A Three-Tiered Approach to Long Term Monitoring Program Optimization

Long term monitoring optimization (LTMO) has proved a valuable method for reducing costs, assuring proper remedial decisions are made, and streamlining data collection and management requirements over the life of a monitoring program. A three-tiered approach for LTMO has been developed that combines a qualitative evaluation with an evaluation of temporal trends in contaminant concentrations, and a spatial statistical analysis. The results of the three evaluations are combined to determine the degree to which a monitoring program addresses the monitoring program objectives, and a decision algorithm is applied to assess the optimal frequency of monitoring and spatial distribution of the components of the monitoring network. Ultimately, application of the three-tiered method can be used to identify potential modifications in sampling locations and sampling frequency that will optimally meet monitoring objectives. To date, the three-tiered approach has been applied to monitoring programs at 18 sites and has been used to identify a potential average reduction of over one-third of well sampling events per year. This paper discusses the three-tiered approach methodology, including data compilation and site screening, qualitative evaluation decision logic, temporal trend evaluation, and spatial statistical analysis, illustrated using the results of a case study site. Additionally, results of multiple applications of the three-tiered LTMO approach are summarized, and future work is discussed.     

Investigating the causes of fish community change in the Dahan River (Taiwan) using an autecology matrix

Recent work in river restoration and water resources management has seen the need to change the focus of analysis from reach to watershed scales to better define causes of watershed impairment. However, comprehensive investigations at a watershed scale are hindered by difficulties in using reach data that was collected for analysis at small spatial and short temporal scales. This is especially true for ecological and biological data. The approach assembles assessment and monitoring data and uses an autecology matrix to identify the changes in environmental and ecological conditions that may be associated with community change over spatial and temporal scales appropriate for ecosystem analysis in watersheds. The analysis uses a weight-of-evidence approach based on the percent of the community associated with a matrix factor. We have used the autecology matrix to examine historical fish community data from the Dahan River, Taiwan. The results show that the method provides an improved understanding of historical influences on fish community structure and supports a process-based analysis of community change over watershed scales and historic time periods. Further the method helps identify habitat requirements for the fish communities at each sampling site, supporting management and ecological restoration objectives.     

Strategies and Decision-Support Tools for Optimizing Long-Term Groundwater Monitoring Plans—MAROS 2.0

Existing long-term groundwater monitoring programs can be optimized to increase their effectiveness/efficiency with the potential to generate considerable cost savings. The optimization can be achieved through an overall evaluation of conditions of the contaminant plume and the monitoring network, focused spatial and temporal sampling analyses, and automated and efficient management of data, analyses, and reporting. Version 2.0 of the Monitoring and Remediation Optimization System (MAROS) software, by integrating long-term monitoring analysis strategies and innovative optimization methods with a data management, processing, and reporting system, allows site managers to quickly and readily develop cost-effective long-term groundwater monitoring plans. The MAROS optimization strategy consists of a hierarchical combination of analysis methods essential to the decision-making process. Analyses are performed in three phases: 1) evaluating site information and historical monitoring data to obtain local concentration trends and an overview of the plume status; 2) developing optimal sampling plans for future monitoring at the site with innovative optimization methods; and 3) assessing the statistical sufficiency of the sampling plans to provide insights into the future performance of the monitoring program. Two case studies are presented to demonstrate the usefulness of the developed techniques and the rigor of the software.     

Strategies and Decision-Support Tools for Optimizing Long-Term Groundwater Monitoring Plans—MAROS 2.0

Existing long-term groundwater monitoring programs can be optimized to increase their effectiveness/efficiency with the potential to generate considerable cost savings. The optimization can be achieved through an overall evaluation of conditions of the contaminant plume and the monitoring network, focused spatial and temporal sampling analyses, and automated and efficient management of data, analyses, and reporting. Version 2.0 of the Monitoring and Remediation Optimization System (MAROS) software, by integrating long-term monitoring analysis strategies and innovative optimization methods with a data management, processing, and reporting system, allows site managers to quickly and readily develop cost-effective long-term groundwater monitoring plans. The MAROS optimization strategy consists of a hierarchical combination of analysis methods essential to the decision-making process. Analyses are performed in three phases: 1) evaluating site information and historical monitoring data to obtain local concentration trends and an overview of the plume status; 2) developing optimal sampling plans for future monitoring at the site with innovative optimization methods; and 3) assessing the statistical sufficiency of the sampling plans to provide insights into the future performance of the monitoring program. Two case studies are presented to demonstrate the usefulness of the developed techniques and the rigor of the software.     

