Long-term ecological research and the COVID-19 anthropause: A window to understanding social-ecological disturbance

Abstract

The period of disrupted human activity caused by the COVID-19 pandemic, coined the "anthropause," altered the nature of interactions between humans and ecosystems. It is uncertain how the anthropause has changed ecosystem states, functions, and feedback to human systems through shifts in ecosystem services. Here, we used an existing disturbance framework to propose new investigation pathways for coordinated studies of distributed, long-term social-ecological research to capture effects of the anthropause. Although it is still too early to comprehensively evaluate effects due to pandemic-related delays in data availability and ecological response lags, we detail three case studies that show how long-term data can be used to document and interpret changes in air and water quality and wildlife populations and behavior coinciding with the anthropause. These early findings may guide interpretations of effects of the anthropause as it interacts with other ongoing environmental changes in the future, particularly highlighting the importance of long-term data in separating disturbance impacts from natural variation and long-term trends. Effects of this global disturbance have local to global effects on ecosystems with feedback to social systems that may be detectable at spatial scales captured by nationally to globally distributed research networks.

Keywords: LTER; ecosystems; feedback; press; pulse; recovery; reorganization; resilience.

FIGURE 1

A framework for understanding the pause in human activity resulting from a global pandemic as a disturbance event (a) that disrupts the existing ecosystem state dynamics (b) through direct and indirect effects on air, water soils, and biota (c, d) and via social–ecological feedback (e) that results in a reordered ecosystem state with different spatiotemporal dynamics (f). A key attribute of long‐term ecological research is the ability to capture whether this reorganization occurs, and if it does, how it affects resilience to subsequent disturbance and the potential for sustainable solutions (g). Simplified from Gaiser et al. (2020)

FIGURE 2

Anticipated effects of the anthropause disturbance on terrestrial and aquatic biogeochemistry (y‐axis) and plant, wildlife, and agriculture (x‐axis) at U.S. Long Term Ecological Research network sites (see Table 1 for abbreviations and detailed rationale for the qualitative placement of sites)

FIGURE 3

March–April averaged NO₂ tropospheric column density for the contiguous United States in 2019 (a) and 2020 (b). Source: NO₂ data were derived from the TROPOspheric Monitoring Instrument onboard European Space Agency's Copernicus Sentinel‐5 precursor satellite. A notable decline in NO₂ density was observed across major urban centers, presumably due to various lockdown measures. The most noticeable reduction was seen in the northeast United States, the region where COVID‐19 was most prevalent in the early phases of the pandemic.Note: Vertical column NO₂ (daily images) have higher uncertainty over less polluted regions yielding negative values at times. We have averaged NO₂ concentration over 2 months (March–April) to minimize the variability due to sensor noise

FIGURE 4

Trends in annual mean (a) chlorophyll a (mg/L) and (b) photosynthetically active radiation (PAR; nm) in Green Lake 4 at Niwot Ridge Long Term Ecological Research (LTER). Measurements were taken mid‐lake at a 3‐m depth and up to six sampling events occurred per year although PAR sampling was suspended between 2005 and 2015. A smoothing function has been added to both figures for demonstration. No apparent effect of the anthropause was detectable at annual resolution of the data amid a decadal‐scale trend in both parameters

FIGURE 5

Sixteen‐day mean enhanced vegetation index (EVI) for estuarine and marine wetlands within the Georgia Coastal Ecosystems Long Term Ecological Research (LTER) domain (USFWS National Wetland Inventory) (a). EVI is a well‐recognized index for evaluating vegetation “greenness,” and was derived from NASA MODIS MOD90GA surface reflectance. Wetland MODIS pixels were filtered following O'Connell et al. (2017) to remove intermittent tidal flooding effects on spectral reflectance. (b) Cropland EVI time series sampled from the coastal plain region of Georgia. The uninterrupted green‐up in croplands in spring 2020 is indicative of a reduction in human interventions (e.g., harvesting). Data for 2013–2019 are represented as means (black points) and SDs (gray shaded region)

FIGURE 6

Comparison of (a) monthly true color images and (b) estimates of absorption by colored dissolved organic matter (CDOM [aCDOM]) at 355 nm (m⁻¹) in Georgia coastal waters in 2019 and 2020. Time series of (c) Landsat 8 derived a CDOM for the Georgia Coastal Ecosystems Long Term Ecological Research (LTER) domain showing monthly means for the years 2013–2019 compared to 2020. Time series of (d) area‐averaged monthly means (2013–2019) of surface runoff compared to 2020 and (e) monthly means of surface precipitation (2013–2019) compared to 2020 for coastal Georgia. The surface runoff and precipitation data were derived from NASA's MERRA‐2 long‐term global re‐analysis database (MERRA‐2 Model M2TMNXLND v5.12.4). Data for 2013–2019 are represented as means (black points) and SDs (gray shaded region), and 2020 data are represented as red points. Source: Landsat 8‐OLI. The CDOM model (R ² = 0.74) used in this study was originally developed for Landsat 5‐TM (Joshi & D'Sa, 2015). However, both Landsat 8‐OLI and Landsat 5‐TM have similar green and red bands (band centers and bandwidths) that were used in the CDOM model, and therefore, we assume the impact of the sensor differences would be minimal in CDOM estimation. Note about uncertainty: The model was developed for Barataria Bay, Louisiana. We have not tuned the model for coastal Georgia because of the lack of in situ data

FIGURE 7

(a) Fishing practices at three different Long Term Ecological Research (LTER) sites including commercial lobster trapping at the Santa Barbara Coastal LTER (SBC, photo by Jono Wilson), local spearfishing from shore at Moorea Coral Reef LTER (MCR, photo by Jean Wencélius), and recreational angling from a boat at North Temperate Lakes LTER (NTL, photo by Noah Lottig). (b) Abundance of selected taxa subjected to fisheries pressure at these three sites. Solid lines connect observed average abundance/biomass per sample per year. Blue dotted lines represent a LOESS smoother to capture the trends in fish abundance. Gray shaded areas show the SE around the mean (dotted line). Gray dashed lines indicate hypothesized directions of response to the anthropause based on anecdotal evidence about human changes in fishing activity. For each site, data presented represent major fisheries at that location: For SBC, data presented are for lobster capture at sites where commercial fishing is permitted (Reed, 2020); for MCR, data reported are in units of fishable biomass compiled for targeted species >15 cm in length (Brooks, 2021); for NTL data reported are from Lake Monona and consist of the top two game species fished in that location, Bluegill and Largemouth Bass, harvested by electrofishing (Magnuson et al., 2019)

FIGURE 8

Long Term Ecological Research (LTER), Long Term Agricultural Research (LTAR), and National Ecological Observatory Network (NEON) sites depicted along axes of population density and percent of built environment. Note that data describing population and built environment were not available for marine LTER sites (BLE, NGS, and NGA) and that some sites are members of multiple networks. See Table 1 for the LTER site abbreviation key