack of model testing by means of field data and of sensitivity and uncertainty analyses. Accurate and robust modeling of PPP effects and other stressors on living organisms, from their application in the field to their functional consequences on the ecosystems at different scales of time and space, would help going toward a more sustainable management of the environment.
Before their placing on the market, the safety of plant protection products (PPP) towards both human and animal health, and the environment has to be assessed using experimental and modelling approaches. Models are crucial tools for PPP risk assessment and some even help to avoid animal testing. This review investigated the use of modelling approaches in the ecotoxicology section of PPP active substance assessment reports prepared by the authorities and opened to consultation from 2011 to 2021 in the European Union. Seven categories of models (Structure-Activity, ToxicoKinetic, ToxicoKinetic-ToxicoDynamic, Species Sensitivity Distribution, population, community and mixture) were searched for into the reports of 317 active substances. At least one model category was found for 44 % of the investigated active substances. The most detected models were Species Sensitivity Distribution, Structure-Activity and ToxicoKinetic for 27, 21 and 15 % of the active substances, respectively. The use of modelling was of particular importance for conventional active substances such as sulfonylurea or carbamates contrary to microorganisms and plant derived substances. This review also highlighted a strong imbalance in model usage among the biological groups considered in the European Regulation (EC) No 1107/2009. For example, models were more often used for aquatic than for terrestrial organisms (e.g., birds, mammals). Finally, a gap between the set of models used in reports and those existing in the literature was observed highlighting the need for the implementation of more sophisticated models into PPP regulation.
Despite over 50 years of research on the use of population models in chemical risk assessment, their practical utility has remained elusive. A novel application and interpretation of ecotoxicological models, Endogenous Lifecycle Models (ELM), is proposed that offers some of the benefits sought from population models, at much lower cost of design, parametrization, and verification. ELMs capture the endogenous lifecycle processes of growth, development, survival, and reproduction and integrate these to estimate and predict expected fitness. Two measures of fitness are proposed as natural model predictions in the context of chemical risk assessment, lifetime reproductive success, and the expected annual propagation of genetic descendants, including self (intrinsic fitness). Six characteristics of the ELM approach are reviewed and illustrated with two ELM examples, the first for a general passerine lifecycle and the second for bald eagle (Haliaeetus leucocephalus). Throughout, the focus is on development of robust qualitative model predictions that depend as little as possible on specific parameter values. Thus, ELMs sacrifice precision to optimize generality in understanding the effects of chemicals across the diversity of avian lifecycles. Notably, the ELM approach integrates naturally with the adverse outcome pathway framework; this integration can be employed as a midtier risk assessment tool when lower tier analyses suggest potential risk.
Abstract. In the last decades the use of pesticides has increased, and with this, the risk that these substances represent for the environment and the human health. With several international agreements, Mexico has committed itself to reduce the risk that results from the use of pesticides; however, so far progress on the subject has not been satisfactory. On the one hand, the programs to evaluate and monitor environmental risk of pesticides have not been fully implemented, among other things, due to financial and technological limitations as well as lack of modeling approaches appropriate to the environmental conditions of tropical areas. On the other hand, risk communication programs have had limited impact. One of the reasons is that, like in other countries, there are great differences between the risk perception of those who communicate the risk and the population. The main objective of this doctoral thesis is to propose methods for risk assessment and the analysis of the risk perceptions on pesticides which facilitate the design of policies related to risk assessment and risk communication. To do so, two studies are presented. The fist one consists in an environmental risk assessment of pesticides. In this study, a modeling approach has been developed and used to calculate
Predicted Environmental Concentrations (PEC) and risk probabilities for freshwater ecosystems in the humid tropics, appropriate in a context of scarce economic and technological resources. The second study is an analysis of the risk perception of different actors involved in the use and management of pesticides. In this second study similarities and differences between experts and lay people are explained from a social science perspective. In the conclusions of this thesis we present, on the one hand, a first advance in the performing of a modeling approach to predict environmental risks of pesticides, and, on the other hand, a conceptual model is proposed that explains the factors that influence the risk perception of experts and lay people about pesticides. Both modeling tools, which may be used independently, form the basis for the design of public policies and for new research on the topic of pesticide risk.
Resumen. En las últimas décadas el uso de plaguicidas ha aumentado, y con ello, los riesgos al ambiente y a la salud humana que esto implica. A través de diversos convenios internacionales, México se ha comprometido a disminuir el riesgo por uso de plaguicidas. No obstante, los avances en esta materia no han sido los requeridos. Por un lado, no se han implementado programas sistemáticos de evaluación y monitoreo de riesgos por el uso de plaguicidas debido, entre otras cosas, a condiciones económicas y tecnológicas limitadas. Por otro lado, los alcances de los programas de comunicación han sido aún muy limitados, tal como sucede en otros países, debido, entre otras razones, a las diferencias en la percepción de riesgos entre quienes comunican el riesgo y la
población. El objetivo central de esta tesis doctoral es el de proponer métodos de evaluación de riesgo ambiental y de análisis de percepción de riesgo por el uso de plaguicidas que apoyen al diseño de políticas en evaluación y comunicación de riesgos. Para ello, se presentan dos estudios. El primero consistente en una evaluación ambiental de riesgo por plaguicidas. En este estudio se desarrolló un enfoque de modelado que, en un contexto de escasos recursos económicos y tecnológicos, sea posible calcular las concentraciones ambientales de plaguicidas y los riesgos que esto implica para la vida acuática. El segundo estudio consistió en un análisis de la percepción de riesgo por plaguicidas de los diferentes actores involucrados en el uso y manejo de plaguicidas. En este segundo estudio se analizaron diferencias y similitudes en la percepción de riesgo por el uso de plaguicidas entre diversos actores involucrados, desde un enfoque de las ciencias sociales. A manera de conclusión en esta tesis doctoral se presenta: por un lado, un primer acercamiento a un método de modelación para predecir los riesgos ambientales en aéreas agrícolas y, por otro lado, un modelo conceptual que explica los factores que influyen en la percepción del riesgo por el uso de plaguicidas. Utilizados de manera independiente, estas herramientas podrían servir de base para el diseño de políticas públicas, así como nuevos estudios en materia de plaguicidas.