How Much is Enough? Estimating the Minimum Sampling Required for Effective Monitoring of African Reserves

Effective biological monitoring in developing countries requires a balance of rigour and practicality. Unfortunately, there exist few general guidelines to help practitioners design monitoring programs that reach this balance. Here, we analyse a 33-year monitoring program from Ghana, West Africa, to provide both specific and general suggestions for monitoring in developing countries. Since the late 1960s the Ghana Wildlife Division has monitored more than 40 wildlife species with monthly surveys at sites throughout Ghana's nature reserves. These data present unparalleled opportunities to illuminate the scale and pattern of changes in animal abundance over time and the forces that drive these changes. We used sub-sampling of the Ghana monitoring data for four species in two savanna reserves to identify the minimum level of monitoring necessary to reliably detect changes in wildlife populations over 5-year intervals. We used a similar approach to estimate the minimum sampling needed to infer changes in abundance of hunters in reserves. Our results highlight the relative importance of comprehensive spatial and temporal sampling and suggest a requirement of no less than one monitoring site per every 285 km² in large reserves and 65 km² in smaller reserves. We discuss briefly the cost of effective monitoring and the relevance of our results to other regions of Africa and the world.     

Monitoring temporal trends in spatially structured populations: how should sampling effort be allocated between space and time?

Estimating temporal trends in spatially structured populations has a critical role to play in understanding regional changes in biological populations and developing management strategies. Designing effective monitoring programmes to estimate these trends requires important decisions to be made about how to allocate sampling effort among spatial replicates (i.e. number of sites) and temporal replicates (i.e. how often to survey) to minimise uncertainty in trend estimates. In particular, the optimal mix of spatial and temporal replicates is likely to depend upon the spatial and temporal correlations in population dynamics. Although there has been considerable interest in the ecological literature on understanding spatial and temporal correlations in species’ population dynamics, little attention has been paid to its consequences for monitoring design. We address this issue using model‐based survey design to identify the optimal allocation of sampling effort among spatial and temporal replicates for estimating population trends under different levels of spatial and temporal correlation. Based on linear trends, we show that how we should allocate sampling effort among spatial and temporal replicates depends crucially on the spatial and temporal correlations in population dynamics, environmental variation, observation error and the spatial variation in temporal trends. When spatial correlation is low and temporal correlation is high, the best option is likely to be to sample many sites infrequently, particularly when observation error and/or spatial variation in temporal trends are high. When spatial correlation is high and temporal correlation is low, the best option is likely to be to sample few sites frequently, particularly when observation error and/or spatial variation in temporal trends are low. When abundances are spatially independent, it is always preferable to maximise spatial replication. This provides important insights into how spatio‐temporal monitoring programmes should be designed to estimate temporal trends in spatially structured populations.     

Consequences of ignoring spatial variation in population trend when conducting a power analysis

