Best management practices (BMPs) for reducing agricultural non-point source pollution are widely available. However, agriculture remains a major global contributor to degradation of waters because farmers often do not adopt BMPs. To improve water quality, it is necessary to understand the factors that influence BMP adoption by farmers. We review the findings of BMP adoption studies from both developed and developing countries, published after (or otherwise not included in) two major literature reviews from 2007 and 2008. We summarize the study locations, scales, and BMPs studied; the analytical methods used; the factors evaluated; and the directionality of each factor's influence on BMP adoption. We then present a conceptual framework for BMP adoption decisions that emphasizes the importance of scale, the tailoring or targeting of information and incentives, and the importance of expected farm profits. We suggest that future research directions should focus on study scale, on measuring and modeling of adoption as a continuous process, and on incorporation of social norms and uncertainty into decision-making. More research is needed on uses of social media and market recognition approaches (such as certificate schemes and consumer labeling) to influence BMP adoption.

Ecological production functions (EPFs) link ecosystems, stressors, and management actions to ecosystem services (ES) production. Although EPFs are acknowledged as being essential to improve environmental management, their use in ecological risk assessment has received relatively little attention. Ecological production functions may be defined as usable expressions (i.e., models) of the processes by which ecosystems produce ES, often including external influences on those processes. We identify key attributes of EPFs and discuss both actual and idealized examples of their use to inform decision making. Whenever possible, EPFs should estimate final, rather than intermediate, ES. Although various types of EPFs have been developed, we suggest that EPFs are more useful for decision making if they quantify ES outcomes, respond to ecosystem condition, respond to stressor levels or management scenarios, reflect ecological complexity, rely on data with broad coverage, have performed well previously, are practical to use, and are open and transparent. In an example using pesticides, we illustrate how EPFs with these attributes could enable the inclusion of ES in ecological risk assessment. The biggest challenges to ES inclusion are limited data sets that are easily adapted for use in modeling EPFs and generally poor understanding of linkages among ecological components and the processes that ultimately deliver the ES. We conclude by advocating for the incorporation into EPFs of added ecological complexity and greater ability to represent the trade-offs among ES. Integr Environ Assess Manag 2017;13:52-61. © 2016 SETAC.

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We demonstrate how mechanistic modeling can be used to predict whether and how biological responses to chemicals at (sub)organismal levels in model species (i.e., what we typically measure) translate into impacts on ecosystem service delivery (i.e., what we care about). We consider a hypothetical case study of two species of trout, brown trout (Salmo trutta; BT) and greenback cutthroat trout (Oncorhynchus clarkii stomias; GCT). These hypothetical populations live in a high-altitude river system and are exposed to human-derived estrogen (17α‑ethinyl estradiol, EE2), which is the bioactive estrogen in many contraceptives. We use the individual-based model inSTREAM to explore how seasonally varying concentrations of EE2 could influence male spawning and sperm quality. Resulting impacts on trout recruitment and the consequences of such for anglers and for the continued viability of populations of GCT (the state fish of Colorado) are explored. inSTREAM incorporates seasonally varying river flow and temperature, fishing pressure, the influence of EE2 on species-specific demography, and inter-specific competition. The model facilitates quantitative exploration of the relative importance of endocrine disruption and inter-species competition on trout population dynamics. Simulations predicted constant EE2 loading to have more impacts on GCT than BT. However, increasing removal of BT by anglers can enhance the persistence of GCT and offset some of the negative effects of EE2. We demonstrate how models that quantitatively link impacts of chemicals and other stressors on individual survival, growth, and reproduction to consequences for populations and ecosystem service delivery, can be coupled with ecosystem service valuation. The approach facilitates interpretation of toxicity data in an ecological context and gives beneficiaries of ecosystem services a more explicit role in management decisions. Although challenges remain, this type of approach may be particularly helpful for site-specific risk assessments and those in which tradeoffs and synergies among ecosystem services need to be considered.

. 2017. Model application niche analysis: assessing the transferability and generalizability of ecological models. Ecosphere 8(10):e01974. 10. 1002/ecs2.1974 Abstract. The use of models by ecologists and environmental managers, to inform environmental management and decision-making, has grown exponentially in the past 50 yr. Due to logistical, economical, and theoretical benefits, model users frequently transfer preexisting models to new sites where data are scarce. Modelers have made significant progress in understanding how to improve model generalizability during model development. However, models are always imperfect representations of systems and are constrained by the contextual frameworks used during their development. Thus, model users need better ways to evaluate the possibility of unintentional misapplication when transferring models to new sites. We propose a method of describing a model's application niche for use during the model selection process. Using this method, model users synthesize information from databases, past studies, and/or past model transfers to create model performance curves and heat maps. We demonstrated this method using an empirical model developed to predict the ecological condition of plant communities in riverine wetlands of the Appalachian Highland physiographic region, USA. We assessed this model's transferability and generalizability across (1) riverine wetlands in the contiguous United States, (2) wetland types in the Appalachian Highland physiographic region, and (3) wetland types in the contiguous United States. With this methodology and a discussion of its critical steps, we set the stage for further inquiries into the development of consistent and transparent practices for model selection when transferring a model.