The assimilation of population models into Ecological Risk Assessment (ERA) has been hindered by their range of complexity, uncertainty, resource investment, and data availability. Likewise, ensuring that the models address risk assessment objectives has been challenging. Recent research efforts have begun to tackle these challenges by creating an integrated Modeling Framework and Decision Guide to aid the development of population models with respect to ERA objectives and data availability. In the Framework, the trade‐offs associated with the generality, realism, and precision of an assessment are used to guide the development of a population model commensurate with the protection goal. The Decision Guide provides risk assessors with a stepwise process to assist them in developing a conceptual model that is appropriate for the assessment objective and available data. We have merged the Decision Guide and Modeling Framework into a comprehensive approach (Pop‐GUIDE, Population modeling Guidance, Use, Interpretation, and Development for Ecological risk assessment) for the development of population models for ERA that is applicable across regulatory statutes and assessment objectives. In Phase 1 of Pop‐GUIDE, assessors are guided through the trade‐offs of ERA generality, realism, and precision, which are translated into model objectives. In Phase 2, available data are assimilated and characterized as general, realistic, and/or precise. Phase 3 provides a series of dichotomous questions to guide development of a conceptual model that matches the complexity and uncertainty appropriate for the assessment that is in concordance with the available data. This phase guides model developers and users to ensure consistency and transparency of the modeling process. We introduce Pop‐GUIDE as the most comprehensive guidance for population model development provided to date and demonstrate its use through case studies using fish as an example taxon and the US Federal Insecticide Fungicide and Rodenticide Act and Endangered Species Act as example regulatory statutes. This article is protected by copyright. All rights reserved.
Anthropogenic interference the ecosystem unavoidably changes the physical and biological environment. The biodiversity of the Amazon region has been threatened by increased agricultural production and pesticide use. Considering that monitoring pesticides in environments close to their application is one of the ways to preserve the ecosystem, this study investigated the levels of pesticide residues in different environmental compartments (soil, sediment, and water samples). Thirty-one active ingredients of pesticides of different classes were analyzed by UHPLC-MS/MS. For this purpose, we performed quarterly collections in dry and rainy seasons in the region, which helped to evaluate the impact of pesticides on the biodiversity of the study site. Sampling points were the river banks in the area of an agricultural project in Formoso do Araguaia city, Tocantins State. After analysis, we detected the following substances in the water matrix: clomazone, fluazifop-p-butyl, flutolanil, metsulfuron-methyl, propanil, and imidacloprid. Nevertheless, we did not detect any active ingredient in sediment and soil matrices. The active ingredient clomazone was present in all points in the trials, with concentrations reaching up to 0.538 μg L⁻¹. These substances have potential for groundwater contamination. Even at low concentrations in the aquatic ecosystem, these substances can damage human populations and wildlife species, given their toxicological classification. Thus, the study showed an environmental risk of bioaccumulation and/or biomagnification in the region, which may affect environmental biodiversity as well as human health.
Herbicide use is a key element in the current intensification of agricultural production systems that usually leads to increases in crop yield. However, development of theoretical frameworks and tools is necessary to allow for environmental assessment of herbicides. In this chapter, we present a series of elements that should be considered for designing these types of tools. In addition, we describe the structure of RIPEST, a simple model based on fuzzy logic that evaluates the ecotoxicological hazard of pesticides (herbicides, fungicides and insecticides). RIPEST was run using a time series of pesticide use and actual soybean yields from Argentina. Results from this cropping system assessment allows for discussion of the ecotoxicological risk of herbicide use, in particular, and pesticides, in general.
Risk assessment and management of pesticides are directly related to sustainable agriculture concept because, besides playing an important role in intensified agriculture by protecting crops from pests and diseases and reducing competition from weeds, the use of pesticides can cause human health and ecological problems. Several pesticides have been shown to reduce water quality and result in adverse effects to sensitive organisms, aquatic ecosystems, and human health. Pesticides enter water systems through different pathways, and therefore, it is important to understand the environmental behavior and fate of pesticides and assess their potential exposure and associated risks to the environment. Ecological risk assessment—ERA—has been adopted in many countries for regulatory purpose and as basis for management of pesticides. Models can be used during different stages of the ERA process and include fate-exposure models, exposure-effect models, and integrated models. In this chapter, definitions of ERA are stated. Pesticide environmental behavior processes and modeling approaches are briefly discussed. Tools for ecological exposure characterization in the regulatory context of agricultural pesticides concerning surface water and groundwater bodies are presented.
Population models are used as tools in species management and conservation and are increasingly recognized as important tools in pesticide risk assessments. A wide variety of population model applications and resources on modeling techniques, evaluation and documentation can be found in the literature. In this paper, we add to these resources by introducing a systematic, transparent approach to developing population models. The decision guide that we propose is intended to help model developers systematically address data availability for their purpose and the steps that need to be taken in any model development. The resulting conceptual model includes the necessary complexity to address the model purpose on the basis of current understanding and available data.
Current environmental risk assessments (ERA) do not account explicitly for ecological factors (e.g. species composition, temperature or food availability) and multiple stressors. Assessing mixtures of chemical and ecological stressors is needed as well as accounting for variability in environmental conditions and uncertainty of data and models. Here we propose a novel probabilistic ERA framework to overcome these limitations, which focusses on visualising assessment outcomes by construct-ing and interpreting prevalence plots as a quantitative prediction of risk. Key components include environmental scenarios that integrate exposure and ecology, and ecological modelling of relevant endpoints to assess the effect of a combination of stressors. Our illustrative results demonstrate the importance of regional differences in environmental conditions and the confounding interactions of stressors. Using this framework and prevalence plots provides a risk-based approach that combines risk assessment and risk management in a meaningful way and presents a truly mechanistic alternative to the threshold approach. Even whilst research continues to improve the underlying models and data, regulators and decision makers can already use the framework and prevalence plots. The integration of multiple stressors, environmental conditions and variability makes ERA more relevant and realistic.
Ecological risk assessment (ERA) is the process used to evaluate the safety of manufactured chemicals to the environment. Here we review the pros and cons of ERA across levels of biological organization, including suborganismal (e.g., biomarkers), individual, population, community, ecosystem and landscapes levels. Our review revealed that level of biological organization is often related negatively with ease at assessing cause-effect relationships, ease of high-throughput screening of large numbers of chemicals (it is especially easier for suborganismal endpoints), and uncertainty of the ERA because low levels of biological organization tend to have a large distance between their measurement (what is quantified) and assessment endpoints (what is to be protected). In contrast, level of biological organization is often related positively with sensitivity to important negative and positive feedbacks and context dependencies within biological systems, and ease at capturing recovery from adverse contaminant effects. Some endpoints did not show obvious trends across levels of biological organization, such as the use of vertebrate animals in chemical testing and ease at screening large numbers of species, and other factors lacked sufficient data across levels of biological organization, such as repeatability, variability, cost per study and cost per species of effects assessment, the latter of which might be a more defensible way to compare costs of ERAs than cost per study. To compensate for weaknesses of ERA at any particular level of biological organization, we also review mathematical modeling approaches commonly used to extrapolate effects across levels of organization. Finally, we provide recommendations for next generation ERA, submitting that if there is an ideal level of biological organization to conduct ERA, it will only emerge if ERA is approached simultaneously from the bottom of biological organization up as well as from the top down, all while employing mathematical modeling approaches where possible to enhance ERA. Because top-down ERA is unconventional, we also offer some suggestions for how it might be implemented efficaciously. We hope this review helps researchers in the field of ERA fill key information gaps and helps risk assessors identify the best levels of biological organization to conduct ERAs with differing goals.