Long‐term, large‐scale monitoring programs are becoming increasingly common to document status and trends of wild populations. A successful program for monitoring population trend hinges on the ability to detect the trend of interest. Power analyses are useful for quantifying the sample size needed for trend detection, given expected variation in the population. Four components of variation (within‐year variation at a given site, interannual variation within a site, variation among sites in the interannual variation, and variation among sites in mean abundance or density) are commonly considered in power analyses for population trend, but a fifth is rarely considered: variation among sites in the local trend. Spatial variation in trend is expected to reduce statistical power, but the magnitude of this reduction has not been fully explored. We used computer simulations to evaluate the consequences of ignoring spatial variation in trend under a variety of sampling designs and wide ranges of other components of variation. The effect of spatial variation in trend on power was minor when other input parameters took extreme values that made the trend either very difficult or very easy to detect. However, at moderate values of the other parameters, spatial variation in trend had a strong effect, reducing statistical power by up to 60%. In some cases, ignoring spatial variation in trend resulted in an 80% probability of a type I error (falsely detecting a trend in a stable population). Spatial variation in trend is therefore an important consideration when designing a long‐term monitoring program for many species, especially those affected by local conditions at sites that are repeatedly surveyed. If variation in trend is ignored, as in many previous power analyses, the recommended sampling design will likely be insufficient to detect the trend of interest and lead to potentially false conclusions of a stable population.     

Long-term monitoring data meet freshwater species distribution models: Lessons from an LTER-site

Long-term monitoring datasets provide a solid framework for ecological research. Such a dataset from the German long-term ecological research (LTER) site Rhine-Main-Observatory was used to set up a species distribution model (SDM) for the Kinzig catchment. The extensive knowledge on the monitoring data provided by the LTER-site framework allowed to calibrate a robust model for 175 taxa of stream macroinvertebrates and to project their distributions on the Kinzig River stream network using bioclimatic, topographical, hydrological, land use and geological predictors. On average, model performance was good, with a TSS of 0.83 (±0.09 SD) and a ROC of 0.95 (±0.03 SD). The model delivered valuable insights on three sources of bias that plague SDMs in general: (a) level of taxonomic identification of the modeled organisms, (b) the spatial arrangement of sampling sites, and (c) the sampling intensity at each sampling site. Taxonomic resolution did not affect SDM performance. The distribution of high predicted probabilities of occurrence in the stream network coincided with those segments in the stream network most densely and frequently sampled, indicating both a spatial and temporal sampling bias. Species richness curves confirmed the temporal sampling bias. Next to spatial bias, sampling frequency also plays an important role in data collection, affecting further analysis and modeling procedures. Results indicate an underrepresentation of low order streams, an important aspect that should be addressed by both monitoring schemes and modeling approaches.     

A Bayesian hierarchical trend analysis finds strong evidence for large-scale temporal declines in stream ecological condition around Melbourne, Australia

Knowledge of temporal change in ecological condition is important for the understanding and management of ecosystems. However, analyses of trends in biological condition have been rare, as there are usually too few data points at any single site to use many trend analysis techniques. We used a Bayesian hierarchical model to analyse temporal trends in stream ecological condition (as measured by the invertebrate-based index SIGNAL) across Melbourne, Australia. The Bayesian hierarchical approach assumes dependency amongst the sampling sites. Results for each site "borrow strength" from the other data because model parameter values are assumed to be drawn from a larger common distribution. This leads to robust inference despite the few data that exist at each site. Utilising the flexibility of the Bayesian approach, we also modelled change over time as a function of catchment urbanisation, allowed for potential temporal and spatial autocorrelation of the data and trend estimates, and used prior information to improve the estimate of data uncertainty. We found strong evidence of a widespread decline in SIGNAL scores for edge habitats (areas of little or no flow). The rate of decline was positively associated with catchment urbanisation. There was no evidence of such declines for riffle habitats (areas with rapid and turbulent flow). Melbourne has experienced a decline in rainfall, indicative of either drought and/or longer-term climate change. The results are consistent with the expected coupled effects of these rainfall changes and increasing urbanisation, but more research is needed to isolate a causal mechanism. More immediately, however, the Bayesian hierarchical approach has allowed us to identify a pattern in a biological monitoring data set that might otherwise have gone un-noticed, and to demonstrate a large-scale temporal decline in biological condition.     

