Minimum Time Required to Detect Population Trends: The Need for Long-Term Monitoring Programs

By Easton R. White. BioScience, 2018

236 citations recorded by the source corpus.

Long-term time series are necessary to better understand population dynamics, assess species' conservation status, and make management decisions. However, population data are often expensive, requiring a lot of time and resources. What is the minimum population time series length required to detect significant trends in abundance? I first present an overview of the theory and past work that has tried to address this question. As a test of these approaches, I then examine 822 populations of vertebrate species. I show that 72% of time series required at least 10 years of continuous monitoring in order to achieve a high level of statistical power. However, the large variability between populations casts doubt on commonly used simple rules of thumb, like those employed by the IUCN Red List. I argue that statistical power needs to be considered more often in monitoring programs. Short time series are likely under-powered and potentially misleading.

Subfields: Demography, Gender Studies, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/ceeccbcc6df8daf63ccbc103590e022821ae048e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Demography

In Gender Studies

In Statistics and Probability

Citations in the corpus

Insect responses to global change offer signposts for biodiversity and conservation (2020, background): “Bioclimate or species distribution models (SDMs) are based on the premise that the environmental niches of species include restricted climatic tolerances, which limit species’ geographic distributions (Guisan et al., 2017).”
Interpreting insect declines: seven challenges and a way forward (2020, methodology): “…the norm rather than the exception (e.g. Redfearn & Pimm, 1988; Roubik, 2001), but it poses serious problems in determining what the baseline reference state should be for historical abundance, and inherently increases the length of time series required to separate signal from noise (White, 2019).”