Ecology

A convincing argument that weather influences COVID-19 can be formulated in three parts: (1) experimental data suggest SARS-CoV-2 persistence on surfaces or in the air is sensitive to temperature, humidity, and ultraviolet light; (2) other environmentally sensitive respiratory viruses are seasonal, and more common in winter; and therefore, (3) climatic effects could be protective over space (hot, dry places might have less transmission) and time (summer might see reduced transmission compared to winter). All three are plausible and are generally consistent, but in many places (including, and especially, on social media), the basic premise of each has been communicated to the public, and policymakers, in a way that obscures key nuance and creates false confidence.
Experimental evidence shows that SARS-CoV-2 is environmentally sensitive
Like other viruses with a lipid envelope, SARS-CoV-2 is probably sensitive to temperature, humidity, and solar radiation; this affects its ability to persist on surfaces and in air, and might have subtle impacts on transmission. But the finer details of microbiology are often lost, leading to false confidence in how lab studies could scale up to the real world. For example, studies showing that germicidal ultraviolet radiation in hospitals and laboratories (ultraviolet C (UV-C) wavelengths) kills the virus have been misconstrued as evidence that sunlight (a mix of UV-A and UV-B) would effectively neutralize the virus in outdoor public spaces, possibly at a scale detectable from case data1. Newer experimental evidence supports the hypothesis that sunlight might also have an effect on SARS-CoV-22, although a global study found only a 1% reduction in transmission linked to environmental UV radiation3. In the real world, these effects will be slight, and unlikely to set hard limits on transmission anywhere in the world4.
Other upper respiratory tract infections are seasonal, with declines in warmer months
Influenza, the common cold, and other respiratory infections show seasonal transmission that coincides with changes in temperature, humidity, and solar radiation. But seasonal epidemics are also a product of the transmissibility of a virus, the initial susceptibility of a population, and the degree and nature of immunity conferred by infections. In basic epidemiological models, stable “oscillations” like seasonal epidemic waves usually require some degree of immunity5,6; at the start of a pandemic, when transmissibility is high and immunity is low, even strong environmental drivers are unlikely to curb transmission. Previous influenza pandemics show the importance of this nuance7. “Seasonal” versus “pandemic” influenza refers not just to different epidemic phases, but entirely different viral strains, and population susceptibility to pandemic strains starts high enough for rapid spread regardless of the season. During the first wave of the 2009 A/H1N1 pandemic, epidemic growth was still possible in August, the most environmentally unfavorable point in the year, with immunity under 20%; months later, immunity—and consequently environmental sensitivity—may eventually have been high enough to lead to a winter-driven third wave8. Scientists anticipate a similar pattern for COVID-19: while the virus could develop seasonal oscillations if it becomes endemic9 (i.e., if pandemic control fails in the long term), current susceptibility to SARS-CoV-2 is high enough that summer weather is unlikely to be protective10.
Environmental drivers could plausibly create seasonal or geographic differences in COVID-19 outbreak intensity
But those drivers’ impacts are heavily confounded by immunity, interventions, human behavior, and other details that are usually left out of models, leading to potentially spurious conclusions. For example, most available contact tracing data indicate that the proportion of indoor transmission is high11,12, a pattern likely caused by a combination of social contact patterns (including both number, intensity, and duration of contacts), air circulation, and potential weather drivers like sunlight or humidity. However, when studies attempt to model links between temperature and transmission, they almost always use gridded climate data or local weather data that represents the outdoors, and is unrepresentative of indoor conditions a virus particle or aerosolized cloud would actually experience in most transmission events.
Perhaps the biggest confounder, social behavior is environmentally driven and seasonal, but is rarely weighed alongside environmental and immunity drivers as a hypothesis for why infectious diseases show seasonality13,14. For example, school terms are seasonal and have a marked influence on social mixing patterns relevant to influenza transmission, even in pandemics15,16,17. Without individual-level transmission data, it can be difficult to distinguish direct biological impacts of weather from behaviorally mediated seasonality, and in some cases, the two are blurred (e.g., vitamin D levels are driven by both weather and seasonal behavior). Confusing the two could easily lead to spurious predictions. If behavioral patterns become unpredictable—either because of externalities like social distancing restrictions or a feedback loop between science and public risk perceptions around seasonality—attempts at forecasting the pandemic based on environmental seasonality will only become more unreliable. More