Demonstration of an integrated watershed assessment using a three-tiered assessment framework

Watersheds are useful templates for wetland protection and land use planning because they integrate cumulative effects that better inform site-specific management decisions. The goal of this study was to demonstrate application of a three-tiered assessment paradigm in the San Gabriel watershed (Los Angeles County, California) that incorporates monitoring at varying spatial scales and intensities. Data on wetland extent and distribution, habitat condition using rapid assessment, and intensive site monitoring were used to show how different levels of assessment can be used together to provide a deeper contextual understanding of overall wetland condition. Wetland sites in the less developed portions of the watershed were of higher overall condition compared to sites located in the more urbanized portions of the watershed. GIS analysis revealed that percent impervious surface is a useful landscape-scale indicator of riverine wetland condition. Furthermore, rapid assessment metrics were significantly correlated with stressors found at sites. Significant correlations also existed between riverine habitat condition, water chemistry, and benthic macroinvertebrate communities across streams in this watershed. This study highlights the following key concepts: (1) application of a multiple indicator approach at different spatial scales and sampling intensities promotes a better understanding of the causal relationships between land use, wetland condition, and anthropogenic stress, (2) a multi-tiered monitoring approach can provide a cost-effective means of integrating wetland status and trends assessments into routine watershed monitoring programs, and (3) a three tiered approach to monitoring provides wetland managers with an effective organizational tool that can be used to prioritize management activities.     

Analysing the performance of a micro soil solution sampling device in a laboratory examination and a field experiment

The performance of a micro soil solution sampling device was tested in a laboratory examination and in a field experiment. The instrument allows detection of temporal and spatial changes in soil solution chemistry at a spatially high resolution. The flexible tube of the suction cell is made of a porous polymer with a diameter of 2.3 mm. To achieve more stability and to minimize disturbance of the instrument during field installation, the original device was modified by embedding the suction cell in a stainless steel and pressure absorbing corpus. During a laboratory test the new sampling system was compared to ceramic P-80 suction cells. Solution samples taken with the new device adapted more quickly to the given concentrations compared to the ceramic suction cells. In a field test, micro samplers were implanted in an existing soil solution monitoring plot, equipped with standard ceramic samplers. Bi-weekly sampling using the micro cells indicated high temporal and spatial variation, and in June 1995 it was possible, to identify a distinct nitrification. However, in a statistical comparison of the entire sampling period and respective sub-sampling areas the two sampler types indicated identical concentration ranges for nitrate. It is concluded that the new micro samplers can help to identify processes in soils which may cause short-term changes in the soil solution chemistry, whereas the standard sampling technique with ceramic cells seems to be still a suitable tool if long-term mean soil solution concentrations are to be measured.     

Cost considerations for long-term ecological monitoring

For an ecological monitoring program to be successful over the long-term, the perceived benefits of the information must justify the cost. Financial limitations will always restrict the scope of a monitoring program, hence the program’s focus must be carefully prioritized. Clearly identifying the costs and benefits of a program will assist in this prioritization process, but this is easier said than done. Frequently, the true costs of monitoring are not recognized and are, therefore, underestimated. Benefits are rarely evaluated, because they are difficult to quantify. The intent of this review is to assist the designers and managers of long-term ecological monitoring programs by providing a general framework for building and operating a cost-effective program. Previous considerations of monitoring costs have focused on sampling design optimization. We present cost considerations of monitoring in a broader context. We explore monitoring costs, including both budgetary costs, what dollars are spent on, and economic costs, which include opportunity costs. Often, the largest portion of a monitoring program budget is spent on data collection, and other, critical aspects of the program, such as scientific oversight, training, data management, quality assurance, and reporting, are neglected. Recognizing and budgeting for all program costs is therefore a key factor in a program’s longevity. The close relationship between statistical issues and cost is discussed, highlighting the importance of sampling design, replication and power, and comparing the costs of alternative designs through pilot studies and simulation modeling. A monitoring program development process that includes explicit checkpoints for considering costs is presented. The first checkpoint occurs during the setting of objectives and during sampling design optimization. The last checkpoint occurs once the basic shape of the program is known, and the costs and benefits, or alternatively the cost-effectiveness, of each program element can be evaluated. Moving into the implementation phase without careful evaluation of costs and benefits is risky because if costs are later found to exceed benefits, the program will fail. The costs of development, which can be quite high, will have been largely wasted. Realistic expectations of costs and benefits will help ensure that monitoring programs survive the early, turbulent stages of development and the challenges posed by fluctuating budgets during implementation.     

