Neglected biological patterns in the residuals

One of the fundamental assumptions underlying linear regression models is that the errors have a constant variance (i.e., homoscedastic). When this assumption is violated, standard errors from a regression can be biased and inconsistent, meaning that the associated p values and 95% confidence intervals cannot be trusted. The assumption of homoscedasticity is made for statistical reasons rather than biological reasons; in most real datasets, some form of heteroscedasticity is likely to exist. However, a survey of the behavioural ecology literature showed that only about 5% of articles explicitly mentioned heteroscedasticity, leaving 95% of articles in which heteroscedasticity was apparently absent. These results strongly indicate that the prevalence of heteroscedasticity is widely under-reported within behavioural ecology. The aim of this article is to raise awareness of heteroscedasticity amongst behavioural ecologists. Using topical examples from fields in behavioural ecology such as sexual dimorphism and animal personality, we highlight the biological importance of considering heteroscedasticity. We also emphasize that researchers should pay closer attention to the variance in their data and consider what factors could cause heteroscedasticity. In addition, we introduce some simple methods of dealing with heteroscedasticity. The two methods we focus on are: (1) incorporating variance functions within a generalised least squares (GLS) framework to model the functional form of heteroscedasticity and; (2) heteroscedasticity-consistent standard error (HCSE) estimators, which can be used when the functional form of heteroscedasticity is unknown. Using case studies, we show how both methods can influence the output from linear regression models. Finally, we hope that more researchers will consider heteroscedasticity as an important source of additional information about the particular biological process being studied, rather than an impediment to statistical analysis.     

Research methods and reporting practices in zoo and aquarium conservation-education evaluation

Zoos and aquariums are increasingly incorporating conservation education into their mission statements and visitor experiences to address global biodiversity loss. To advance knowledge and practice in the field, research is being conducted to evaluate the effect of zoo conservation-education experiences on visitor psychosocial outcomes (e.g., knowledge, attitude, emotions, motivations, behavior). Following recent discussions among scholars and practitioners concerning logistical and methodological challenges that likely undermine the conclusions of such research, we identified and reviewed the methods and reporting practices in peer-reviewed articles published in English from May 1998 to June 2016 that focused on adult visitor samples (47 articles, 48 studies). We examined elements of internal, external, construct, and statistical conclusion validity. Methodological quality of quantitative methods and reporting practices was determined using the Effective Public Health Practice Project Quality Assessment Tool. Each study was coded as either strong (no weak ratings), moderate (1 weak rating), or weak (≥2 weak ratings). The quantitative methods of 83.3% of studies were weak. The remaining 16.7% had methods of moderate quality. Using an existing checklist, we also assessed the quality and rigor of qualitative methods and reporting practices and found that some aspects of these methods were reported more comprehensively than others. For example, 69.6% of articles discussed methods for identifying key themes from the data, whereas only 34.8% reported how data verification was performed. We suggest increased application of intensive longitudinal methods (e.g., daily diary) to strengthen self-reported data, experimental and repeated-measures designs, and mixed-methods approaches. Our findings and recommendations could strengthen and guide the research and evaluation agenda for the field and ultimately enhance the contribution zoos make to global biodiversity conservation.     

The Use of Bayesian Model Averaging to Better Represent Uncertainty in Ecological Models

Abstract:   In conservation biology, uncertainty about the choice of a statistical model is rarely considered. Model-selection uncertainty occurs whenever one model is chosen over plausible alternative models to represent understanding about a process and to make predictions about future observations. The standard approach to representing prediction uncertainty involves the calculation of prediction (or confidence) intervals that incorporate uncertainty about parameter estimates contingent on the choice of a "best" model chosen to represent truth. However, this approach to prediction based on statistical models tends to ignore model-selection uncertainty, resulting in overconfident predictions. Bayesian model averaging (BMA) has been promoted in a range of disciplines as a simple means of incorporating model-selection uncertainty into statistical inference and prediction. Bayesian model averaging also provides a formal framework for incorporating prior knowledge about the process being modeled. We provide an example of the application of BMA in modeling and predicting the spatial distribution of an arboreal marsupial in the Eden region of southeastern Australia. Other approaches to estimating prediction uncertainty are discussed.     