NEWS Q&A
27 August 2020

The spill released a new type of low-sulfur fuel, and its ecological effects aren’t well studied, says environment advocate Jaqueline Sauzier.

Dyani Lewis

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Around 1,000 tonnes of oil have leaked from the MV Wakashio off Mauritius since 6 August.Credit: Pierre Dalais/EPA-EFE/Shutterstock

When the cargo ship MV Wakashio ran aground on a coral reef on the southeast tip of Mauritius, in the Indian Ocean almost exactly a month ago, it unleashed a vast oil spill. The Japanese-owned vessel held 200 tonnes of diesel and 3,900 tonnes of fuel oil, an estimated 1,000 tonnes of which leaked into the sea when the ship’s hull cracked on 6 August. It is the first reported spill of a new type of low-sulfur fuel that has been introduced to reduce air pollution. The spill has left a 15-kilometre stretch of the coastline — an internationally recognized biodiversity hotspot — smeared with oil.
Jacqueline Sauzier, president of the non-profit Mauritius Marine Conservation Society in Phoenix, has been helping with volunteer efforts to contain the spill. She spoke to Nature about how the clean-up is progressing.
What has been the response to the spill?
Mauritius is not geared up to deal with a catastrophe of this size, so other countries have sent experts to help. A French team arrived first, from the nearby island Réunion, to erect ocean booms — floating structures that contain the spill. The United Nations sent a team including experts in oil spills and crisis management. They’ve been working with communities, the private sector and the government to coordinate clean-up efforts. Marine ecologists and others have arrived from Japan and the United Kingdom.
Mauritians were also very proactive. In one weekend, we made nearly 80 kilometres of make-shift ocean booms out of cane trash — the leftover leaves and waste from sugar-cane processing — to contain the oil. Empty bottles were put in the middle of the booms to make them float, and anchors were attached to keep them from drifting away with the current.
For ten days, people worked night and day to contain as much oil as possible so that it wouldn’t reach the shoreline, where it is more difficult to clean. We managed to contain and remove nearly 75% of the spilled oil. Only a small amount reached the shore. But there’s still the issue of water-soluble chemicals that come from the oil, but dissolve into the water and therefore aren’t scooped out with the oil that sits on the water’s surface.
What ecosystems have been affected?
When you look at images in the media, it can feel like the whole of Mauritius is under oil. But the oil reached only 15 kilometres of the 350-kilometre shoreline, so it could have been much worse.
Unfortunately, there are a lot of environmentally sensitive areas in the region affected. The ship ran aground off Pointe d’Esny and just to the north of Blue Bay Marine Park. These sites are listed under the Ramsar Convention on Wetlands of International Importance as biodiversity hotspots. Ocean currents carried the oil northwards, so fortunately there’s none in the Blue Bay Marine Park, but the mangroves on the shoreline north of Pointe d’Esny have been covered. This will definitely have an impact, because mangroves are the nursery of the marine environment.
The Île aux Aigrettes, a small island near the wreck, has also been affected. The island is home to vulnerable pink pigeons (Nesoenas mayeri) and other native birds, and Telfair’s skink (Leiolopisma telfairii). The Mauritian Wildlife Foundation in Port Louis was already working to restore the island’s unique plants and remove invasive species. The oil didn’t go onto the island itself, but chemicals might have seeped into the corals and fumes from the spill could also have an impact.
Two rivers open into the bay where the oil spill is. The brackish water at the mouths of the rivers is an important ecosystem, and the oil has managed to go up parts of the rivers. The oil slick also floated above a large and rare area of seagrass, which is home to seahorses. Although the oil didn’t touch the seagrass, we fear that chemicals in the water could reach them.