Quantifying spatial and temporal variability of macroinvertebrate metrics

Since the introductions of the Habitat Directive and the European Water Framework Directive, water authorities are now obliged to monitor changes in conservation value/ecological quality on larger spatial scales (opposed to site scale), as well as to indicate the level of confidence and precision of the results provided by the monitoring programs in their river basin management plans (European Commission, 2000). To meet these requirements, analyses of the statistical power of the monitoring programs should be implemented. Currently, the statistical properties associated with aquatic monitoring programs are often unknown. We collected macroinvertebrate samples from 25 meso-eutrophic drainage ditches in the Netherlands and selected 7 taxonomic richness metrics for the evaluation of spatial and temporal variability. Simulations were performed to investigate the effects of changes in (1) the total number of species included in a taxonomic richness metric and (2) the relative number of rare species included in a taxonomic richness metric. Of the 7 metrics evaluated, the number of common species required the smallest number of monitoring sites, followed by the number of Gastropoda species, and the number of species. Also, results showed that metric variability will decrease when the proportion of rare species included in a taxonomic richness metric is reduced or the total number of species included is increased. Irrespective of the metric applied a large effort will be required to detect change within drainage ditches in the Wieden, due to high spatial variability. Therefore, we need to explore the possibilities of applying alternative more cost-effective methods for sampling and sample processing in biomonitoring programs.     

Power, precaution, Type II error and sampling design in assessment of environmental impacts

Increasingly, environmental managers attempt to incorporate precautionary principles into decision making. In any quantitative analysis of impacts, precaution is closely related to the power of the analysis to detect an impact. Designs of sampling to detect impacts are, however, complex because of natural spatial and temporal variability and the intrinsic nature of the statistical interactions which define impacts. Here, pulse and press responses and impacts that affect time courses (temporal variance) were modelled to determine the influences of increasing temporal replication—sampling more times in each of several longer periods before and again after an impact.Increasing the number of control or reference locations and number of replicate sample units at each time and place of sampling investigated the influence of spatial replication on power. From numerous scenarios of impacts, with or without natural spatial and temporal interactions (i.e. not caused by an impact), general recommendations are possible. Detecting press impacts requires maximal numbers of control locations. Shorter-term pulse impacts are best detected when the number of periods sampled is maximized. Impacts causing changes in temporal variance are most likely to be detected by sampling with the greatest possible number of periods or times within periods.To allow precautionary decision making, the type of predicted impact should be specified with its magnitude and duration. Only then can sampling be designed to be powerful, thereby allowing precautionary concepts to be invoked.     

Implementation of an Electronic Monitoring and Evaluation System for the Antiretroviral Treatment Programme in the Cape Winelands District,South Africa: A Qualitative Evaluation

BackgroundA pragmatic three-tiered approach to monitor the world’s largest antiretroviral treatment (ART) programme was adopted by the South African National Department of Health in 2010. With the rapid expansion of the programme, the limitations of the paper-based register (tier 1) were the catalyst for implementation of the stand-alone electronic register (tier 2), which offers simple digitisation of the paper-based register. This article engages with theory on implementation to identify and contextualise enabling and constraining factors for implementation of the electronic register, to describe experiences and use of the register, and to make recommendations for implementation in similar settings where standardisation of ART monitoring and evaluation has not been achieved.MethodsWe conducted a qualitative evaluation of the roll-out of the register. This comprised twenty in-depth interviews with a diverse sample of stakeholders at facility, sub-district, and district levels of the health system. Facility-level participants were selected across five sub-districts, including one facility per sub-district. Responses were coded and analysed using a thematic approach. An implementation science framework guided interpretation of the data.ConclusionIn this study we found that relative advantage of an intervention and stakeholder engagement are critical to implementation. We suggest that without these aspects of implementation, formative and summative outcomes of implementation at both the adoption and coalface stages of implementation would be negatively affected.     