Ecological risk assessment is the process of evaluating how likely it is that the environment may be impacted as the result of exposure to one or more chemicals and/or other stressors. It is not playing as large a role in environmental management decisions as it should be. A core challenge is that risk assessments often do not relate directly or transparently to protection goals. There have been exciting developments in in vitro testing and high-throughput systems that measure responses to chemicals at molecular and biochemical levels of organization, but the linkage between such responses and impacts of regulatory significance – whole organisms, populations, communities, and ecosystems – are not easily predictable. This article describes some recent developments that are directed at bridging this gap and providing more predictive models that can make robust links between what we typically measure in risk assessments and what we aim to protect.
For a number of scientific and other reasons, the procedures currently used for both
human and ecological risk assessment are anticipated to change substantially over the
next few decades. However, the roadmaps for these changes are expected to be
distinctly different for ecological and human health risk assessments. This opinion focuses
on the future of risk assessment of organic chemicals although many of the techniques
discussed would be applicable to other stressors too.
ECOLOGICAL RISK ASSESSMENT
The approaches in current use for ecological risk assessment are likely to suffice for
regulatory purposes as sufficiently protective for ecosystems. However they lack
environmental realism. This entails high uncertainty on the actual consequences of
environmental contaminations on the ecosystem structure and functions that has to be
addressed by the application of uncertainty/safety/default factors.
The main challenge for ecological risk assessment is to develop tools that take account of
the complexity of the potentially exposed ecosystems and enable assessment of sitespecific
effects.
Exposure considerations
• Verification and harmonization of physico-chemical data is necessary.
• Current models for fate prediction are better suited for apolar compounds. New
and improved models are needed for polar and ionized chemicals and metals.
• Methods are needed to characterize the exposure to nanomaterials
• Criteria and protocols are required for obtaining and comparing monitoring data
especially for evaluating the fate of chemical mixtures, including metabolites and
breakdown products. Data at short temporal resolution (e.g. hours) are needed
for developing/calibrating predictive approaches also in view of the rapid
conversion of chemicals in some compartments of the ecosphere.
New models are necessary for a number of purposes including:
- The development of realistic scenarios, especially to predict temporal and
spatial variations as well as bioavailability of chemicals;
- Assessment of specific organism parameters to extend the applicability of
bioaccumulation models in aquatic and terrestrial systems;
- Description of the food web path of chemicals, especially for terrestrial
systems.
Effects considerations
The Protection of high hierarchical levels of ecological organisation (communities,
ecosystems) is the main goal of environmental protection. Mesocosm data and SSD are
already a powerful tool for improving ecological realism of risk assessment. The
usefulness of molecular approaches in ecological risk assessment remains to be
established. They may be suitable as early warning systems.
Priorities for improvements are:
• The assessment of the effects of variable exposure due to space and time
variability of chemical concentrations.
• The development of improved models to examine the vulnerability of aquatic and
terrestrial ecosystems to different kinds of stressors, particularly for site-specific
risk assessment.
• The improvement of knowledge on the interactions of toxicants with other
environmental factors in natural ecosystems.
• The improvement of the application of trait-based ecological risk assessment.
• The development of ecological models capable to describe and predict direct and
indirect effects of stress factors on structure and functions of ecosystems.
• A concerted action is needed to agree on standard scenarios, ecologically relevant
test species and endpoints, acceptance criteria of ecological models, and to
develop well-tested, flexible models.
• The increased complexity of the assessment would require statistically-based tools
capable to quantitatively assess uncertainties and to improve the transparent use
of these approaches.
HUMAN RISK ASSESSMENT
There is a trend/need to change the basis of risk assessment from the one based on
standard tests to one that is centred on modes of action. A prerequisite for major
advances is the development of improved databases to enable more appropriate test
selection through advancement of in silico approaches – such as (Q)SAR and read-across.
This will require fully validated databases for:
-Effects of chemicals in humans;
-Exposure information;
-Effects in animal models;
-Effects in in vitro models;
-Mode of action information.
Exposure considerations
A paradigm shift is likely from a hazard-driven process to one that is exposure-driven.
Achieving this will require major improvements in the assessment of exposure to
individual chemicals and groups of chemicals.
Priorities for improvement are:
-Advances in the identification and use of biomarkers for exposure;
-Wide availability of low-cost personal monitors;
-Better modelling of external and internal exposure.
Hazard considerations
Major changes are also needed in the identification and characterisation of hazards to
humans. The development of alternatives to using laboratory animals for the
identification and characterisation of hazardous properties of chemicals is a priority
because of the political, ethical, and other pressures to reduce the use of laboratory
animals for testing purposes. In investigations using laboratory animals, increasing
importance should be directed to characterising the mode of action with less emphasis to
endpoints based on histopathological criteria, body and organ weight, and blood
chemistry.
Priorities for improvements are:
• The progressive replacement of in vivo laboratory animal tests by in vitro tests is
critically dependent on the development of in vitro preparations that maintain the
in vivo characteristics of various tissues and organs over long periods (weeks to
months).
• New, more sensitive methods for characterising the effects of chemicals, in
particular genomics, are likely to provide a very important tool for identifying
modes of action which will increasingly become crucial for characterising the risks.
