A road map for designing and implementing a biological monitoring program

Reynolds, Joel H.; Knutson, Melinda G.; Newman, Ken; Silverman, Emily; Thompson, William L.

doi:10.1007/s10661-016-5397-x

Cited by 42 publications

(93 citation statements)

References 73 publications

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“…First, increasing the number of study sites to hundreds rather than tens of sites would improve spatial replication and thus help to offset the spatial variation in trend. Managers and scientists seeking to design a monitoring program could start by carefully considering the magnitude of the trend (positive or negative) that is important to detect (Reynolds et al 2016). If it is not feasible to monitor a sufficient number of sites for a sufficient time period, the results must be interpreted carefully, as the analysis might falsely indicate that the population is stable if power is insufficient to detect a trend.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2018

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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.

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Section: Discussionmentioning

confidence: 99%

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

et al. 2018

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“…By following the steps outlined in Reynolds et al. () and using the master sample for point generation, we believe that the monitoring programmes undertaken at all levels will have improved efficiency and contribute to the overall knowledge of the population of interest.…”

Section: Discussionmentioning

confidence: 99%

“…The actual required sample size and visitation frequency should reflect the monitoring objectives and follow a similar process as outlined by Reynolds et al. (). See Figure for an example of clipping the master sample on the South Island into the first 500 samples that fall within EMUs.…”

Section: Application: New Zealand Terrestrial Monitoringmentioning

confidence: 99%

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Using balanced acceptance sampling as a master sample for environmental surveys

Dam‐Bates¹,

Gansell²,

Robertson

2018

Methods Ecol Evol

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Well‐designed environmental monitoring programmes for management organisations are important for evidence‐based decision making. However, many environmental problems are not single agency issues that require intervention or monitoring at one spatial scale. A master sample can be used to coordinate and scale monitoring designs to ensure consistency in information gathered and robustness of estimators at the different spatial scales. We propose using balanced acceptance sampling (BAS) to generate a master sample. In this context, practical applications and justification of BAS as a master sample are addressed. These include sample generation, stratification, unequal probability sampling, rotating panel designs, and regional intensification. A method for incorporating legacy sites is also provided. Using BAS as a master sample is conceptually simple, gives good spatial balance over different spatial scales, and is computationally efficient to generate. An example for terrestrial biodiversity monitoring in New Zealand is provided. Environmental monitoring can benefit from increased coordination between agencies. A master sample is an excellent way to incorporate coordination directly into the sample design. BAS improves on methods previously described and provides an effective method to monitor populations at multiple spatial scales.

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“…Study design is a critical component of any monitoring program (Reynolds et al ), and the amount of observation error expected can often be reduced by standardizing data collection (Lindenmayer and Likens ). Although careful data standardization may result in substantial reductions in estimator bias, failing to design sampling that would rigorously account for known imperfect observation necessarily assumes that observation error remains spatially and temporally constant (Kéry and Schmidt ).…”

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confidence: 99%

Improving bald eagle nest monitoring with a second spring survey

2017

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Failure to account for observation error can cause bias in estimators of variables important for wildlife management and conservation, such as abundance, occupancy, and species composition. Therefore, long‐term monitoring programs need to evaluate sources of bias to ensure that objectives of the program can be met. Although trend estimation is possible using biased estimators, population status, and management decisions can be sensitive to random and systematic heterogeneity in the observation process. For example, standard methods used to monitor reproductive attempts at bald eagle (Haliaeetus leucocephalus) nests fail to quantify observation error. We used bald eagle nest surveys from 3 national parks in Alaska, USA, to examine their ability to accurately estimate nesting attempts and hypothesized that our ability to perfectly observe nesting activity was related to the timing of the survey relative to peak nest attendance by eagles. We therefore, developed 3 scenarios that evaluated our ability to correctly identify nesting status metrics: peak nesting remained unchanged (observed variation scenario), reduced ability to predict peak nest attendance (high heterogeneity scenario), and peak nest attendance occurred earlier than the survey window (low detection scenario). Eagle nest attendance was consistently underestimated using naïve estimators, but productivity (predicted no. chicks/nesting attempt) was not affected by imperfect detection. All scenarios showed that using replicate observations to model imperfect detection corrected estimator bias for nest attendance and improved the estimator precision for both metrics. Our study demonstrates that imperfect observation of bald eagle nesting activity using standard methods results in estimator bias and reduced precision if not corrected. Further, our simulations show that changes in observation conditions can affect estimator bias and precision, which is an important consideration for long‐term monitoring programs. Therefore, formally modeling imperfect detection should be completed for monitoring programs designed to use nesting status to inform management or conservation decisions. © 2017 The Wildlife Society.

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A road map for designing and implementing a biological monitoring program

Cited by 42 publications

References 73 publications

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

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

Using balanced acceptance sampling as a master sample for environmental surveys

Improving bald eagle nest monitoring with a second spring survey

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