Quantifying the role of online news in linking conservation research to Facebook and Twitter

Conservation science needs to engage the general public to ensure successful conservation interventions. Although online technologies such as Twitter and Facebook offer new opportunities to accelerate communication between conservation scientists and the online public, factors influencing the spread of conservation news in online media are not well understood. We explored transmission of conservation research through online news articles with generalized linear mixed‐effects models and an information theoretic approach. In particular, we assessed differences in the frequency conservation research is featured on online news sites and the impact of online conservation news content and delivery on Facebook likes and shares and Twitter tweets. Five percent of articles in conservation journals are reported in online news, and the probability of reporting depended on the journal. There was weak evidence that articles on climate change and mammals were more likely to be featured. Online news articles about charismatic mammals with illustrations were more likely to be shared or liked on Facebook and Twitter, but the effect of news sites was much larger. These results suggest journals have the greatest impact on which conservation research is featured and that news site has the greatest impact on how popular an online article will be on Facebook and Twitter. Cuantificación del Papel de las Noticias En Línea en el Enlazamiento de la Investigación para la Conservación con Facebook y Twitter     

Variances and variance estimators of the improved ratio estimators under adaptive cluster sampling

Although not design-unbiased, the ratio estimator is recognized as more efficient when a certain degree of correlation exists between the variable of primary interest and the auxiliary variable. Meanwhile, the Rao–Blackwell method is another commonly used procedure to improve estimation efficiency. Various improved ratio estimators under adaptive cluster sampling (ACS) that make use of the auxiliary information together with the Rao–Blackwellized univariate estimators have been proposed in past research studies. In this article, the variances and the associated variance estimators of these improved ratio estimators are proposed for a thorough framework of statistical inference under ACS. Performance of the proposed variance estimators is evaluated in terms of the absolute relative percentage bias and the empirical mean-squared error. As expected, results show that both the absolute relative percentage bias and the empirical mean-squared error decrease as the initial sample size increases for all the variance estimators. To evaluate the confidence intervals based on these variance estimators and the finite-population Central Limit Theorem, the coverage rate and the interval width are used. These confidence intervals suffer a disadvantage similar to that of the conventional ratio estimator. Hence, alternative confidence intervals based on a certain type of adjusted variance estimators are constructed and assessed in this article.     

Empirical likelihood confidence intervals for adaptive cluster sampling

Adaptive cluster sampling (ACS) is an efficient sampling design for estimating parameters of rare and clustered populations. It is widely used in ecological research. The modified Hansen-Hurwitz (HH) and Horvitz-Thompson (HT) estimators based on small samples under ACS have often highly skewed distributions. In such situations, confidence intervals based on traditional normal approximation can lead to unsatisfactory results, with poor coverage properties. Christman and Pontius (Biometrics 56:503–510, 2000) showed that bootstrap percentile methods are appropriate for constructing confidence intervals from the HH estimator. But Perez and Pontius (J Stat Comput Simul 76:755–764, 2006) showed that bootstrap confidence intervals from the HT estimator are even worse than the normal approximation confidence intervals. In this article, we consider two pseudo empirical likelihood functions under the ACS design. One leads to the HH estimator and the other leads to a HT type estimator known as the Hájek estimator. Based on these two empirical likelihood functions, we derive confidence intervals for the population mean. Using a simulation study, we show that the confidence intervals obtained from the first EL function perform as good as the bootstrap confidence intervals from the HH estimator but the confidence intervals obtained from the second EL function perform much better than the bootstrap confidence intervals from the HT estimator, in terms of coverage rate.     

Model averaging,missing data and multiple imputation: a case study for behavioural ecology

Model averaging, specifically information theoretic approaches based on Akaike’s information criterion (IT-AIC approaches), has had a major influence on statistical practices in the field of ecology and evolution. However, a neglected issue is that in common with most other model fitting approaches, IT-AIC methods are sensitive to the presence of missing observations. The commonest way of handling missing data is the complete-case analysis (the complete deletion from the dataset of cases containing any missing values). It is well-known that this results in reduced estimation precision (or reduced statistical power), biased parameter estimates; however, the implications for model selection have not been explored. Here we employ an example from behavioural ecology to illustrate how missing data can affect the conclusions drawn from model selection or based on hypothesis testing. We show how missing observations can be recovered to give accurate estimates for IT-related indices (e.g. AIC and Akaike weight) as well as parameters (and their standard errors) by utilizing ‘multiple imputation’. We use this paper to illustrate key concepts from missing data theory and as a basis for discussing available methods for handling missing data. The example is intended to serve as a practically oriented case study for behavioural ecologists deciding on how to handle missing data in their own datasets and also as a first attempt to consider the problems of conducting model selection and averaging in the presence of missing observations.     