Jacqueline Sauzier is president of the Mauritius Marine Conservation Society in Phoenix.Credit: Jacqueline Sauzier

Are there particular species affected?
It is not one species that could be at risk. It is the whole ecosystem, because of the dispersal of water-soluble chemicals in the water. Filter feeders, such as corals and crustaceans and molluscs, are probably the first to be impacted. We haven’t seen lots of animals dying, but we will need to monitor for signs.
Bad weather over the past two weeks has also forced the ship against the coral reef. That pushed a lot of sand and broken coral over the reef into the lagoon, creating a sand bar just inside the reef. That could change the currents in the lagoon and will have an impact on coral growth.
The social impacts are also a big concern for us. Fishing communities living in the region cannot fish anymore, because the fish that have been caught contain high levels of arsenic.
Something that is also concerning is that we don’t know the possible long-term effects. The oil is a new low-sulfur fuel oil that is being introduced to reduce air pollution. This is the first time that type of oil has spilled, so there have been no long-term studies on the impacts.
What steps are being taken now?
As soon as the ship grounded, people started monitoring the quality of the water. So we have this baseline from before the spill and we know the target that we have to reach for remediation of the water.
Oil-spill experts are formulating a plan to clean the shoreline properly. The impact on the mangroves could be worse if the cleaning is done badly. It could also push chemicals into the sand, which could be released in warm weather a year or two from now.
The front part of the vessel has been tugged away to be sunk along the shipping route. This was the least bad option. The rear is still on the reef. It has been cleaned of fuel, but rust and paint could still cause damage. It’s also falling apart, which can break the coral
Can future spills be avoided?
We were lucky this time that the spill was small and the boat grounded when it did. First, if it had happened in April, we wouldn’t have been able to go out, because we were locked down because of COVID. Second, the cane harvest started at the end of June. So if the wreck had happened earlier, we wouldn’t have had the cane trash readily available to contain the oil.
The spill has opened people’s eyes. Mauritius lies near a shipping highway. Around 2,500 large vessels pass close to Mauritius every month. It is difficult to describe how very big the Wakashio is. From the shore, it’s as if something foreign is sitting on your veranda. But this is the third stranding that we have had in ten years. Each time, there is uproar from the community, saying large vessels are much too close to the island.
It is not the first time, but I really hope it is the last.

doi: 10.1038/d41586-020-02446-7

This interview has been edited for length and clarity.

