Ecology

Study site
Our study site is the species-rich sub-alpine meadows located in the eastern part of the Qinghai-Tibetan plateau, Hezuo, China (34°55′N, 102°53′E) with mean elevation approximately 3000 m above sea level. Although the Tibetan Plateau Monsoon and Asian Monsoon28 brings rain, the study region has cold and dry climate, with mean annual temperature of 2.4° C and mean annual precipitation of just 530 mm23. The vegetation is dominated by herbaceous species such as Elymus nutans Griseb (Poaceae), Kobresia humilis (C.A. Mey.) Serg. (Cyperaceae) and Thermopsis lanceolata R. Br. (Fabaceae)23. Human impacts include agricultural exploitation and pastoralism are the primary current land use, which in places have caused serious land degradation. In response, local governments have stopped further agricultural exploitation and constructed fences to restrict livestock grazing. These efforts gave rise to successional chronosequences, such as the ones we use in our study.
We identified a chronosequence of fields that had been undisturbed for 4-, 6-, 10-, 13-, and 40-years (the control)19,23. All our sample sites, except for the control meadows, had been used for agriculture to grow highland barley in the recent past, with cessation of cultivation within the last 4–13 years. The time since last agricultural use was determined by interviews with local farmers. There are 1–10 km apart among the five meadows and all meadows possessed comparable topographic characteristics (e.g., orientation and slope), soil types and climate (Fig. 1A). This chronosequence is one of the same chronosequence in our previous work23 and we have observed that species richness increased from 61 to 82 species during succession, with 50 species sharing among all five successional meadows. Species composition was similar between 4-year and 6-year meadows, with 60 species sharing between these two meadows. Similar patterns were found in late successional meadows, with 70 species shared among 10-year, 13-year and undisturbed meadows.
Figure 1

Location map of our study sites and our quadrat sampling design. (A) locations of five sites representing each of the five successional ages (4-, 6-, 10-, 13-year and undisturbed grassland), (B) the 30 0.5 × 0.5 m2 quadrats sampling design in each of the five successional meadows. The map of Fig. 1A was obtained from Google Earth online version (https://earth.google.com/, access on 12/10/2018). Figure labels on the map were added using Google Earth online toolkit and text labels using Windows image processing software Paint.

Full size image

Field sampling
The vegetation in each field was sampled in August 2013. An area of 120 × 120 m2 was randomly selected in each meadow. Within this area, thirty 0.5 × 0.5 m2 quadrats were regularly arranged in six parallel transects, with 20 m intervals between each two adjacent quadrats (detail please see Fig. 1B). To determine species richness and abundances, in each quadrat we recorded all the aboveground ramets and identified them to species.
To determine aboveground biomass, we removed all the ramets in each quadrat and took them to the laboratory, where they were oven-dried at 100℃ for 2 days and then weighed. Productivity is typically the amount of carbon fixed per unit time, not standing biomass. Here we follow methods of previous diversity–productivity studies in grasslands29,30, which have used aboveground biomass as proxy for productivity.
Functional trait data collection
We quantified the carbon economy of leaves by measuring specific leaf area (SLA, cm2 g−1). We quantified light capture strategy via photosynthesis rate (A, u mol−1). We estimated resistance to abiotic stress via leaf proline content (Pro, mg/kg), seed mass (SM, g) and seed germination rate (SG, %). Importantly, the functional traits for the same species at each successional age separately if they occurred in multiple meadows were measured to ensure that successional age-related intraspecific variation was appropriately incorporated into our analyses. All functional traits were determined as described in our previous work19,22,23 and the detailed procedures were given in the Supplementary Material.
Statistical methods
First, we compared variation during successional change in the proportion of total biomass for the three main functional groups of plants: forbs (dominant in early succession), legumes, and graminoids (both dominant in later succession) to check whether there are significant turnovers in the dominant plant taxa from early to late succession. Then, we used Spearman correlation analysis to quantify whether significantly positive correlations between empirical species diversity (S, numbers of species richness per square meters) and productivity (aboveground biomass per square meters, P) can be observed in each successional meadow.
For each of the five functional traits (SLA, A, Pro, Sm, and SG), we calculated two functional diversity indices: the community-weighted mean (CWM) and functional diversity (FD) represented by Rao’s quadratic entropy (RaoQ).
The two indices were calculated as follows:

$$ {CWM} = sumlimits_{i = 1}^{n} {p_{ij} times t_{ij} } $$
(1)

where pij is the relative abundance of the species i in each 0.5 × 0.5 m2 quadrat j, and tijis the mean trait value of the species i in each successional meadow j.

