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    Can the world save a million species from extinction?

    Indonesia’s bleeding toad (Leptophryne cruentata) is critically endangered.Credit: Pepew Fegley/Shutterstock

    One-quarter of all plant and animal species are threatened with extinction owing to factors such as climate change and pollution. Starting this week, negotiators and ministers from more than 190 countries are meeting at a United Nations biodiversity summit called COP15 in Montreal, Canada, to address the emergency.
    10 startling images of nature in crisis — and the struggle to save it
    From 7 to 19 December, they will be trying to seal a new deal to save Earth’s biodiversity. The treaty, known as the post-2020 Global Biodiversity Framework, is intended to establish precise targets for countries to protect and restore nature, including conserving 30% of the planet by 2030 and cutting nutrient pollution, such as reducing nitrogen fertilizer loss from farmland.Time is running out. “We’re driving species to extinction at a rate about 1,000 times faster than they are created through evolution,” says Stuart Pimm, an ecologist at Duke University in Durham, North Carolina, and head of Saving Nature, a non-profit conservation organization.As COP15 kicks off, researchers and policy experts are concerned that countries still disagree on too many issues to secure a deal that will protect species and ecosystems effectively. Here, Nature looks at the extent of the crisis, and what scientists say countries must do to succeed.Which species are most at risk, and what’s threatening them?Among the most at-risk groups are amphibians and reef-forming corals. A global assessment shows that more than 40% of amphibians are threatened with extinction1, including the critically endangered bleeding toad (Leptophryne cruentata), which lives in Mount Gede Pangrango National Park in Java, Indonesia.These toads were thought to be extinct until the year 2000, when some were spotted by a team led by Mirza Kusrini, a herpetologist at Bogor Agricultural University in Indonesia. But the researchers found that the amphibians were infected with chytrid (Chytridiomycota sp.), a fungus that has devastated global amphibian populations. Kusrini says that climate change is probably making life hard for the tiny toad, which got its common name from the crimson, splatter-like spots covering its body. Warm weather can stimulate fungal outbreaks and shift the timing of behaviours, such as the toads’ breeding season, making the amphibians vulnerable.

    Source: Red List Index/IUCN

    Global warming, which has been raising sea temperatures, is also responsible for harming coral reefs around the globe (see ‘Threat assessment’). Over a period of 9 years, up to 2018, 14% of the world’s coral died out — a massive problem, because today, coral reefs support one-quarter of all marine species.Research shows that climate change is quickly becoming a large threat to biodiversity2. But still, the most-destructive forces are the conversion of land and seas for agricultural uses and people exploiting natural resources through fishing, logging, hunting and the wildlife trade. About 75% of land and 66% of ocean areas have been significantly altered, usually for producing food.What might happen if species disappear?It’s difficult to predict, because doing so requires knowledge of which species are present in a particular ecosystem, such as a rainforest, and what functions they have, says Shahid Naeem, an ecologist at Columbia University in New York City. Much of that information is often unknown. However, scientists have shown3 that ecosystems with less biodiversity are not as good at capturing and converting resources into biomass, such as happens when plants capture nutrients or sunlight used for growth.
    Why deforestation and extinctions make pandemics more likely
    Neither are less-diverse ecosystems as good at decomposing and recycling biological materials and nutrients. For example, studies show that dead organisms are broken down, and their nutrients recycled, more quickly when a high variety of plant litter covers the forest floor4. Ecosystems with low biodiversity also have low resilience — they are not as able to bounce back after a perturbation or shock, such as a fire, as more-diverse systems are, Naeem says.“If we lose parts of our system, it simply won’t function very efficiently, and it won’t be very robust,” he adds. “The science behind that is rock solid.”Ecosystems also provide clean water and can sometimes prevent diseases from spreading to humans. When species are lost, these services deteriorate, Kusrini says. For example, most amphibians eat insects, many of which are considered pests, such as cockroaches, termites and mosquitoes. Studies have shown a rise in cases of malaria — spread by mosquitoes — in areas in Central America where amphibian populations have collapsed5. “You know when they disappear”, Kusrini says, because insect numbers rise and people start using more pesticides to kill them.What solutions do researchers say are needed to protect biodiversity?Protecting and conserving habitats is central to saving species. This idea is captured in the framework being negotiated at COP15. The draft includes the goal of conserving at least 30% of the world’s land and sea by 2030. But for protections to be most effective, they must include regions that are rich in biodiversity, such as tropical forests, Pimm says. Despite an increase in protected areas worldwide over the past ten years, species numbers have still declined, because these safeguards were not in the right places, studies show6.