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Assembling and monitoring the synthetic bacterial communities
We selected three bacterial strains, a Chryseobacterium sp., a Staphylococcus sp., and a Bacillus sp., from a large collection of soil isolates, and we screened them with fluorescence-independent flow cytometry as we did previously [17]. We measured at an acquisition speed of 14 μl min−1 for 2 min per sample and setting a threshold of 10,000 regarding the height signal of the front scatter (FSC-H). Differently than in our previous work, here we acquired all scattering profiles based on growth assays at 30 °C that was the temperature at which we performed all the experiments. In addition, we screened the growing cultures with a temporal resolution of 20, rather than 30, min. We recorded significant interactions among the strains by comparing their single and mixed growth profiles at 30 °C (Dataset 1). These interactions were mainly positive, similarly to what we found previously at other temperatures [17]. We performed all the related growth assays in biological triplicates. All flow cytometry data are available in .fcs format online (http://flowrepository.org) under the “FR-FCM-Z25Q” identifier. Henceforth, when referring to the experiments we use the term “population” to describe the cells of a given strain within a flask at a given assay and the term “community” to describe the total bacterial cells within a flask at a given assay.
Quantification of “background noise”
Our flow cytometry method for screening the mixed bacterial cultures has an accuracy of 97% for sample densities above 105 cells ml−1 [17]. However, at lower densities sampling errors and instrument inconsistencies become increasingly important because the signal-to-noise ratio drops. This can result in substantially different counts among identical samples and can artificially inflate the observed variability. Thus, it was essential to quantify this “noise” before performing the main experiments and subtract it from the observed variability when quantifying drift.
To that end, we made a series of separate experiments to quantify “noise”. In these experiments, we mixed overnight cultures of the three strains in all the seven possible combinations and in final cell densities ranging from 1.6 × 104 to 6.3 × 107 strain−1 ml−1 (corresponding to the expected range of cell densities in the main experiment, see below), and we measured repeated aliquots from the same flask to determine the coefficient of variation (CV—Fig. 1a). We treated the samples in exactly the same way as in the main experiments to include the effect of sampling errors in our calculations. We acquired in total of 99 triplicate measurements of 1–3 populations for a total of 148 observations (Fig. 1a, Dataset 2). We hypothesized that the level of “noise” should be inversely related to the cell density of the sample, because the signal-to-noise ratio decreases at low cell densities in the flow cytometer. Accordingly, we fit different functions for the dependency of CV to cell density (Supplementary Table S1). Finally, we calculated the 99.5% confidence intervals of the best-fitting function (i.e., Michaelis-Menten) using the confint function of the MASS package [18] in R [19], and we defined the false discovery rate based on the number of observations that were above the upper 99.5% confidence interval (Supplementary Fig. S1). Finally, we verified that the levels of noise determined in this study are similar to the variability recorded from technical replicate samples taken during our previous experiment where we used the same bacterial system and instrument with identical settings [17] (Supplementary Fig. S2).
Main experiments
To quantify drift, we monitored the changes in population densities across identical starting communities incubated under the same environmental conditions (Fig. 1b). To that end, we mixed the three strains in all seven possible combinations, i.e., three monocultures, three mixed cultures of two strains and a mixed culture of all three strains, and in three different starting total cell densities (5 × 104 cells ml−1, 105 cells ml−1, and 106 cells ml−1). To perform each growth assay, we first inoculated the respective strains from overnight pure cultures in a single flask containing 300 ml of Luria–Bertani medium (Sigma). To reach the desired starting total cell density, we estimated the cell density of the overnight pure cultures with flow cytometry [17] and we inoculated the respective volume. We then mixed the culture thoroughly and we immediately split the volume equally into three flasks. We next sampled 500 μl from each flask and we compared the variability in the bacterial populations across the three flasks to the expected “background noise” for the same cell density. In specific, we examined the CVs of the bacterial populations and their z-scores compared to the “background noise”, i.e., how many standard deviations an observed CV differs from the expected “background noise” CV at a given cell density. If the observed z-scores were larger than 2 (95% CI), we aborted the given experiment because it indicated that we introduced variability when we mixed and split the cultures and thus the starting cultures could not be considered identical; this happened in ~50% of the cases. If the observed z-scores were lower than 2, indicating that the recorded variability was not statistically different or was less than the expected variability based on the “background noise”, we proceeded with the experiment, incubating the three flasks in the same chamber (New Brunswick Innova 42R, Eppendorf) at 30 °C and with shaking at 80 rpm.
We recorded the starting densities (Dataset 3, z-scores between −6.68 and 1.17) and the densities every 20 min until the end of the fourth hour of incubation starting from the 60th minute. To detect and quantify drift, our main assumption was that any larger-than-expected deviations in the population densities of identical starting communities incubated under the same environmental conditions could only be because of drift. Thus, we compared the observed CVs to the expected CVs based on the “background noise” by deploying the z-score. We quantified drift using two different thresholds:
1.
The “upper threshold” that focused on excluding false-positive observations. In this quantification, we used a cutoff significance level of z  > 3 (99.5% CI) and we ignored the lowest 17.57% of positive observations (i.e., 15 observations, corresponding to the FDR level of the “background noise”) to minimize the detection of false positives.