$$ RaoQ_{i} = sumlimits_{i = 1}^{n} {sumlimits_{i = 1}^{n} {p_{i} times p_{k} times d_{ik} } } $$
(2)

where pi and pkare the relative abundance of species i and k in each 0.5 × 0.5m2 quadrat j respectively and dik is the dissimilarity coefficient based on Euclidean distance between two species i and k in the multivariate trait space of each successional meadow j.
Then, a variance partitioning analysis was used to test the relative contributions of species richness, the CWM and FD represented by RaoQ of these five traits to productivity in each successional meadow. We also used variance partitioning to allocate changes in productivity in each successional meadow arising from four complementary components: (a) variation explained by species richness, (b) variation explained CWM of each of the five traits, (c) variation explained by FD of each of the five traits only, and (d) “unexplained variation”31. Across all successional meadows, species richness, and aboveground biomass, CWM and FD of all five traits (SLA, A, Pro, SM, and SG) were strongly right-skewed, so we log-transformed species richness, and aboveground biomass, CWM and FD of all five traits to meet the assumption of normality required by variance partitioning. At each successional meadow, variance partitioning was done using the function of “varpart” in “vegan” package in R32. All analyses above were performed in R (R Core Team 2019). More

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We observed a contaminant colony that paradoxically grew preferentially on small, spot-plated lawns of C. jejuni cells on Mueller Hinton (MH) agar (1.5% w/v). The MH plate had previously been inoculated with C. jejuni cells spotted and incubated microaerobically at 38 °C overnight before being stored for several days aerobically at room temperature. Transfer of contaminant cells onto new, similarly prepared spot-plated lawns of C. jejuni resulted in the contaminant again growing preferentially atop the C. jejuni lawns, with minimal growth on the rich agar in between spots of C. jejuni lawns (Fig. 1a). The contaminant was isolated for further study and the strain named ML-A2C4.
Fig. 1: Identification of the filamentous motile environmental isolate as Bacillus mobilis ML-A2C4.

a ML-A2C4 filamentous growth on C. jejuni lawn spots (small circles). b ML-A2C4 growth on a control 1.5% agar MH plate (left) and on a MH plate spread with a full confluent C. jejuni lawn (center) after 48 h aerobic incubation at 30 °C. The red box shows a close-up view of the filaments at the growth edge (right). c Quantification of the visible growth diameter on control MH plates (black bars) and plates with C. jejuni lawns (red bars) over time (n = 5) with error bars indicating standard deviation (SD). Statistical analysis was performed for growth diameter on C. jejuni lawn plates versus control plates using the Student’s t test with Welch’s correction, and for 48 vs. 24 h using repeated measures one-way ANOVA, with ****p  More

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NEWS
07 August 2020

Researchers are redoubling efforts to understand links between species loss and emerging diseases — and use that information to predict and stop future outbreaks.

Jeff Tollefson

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Controlling deforestation (shown here, in a tropical rainforest in the Congo Basin) could decrease the risk of future pandemics, experts say.Credit: Patrick Landmann/Science Photo Library

As humans diminish biodiversity by cutting down forests and building more infrastructure, they’re increasing the risk of disease pandemics such as COVID-19. Many ecologists have long suspected this, but a new study helps to reveal why: while some species are going extinct, those that tend to survive and thrive — rats and bats, for instance — are more likely to host potentially dangerous pathogens that can make the jump to humans.
The analysis of around 6,800 ecological communities on 6 continents adds to a growing body of evidence that connects trends in human development and biodiversity loss to disease outbreaks — but stops short of projecting where new disease outbreaks might occur.
“We’ve been warning about this for decades,” says Kate Jones, an ecological modeller at University College London and an author on the study, published on 5 August in Nature1. “Nobody paid any attention.”
Jones is one of a cadre of researchers that has long been delving into relationships among biodiversity, land use and emerging infectious diseases. Their work has mostly flown below the radar, but now, as the world reels from the COVID-19 pandemic, efforts to map risks in communities across the globe and to project where diseases are most likely to emerge are taking centre stage.