* Quantitative histochemistry and high content cell imaging will be important tools in
linking biochemical changes to morphological (including histopathological) ones
The evolution of toxic effects at a relevant scale is an important challenge for the ecosystem protection. Indeed, pollutants may impact populations over long-term and represent a new evolutionary force which can be adding itself to the natural selection forces. Thereby, it is necessary to acquire knowledge on the phenotypics and genetics changes that may appear in populations submitted to stress over several generations. Usually statistical analyses are performed to analyse such multigenerational studies. The use of a mechanistic mathematical model may provide a way to fully understand the impact of pollutants on the populations' dynamics. Such kind of model allows the integration of biological and toxic processes into the analysis of ecotoxicological data and the assessment of interactions between these processes. The aim of this Ph.D. project was to assess the contributions of the mechanistical modelling to the analysis of evolutionary experiment assessing long-term exposure. To do so, a three step strategy has been developed. Foremost, a multi-generational study was performed to assess the evolution of two populations of the ubiquitous nematode Caenorhabditis elegans in control conditions or exposed to 1.1 mM of uranium. Several generations were selected to assess growth, reproduction, and dose-responses relationships, through exposure to a range of concentrations (from 0 to 1.2 mM U) with all endpoints measured daily. A first statistical analysis was then performed. In a second step, a bioenergetic model adapted to the assessment of ecotoxicological data (DEBtox) was developed on C. elegans. Its numerical behaviour was analysed. Finally, this model was applied to all the selected generations in order to infer parameters values for the two populations and to assess their evolutions. Results highlighted an impact of the uranium starting from 0.4 mM U on both C. elegans' growth and reproduction. Results from the mechanistical analysis indicate this effect is due to an impact on the assimilation of energy from food. Both the mechanistic and the classic approaches highlighted individuals' adaptation to environmental conditions. Despite this, differential evolutions of the individuals from the uranium-selected population were also highlighted. All these results were more in-depth described by the mechanistical analysis. Overall, this work contributes to our knowledge on the effects of pollutants on population dynamics, and demonstrates the contributions of mechanistical modelling which can be applied in other contexts to achieve in fine a better assessment of environmental risks of pollutants.
Current pesticide risk assessment for honey bees is based on laboratory tests and on semi-field and field studies. Risk assessment schemes focus on quotients of the hazard imposed by a compound and the predicted exposure to this compound in the field. This chapter gives a brief introduction into the rationale and approaches of ecological modeling of population dynamics. It presents an example model to demonstrate the potential insights that can be gained from such ecological models, summarizes current modeling practice, and describes recent attempts to establish good modeling practice (GMoP), which is needed to make mechanistic effect models applicable for regulatory risk assessment. It provides an overview of existing models of honey bee colonies and gives recommendations for the potential use of these models for pesticide risk assessment. The chapter briefly discusses how ecological modeling could support risk assessment of non-Apis pollinators.
Introduction, L.W. Barnthouse, W.R. Munns, Jr., and M.T. Sorensen THE MANAGEMENT-SCIENCE INTERFACE Managing Risk to Ecologic Populations, G.R. Biddinger, P. Calow, P. Delorme, G. Harris, B. Hope, B.L. Lin, M. Sorensen, and P. van den Brink Population Protection Goals, C. Menzie, N. Bettinger, A. Fritz, L. Kapustka, H. Regan, V. Moller, and H. Noel SCIENTIFIC ISSUES IN POPULATION-LEVEL ECOLOGIC RISK ASSESSMENT Density Dependence in Ecologic Risk Assessment, S.J. Moe Genetic Variation in Population-Level Ecologic Risk Assessment, D. Nacci and A. Hoffman The Spatial Structure of Populations and Ecologic Risk Assessment, W.G. Landis, and A. Deines What Conservation Biology and Natural Resource Management Can Offer Population-Level Ecologic Risk Assessment, J.A. Gervais and H.M. Regan APPROACHES TO POPULATION-LEVEL ECOLOGIC RISK ASSESSMENT Empiric Approaches to Population-Level Ecologic Risk Assessment, T.M. Carlsen, P.F. Chapman, S. Brassfield, N. Elmegaard, A. Hoffman, W. Landis, S. J. Moe, D. Nacci, H.M. Noel, and J. Spromburg Modeling Approaches to Population-Level Ecologic Risk Assessment, W.R. Munns, Jr., J.A. Gervais, A.A. Hoffman, U. Hommen, D.E. Nacci, M. Nakamaru, R. Sibly, and C.J. Topping A Framework for Population-Level ERA, R. Wentsel, N. Beyer, V. Forbes, S. Maund, and R. Pastorok A PATH FORWARD Issues and Recommendations, W.R. Munns, Jr, L.W. Barnthouse, and M.T. Sorensen REFERENCES AND APPENDICES References Appendix 1 Decision Context Scenarios Appendix 2 Workshop Exercise: Application of 2 Modeling Techniques in a Theoretical Assessment for Agricultural Pesticide Registration Appendix 3 Supplemental Reading List
Management of many species is currently based on an inadequate under- standing of their population dynamics. Lack of age-specific demographic information, particularly for long-lived iteroparous species, has impeded development of useful models. We use a Lefkovitch stage class matrix model, based on a preliminary life table developed by Frazer (1983a), to point to interim management measures and to identify those data most critical to refining our knowledge about the population dynamics of threatened log- gerhead sea turtles (Caretta caretta). Population projections are used to examine the sen- sitivity of Frazer's life table to variations in parameter estimates as well as the likely response of the population to various management alternatives. Current management practices appear to be focused on the least responsive life stage, eggs on nesting-beaches. Alternative protection efforts for juvenile loggerheads, such as using turtle excluder devices (TEDs), may be far more effective.
Individual-based models (IBMs) allow the explicit inclusion of indi-vidual variation in greater detail than do classical differential-equation and difference-equation models. Inclusion of such variation is important for continued progress in ecological and evolutionary theory. We provide a conceptual basis for IBMs by de-scribing five major types of individual variation in IBMs: spatial, ontogenetic, pheno-typic, cognitive, and genetic. IBMs are now used in almost all subfields of ecology and evolutionary biology. We map those subfields and look more closely at selected key papers on fish recruitment, forest dynamics, sympatric speciation, metapopulation dynamics, maintenance of diversity, and species conservation. Theorists are currently divided on whether IBMs represent only a practical tool for extending classical theory to more complex situations, or whether individual-based theory represents a radically new research program. We feel that the tension between these two poles of thinking can be a source of creativity in ecology and evolutionary theory.
The demography of Primula veris, a typical species of the species-rich Mesobromion grasslands, was investigated at two contrasting habitats in Eastern Belgium. Both a forested site and a clear-cut parcel were part of formerly larger calcareous grassland areas. By monitoring the demographic response of the target species, this study attempts to clarify the differences in fecundity, growth and survival between a restored and degraded calcareous grassland site. A study of plant traits showed a decrease in number of flowers, inflorescence stalks and plant size under a closing tree canopy. We surveyed individuals in permanent plots between 1999 and 2001. At the forested site, a first sign of decline included lower proportions of flowering individuals and afterwards an increase in mortality was found. Alternatively, removing canopy resulted in an immediate flowering response and both increased growth and seedling recruitment the year after. At both sites, survival rates strongly depended on the initial state, location and year. Projection matrix analysis revealed large differences in modelled population growth rates between sites and years. Under closing canopy the species showed only slow population decline, but the decrease was larger in the last survey year when higher mortality affected the number of reproductive individuals. Transitions between stages with the highest impact on population growth rate were identified by elasticity analysis. If calcareous grassland is forested, survival of reproductive adults is predicted to be very important for conservation of P. veris populations. However, seedling recruitment needs to be raised to guarantee long-term persistence of the populations. On the other hand, clear-felling of a site for restoration of species-rich calcareous grasslands may result in a rapid recovery of certain species.