    Delegates at COP15 in Montreal show their support for a new agreement among nations to protect Earth’s biodiversity.Credit: UN Convention on Biological Diversity (CC BY 2.0)

    “What we’re going to be looking for at COP15 is more quality, not just more quantity,” Pimm says.Eradicating invasive species is another important conservation strategy, and the framework’s draft currently calls for cutting the introduction of such species in half. Some estimates suggest that invasive predators, such as cats and rats, are responsible for more than half of all extinctions of birds, mammals and reptiles7.It’s important that nations agree on a framework with at least some quantifiable targets, so that progress can be measured, and so that countries can be held accountable if they fail to meet their targets, researchers say. “I’m afraid what will happen is, they will produce a long list of ‘waffle’,” Pimm says. “We need quantification.”Will nations manage to agree on a new deal to protect nature?As COP15 begins, the outlook is not good. The text of the draft is still littered with unresolved issues. At a press conference on 6 December, Elizabeth Mrema, executive secretary of the Convention on Biological Diversity — the global treaty that underpins the new biodiversity deal — said that national negotiators had made insufficient progress in a final round of discussions before the start of the summit. She urged countries to compromise, otherwise they will fail to reach a deal. “The state of the planet is in crisis,” Mrema said. “This is our last chance to act.”
    Troubled biodiversity plan gets billion-dollar funding boost
    One key contentious issue is how to finance biodiversity conservation, particularly in low- and middle-income countries, which are home to much of the world’s biodiversity. These nations, including Brazil and Gabon, would like a new fund to be established with US$100 billion added per year in aid. So far, that proposal has not gained traction with wealthier countries. “They really need to have the financial commitments, because things don’t get done without the money,” Naeem says.Despite the pessimism, Naeem is certain that scientists and advocates will keep pushing for a deal. “There would be real change” if countries were able to achieve a universal decrease in biodiversity loss, he says. More

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    Hydrochemical and isotopic baselines for understanding hydrological processes across Macquarie Island

    Field parameters and major ionsThe results of the hydrochemistry and environmental isotopes for the 40 lakes are presented spatially in Figs. S1–S11 and are located in Tables S1 and S2.The lake waters are oxic (8.6–12.6 mg l−1) and range from slightly acidic (pH 6.0) to slightly alkaline (pH 9.2). Lake water temperatures are generally highest for lakes along the west coast (greater than 10 °C, Table S2). Phosphate concentrations are below detection level (0.1 mg l−1) for all lakes and nitrate was low ranging from below detection limit ( More

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    Co-seeding grasses and forbs supports restoration of species-rich grasslands and improves weed control in ex-arable land

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    RNA-Seq comparative study reveals molecular effectors linked to the resistance of Pinna nobilis to Haplosporidium pinnae parasite

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    Land use and land cover changes influence the land surface temperature and vegetation in Penang Island, Peninsular Malaysia