Last week, the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) hosted an online workshop on the nexus between biodiversity loss and emerging diseases. The organization’s goal now is to produce an expert assessment of the science underlying that connection ahead of a United Nations summit in New York that’s planned for September, where governments are expected to make new commitments to preserve biodiversity.
Others are calling for a more wide-ranging course of action. On 24 July, an interdisciplinary group of scientists, including virologists, economists and ecologists, published an essay in Science2, arguing that governments can help reduce the risk of future pandemics by controlling deforestation and curbing the wildlife trade, which involves the sale and consumption of wild — and often rare — animals that can host dangerous pathogens.
Most efforts to prevent the spread of new diseases tend to focus on vaccine development, early diagnosis and containment, but that’s like treating the symptoms without addressing the underlying cause, says Peter Daszak, a zoologist at the non-governmental organization EcoHealth Alliance in New York, who chaired the IPBES workshop. He says COVID-19 has helped to clarify the need to investigate biodiversity’s role in pathogen transmission.
The latest work by Jones’s team bolsters the case for action, Daszak says. “We’re looking for ways to shift behaviour that would directly benefit biodiversity and reduce health risks.”
Concentrating risk
Previous research has shown that outbreaks of diseases such as severe acute respiratory syndrome (SARS) and bird influenza that cross over from animals to humans have increased in the past few decades3,4. This phenomenon is likely to be the direct result of increased contact between humans, wildlife and livestock, as people move into undeveloped areas. These interactions happen more frequently on the frontier of human expansion because of changes to the natural landscape and increased encounters with animals. A study published in April by researchers at Stanford University in California found that deforestation and habitat fragmentation in Uganda increased direct encounters between primates and people, as primates ventured out of the forest to raid crops and people ventured in to collect wood5.
But a key question over the past decade has been whether the decline in biodiversity that inevitably accompanies human expansion on the rural frontier increases the pool of pathogens that can make the jump from animals to humans. Work by Jones and others6 suggests that the answer in many cases is yes, because a loss in biodiversity usually results in a few species replacing many — and these species tend to be the ones hosting pathogens that can spread to humans.
For their latest analysis, Jones and her team compiled more than 3.2 million records from several hundred ecological studies at sites around the world, ranging from native forests to cropland to cities. They found that the populations of species known to host diseases transmissible to humans — including 143 mammals such as bats, rodents and various primates — increased as the landscape changed from natural to urban, and as biodiversity generally decreased.

The next step for Jones’s team is to examine the likelihood of disease transmission to the human population. The group has already made this type of evaluation for Ebola virus outbreaks in Africa, creating risk maps based on development trends, the presence of probable host species, and socio-economic factors that determine the pace at which a virus might spread once it enters the human population7. The group’s risk maps accurately captured where outbreaks occurred in the Democratic Republic of the Congo (DRC) in the past few years, suggesting that it is possible to understand and project risks on the basis of relationships between factors such as land use, ecology, climate and biodiversity.
Some researchers urge caution when communicating that biodiversity hotspots are where outbreaks are likely to occur. “My worry, frankly, is that people are going to cut down the forests more if this is where they think the next pandemic is going to come from,” says Dan Nepstad, a tropical ecologist and founder of the Earth Innovation Institute based in San Francisco, California, a non-profit organization that campaigns for sustainable development. Efforts to preserve biodiversity will only work, he says, if they address the economic and cultural factors that drive deforestation and the rural poor’s dependency on hunting and trading wild animals.
Ibrahima Socé Fall, an epidemiologist and head of the World Health Organization’s emergency operations in Africa, agrees that understanding the ecology — as well as the social and economic trends — of the rural frontier will be crucial to projecting the risk of future disease outbreaks. “Sustainable development is crucial,” he says. “If we continue to have this level of deforestation, disorganized mining and unplanned development, we are going to have more outbreaks.”
Coordinating efforts
One message that the IPBES’s upcoming report is likely to deliver is that scientists and policymakers need to treat the rural frontier more holistically, addressing issues of public health, the environment and sustainable development in tandem. In the wake of the COVID-19 pandemic, many scientists and conservationists have emphasized curbing the wildlife trade — an industry worth an estimated US$20 billion annually in China, where the first coronavirus infections appeared. China has temporarily suspended its trade. But Daszak says the industry is just one piece in a larger puzzle that involves hunting, livestock, land use and ecology.