Modelling temporal and spatial variability in tag reporting-rates for Newfoundland cod (<Emphasis Type="Italic">Gadus morhua</Emphasis>)

Mark–recapture experiments can be used to estimate the exploitation rate of a fishery; however, the estimate is influenced by the tag reporting-rate by the fishers. We present two methods to estimate the reporting rates in high/low reward ($100 and $10 CAD respectively) long-term cod tagging experiments. We fit two binomial logistic mixed-effect models, one with temporal auto-correlation in the reporting-rate year-effects and one with independent year-effects. We estimate reporting-rates separately for recreational and commercial fishers, and test for spatial variation using fixed-effects for spatial regions. Due to the complexity of the fishery, our models account for factors such as recapture-fishery type, fish-size and time-at-liberty. Our results indicate that the recreational fishers reporting-rate was constant at 0.51 across all regions and years. The commercial fishery showed more spatial and temporal variation, with the reporting-rates estimates lying between 0.67 and 0.87 for the independent year-effect model, and between 0.57 and 0.84 for the random walk model. Furthermore, we assessed the model performance as well as the coverage probability of nominal 95 % confidence intervals using simulations. We found that the models performed adequately; however, the nominal 95 % confidence intervals tended to be too narrow.     

Bayesian estimation of age-specific bird nest survival rates with categorical covariates

The populations of many North American landbirds are showing signs of declining. Gathering information on breeding productivity allows critical assessment of population performance and helps identify good habitat-management practices. He (Biometrics (2003) 59 962–973) proposed a Bayesian model to estimate the age-specific nest survival rates. The model allows irregular visiting schedule under the assumption that the observed nests have homogeneous nest survival. Because nest survival studies are often conducted in different sites and time periods, it is not realistic to assume homogeneous nest survival. In this paper, we extend He’s model by incorporating these factors as categorical covariates. The simulation results show that the Bayesian hierarchical model can produce satisfactory estimates on nest survival and capture different factor effects. Finally the model is applied to a Missouri red-winged blackbird data set.     

Issues in information theory-based statistical inference—a commentary from a frequentist’s perspective

After several decades during which applied statistical inference in research on animal behaviour and behavioural ecology has been heavily dominated by null hypothesis significance testing (NHST), a new approach based on information theoretic (IT) criteria has recently become increasingly popular, and occasionally, it has been considered to be generally superior to conventional NHST. In this commentary, I discuss some limitations the IT-based method may have under certain circumstances. In addition, I reviewed some recent articles published in the fields of animal behaviour and behavioural ecology and point to some common failures, misunderstandings and issues frequently appearing in the practical application of IT-based methods. Based on this, I give some hints about how to avoid common pitfalls in the application of IT-based inference, when to choose one or the other approach and discuss under which circumstances a mixing of the two approaches might be appropriate.     

Assessing conservation status with extensive but low-resolution data: Application of frequentist and Bayesian models to endangered Athabasca River rainbow trout

Use of extensive but low-resolution abundance data is common in the assessment of species at-risk status based on quantitative decline criteria under International Union for Conservation of Nature (IUCN) and national endangered species legislation. Such data can be problematic for 3 reasons. First, statistical power to reject the null hypothesis of no change is often low because of small sample size and high sampling uncertainty leading to a high frequency of type II errors. Second, range-wide assessments composed of multiple site-specific observations do not effectively weight site-specific trends into global trends. Third, uncertainty in site-specific temporal trends and relative abundance are not propagated at the appropriate spatial scale. A common result is the propensity to underestimate the magnitude of declines and therefore fail to identify the appropriate at-risk status for a species. We used 3 statistical approaches, from simple to more complex, to estimate temporal decline rates for a designatable unit (DU) of rainbow trout in the Athabasca River watershed in western Canada. This DU is considered a native species for purposes of listing because of its genetic composition characterized as >0.95 indigenous origin in the face of continuing introgressive hybridization with introduced populations in the watershed. Analysis of abundance trends from 57 time series with a fixed-effects model identified 33 sites with negative trends, but only 2 were statistically significant. By contrast, a hierarchical linear mixed model weighted by site-specific abundance provided a DU-wide decline estimate of 16.4% per year and a 3-generation decline of 93.2%. A hierarchical Bayesian mixed model yielded a similar 3-generation decline trend of 91.3% and the posterior distribution showed that the estimate had a >99% probability of exceeding thresholds for an endangered listing. We conclude that the Bayesian approach was the most useful because it provided a probabilistic statement of threshold exceedance in support of an at-risk status recommendation.     