    Study areaPenang is situated in the northern part of Peninsular Malaysia and lies within the latitudes 5°12’N to 5°30′ N and longitudes 100°09’E to 100°26’E (Fig. 7). Penang with a land area of 295 Km2, has an estimated population of 720,000 and is regarded as the most populated island in Malaysia. Penang shares the same border on the north and east with Kedah State and the south with Perak State. There are two main parts of Penang State: Penang Island and the mainland which is also regarded as Seberang Perai. These two parts of the State are connected by the two bridges. The eastern part of Penang Island is the most urbanized area comprising industries, commercial centres and residential buildings. However, the western part is less developed comprising mainly hilly terrain and forests22. This study is focused on the Island part of Penang. This island is endowed with a yearly equatorial climate (hot and humid). It has a mean annual temperature ranging between 27 and 30 °C while the mean annual relative humidity ranges between 70 and 90%. Also, the mean annual rainfall is about 267–624 cm.Figure 7The map of Penang State showing the Penang Island (created by the authors using ArcMap 10.8 software).Full size imageData acquisitionThe flow chart of the methodology is presented in Fig. 8. Landsat satellite images were used for the assessment of changes in land use covering a period of 2010–2021 (11 years).Figure 8The flow chart of the methodology.Full size imageThese images were gotten from the website of the United State Geological Survey (https://earthexplorer.usgs.gov). The Landsat images include the Landsat 5 TM (thematic mapper) and Landsat 8 OLI / TIRS (operational land imager / thermal infrared sensor). These were downloaded from the Landsat level 1 dataset (Table 6) with additional criteria which reduced the.Table 6 The characteristics of the satellite data used.Full size tableDetermination of LST and NDVI for Landsat 5 and 8Band 6 of Landsat 5 and band 10 of Landsat 8 were used for the determination of the land surface temperature (LST). The LST and normalized difference vegetation index were determined using the following steps:Conversion of top of atmosphere (TOA) radianceUsing the radiance rescaling factor, thermal infra-red digital numbers were converted to TOA spectral radiance using the equation below29: (frac{Red – NIR}{{Red + NIR}}) (frac{Red – NIR}{{Red + NIR}}) For Landsat 8,$$ {text{L}}lambda = left( {{text{ML}} times {text{ Qcal}}} right) + left( {{text{AL}} – {text{Oi}}} right) $$
    (1)
    For Landsat 5,$$ {text{L}}lambda = left( {{ }frac{{{text{LMax}}lambda – {text{LMin}}lambda }}{{{text{QcalMax}} – {text{QcalMin}}}}} right) times left( {left( {{text{Qcal }} – {text{QcalMin}}} right) + {text{LMin}}lambda } right) $$
    (2)
    where Lλ is TOA spectral radiance, ML is radiance multiplicative band Number, AL is radiance add band number, Qcal is quantized and calibrated standard product pixel values (DN for band 6 or band 10), Oi is the correction value for the respective bands, LMaxλ is spectral radiance scaled to QcalMax, LMinλ is spectral radiance scaled to QcalMin, QcalMax is maximum quantized calibrated pixel value, and QcalMin is minimum quantized calibrated pixel value.Conversion to TOA brightness temperature (BT)Spectral radiance data were converted to TOA brightness temperature using the thermal constant values in the Metadata file29.Kelvin (K) to Celcius (°C) degrees$$ BT = {raise0.5exhbox{$scriptstyle {K2}$} kern-0.1em/kern-0.15em lower0.25exhbox{$scriptstyle {ln left( {frac{K1}{{{text{L}}lambda { } + { }1}}} right)}$}} – 273.15 $$
    (3)
    where BT is the Top of atmosphere brightness temperature (°C), Lλ is TOA spectral radiance (W.m−2 .sr−1 .µm−1)), K1 is the K1 constant band number, and K2 is the K2 constant band number. For Landsat 5, K1 is 607.76, and K2 is 1260.56.Normalized difference vegetation index (NDVI)The Normalized Difference Vegetation Index (NDVI) is a standardized vegetation index which reveals the intensity of greenness and surface radiant temperature of the area30,31. The index value of NDVI usually ranges from − 1 to 1. The higher NDVI value indicates that the vegetation of the area is denser and healthier. This shows that the NDVI values of normal healthy vegetation range from 0.1– 0.75, while it is almost zero for rock and soil, and negative value for water bodies24. The NDVI is calculated using the followings:$$ {text{NDVI }} = frac{{left( {{text{NIR }}{-}{text{ RED}}} right){ }}}{{left( {{text{NIR }} + {text{ RED}}} right)}} $$