In the last decade there has been increasing evidence of amphibian declines from relatively pristine areas. Some declines are hypothesized to be the result of egg mortality caused by factors such as elevated solar UV-B irradiation, chemical pollutants, pathogenic fungi, and climate change. However, the population-level consequences of egg mortality have not been examined explicitly, and may be complicated by density dependence in intervening life-history stages. Here we develop a demographic model for two amphibians with contrasting life-history strategies, Bufo boreas and Ambystoma macrodactylum. We then use the complementary approaches of elasticity and limitation to examine the relationships among stage-specific survival rates, larval-stage density dependence and amphibian population dynamics. Elasticity analyses showed that for a range of density dependence scenarios both species were more sensitive to changes in post-embryonic survival parameters, particularly juvenile survival, than to egg survival, suggesting that mortality of later stages may play an important role in driving declines. Limitation analyses revealed that larval density dependence can dramatically alter the consequences of early mortality, reducing or even reversing the expected population-level effects of egg mortality. Thus, greater focus on later life stages and density dependence is called for to accurately assess how stressors are likely to affect amphibian populations of conservation concern.
The major tools used to make population viability analyses (PVA) quantitative are stochastic models of population dynamics. Since a specially tailored model cannot be developed for every threatened population, generic models have been designed which can be parameterised and analysed by non-modellers. These generic models compromise on detail so that they can be used for a wide range of species. However, generic models have been criticised because they can be employed without the user being fully aware of the concepts, methods, potentials, and limitations of PVA. Here, we present the conception of a new generic software package for metapopulation viability analysis, META-X. This conception is based on three elements, which take into account the criticism of earlier generic PVA models: (1) comparative simulation experiments; (2) an occupancy-type model structure which ignores details of local population dynamics (these details are integrated in external submodels); and (3) a unifying currency to quantify persistence and viability, the intrinsic mean time to extinction. The rationale behind these three elements is explained and demonstrated by exemplary applications of META-X in the three fields for which META-X has been designed: teaching, risk assessment in the field, and planning. The conception of META-X is based on the notion that PVA is a tool to deal with rather than to overcome uncertainty. The purpose of PVA is to produce relative, not absolute, assessments of extinction risk which support, but do not supplant, management decisions.
Simulation models that describe autonomous individual organisms (individual based models, IBM) or agents (agent-based models, ABM) have become a widely used tool, not only in ecology, but also in many other disciplines dealing with complex systems made up of autonomous entities. However, there is no standard protocol for describing such simulation models, which can make them difficult to understand and to duplicate. This paper presents a proposed standard protocol, ODD, for describing IBMs and ABMs, developed and tested by 28 modellers who cover a wide range of fields within ecology. This protocol consists of three blocks (Overview, Design concepts, and Details), which are subdivided into seven elements: Purpose, State variables and scales, Process overview and scheduling, Design concepts, Initialization, Input, and Submodels. We explain which aspects of a model should be described in each element, and we present an example to illustrate the protocol in use. In addition, 19 examples are available in an Online Appendix. We consider ODD as a first step for establishing a more detailed common format of the description of IBMs and ABMs. Once initiated, the protocol will hopefully evolve as it becomes used by a sufficiently large proportion of modellers.
The ecological literature reveals considerable confusion about the meaning of validation in the context of simulation models. The confusion arises as much from semantic and philosophical considerations as from the selection of validation procedures. Validation is not a procedure for testing scientific theory or for certifying the ‘truth’ of current scientific understanding, nor is it a required activity of every modelling project. Validation means that a model is acceptable for its intended use because it meets specified performance requirements.Before validation is undertaken, (1) the purpose of the model, (2) the performance criteria, and (3) the model context must be specified. The validation process can be decomposed into several components: (1) operation, (2) theory, and (3) data. Important concepts needed to understand the model evaluation process are verification, calibration, validation, credibility, and qualification. These terms are defined in a limited technical sense applicable to the evaluation of simulation models, and not as general philosophical concepts. Different tests and standards are applied to the operational, theoretical, and data components. The operational and data components can be validated; the theoretical component cannot.The most common problem with ecological and environmental models is failure to state what the validation criteria are. Criteria must be explicitly stated because there are no universal standards for selecting what test procedures or criteria to use for validation. A test based on comparison of simulated versus observed data is generally included whenever possible. Because the objective and subjective components of validation are not mutually exclusive, disagreements over the meaning of validation can only be resolved by establishing a convention.
Control of animal-born diseases is a major challenge faced by applied ecologists and public health managers. To improve cost-effectiveness, the effort required to control such pathogens needs to be predicted as accurately as possible. In this context, we reviewed the anti-rabies vaccination schemes applied around the world during the past 25 years.
We contrasted predictions from classic approaches based on theoretical population ecology (which governs rabies control to date) with a newly developed individual-based model. Our spatially explicit approach allowed for the reproduction of pattern formation emerging from a pathogen's spread through its host population.
We suggest that a much lower management effort could eliminate the disease than that currently in operation. This is supported by empirical evidence from historic field data. Adapting control measures to the new prediction would save one-third of resources in future control programmes.
The reason for the lower prediction is the spatial structure formed by spreading infections in spatially arranged host populations. It is not the result of technical differences between models.
Synthesis and applications. For diseases predominantly transmitted by neighbourhood interaction, our findings suggest that the emergence of spatial structures facilitates eradication. This may have substantial implications for the cost-effectiveness of existing disease management schemes, and suggests that when planning management strategies consideration must be given to methods that reflect the spatial nature of the pathogen–host system.
Northern right whales (Eubalaena glacialis) were formerly abundant in the northwestern Atlantic, but by 1900 they had been hunted to near extinction. After the end of commercial whaling the population was thought to be recovering slowly; however, evidence indicates that it has been declining since about 1990 (ref. 1). There are now fewer than 300 individuals, and the species may already be functionally extinct owing to demographic stochasticity or the difficulty of females locating mates in the vast Atlantic Ocean (Allee effect). Using a data set containing over 10,000 sightings of photographically identified individuals we estimated trends in right whale demographic parameters since 1980. Here we construct, using these estimates, matrix population models allowing us to analyse the causes of right whale imperilment. Mortality has increased, especially among mother whales, causing declines in population growth rate, life expectancy and the mean lifetime number of reproductive events between the period 1980-1995. Increased mortality of mother whales can explain the declining population size, suggesting that the population is not doomed to extinction as a result of the Allee effect. An analysis of extinction time shows that demographic stochasticity has only a small effect, but preventing the deaths of only two female right whales per year would increase the population growth rate to replacement level.