Corporate Social Responsibility (CSR) practices developed by Brazilian companies: an exploratory study

In a world where companies have major influence over the economy, politics, and environment, they must be held accountable for their actions. With this in mind, the publication of sustainability reports is in a company’s best interest, since the disclosure of Corporate Social Responsibility (CSR) and sustainable practices can bring enormous advantages, largely related to a company’s image. The amount of information presented in sustainability reports turns them into valuable sources for research data, allowing analyses related to CSR and management practices, stakeholder relations, language used, among others. However, there is no a paper in the literature that analyzes theses information jointly for the Brazilian companies, and then we decided to explore this theme. This research aimed to identify advances and gaps of CSR practices performed by Brazil companies, through the analysis of 30 sustainability reports published between 2014 and 2015. The main findings indicate that, in general, the analyzed companies are experienced in sustainability reporting; however, there is still room for improvements, including a better mapping and reporting of practices involving the integration of CSR to organizational structure, stakeholder engagement, and continuous improvement practices.     

Use of DNA barcoding to reveal species composition of convenience seafood

Increased education of consumers can be an effective tool for conservation of commercially harvested marine species when product labeling is accurate and allows an informed choice. However, generic labeling (e.g., as white fish or surimi) and mislabeling of seafood prevents this and may erode consumer confidence in seafood product labels in general. We used DNA barcoding to identify the species composition of two types of convenience seafood (i.e., products processed for ease of consumption): fish fingers (long pieces of fish covered with bread crumbs or batter, n = 241) and seafood sticks (long pieces of cooked fish, n = 30). In products labeled as either white fish or surimi, four teleost species were present. Less than 1.5% of fish fingers with species-specific information were mislabeled. Results of other studies show substantially more mislabeling (e.g., >25%) of teleost products, which likely reflects the lower economic gains associated with mislabeling of convenience seafood compared with whole fillets. In addition to species identification, seafood product labels should be required to contain information about, for example, harvesting practices, and our data indicate that consumers can have reasonable confidence in the accuracy of the labels of convenience seafood and thus select brands on the basis of information about current fisheries practice.     

Limited contributions of released animals from zoos to North American conservation translocations

With the loss of biodiversity accelerating, conservation translocations such as reintroductions are becoming an increasingly common conservation tool. Conservation translocations must source individuals for release from either wild or captive-bred populations. We asked what proportion of North American conservation translocations rely on captive breeding and to what extent zoos and aquaria (hereafter zoos) fulfill captive breeding needs. We searched for mention of captive breeding and zoo involvement in all 1863 articles included in the North American Conservation Translocations database, which comprises journal articles and grey literature published before 2014 on conservation translocations in Canada, the United States, Mexico, the Caribbean, and Central America before 2014 as identified by a comprehensive literature review. Conservation translocations involved captive breeding for 162 (58%) of the 279 animal species translocated. Fifty-four zoos contributed animals for release. The 40 species of animals bred for release by zoos represented only 14% of all animal species for which conservation translocations were published and only 25% of all animal species that were bred for releases occurring in North America. Zoo contributions varied by taxon, ranging from zoo-bred animals released in 42% of amphibian conservation translocations to zero contributions for marine invertebrates. Proportional involvement of zoos in captive-breeding programs for release has increased from 1974 to 2014 (r = 0.325, p = 0.0313) as has the proportion of translocation-focused scientific papers coauthored by zoo professionals (from 0% in 1974 to 42% in 2013). Although zoos also contribute to conservation translocations through education, funding, and professional expertise, increasing the contribution of animals for release in responsible conservation translocation programs presents a future conservation need and opportunity. We especially encourage increased dialogue and planning between the zoo community, academic institutions, and governments to optimize the direct contribution zoos can make to wildlife conservation through conservation translocations.     