Assessment of the required performance and the development of corresponding program decision rules for neglected tropical diseases diagnostic tests: Monitoring and evaluation of soil-transmitted helminthiasis control programs as a case study

Recently, the World Health Organization established the Diagnostic Technical Advisory Group to identify and prioritize diagnostic needs for neglected tropical diseases, and to ultimately describe the minimal and ideal characteristics for new diagnostic tests (the so-called target product profiles (TPPs)). We developed two generic frameworks: one to explore and determine the required sensitivity (probability to correctly detect diseased persons) and specificity (probability to correctly detect persons free of disease), and another one to determine the corresponding samples sizes and the decision rules based on a multi-category lot quality assurance sampling (MC-LQAS) approach that accounts for imperfect tests. We applied both frameworks for monitoring and evaluation of soil-transmitted helminthiasis control programs. Our study indicates that specificity rather than sensitivity will become more important when the program approaches the endgame of elimination and that the requirements for both parameters are inversely correlated, resulting in multiple combinations of sensitivity and specificity that allow for reliable decision making. The MC-LQAS framework highlighted that improving diagnostic performance results in a smaller sample size for the same level of program decision making. In other words, the additional costs per diagnostic tests with improved diagnostic performance may be compensated by lower operational costs in the field. Based on our results we proposed the required minimal and ideal diagnostic sensitivity and specificity for diagnostic tests applied in monitoring and evaluating of soil-transmitted helminthiasis control programs.     

A proposed framework for the development and qualitative evaluation of West Nile virus models and their application to local public health decision-making

West Nile virus (WNV) is a globally distributed mosquito-borne virus of great public health concern. The number of WNV human cases and mosquito infection patterns vary in space and time. Many statistical models have been developed to understand and predict WNV geographic and temporal dynamics. However, these modeling efforts have been disjointed with little model comparison and inconsistent validation. In this paper, we describe a framework to unify and standardize WNV modeling efforts nationwide. WNV risk, detection, or warning models for this review were solicited from active research groups working in different regions of the United States. A total of 13 models were selected and described. The spatial and temporal scales of each model were compared to guide the timing and the locations for mosquito and virus surveillance, to support mosquito vector control decisions, and to assist in conducting public health outreach campaigns at multiple scales of decision-making. Our overarching goal is to bridge the existing gap between model development, which is usually conducted as an academic exercise, and practical model applications, which occur at state, tribal, local, or territorial public health and mosquito control agency levels. The proposed model assessment and comparison framework helps clarify the value of individual models for decision-making and identifies the appropriate temporal and spatial scope of each model. This qualitative evaluation clearly identifies gaps in linking models to applied decisions and sets the stage for a quantitative comparison of models. Specifically, whereas many coarse-grained models (county resolution or greater) have been developed, the greatest need is for fine-grained, short-term planning models (m–km, days–weeks) that remain scarce. We further recommend quantifying the value of information for each decision to identify decisions that would benefit most from model input.     

Temporal scaling of bacterial taxa is influenced by both stochastic and deterministic ecological factors

Microorganisms operate at a range of spatial and temporal scales acting as key drivers of ecosystem properties. Therefore, many key questions in microbial ecology require the consideration of both spatial and temporal scales. Spatial scaling, in particular the species-area relationship (SAR), has a long history in ecology and has recently been addressed in microbial ecology. However, the temporal analogue of the SAR, the species-time relationship, has received far less attention even in the science of general ecology. Here we focus upon the role of temporal scaling in microbial ecological patterns by coupling molecular characterization of bacterial communities in discrete island (bioreactor) systems with a macroecological approach. Our findings showed that the temporal scaling exponent (slope), and therefore taxa turnover of the bacterial taxa-time relationship decreased as selective pressure (industrial wastewater concentration) increased. Also, as the concentration of industrial wastewater increased across the bioreactors, we observed a gradual switch from stochastic community assembly to more deterministic (niche)-based considerations. The identification of broad-scale statistical patterns is particularly relevant to microbial ecology, as it is frequently difficult to identify individual species or their functions. In this study, we identify wide-reaching statistical patterns of diversity and show that they are shaped by the prevalent underlying ecological factors.     