Wildlife markets like this one in Bali, Indonesia, sustain the livelihoods of many people. But they are also under scrutiny as hotspots for pathogen transmission.Credit: Amilia Roso/The Sydney Morning Herald via Getty

“Ecologists should be working with infectious-disease researchers, public-health workers and medics to track environmental change, assess the risk of pathogens crossing over and reduce risky human activities,” he says.
Daszak was an author of last month’s essay in Science, which argued that governments could substantially reduce the risk of future pandemics such as COVID-19 by investing in efforts to curb deforestation and the wildlife trade, as well as in efforts to monitor, prevent and control new virus outbreaks from wildlife and livestock. The team estimated that the cost of these actions would ring in at $22 billion to $33 billion annually, including $19.4 billion for ending trade in wild meat in China — a step that not all experts think is desirable or necessary — and up to $9.6 billion to help curb tropical deforestation. The total investment would be two orders of magnitude less than the $5.6-trillion price tag estimated for the COVID-19 pandemic, the team estimates.

Fall says the key is to align efforts by government and international agencies focused on public health, animal health, the environment and sustainable development. The latest Ebola outbreak in the DRC, which began in 2018 and ended last month, had its roots not just in disease but also in deforestation, mining, political instability and the movement of people. The goal must be to focus resources on the riskiest areas and manage interactions between people and animals, both wild and domestic, Fall says.
With the right collaboration between human health, animal health and environmental authorities, Fall says, “you have some mechanisms for early warnings”.

doi: 10.1038/d41586-020-02341-1

References

1.
Gibb, R. et al. Nature https://doi.org/10.1038/s41586-020-2562-8 (2020).

2.
Dobson, A. P. et al. Science 369, 379–381 (2020).

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Jones, K. E. et al. Nature 451, 990–993 (2008).

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Smith, K. F. et al. J. R. Soc. Interface 11, 20140950 (2014).

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Bloomfield, L. S. P., McIntosh, T. L. & Lambin, E. Landscape Ecol. 35, 985–1000 (2020).

6.
Faust, C. L. et al. Ecol. Lett. 21, 471–483 (2018).

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Redding, D. W. et al. Nature Commun. 10, 4531 (2019).

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A space-based sensor has detected new colonies of emperor penguins on Antarctic sea ice. Credit: Christopher Walton

Ecology
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Images from space bolster the population count, but the birds remain vulnerable to climate change.

From their vantage point high above Antarctica, sharp-eyed satellites have spotted eight previously unknown colonies of emperor penguins. The discovery boosts emperor penguin numbers by 5–10%.
The iconic birds breed and raise their young on sea ice frozen to Antarctica’s shoreline. These habitats are threatened by climate change, so scientists have been working to get a complete census of emperor penguins (Aptenodytes forsteri) to assess how the bird’s populations might change.
Peter Fretwell and Philip Trathan at the British Antarctic Survey in Cambridge, UK, used the European Space Agency’s Sentinel-2 satellites to search for dark smudges of guano-stained ice. They identified eight newfound penguin colonies located around the rim of the continent; one was on sea ice frozen around icebergs grounded far offshore. Using the images, the authors also pinpointed three colonies that had been reported in the 1960s and 1980s but not confirmed since.
The findings bring the total number of emperor penguin colonies to 61. Many are in areas vulnerable to climate change. More