Optimizing landscape-scale monitoring programmes to detect the effects of megafires

13Citations
Citations of this article
25Readers
Mendeley users who have this article in their library.

This article is free to access.

Abstract

Aim: By virtue of their spatial extent, landscape-scale monitoring programmes may be well-suited to support before-after/control-impact (BACI) studies of major disturbances such as megafires, but they may only have a few affected sites, reducing statistical power. I tested whether a variety of hypothetical but logistically plausible survey designs could provide statistically powerful assessments of hypothetical but empirically based population responses to megafire. Location: Simulation focused on fire-prone forests. Methods: I varied the number of affected sites, detection probability, baseline occupancy and species’ responses to fire to create 72 monitoring scenarios. I then simulated 10 years of site occupancy data in which a megafire or other disturbance occurred between years 5 and 6 (n = 500 iterations). Results: Statistical power to correctly reject the null hypothesis of no population response to megafire was high (mean across all scenarios = 0.78), but power to identify the correct population response (e.g. post-fire occupancy declined and then recovered) was low (mean = 0.29). Statistical power to not underestimate the effect of a megafire on site occupancy was fairly high (mean = 0.66), but power to accurately estimate site occupancy was low (mean = 0.25). Statistical power increased with the number of affected sites (i.e., sample size) and with the intensity of the focal species’ response to megafire. Case study simulations based on an existing acoustic monitoring programme in the Sierra Nevada, USA, indicate that it is likely to identify Spotted Owl and Black-backed Woodpecker population responses to megafires. Researchers can use the included simulation tutorial to implement their own power analyses. Main conclusions: Landscape-scale monitoring programmes can identify population changes following megafires, but cannot reliably produce nuanced results, especially with only five years of post-fire data. Smaller-bodied species, which could be studied at more sites, are likely to be better focal species for megafire BACI studies. Aggregating landscape-scale studies’ sampling coverage across many fire footprints to study the overall effects of megafires—rather than the effects of individual fires—may be a more informative approach.

References Powered by Scopus

Uninformative parameters and model selection using akaike's information criterion

2777Citations
N/AReaders
Get full text

Increasing western US forest wildfire activity: Sensitivity to changes in the timing of spring

936Citations
N/AReaders
Get full text

Quantitative evidence for increasing forest fire severity in the Sierra Nevada and southern Cascade Mountains, California and Nevada, USA

605Citations
N/AReaders
Get full text

Cited by Powered by Scopus

Design considerations for rapid biodiversity reconnaissance surveys and long-term monitoring to assess the impact of wildfire

14Citations
N/AReaders
Get full text

Fire ecology for the 21st century: Conserving biodiversity in the age of megafire

13Citations
N/AReaders
Get full text

Combining financial costs and statistical power to optimize monitoring to detect recoveries of species after megafire

5Citations
N/AReaders
Get full text

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Cite

CITATION STYLE

APA

Wood, C. M. (2022). Optimizing landscape-scale monitoring programmes to detect the effects of megafires. Diversity and Distributions, 28(3), 479–492. https://doi.org/10.1111/ddi.13308

Readers over time

‘21‘22‘23‘240481216

Readers' Seniority

Tooltip

PhD / Post grad / Masters / Doc 12

60%

Researcher 7

35%

Professor / Associate Prof. 1

5%

Readers' Discipline

Tooltip

Agricultural and Biological Sciences 8

50%

Environmental Science 6

38%

Social Sciences 1

6%

Earth and Planetary Sciences 1

6%

Save time finding and organizing research with Mendeley

Sign up for free
0