Multivariate statistical approach to the temporal and spatial patterns of selected bioindicators observed in the North Sea during the years 1995–1997

A comprehensive database, containing biological and chemical information, collected in the framework of the bilateral interdisciplinary MARS project (”biological indicators of natural and man-made changes in marine and coastal waters”) during the years 1995–1997 in the coastal environment of the North Sea, was subjected to a multivariate statistical evaluation. The MARS project was designated to combine a variety of approaches and to develop a set of methods for the employment of biological indicators in pollution monitoring and environmental quality assessment. In total, nine ship cruises to four coastal sampling sites were conducted; 765 fish and 384 mussel samples were analysed for biological and chemical parameters. Additional information on the chemical background at the sampling sites was derived from sediment samples, collected at each of the four sampling sites. Based on the available chemical data in sediments and black mussel (Mytilus edulis) a pollution gradient between the selected sites, was established. The chemical body burden of flounder (Platichthys flesus) from these sites, though, did not reflect this gradient equally clear. In contrast, the biological information derived from measurements in fish samples displayed significant a regional as well as a temporal pattern. A multivariate bioindicator data matrix was evaluated employing a factor analysis model to identify relations between selected biological indicators, and to improve the understanding of a regional and temporal component in the parameter response. In a second approach, applying the k-means algorithm on the data matrix, two significantly different clusters of samples, characterised by the current health status of the fish, were extracted. Using this classification a temporal, and in the second order, a less pronounced spatial effect was evident. In particular, during July 1996, a clear sign of deteriorating environmental conditions was extracted from the biological data matrix. Received: 20 June 1999 / Received in revised form: 8 November 1999 / Accepted: 8 November 1999     

Using value of information to prioritize research needs for migratory bird management under climate change: a case study using federal land acquisition in the United States

In response to global habitat loss, many governmental and non‐governmental organizations have implemented land acquisition programs to protect critical habitats permanently for priority species. The ability of these protected areas to meet future management objectives may be compromised if the effects of climate change are not considered in acquisition decisions. Unfortunately, the effects of climate change on ecological systems are complex and plagued by uncertainty, making it difficult for organizations to prioritize research needs to improve decision‐making. Herein, we demonstrate the use of qualitative value of information analysis to identify and prioritize which sources of uncertainty should be reduced to improve land acquisition decisions to protect migratory birds in the face of climate change. The qualitative value of information analysis process involves four steps: (i) articulating alternative hypotheses; (ii) determining the magnitude of uncertainty regarding each hypothesis; (iii) evaluating the relevance of each hypothesis to acquisition decision‐making; and (iv) assessing the feasibility of reducing the uncertainty surrounding each hypothesis through research and monitoring. We demonstrate this approach using the objectives of 3 U.S. federal land acquisition programs that focus on migratory bird management. We used a comprehensive literature review, expert elicitation, and professional judgement to evaluate 11 hypotheses about the effect of climate change on migratory birds. Based on our results, we provide a list of priorities for future research and monitoring to reduce uncertainty and improve land acquisition decisions for the programs considered in our case study. Reducing uncertainty about how climate change will influence the spatial distribution of priority species and biotic homogenization were identified as the highest priorities for future research due to both the value of this information for improving land acquisition decisions and the feasibility of reducing uncertainty through research and monitoring. Research on how changes in precipitation patterns and winter severity will influence migratory bird abundance is also expected to benefit land acquisition decisions. By contrast, hypotheses about phenology and migration distance were identified as low priorities for research. By providing a rigorous and transparent approach to prioritizing research, we demonstrate that qualitative value of information is a valuable tool for prioritizing research and improving management decisions in other complex, high‐uncertainty cases where traditional quantitative value of information analysis is not possible. Given the inherent complexity of ecological systems under climate change, and the difficulty of identifying management‐relevant research priorities, we expect this approach to have wide applications within the field of natural resource management.