2.
The “mean threshold” that focused on excluding false negatives and increasing detectability. In this quantification, we used a cutoff significance level of z  > 0, meaning that we scored any observation greater than the mean noise function as positive.

The “upper threshold” quantification probably overestimates drift by taking into account only the highest among the recorded CV values while the “mean threshold” quantification underestimates drift by taking into account some low CV values that are very close to the noise levels. Thus, the “upper threshold” and “mean threshold” quantifications do not represent the true levels of drift (which are hard to define whatsoever in the presence of noise) but they rather represent the upper and lower boundaries within which the true levels of drift lie.
Estimating potential growth variability due to temperature differences within the incubation chamber
To ensure that the recorded variability in the population counts was not due to slight differences in temperature within the incubation chamber, we estimated the potential variability that could have resulted if each strain grew within the extremes of the recorded temperatures in the chamber. For that, we first measured the temperature within each flask at each experimental time point, five times per flask, using a digital immersion thermometer with an accuracy of 0.1 °C. The temperature varied by 0.15 °C ± 0.08 °C on average and by 0.28 °C at maximum. We then calculated the growth rates of each of the strains under the recorded temperature extremes at each time point by interpolating from previously recorded growth rates [17]. We interpolated both with respect to time and with respect to temperature because the previous data were recorded at intervals of 30 min and 0.5 °C (the latter at a range of 25–42 °C). Finally, for each strain, we calculated how the CV in the hypothetical population densities would increase if the strains were constantly growing within the recorded temperature extremes for the duration of the experiment and if the CV was calculated from three observations (like in the real experiments) of the resulting population density distributions. We note that with this analysis, we probably overestimated the hypothetical increase in population densities because we used growth rates from mixed cultures that were generally higher than those in monocultures because of the positive interactions among the strains (Dataset 1).
Simulations
To simulate drift in complex bacterial communities, we used in silico communities with diversity and abundance distributions similar to nature [20] where drift acts with magnitude according to our experimental data. A conceptual flowchart of the simulations can be found in Supplementary Information (Supplementary Fig. S3). Each simulation involved a metacommunity of 100 communities that were connected with dispersal and that initially contained 2000 species each.
We simulated dispersal occurring in a unidirectional way within a closed system; individuals from community n disperse to the community (n + 1) and individuals from community 100 disperse back to community 1. The strength of dispersal equaled to the percentage of individuals that disperse to the respective community and it varied between 2 and 20%. Our aim in modeling dispersal in this way was to create a setting where habitat fragmentation was high and therefore drift’s importance is expected to be more pronounced [21, 22], and where there was no gain or loss of individuals from outside the metacommunity.
We simulated selection as differences in the growth rates among species within a community. The growth rates were distributed normally with a mean of 1 (resembling systems at their carrying capacity) and with a standard deviation between 0.071 and 0.167. Growth rates changed at every generation by being re-drawn from the same distribution in an effort to represent fluctuating habitats where a given species is not always favored or disfavored. Therefore, in our simulations, the standard deviation in the growth rates represents the strength of selection, because the higher it is the bigger are the differences in the growth rates in a community and the changes in the growth of a species from generation to generation. The distribution of the abundances in a community at time zero was log-normal (mean = 4, sd = 1.1) and the distribution of the abundances of a given species across all communities was normal with a standard deviation equal to the strength of selection.
The metacommunity grew for 1000 generations under given dispersal and selection conditions with drift, where drift changed the assigned growth rates at every generation according to a distribution based on the defined threshold from the experimental data (“upper” or “mean” threshold). In parallel, an initially identical metacommunity grew under the same dispersal and selection conditions but without drift, meaning that the assigned growth rates at every generation did not change further. More details and an example on how we modeled changes in growth rates due to drift are presented in the Supplementary Text in Supplementary Information.
For a given generation, we calculated the effect of drift by comparing a given community in the metacommunity that grew under drift to the same community in the metacommunity that grew without drift. In specific, we examined the β-diversity by means of the Bray–Curtis (BC) community similarity and the differences in species richness and in Pielou’s evenness among drift-impacted and drift-free communities, calculating the metacommunity-wise mean and standard deviation on all these properties. Moreover, we kept track of the extinct species at the end of each simulation and we mapped their initial relative abundances, but here we report the metacommunity-wise median because the distribution of the relative abundances is skewed (Supplementary Fig. S4). We ran simulations under 50 different scenarios resulting from five levels of selection strength over ten levels of dispersal rate. To estimate the effect of drift on Bray–Curtis similarity in metacommunities with increasing number of species, we ran the same simulation at the highest selection and lowest dispersal levels, at intermediate selection and dispersal and at the lowest selection and highest dispersal, but we changed the number of species; we ran the simulation three times in metacommunities of 500, 1000, 2000, 4000, 6000, 8000, and 10,000 species. We performed all simulations in R. All code is available on GitHub (https://github.com/sfodel/Drift).
Reported β-diversity in stochastically assembled communities in nature
To compare our simulation results with the results from environmental surveys regarding the β-diversity in stochastically assembled communities, we searched for related studies using the following two criteria: (1) the study cites the works of Stegen and colleagues [12, 13], where the term “undominated” community assembly is presented formally for microbial ecology, (2) the study reports data on the range of the observed β-diversity in terms of Bray–Curtis dissimilarity (or similarity) in stochastically assembled communities, or this range can be inferred from the data presented in that study. More

