NEWS
2024 Report (Partial)
Background
One of our main goals is to determine if insect abundance is currently changing across the continent. Declines in insect abundance and diversity have been found in many countries and regions, but the scope and causes of the declines are unclear and controversial (Wagner et al. 2021). Much of the evidence for declines comes from long-term (10+ years) studies in western European countries (48% of studies), and there have been relatively few studies in other areas (29% in North America and 10% in Asia; n = 165 studies in van Kink et al. 2020).
Highlights
1) Total biomass (wet mass/day) is approximately the same in North America as in Germany where biomass declined 75% over 35 years (data from Hallman et al. 2017). So we already have “low” insect abundance relative to some other places. It is unknown whether we have been at this level for awhile or it is still changing, but see below. Note that last year I mentioned our biomass was lower than in Germany, but I had not adjusted for a difference in size of the traps (7%).
2) Over the six years of study 2019-2024, total insect biomass (after 1 hr of drying) generally declined from 2019 to 2020, but then increased. In terms of linear trends, total biomass is slightly increasing over the six years of study.
3) However, biomass of individual orders showed both declines (Nematocera 3.7% per year) and increases (eg, Lepidoptera, Hymenoptera).
4) Some of these results are borderline in significance (P ~ 0.05), so it would help to continue for another season (2025). It is particularly important to get multiple years of data from the same trap site to increase our statistical power by reducing between-site variation. These analyses are still in progress.
Results
We now have a total of 2102 samples from 217 trap locations and 113 research groups over the six years (2019-2024) of the project (Fig. 1). Note that sampling is not yet complete, and some ( n= 73) samples still need to be processed.
Fig. 1. Biomass per day (g) across North America at NAIAN sampling locations. Size of the bubble indicates the amount of biomass (after 1 hr of drying at room temperature) and color indicates year of sampling (earlier years may be hidden). Note that there are 73 more samples to process and add to this map. Regions correspond to the ones used in our Ecology paper (Dunn et al. 2023). The large circle in southern FL in 2023 was due to a very high biomass (30 g) of lovebugs (Plecia nearctica, a bibionid fly) at Buck Island Ranch (Archbold Biological Station). The average daily biomass (dried 1 hr) across all traps was 0.97 g (SD = 1.284, N = 1920).
Total “wet” biomass in North America and Germany
The Malaise traps used in Germany by Hallman et al. (2017) had an intercept area of 1.89 m2 compared to 1.82 m2 (1.65 x 1.10 m) for the BugDorm Model I and II used in this study. Thus, we adjusted the total “wet” biomass for this study by the difference in area (7.1%).
After controlling for maximum temperature (averaged over each sampling period), wet biomass in North America was similar to wet biomass in Germany (Fig. 2). This result is based on a mixed model predicting wet biomass per day with year group (the five groups shown in Fig. 2), maximum temperature (Tmax), year group and year group * Tmax as fixed factors; trap location was a random effect. All of these fixed effects were significant (P<0.002).
Fig. 2. Least squares means (LSM) of total “wet” biomass per day (log10 +0.1, adjusted for size of the trap). LSM not connected by the same letter are not different from a Tukey’s test (P<0.05). Thus, after correcting for temperature, wet biomass in North America (NAIAN) is not significantly different from any of the time periods in Germany (Hallman et al. years). LSM are from a mixed model predicting wet biomass per day (adjusted for trap size) with year, maximum temperature (Tmax), year group and year group * Tmax as fixed factors, and trap location as a random effect. All fixed effects were significant (P<0.002). Vertical bars around each mean are 95% CIs.
Temporal trends for Individual Taxa
Our previous results (Dunn et al. 2023) showed that taxa differed greatly in phenology and their responses to temperature. So it is possible that certain taxa are declining (or increasing) even though there is no trend for total biomass. Thus, I also analyzed some of the main orders with a mixed model that included year, starting date, maximum temperature and region. Here I used a continuous version of “year” to examine consistent linear trends over time, rather than any annual change (up or down).
Nematocera (midges, crane flies, mosquitoes, etc) biomass showed a significant decline of 3.7 percent per year (P= 0.047). However, Coleoptera (beetles), Hemiptera (true bugs) and Hymenoptera (wasps, bees, etc) showed significant increases of 4 to 9.5 percent per year. Total biomass tended to increase as well (1.9%/yr. P=0.059).
Conclusions
There was a significant decline in Nematocera over the six years of study. Nematocerans are an important food source for birds and mammals that feed on aerial insects, such as swallows, nightjars, and bats (Shipley et al. 2022). Other groups, such as Coleoptera and Hymenoptera are increasing (~ 4.3% per year). In my experience, we do not capture many large bees (Family Apidae) in our Hymenoptera collections, so we may be sampling a different group of insects than the honey bees and bumblebees that are declining in other surveys that use different methods, particularly different types of traps (eg, Kerr et al. 2015, Ascher et al. 2020). Similarly, there are reports of butterflies declining (Crone et al. 2019, Davis et al. 2024), but our Lepidoptera collections show a borderline increasing trend (b = 0.039, P =0.054). Again, we may be catching more moths (particularly at night) while most butterfly surveys focus on day-flying and possibly larger and more colorful species, although I do see substantial numbers of sulphurs (Family Pieridae).
There is more to analyze, including more detailed analyses of each taxonomic group and additional weather and environmental predictors, such as land cover.
Questions?
Contact Peter Dunn pdunn@uwm.edu
Literature Cited