Scaling up private land conservation to meet recovery goals for grassland birds

Long-term population declines have elevated recovery of grassland avifauna to among the highest conservation priorities in North America. Because most of the Great Plains is privately owned, recovery of grassland bird populations depends on voluntary conservation with strong partnerships between private landowners and resource professionals. Despite large areas enrolled in voluntary practices through U.S. Department of Agriculture's Lesser Prairie-chicken (Tympanuchus pallidicinctus) Initiative (LPCI), the effectiveness of Farm Bill investments for meeting wildlife conservation goals remains an open question. Our objectives were to evaluate extents to which Conservation Reserve Program (CRP) and LPCI-grazing practices influence population densities of grassland birds; estimate relative contributions of practices to regional bird populations; and evaluate percentages of populations conserved relative to vulnerability of species. We designed a large-scale impact-reference study and used the Integrated Monitoring in Bird Conservation Regions program to evaluate bird population targets of the Playa Lakes Joint Venture. We used point transect distance sampling to estimate density and population size for 35 species of grassland birds on private lands enrolled in native or introduced CRP plantings and LPCI-prescribed grazing. Treatment effects indicated CRP plantings increased densities of three grassland obligates vulnerable to habitat loss, and LPCI grazing increased densities of four species requiring heterogeneity in dense, tall-grass structure (α = 0.1). Population estimates in 2016 indicated the practices conserved breeding habitat for 4.5 million birds (90% CI: 4.0–5.1), and increased population sizes of 16 species , totaling 1.8 million birds (CI: 1.4–2.4). Conservation practices on private land benefited the most vulnerable grassland obligate species (AIC_c weight = 0.53). By addressing habitat loss and degradation in agricultural landscapes, conservation on private land provides a solution to declining avifauna of North America and scales up to meet population recovery goals for the most imperiled grassland birds.     

Development and optimization of an Agro-BGC ecosystem model for C4 perennial grasses

Extrapolating simulations of bioenergy crop agro-ecosystems beyond data-rich sites requires biophysically accurate ecosystem models and careful estimation of model parameters not available in the literature. To increase biophysical accuracy we added C₄ perennial grass functionality and agricultural practices to the Biome-BGC (BioGeochemical Cycles) ecosystem model. This new model, Agro-BGC, includes enzyme-driven C₄ photosynthesis, individual live and dead leaf, stem, and root carbon and nitrogen pools, separate senescence and litter fall processes, fruit growth, optional annual seeding, flood irrigation, a growing degree day phenology with a killing frost option, and a disturbance handler that simulates nitrogen fertilization, harvest, fire, and incremental irrigation. To obtain spatially generalizable vegetation parameters we used a numerical method to optimize five unavailable parameters for Panicum virgatum (switchgrass) using biomass yield data from three sites: Mead, Nebraska, Rockspring, Pennsylvania, and Mandan, North Dakota. We then verified simulated switchgrass yields at three independent sites in Illinois (IL). Agro-BGC is more accurate than Biome-BGC in representing the physiology and dynamics of C₄ grass and management practices associated with agro-ecosystems. The simulated two-year average mature yields with single-site Rockspring optimization have Root Mean Square Errors (RMSE) of 70, 152, and 162 and biases of 43, −87, 156 g carbon m⁻² for Shabbona, Urbana, and Simpson IL, respectively. The simulated annual yields in June, August, October, December, and February have RMSEs of 114, 390, and 185 and biases of −19, −258, and 147 g carbon m⁻² for Shabbona, Urbana, and Simpson IL, respectively. These RMSE and bias values are all within the largest 90% confidence interval around respective IL site measurements. Twenty-four of twenty-six simulated annual yields with Rockspring optimization are within 95% confidence intervals of Illinois site measurements during the mature fourth and fifth years of growth. Ten of eleven simulated two-year average mature yields with Rockspring optimization are within 65% confidence intervals of Illinois site measurements and the eleventh is within the 95% confidence interval. Rockspring optimized Agro-BGC achieves accuracies comparable to those of two previously published models: Agricultural Land Management Alternatives with Numerical Assessment Criteria (ALMANAC) and Integrated Farm System Model (IFSM). Agro-BGC suffers from static vegetation parameters that can change seasonally and as plants age. Using mature plant data for optimization mitigates this deficiency. Our results suggest that a multi-site optimization scheme using mature plant data from more sites would be adequate for generating spatially generalizable vegetation parameters for simulating mature bioenergy crop agro-ecosystems with Agro-BGC.     

Lifting a veil on diversity: a Bayesian approach to fitting relative-abundance models.

Bayesian methods incorporate prior knowledge into a statistical analysis. This prior knowledge is usually restricted to assumptions regarding the form of probability distributions of the parameters of interest, leaving their values to be determined mainly through the data. Here we show how a Bayesian approach can be applied to the problem of drawing inference regarding species abundance distributions and comparing diversity indices between sites. The classic log series and the lognormal models of relative- abundance distribution are apparently quite different in form. The first is a sampling distribution while the other is a model of abundance of the underlying population. Bayesian methods help unite these two models in a common framework. Markov chain Monte Carlo simulation can be used to fit both distributions as small hierarchical models with shared common assumptions. Sampling error can be assumed to follow a Poisson distribution. Species not found in a sample, but suspected to be present in the region or community of interest, can be given zero abundance. This not only simplifies the process of model fitting, but also provides a convenient way of calculating confidence intervals for diversity indices. The method is especially useful when a comparison of species diversity between sites with different sample sizes is the key motivation behind the research. We illustrate the potential of the approach using data on fruit-feeding butterflies in southern Mexico. We conclude that, once all assumptions have been made transparent, a single data set may provide support for the belief that diversity is negatively affected by anthropogenic forest disturbance. Bayesian methods help to apply theory regarding the distribution of abundance in ecological communities to applied conservation.     