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For predators, climate change-induced shifts in prey numbers, behaviours and spatial or temporal locations can be a major threat to food security. For predators that hoard prey to ensure survival through harsh winters, climate variation can have a doubled effect — influencing both food capture and store stability. Although northern latitude autumn and winter temperatures have increased dramatically in recent years, the effects of climate on foraging and storing throughout winter remain understudied.

Credit: Szymon Bartosz / Alamy Stock Photo

Giulia Masoero at the University of Turku, Finland, and colleagues analysed the impact of climate on Eurasian pygmy owl (Glaucidium passerinum) food-hoarding behaviour across 16 years. They found increased freeze–thaw frequency, lower winter precipitation and deeper snow cover were linked to greater hoard consumption. Higher autumn precipitation and an early hoarding start led to food rot, which reduced female owl recapture (indicating death or emigration).

Although owls delayed hoarding in autumns with fewer freeze–thaw events, suggesting some potential for climate change adaptation, the study indicates that altered climates can decrease owl overwinter survival, which may in turn have vast impacts on the boreal food web.

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Armarego-Marriott, T. Owls’ hoards rot. Nat. Clim. Chang. (2020). https://doi.org/10.1038/s41558-020-0903-0
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National Institute of Water and Atmospheric Research, Hataitai, New Zealand
Jade Maggs

Endangered Marine Species Research Unit, Borneo Marine Research Institute, Universiti Malaysia Sabah, Kota Kinabalu, Malaysia
B. Mabel Manjaji-Matsumoto

Department of Marine Biology, Texas A&M University at Galveston, Galveston, TX, USA
Philip Matich

Aquarium of the Pacific, Long Beach, CA, USA
Erin McCombs

Khaled bin Sultan Living Oceans Foundation, Annapolis, MD, USA
Llewelyn Meggs

Department of Biodiversity, Conservation & Attractions, Parks & Wildlife WA, Pilbara Region, Nickol, Western Australia, Australia
Stephen Moore

Large Marine Vertebrates Research Institute Philippines, Jagna, The Philippines
Ryan Murray & Alessandro Ponzo

Wasage Divers, Wakatobi and Buton, Indonesia
Muslimin Kaimuddin

Western Australian Fisheries and Marine Research Laboratories, Department of Primary Industries and Regional Development, Government of Western Australia, North Beach, Western Australia, Australia
Stephen J. Newman & Michael J. Travers