Measurements of toxicity based on individuals, such as the LC50 (concentration that kills 50% of a population), and effects on reproduction are used extensively in determining ecological risk, in particular, for endangered or threatened species. An underlying assumption is that individual-based toxicity metrics for one species can be directly compared with that for another species. However, this assumption overlooks the fact that different species have different life-history strategies and variables, such as lifespan, time to first reproduction, and number of offspring produced over a lifetime. Using a simple model and laboratory-derived parameter values, we tested the impact of differences in life-history traits on predicted responses to stress. The model predicts the delay in population growth. We compared seven invertebrate species by imposing 50% chronic mortality, 50% reduction of offspring, and both of these effects. The model predicted substantial differences in population delay among all of the species. Furthermore, the intrinsic rate of increase of each population was negatively correlated with the delay in population growth; species with high intrinsic rates of increase were less susceptible to equal levels of stress than species with lower intrinsic rates of increase. These results suggest that the susceptibility of species to pollutants is more complicated than previously thought and that differences in life-history variables must be considered in analyses of population persistence for threatened and endangered species.
• toxicity
• conservation biology
• delay-in-population-growth model
• demography
The aim of this investigation was to find patterns in aquatic invertebrate community composition that are related to the effects of pesticides. Investigations were carried out in 20 central European streams. To reduce the site-specific variation of community descriptors due to environmental factors other than pesticides, species were classified and grouped according to their vulnerability to pesticides. They were classified as species at risk (SPEAR) and species not at risk (SPEnotAR). Ecological traits used to define these groups were sensitivity to toxicants, generation time, migration ability, and presence of aquatic stages during time of maximum pesticide application. Results showed that measured pesticide concentrations of 1:10 of the acute 48-h median lethal concentration (LC50) of Daphnia magna led to a short- and long-term reduction of abundance and number of SPEAR and a corresponding increase in SPEnotAR. Concentrations of 1:100 of the acute 48-h LC50 of D. magna correlated with a long-term change of community composition. However, number and abundance of SPEAR in disturbed stream sections are increased greatly when undisturbed stream sections are present in upstream reaches. This positive influence compensated for the negative effect of high concentrations of pesticides through recolonization. The results emphasize the importance of considering ecological traits and recolonization processes on the landscape level for ecotoxicological risk assessment.
Following a workshop exercise, two models, an individual-based landscape model (IBLM) and a non-spatial life-history model were used to assess the impact of a fictitious insecticide on populations of skylarks in the UK. The chosen population endpoints were abundance, population growth rate, and the chances of population persistence. Both models used the same life-history descriptors and toxicity profiles as the basis for their parameter inputs. The models differed in that exposure was a pre-determined parameter in the life-history model, but an emergent property of the IBLM, and the IBLM required a landscape structure as an input. The model outputs were qualitatively similar between the two models. Under conditions dominated by winter wheat, both models predicted a population decline that was worsened by the use of the insecticide. Under broader habitat conditions, population declines were only predicted for the scenarios where the insecticide was added. Inputs to the models are very different, with the IBLM requiring a large volume of data in order to achieve the flexibility of being able to integrate a range of environmental and behavioural factors. The life-history model has very few explicit data inputs, but some of these relied on extensive prior modelling needing additional data as described in Roelofs et al. (2005, this volume). Both models have strengths and weaknesses; hence the ideal approach is that of combining the use of both simple and comprehensive modeling tools.
In the present study we present a population model (Metapopulation model for Assessing Spatial and Temporal Effects of Pesticides [MASTEP]) describing the effects on and recovery of the waterlouse Asellus aquaticus after exposure to a fast-acting, nonpersistent insecticide as a result of spray drift in pond, ditch, and stream scenarios. The model used the spatial and temporal distribution of the exposure in different treatment conditions as an input parameter. A dose-response relation derived from a hypothetical mesocosm study was used to link the exposure with the effects. The modeled landscape was represented as a lattice of 1- by 1-m cells. The model included processes of mortality of A. aquaticus, life history, random walk between cells, density dependence of population regulation, and, in the case of the stream scenario, medium-distance drift of A. aquaticus due to flow. All parameter estimates were based on expert judgment and the results of a thorough review of published information on the ecology of A. aquaticus. In the treated part of the water body, the ditch scenario proved to be the worst-case situation, due to the absence of drift of A. aquaticus. Effects in the pond scenario were smaller because the pond was exposed from one side, allowing migration from the other, less contaminated side. The results of the stream scenario showed the importance of including drift for the population recovery in the 100-m stretch of the stream that was treated. It should be noted, however, that the inclusion of drift had a negligible impact on numbers in the stream as a whole (600 m).
Bringing together more than thirty influential regulators, academics, and industry scientists, Ecological Models for Regulatory Risk Assessments of Pesticides: Developing a Strategy for the Future provides a coherent, science-based view on ecological modeling for regulatory risk assessments. It discusses the benefits of modeling in the context of registrations, identifies the obstacles that prevent ecological modeling being used routinely in regulatory submissions, and explores the actions needed to overcome these obstacles. The book focuses on the following issues: Uncertainties in the process of model development, such as design, analysis, documentation, and communication The availability of data and background information needed for optimal modeling The limited knowledge of modeling The lack of confidence in the outcome of ecological models and their reliability in pesticide risk assessment It also suggests future solutions to these challenges, including: A guidance document on the modeling process Case studies that show how ecological models can provide reliable ecologically relevant risk assessments Training the people who generate or evaluate results obtained by ecological models Focusing on ecological models, such as unstructured population models, stage-structured matrix models, and individual- or agent-based models, this volume helps regulatory authorities, manufacturers, and scientists assess the risk of plant protection products in nontarget organisms. Armed with this knowledge, readers will better understand the challenges of using ecological modeling in the regulatory process.
The desert tortoise is a threatened species living in the deserts of the Amer- ican Southwest. Using size-structured demographic models, we analyzed the status of the tortoise in the Western Mojave desert and evaluated the effectiveness of possible manage- ment measures. Our demographic analyses agree with the trends reported by field censuses in showing rapid population decline. Importantly, simulations that include variation and correlation in vital rates yield highly variable forecasts of population growth, indicating the uncertainties inherent in even short-term projections of tortoise population sizes. To determine where conservation efforts and data collection should be focused, we performed a series of sensitivity analyses in which the effects of changing different vital rates were quantified. We found that the rate of population growth is most sensitive to the survival of large adult females and that improving survival of this size class to reputably "pristine" rates could reverse population declines; in contrast, large improvements in other vital rates will not, alone, reverse population decline. Thus, shooting, off-road vehicles, upper respi- ratory tract disease (URTD), and other major sources of adult mortality should be the primary focus of management strategies. Finally, we discuss the impact of the proposed expansion of the U.S. Army's Fort Irwin, which would reduce the Western Mojave tortoise population by 5 13%. We argue that the expansion could have a grave impact on the long- term population viability of the tortoise, but that this impact will be a function of the management of remaining tortoise habitat. We conclude that, although many anthropogenic impacts threaten the tortoise, the species' future could be bright, provided that research and land-use planning focus on biologically important aspects of its life history.