Assessment of the Conservation Efforts to Prevent Extinction of the Iberian Lynx

Abstract: The Iberian lynx (Lynx pardinus) may be the first charismatic felid to become extinct in a high‐income country, despite decades of study and much data that show extinction is highly probable. The International Union for Conservation of Nature categorizes it as critically endangered; about 200 free‐ranging individuals remain in two populations in southern Spain. Conservation measures aimed at averting extirpation have been extensively undertaken with 4 of the former 10 Iberian lynx populations recorded 25 years ago. Two of the four populations have been extirpated. The number of individuals in the third population have declined by 83%, and in the fourth the probability of extirpation has increased from 34% to 95%. Major drivers of the pending extinction are the small areas to which conservation measures have been applied; lack of incorporation of evidence‐based conservation, scientific monitoring, and adaptive management into conservation efforts; a lack of continuity in recovery efforts, and distrust by conservation agencies of scientific information. In contrast to situations in which conservation and economic objectives conflict, in the case of the Iberian lynx all stakeholders desire the species to be conserved.     

The Importance of the Water Conservation Areas in the Everglades to the Endangered Wood Stork (Mycteria americana)

During 1985–1989, numbers of the endangered Wood Stork ( Mycteria americana ) in the Water Conservation Areas of the Everglades, Florida, showed an annual peak in February or March of 1233 to 7874 birds. The annual maxima are significantly greater in dry years than in wet years (P < 0.05). Most storks are nonbreeders and are found foraging in open habitats along the eastern and southern parts of Conservation Area 1 and Conservation Area 2A and along the west-central side of Conservation Area 3A. In dry years, large numbers move into the southern half of Conservation Area 3A. We estimated that in spring, the total southeastern United States population of Wood Storks is between 14,000 and 20,000 individuals. The annual maxima of the water conservation areas were at least 8% to 10% of the total southeastern U.S. population in wet years and possibly as much as 55% in dry years. The Water Conservation Areas appear to be critical foraging habitat for wintering storks, especially during drought years when most of the rest of south Florida is dry. Changes in the management of these areas could have a major impact on the status of storks throughout the southeastern United States.     

A Bayesian hierarchical framework for calibrating aquatic biogeochemical models

Model practitioners increasingly place emphasis on rigorous quantitative error analysis in aquatic biogeochemical models and the existing initiatives range from the development of alternative metrics for goodness of fit, to data assimilation into operational models, to parameter estimation techniques. However, the treatment of error in many of these efforts is arguably selective and/or ad hoc. A Bayesian hierarchical framework enables the development of robust probabilistic analysis of error and uncertainty in model predictions by explicitly accommodating measurement error, parameter uncertainty, and model structure imperfection. This paper presents a Bayesian hierarchical formulation for simultaneously calibrating aquatic biogeochemical models at multiple systems (or sites of the same system) with differences in their trophic conditions, prior precisions of model parameters, available information, measurement error or inter-annual variability. Our statistical formulation also explicitly considers the uncertainty in model inputs (model parameters, initial conditions), the analytical/sampling error associated with the field data, and the discrepancy between model structure and the natural system dynamics (e.g., missing key ecological processes, erroneous formulations, misspecified forcing functions). The comparison between observations and posterior predictive monthly distributions indicates that the plankton models calibrated under the Bayesian hierarchical scheme provided accurate system representations for all the scenarios examined. Our results also suggest that the Bayesian hierarchical approach allows overcoming problems of insufficient local data by “borrowing strength” from well-studied sites and this feature will be highly relevant to conservation practices of regions with a high number of freshwater resources for which complete data could never be practically collected. Finally, we discuss the prospect of extending this framework to spatially explicit biogeochemical models (e.g., more effectively connect inshore with offshore areas) along with the benefits for environmental management, such as the optimization of the sampling design of monitoring programs and the alignment with the policy practice of adaptive management.