Island Conservation Society Seychelles, Victoria, Mahé, Seychelles
Josep Nogués

CORDIO East Africa, Mombasa, Kenya
Clay Obota & Melita Samoilys

The Centre for Ocean Research and Education, Gregory Town, Eleuthera, Bahamas
Owen O’Shea

Department of Environment and Geography, University of York, York, UK
Kennedy Osuka

Center for Fisheries Research, Ministry for Marine Affairs and Fisheries, Jakarta Utara, Indonesia
Andhika Prasetyo

Universitas Dayanu Ikhsanuddin Bau-Bau, Bau-Bau, Indonesia
L. M. Sjamsul Quamar

Pristine Seas, National Geographic Society, Washington, DC, USA
Enric Sala

Department of Zoology, University of Oxford, Oxford, UK
Melita Samoilys

HJR Reefscaping, Boquerón, Puerto Rico
Michelle Schärer-Umpierre

SalvageBlue, Kingstown, Saint Vincent and the Grenadines
Nikola Simpson

School of Natural and Computational Sciences, Massey University, Auckland, New Zealand
Adam N. H. Smith

Indo Ocean Project, PT Nomads Diving Bali, Nusa Penida, Indonesia
Lauren Sparks

Manchester Metropolitan University, Manchester, UK
Akshay Tanna & Alexandra M. Watts

Reef Check Dominican Republic, Santo Domingo, Dominican Republic
Rubén Torres

Institut de Recherche pour le Développement, UMR ENTROPIE (IRD-UR-UNC-CNRS-IFREMER), Nouméa, New Caledonia
Laurent Vigliola

Secretariat of the Pacific Regional, Environment Programme, Apia, Samoa
Juney Ward

Department of Life Science, Tunghai University, Taichung, Taiwan
Colin Wen

School of Environmental and Forest Sciences, University of Washington, Seattle, WA, USA
Aaron J. Wirsing

Corales del Rosario and San Bernardo National Natural Park, GIBEAM Research Group, Universidad del Sinú, Cartagena, Colombia
Esteban Zarza-Gonzâlez More