Despite their promise for simulating natural complexity, individual-based models (IBMs) are rarely used for ecological research or resource management. Few IBMs have been shown to reproduce realistic patterns of behavior by individual organisms. To test our IBM of stream salmonids and draw conclusions about foraging theory, we analyzed the IBM's ability to reproduce six patterns of habitat selection by real trout in simulations contrasting three alternative habitat-selection objectives: maximizing current growth rate, current survival probability, or ''expected maturity'' (EM). EM is the product of (1) pre dicted survival of starvation and other mortality risks over a future time horizon, and (2) the fraction of reproductive size attained over the time horizon. Minimizing the ratio of mortality risk to growth rate was not tested as a habitat-selection rule because it produces nonsensical results when any habitat yields negative growth rates. The IBM simulates habitat selection in response to spatial and temporal variation in mortality risks and food availability as fish compete for food. The model fish move each daily time step to maximize their habitat-selection objective with no other restrictions (e.g., territoriality) imposed. Simulations with habitat selected to maximize growth reproduced three of the six habitat- selection patterns; maximizing survival reproduced two patterns; and maximizing EM re- produced all six patterns. Two patterns (shifts in habitat with changes in temperature and food availability) were not reproduced by the objectives that consider only current growth and risk but were explained by the EM objective that considers how future starvation risk depends on current energy reserves and energy intake. In 75-d simulations, population- level survival and biomass accumulation were highest for fish moving to maximize EM. These results support the basic assumptions of state-based dynamic-modeling approaches to habitat selection. Our IBM appears successful because it avoids restrictive assumptions, incorporates competition for food, assumes salmonids make good habitat-selection decisions at a daily time step, and uses a habitat objective (EM) that provides reasonable trade-offs between growth and mortality risks.
Summary • Demographic models are assuming an important role in management decisions for endangered species. Elasticity analysis and scope for management analysis are two such applications. Elasticity analysis determines the vital rates that have the greatest impact on population growth. Scope for management analysis examines the effects that feasible management might have on vital rates and population growth. Both methods target management in an attempt to maximize population growth. • The Seychelles magpie robin Copsychus sechellarum is a critically endangered island endemic, the population of which underwent significant growth in the early 1990s following the implementation of a recovery programme. We examined how the formal use of elasticity and scope for management analyses might have shaped management in the recovery programme, and assessed their effectiveness by comparison with the actual population growth achieved. • The magpie robin population doubled from about 25 birds in 1990 to more than 50 by 1995. A simple two-stage demographic model showed that this growth was driven primarily by a significant increase in the annual survival probability of first-year birds and an increase in the birth rate. Neither the annual survival probability of adults nor the probability of a female breeding at age 1 changed significantly over time. • Elasticity analysis showed that the annual survival probability of adults had the greatest impact on population growth. There was some scope to use management to increase survival, but because survival rates were already high (> 0·9) this had a negligible effect on population growth. Scope for management analysis showed that significant population growth could have been achieved by targeting management measures at the birth rate and survival probability of first-year birds, although predicted growth rates were lower than those achieved by the recovery programme when all management measures were in place (i.e. 1992–95). • Synthesis and applications. We argue that scope for management analysis can provide a useful basis for management but will inevitably be limited to some extent by a lack of data, as our study shows. This means that identifying perceived ecological problems and designing management to alleviate them must be an important component of endangered species management. The corollary of this is that it will not be possible or wise to consider only management options for which there is a demonstrable ecological benefit. Given these constraints, we see little role for elasticity analysis because, when data are available, a scope for management analysis will always be of greater practical value and, when data are lacking, precautionary management demands that as many perceived ecological problems as possible are tackled.
Summary • Mechanistic models may be able to predict how changes in agricultural practice influence farmland bird populations. A key component of these models is the link between food and competitor densities and the rate at which birds consume food, i.e. the functional response. • This paper tests whether the functional response of a farmland bird, the rook Corvus frugilegus, can be predicted from three parameters: searching speed, food detection distance and handling time. It is often difficult to measure the functional response of farmland birds directly, but it may be possible to measure behavioural parameters more quickly. • We performed experiments in which rooks fed on a range of artificial food densities in two grass sward heights. Food detection distance was greater in the shorter sward, but sward height did not influence searching speed or handling time. The functional response could be accurately predicted in both sward heights. • We show that the functional response of a farmland bird can be predicted from parameters that can be measured more quickly than the alternative of measuring the functional response directly. This implies that the functional responses of other farmland birds may be predicted using a minimum of information. Functional Ecology (2006) 20, 723–729 doi:10.1111/j.1365-2435.2006.01155.x
I explored the efficacy of alternative actions to recover threatened Snake River chinook salmon ( Onchorhyncus tshawytscha ). I compared the potential to increase population growth rates from two different actions: ( 1 ) habitat restoration efforts, aimed at increasing egg-to-smolt survival rate, and ( 2 ) dam breaching, intended to improve smolt-to-spawner survival. Eight dams obstruct the migration corridor these populations traverse as juveniles ( downstream ) and as adults ( upstream ), and a large portion of the juvenile migrants are collected and transported past most of the dams on barges or trucks. I applied sensitivity, elasticity, and direct perturbation analyses to an age-structured projection matrix to predict potential effects from simultaneous, nonproportional changes in multiple survival rates. Throughout the analyses, I explicitly incorporated alternative assumptions about the effectiveness of transportation, which is known to be influential. Results of the numerical experiments suggest that dam breaching has more potential to increase population growth rates than habitat restoration, except for the most optimistic assumption about the efficacy of transportation. I then fit the matrix to historical data to identify life stages in which actual decreases in survival rates have caused the observed declines in abundance. There was no reduction in egg-to-smolt survival, indicating that neither habitat deterioration nor hatchery impacts ( in that life stage ) caused the stocks to decline. The large decrease in smolt-to-adult survival rate from the historical period, when there were fewer dams, is consistent with the hypothesis that increased stress from transportation and passage through additional dams on the Snake River has elevated delayed mortality levels.
