Experimenting With the Past to Improve Environmental Monitoring

Abstract

Long-term monitoring programs are a fundamental part of both understanding ecological systems and informing management decisions. However, there are many constraints which might prevent monitoring programs from being designed to consider statistical power, site selection, or the full costs and benefits of monitoring. Key considerations can be incorporated into the optimal design of a management program with simulations and experiments. Here, we advocate for the expanded use of a third approach: non-random resampling of previously-collected data. This approach conducts experiments with available data to understand the consequences of different monitoring approaches. We first illustrate non-random resampling in determining the optimal length and frequency of monitoring programs to assess species trends. We then apply the approach to a pair of additional case studies, from fisheries and agriculture. Non-random resampling of previously-collected data is underutilized, but has the potential to improve monitoring programs.

Longer study length, standardized sampling techniques, and broader geographic scope leads to higher likelihood of detecting stable abundance patterns in long term black-legged tick studies

“…In our sampling of 289 unique location datasets from 6 projects, we found several study factors can have an impact on the likelihood of a study reaching stability and the likelihood of tick abundance data leading to misleading results if the study does not reach stability. Our findings are consistent with recent research that indicates resampling existing data can give insights into analyzing existing data and designing future monitoring programs (White and Bahlai, 2021). Study length is important to consider in any deer tick study if the data is to reach stability.…”

“…Our results can help inform if and how black-legged tick monitoring data can be reasonably interpreted based on study parameters to make informed, rigorous claims to support management decisions that can help reduce Lyme disease exposure and risk. Moreover, we further demonstrate the value of resampling existing data to give insights into analyzing existing data and designing future monitoring programs (White and Bahlai, 2021).…”

“…‘ windows ’) of the time series data to gain insights into patterns of how data behaves in arbitrarily selected time periods (Bahlai et al 2021). Non-random resampling of existing monitoring data is a powerful and underused tool to understand trends (White and Bahlai 2021).…”

“…Like for many environmental problems, understanding of how study design and monitoring strategies play out over time is critical to underpin environmental management of ticks and public policy about Lyme disease (White and Bahlai, 2021). Yet many biological and field studies on many taxa are short term and limited in scope, leading to concerns about how to interpret the many short-term studies compared to the relatively fewer long term ecological studies (Wauchope et al, 2019; Bahlai et al, 2020).…”

“…Many biological and field studies are short term and limited in scope, leading to concerns about how to interpret the many short-term studies in comparison with the relatively fewer long term ecological studies (Wauchope et al, 2019;Bahlai et al, 2021). Shorter term studies increase the likelihood of finding misleading trends, potentially leading to misinformed management approaches (White and Bahlai, 2021). Natural variability in a stable population might lead a 2-3 year study to appear to capture a strong upward trend in abundance for a given taxon, simply as a result of natural population cycling (Fournier et al, 2019).…”