Spatial regime shifts over time and space
To assess how the dominance of large predatory fish (Eurasian perch Perca fluviatilis, northern pike Esox lucius) and three-spined stickleback (Gasterosteus aculeatus) changed over time and space, we collated juvenile fish abundance data from 13073 samplings conducted during 39 years (1979–2017) in 486 bays along a 1200 km stretch of the Swedish Baltic Sea coast. We used juvenile fish surveys because they (i) include stickleback, (ii) were conducted along the entire coast, including the outer archipelago, and (iii) capture patterns of recruitment failure; a proposed driver of local perch and pike stock decline34,35,37,45. The samplings were conducted by various monitoring programs and research projects to quantify fish recruitment. Much of the data was extracted from the Swedish national database for coastal fish (http://www.slu.se/kul). Other fish species also occurred in the data, but we—like others25,34,45—focused on the most common and strongly interacting species.
The timing and placement of most of the fish surveys was not chosen with this study in mind. We therefore included data from as many surveys as possible, ensuring that the dataset covered (i) gradients in distance to open sea and wave exposure, (ii) the entire Swedish east coast, and (iii) the longest time period possible (Supplementary Fig. 7). Nearly all sampling (94%) was conducted during July-September. To achieve the best possible spatial coverage we also included some bays only sampled in October and (for one bay) June. Initial exploration of data from bays sampled monthly from June to October suggested there were no large differences in the October and June data. Nine of the 486 bays occurred much further into the archipelago (49–67 km) than the rest (20 km from the open sea prior to 1995, we only used data from 1995–2017. Finally, we used a general linear multiple regression model to test how sampling year, wave exposure (two levels) and their interaction explained the maximum distance.
The bimodality in relative predator dominance seen over time and space (Figs. 1–3) could in theory be caused by variability in local abiotic conditions, and not by predator–prey interactions. We therefore tested whether any of four abiotic conditions estimated locally—water surface temperature, salinity, turbidity, water depth—could explain the variability in deviance residuals from the binomial glm (Supplementary Fig. 8), using a regular linear model (after assessing model assumptions and ruling out multicollinearity; see above). Salinity and turbidity (but not depth and temperature) had statistically significant but weak influences (R2 = 0.11), and the bimodality clearly remained (Supplementary Fig. 9).
Finally, we explored how time, distance to open sea, wave exposure, latitude and their two- and three-way interactions influenced log-transformed abundances of (i) predators (perch and pike pooled) and (ii) stickleback, using general linear models. We identified the most parsimonious models as outlined above.
Temporal regime shift at Forsmark: using the 34-year Forsmark time series, we first tested for temporal breakpoints in logit-transformed relative predator dominance data using change-point detection (strucchange) for linear models47. This method estimates the optimal number and (if identified) position of breakpoints using the Bayesian Information Criterion (BIC). Second, we explored what temporal change(s) in perch and stickleback abundances that preceded the shift. Because of highly non-linear patterns we modeled the temporal changes using generalized additive models (GAM) as implemented in the mgvc package68.
Importance of predator–prey reversal for fish recruitment: to assess the relative importance of predator–prey reversal for perch, pike and stickleback recruitment, we used statistical model selection based on path analyses; a form of structural equation modeling that can be used to tease apart direct vs. indirect (mediated) relationships between multiple ( >2) variables, and thereby assess the relative importance of direct vs. indirect relationships in systems48,69. Initial data exploration using multiple regression showed that one bay was a clear outlier due to 0 juvenile perch and pike, generating (i) too high leverage (influence on statistical relationships), (ii) heteroscedasticity and (iii) non-normally distributed errors. Since we suspected that juveniles had already migrated out of this bay, the bay was excluded (resulting in N = 31). Removing this statistical outlier resulted in that the model fulfilled test assumptions and the overall fit more than doubled (adjusted R2 increased from 0.17 to 0.37).
Based on ecological knowledge of the study system, we expressed 14 multivariate hypotheses of the direct and indirect drivers of perch and stickleback recruitment, as graphical network models of interacting paths36,49. Due to the relatively low sample size we restricted the number of paths to 7. The two simplest models assumed that perch+pike and stickleback juvenile abundance in summer (i.e. recruitment) was influenced by adult abundance in spring (stock recruitment) and cumulative cover of rooted vegetation, while adult abundance was explained by spring cumulative cover of all vegetation species36,46, and distance to open sea or wave exposure46. The more complex models included combinations of known predator–prey interactions: perch and pike controlling adult stickleback in spring through predation36, stickleback feeding on juvenile perch and pike45, and stickleback competing with juvenile perch for zooplankton prey37. We then analyzed each model using piecewise path analysis as implemented in the piecewiseSEM package48. First, we tested the goodness of fit of each model to the data using Shipley’s test of directional separation (D-sep)70. If missing paths were identified, they were included in a new model. For the models that fitted the data (p  > 0.05) we used the Akaike’s Information Criterion corrected for small samples (AICc) (calculated using Shipley’s general approach to calculate AIC for path analysis71) to compare relative model fit. A summary of all candidate models and details of the best-fitting model are presented in Supplementary Tables 1 and 2, respectively. The strength of paths in the best-fitting model are presented using standardized path coefficients, which (based on the best-fitting model, see Fig. 3) mean that 1 SD increase in pooled adult perch and pike abundance reduces adult stickleback abundance by 0.53 SD. We also calculated the amount of variation (R2) in adult stickleback abundance, pooled juvenile perch and pike, and juvenile stickleback abundance, that was explained by the paths. Finally, we tested whether the relative predator dominance of adult and juvenile fish in this smaller dataset (N = 31) was also uni- or bimodal, using Hartigan’s dip test (for details, see above).
Reporting summary
Further information on research design is available in the Nature Research Reporting Summary linked to this article. More