In this paper we consider the relationships between effects of toxicants on population growth rate (r) and the individual-level traits (survival and reproduction) contributing to it by examining population dynamics theory and by reviewing the experimental work that has been carried out in this context. There was no consistent pattern in terms of which individual-level traits were most or least sensitive to toxicant exposure, and it is therefore impractical to select traits for ecotoxicological testing on this basis. Although percent changes in several of the individual-level traits showed significant overall correlations with percent changes in r, changes in any single trait could explain no more than about one-half of the percent change in r. Our conclusion is that r is a better measure of responses to toxicants than are individual-level effects, because it integrates potentially complex interactions among life-history traits and provides a more relevant measure of ecological impact.
Demographic matrix models are an increasingly standard way to evaluate the effects of different impacts and management approaches on species of concern. Although invasive species are now considered among the greatest threats to biodiversity, matrix methods have been little used to explore and integrate the potentially complicated effects of invasions on native species. I developed stage-structured models to assess the impacts of invasive grasses on population growth and persistence of a federally listed (U.S.A.) endemic plant, the Antioch Dunes evening primrose (Oenothera deltoides subsp. howellii [Munz] W. Klein). I used these models to evaluate two frequently made assumptions: (1) when rare plant populations decline in invaded habitats, invasive species are the cause and (2) invasive plants suppress rare plants primarily through direct resource competition. I compared two control and two removal matrices based on previous experimental work that showed variable effects of invasive grasses on different life-history stages of O. deltoides. Matrix analysis showed that these effects translated into substantial changes in population growth rates and persistence, with control matrices predicting a mean stochastic population growth rate (λ) of 0.86 and removal matrices predicting growth rates from 0.92 to 0.93. Yet even the most optimistic invasive removal scenarios predicted rapid decline and a probability of extinction near one in the next 100 years. Competitive suppression of seedlings had much smaller effects on growth rates than did lowered germination, which probably resulted from thatch accumulation and reduced soil disturbance. These results indicate that although invasive grasses have important effects on the population growth of this rare plant, invasion impacts are not solely responsible for observed declines and are likely to be interacting with other factors such as habitat degradation. Further, changes in the disturbance regime may be as important a mechanism creating these impacts as direct resource competition. My results highlight the value of demographic modeling approaches in creating an integrated assessment of the threats posed by invasive species and the need for more mechanistic studies of invasive plant interactions with native plants.Resumen: Los modelos demográficos matriciales son una forma cada vez más utilizada para evaluar los efectos de diferentes impactos y métodos de gestión sobre las especies en cuestión. Aunque actualmente se considera a las plantas invasoras entre las mayores amenazas a la biodiversidad, los modelos matriciales han sido poco utilizados para explorar e integrar los efectos potencialmente complicados de las invasiones sobre las especies nativas. Desarrollé modelos estructurados por etapas para evaluar los impactos de pastos invasores sobre el crecimiento poblacional y la persistencia de una especie de planta endémica, enlistada federalmente (E.U.A.), Oenothera deltoides ssp. howellii [Munz] W. Klein. Utilicé estos modelos para evaluar dos suposiciones frecuentes: (1) cuando las poblaciones de plantas raras declinan en hábitats invadidos, las especies invasoras son la causa y (2) las plantas invasoras suprimen a las plantas raras principalmente mediante la competencia directa por recursos. Comparé dos matrices de control y dos de remoción con base en trabajo experimental previo que mostró efectos variables de los pastos invasores sobre las diferentes etapas de la historia de vida de O. deltoides. El análisis de la matriz mostró que estos efectos se tradujeron en cambios sustanciales en las tasas de crecimiento y persistencia de la población, las matrices de control predijeron una tasa media de crecimiento poblacional estocástica (λ) de 0.86 y las matrices de remoción predijeron tasas de crecimiento de 0.92-0.93. Aun los escenarios más optimistas de remoción de invasores predijeron una rápida declinación y una probabilidad de extinción en 100 años cerca de uno. La supresión competitiva de plántulas tuvo mucho menor efecto sobre las tasas de crecimiento que la disminución en la germinación, que probablemente resultó de la acumulación de paja y reducción en la perturbación del suelo. Estos resultados indican que, aunque los pastos invasores tienen efectos importantes sobre el crecimiento poblacional de esta planta rara, los impactos de la invasión no son los únicos responsables de las declinaciones observadas y probablemente están interactuando con otros factores como la degradación del hábitat. Más aun, los cambios en el régimen de perturbación pueden ser un mecanismo tan importante en la creación de estos impactos como la competencia directa por recursos. Mis resultados resaltan el valor del enfoque de los modelos demográficos para la evaluación integral de las amenazas de especies invasoras y la necesidad de estudios más mecanicistas de las interacciones de plantas invasoras con plantas nativas.
The animal, landscape and man simulation system (ALMaSS) was designed as a predictive tool for answering policy questions regarding the effect of changing landscape structure or management on key animal species in the Danish landscape. By combining agent-based models of animals with a comprehensive and dynamic landscape simulation, it aims to improve predictive ability. The landscape model comprises detailed mapping, weather, farm management, and vegetation growth. Each vegetated area has its own growth model and in the case of farmed areas, management is modelled in detail. Animal models are agent-based, designed using the state/transition concept, and are rule-based. Each animal may interact with others and directly with its local environment. Field vole (Microtus agrestis) is used as an example of the extent to which dynamic landscapes can influence the population dynamics. Simulations of crop diversity and rotation demonstrate significant effects of spatial and temporal heterogeneity on population sizes, population fluctuations and landscape permeability. These two factors interact and thus different responses to temporal factors occur at different levels of spatial heterogeneity. Spatial and temporal heterogeneity in both the model and the real world are often related to changes in land-use and management. Consequently, the impact of landscape changes on any population can be enormous and heavily spatially influenced. Therefore, the use of dynamic landscapes is seen as an important addition to the modeller’s toolkit.
In this study, we use two ecological forest models, namely FORMIX3 and FORREG, to analyse the impact of logging on tropical rain forests and to discuss needs for and problems of an economic extension of these models. The FORMIX3 model simulates spatial–temporal dynamics of tropical forests using an individual and process based approach. The main processes included are growth, mortality, competition, and regeneration of trees. The FORREG model simulates wood growth of tropical forests at landscape scale based on differential equations. Regeneration capabilities of logged forests are analysed and compared for different logging strategies. While conventional management strategies with a short logging cycle (here 20 years) produce low yields and cause severe changes in tree species composition, a reduced impact logging strategy with a longer cycle (here 60 years) leads to relatively high yields and causes moderate changes in species composition. Thus, longer logging cycles are preferable from an ecological point of view. However, also economic aspects influence logging decisions, thus a closer analysis of additional economic aspects of forest management is inevitable. We discuss which economic shortcomings of present rain forest models should be dealt with in the future and which additional data is needed as a consequence.