    (4)
    In Landsat 4–7$$ {text{NDVI }} = , left( {{text{Band 4 }}{-}{text{ Band 3}}} right) , / , left( {{text{Band 4 }} + {text{ Band 3}}} right) $$In Landsat 8$$ {text{NDVI }} = , left( {{text{Band 5 }}{-}{text{ Band 4}}} right) , / , left( {{text{Band 5 }} + {text{ Band 4}}} right) $$where: RED = DN values from the RED band, and NIR = DN values from the Near Infra-red band.Land Surface Emissivity (LSE)Land Surface Emissivity is the average emissivity of an element on the surface of the earth calculated from NDVI values.$$ {text{PV }} = left{ {frac{{left( {{text{NDVI }} – {text{ NDVImin}}} right)}}{{left( {{text{NDVImax }} – {text{ NDVImin}}} right)}}} right}^{2} $$
    (5)
    where PV is the Proportion of vegetation, NDVI is the DN value from the NDVI image, NDVImin is the minimum DN value from the NDVI image, and NDVImax is the maximum DN value from the NDVI image.$$ {text{E }} = left( {0.004{ } times {text{PV}}} right) + 0.986 $$
    (6)
    where E is land surface emissivity, PV is the Proportion of vegetation, 0.986 corresponds to a correction value of the equation.Land Surface Temperature (LST)Land Surface Temperature (LST) is the radiative temperature which is calculated using top of atmosphere brightness temperature, the wavelength of emitted radiance and land surface emissivity.$$ {text{LST}} = {raise0.5exhbox{$scriptstyle {BT}$} kern-0.1em/kern-0.15em lower0.25exhbox{$scriptstyle {left( {1 + left( {{lambda } times { }frac{{{text{BT}}}}{{{text{c}}2}}} right) times {text{ln}}left( {text{E}} right)} right)}$}} $$
    (7)
    Here c2 is 14388. The value of λ for Landsat 5 (Band 6) is 11.5 µm and Landsat 8 (Band 10) is 10.8 µm.Where BT is the top of atmosphere brightness temperature, λ is the wavelength of emitted radiance, and E is land surface emissivity.c2 = h*c/s (1.4388*10–2 mK = 14388 mK), h is Planck’s constant (6.626*1034 Js), s is Boltzmann constant (1.38*1023 JK), c is velocity of light (2.998*108 m/s).Determination of land use and land cover (LULC) of the study areaThe Landsat images were pre-screened and subjected to clipping and classification32. The boundary shape file of Penang was used to clip out the area of study.Image classificationThe unsupervised method involving a random assignment of sample training points and supervised methods of satellite image classification was employed in this study for determining the LULC types. This mixture of image classification methods has been reported as vital in achieving a high accuracy level33. Bands 5, 4 and 3 were used to classify Landsat 8 while bands 4, 3 and 2 were used for classifying Landsat 5. We used the extraction by mask in the spatial analyst tool of ArcMap 10.2.1 software to extract the study area from the selected bands of the Landsat satellite images. A widely used supervised image classification method was adopted for classifying the Landsat bands in this study32,34. The principle of operation of this method involves the identification of known sample training points which are then used to classify other unknown points with related spectral signatures35. The three monochromatic satellite bands were combined to produce the false colour composite (FCC) using the data management tool36. This involves drawing polygons on the LULC type to select the training points. The LULC types adopted for this study include urbanized areas, agricultural land, rocks, forests, bare surfaces, and water bodies. These were modified LULC types from IPOC Good Practice Guidance37. To achieve this, a minimum of 40 sample points were selected randomly for each category of LULC type36. Having prior knowledge of the study area assisted in the selection of the training points38.The multivariate maximum likelihood classification (MLC) technique was used for transforming the images. Other image transformation techniques have been used by researchers. These include the fuzzy set classifier, neural networks (NN) classifier, extraction and classification of homogenous objects (ECHO) classifier, per-field classifier, sub-pixel classifier, decision trees (DTs), support vector machines (SVMs), minimum distance classifier (MDC) and so-on39. The adoption of any of these techniques is dependent on the knowledge of the area of study, band selection, accessibility of data, the complexity of the landscape, the classification algorithm, and the proficiency of the analyst39. We preferred MLC to other techniques in this study due to its reported high level of accuracy in tropical regions32,34. Another reason for choosing MLC is that it is readily incorporated in many widely used GIS software packages. This MLC algorithm operates based on assigning pixels to the highest probability class and establishing the class ownership of such pixels. It is also regarded as a parametric classifier whose data follows almost a normal distribution39. We ensured the accuracy of this classifier by assigning a large number of training sample points using our prior knowledge of the study area.Description of the LULC categoriesThe urbanized area is the developed part of the study area. This includes houses, roads, railways, and industries. This is also known to be a settlement in other literature40. Agricultural land is the part of the study area dominated by agricultural activities and herbaceous plants and grasses. Agricultural land is generally a product of deforestation36. Rocks are part of the study area comprising solid mineral materials (rocks). Bare land is the bare soil which is either made open by natural or human activities.Forests are parts of the study area dominated by trees. They can be primary or secondary forests depending on the rate of disturbances. According to41, forest land is an area having more than 0.5 ha of flora comprising trees (height is above 5 m) with a canopy greater than 10%. The forests in Penang are generally both primary and secondary42. Water bodies are parts of the study area covered by water seasonally or permanently. These include seas, rivers, lakes, ponds, streams, or reservoirs40.Determination of change in the LULCThe rate and extent of change in the LULC of Penang within the periods under consideration were determined following the formula below43:$$ {text{Changed area }}left( {{text{C}}_{{text{a}}} } right) , = {text{ T}}_{{text{a}}} left( {text{year 2}} right) , {-}{text{ T}}_{{text{a}}} left( {text{year 1}} right) $$
    (8)
    $$ {text{Changed extent }}left( {{text{C}}_{{text{e}}} } right) , = {text{ C}}_{{text{a}}} /{text{ T}}_{{text{a}}} left( {text{year 1}} right) $$
    (9)
    $$ {text{Percentage of change }} = {text{ C}}_{{text{e}}} {text{x 1}}00 $$
    (10)
    where Ta means the total area.Determination of relationship between LST and NDVIThe values of LST and NDVI at 20 random points of each LULC class were used. The relationship between the LST and NDVI across all the LULC classes in each year was determined using the bivariate linear regression analysis. This was done in Paleontological Statistical (PAST) package 3.0.Classification accuracy assessmentThe classification accuracy was assessed by taking ground truth coordinate data of the LULC of the study area using a geographical positioning system (GPS) device (Garmin Etrex 10). These data were compared with the LULC classified in this study32. Consequently, an error matrix was generated. This normally uses ground truth data to explain the accuracy of the classified LULC. The error matrix comprises the user’s accuracy, the producer’s accuracy, overall accuracy and the Kappa index32.The producer’s accuracy (omission error) represents the probability of the correctly classified reference pixel and it is determined using this formula below:$${text{Producer’s accuracy }}left( % right) , = { 1}00% , – {text{ error of omission}} $$
    (11)
    Also, the user’s accuracy (commission error) represents the probability that the classified pixel matches the one on the ground36 and it is determined using the formula below:$$ {text{User’s accuracy }}left( % right) , = { 1}00% , – {text{ error of commission}} $$
    (12)
    The statistical accuracy of the matrix was determined using the Kappa coefficient44. This Kappa coefficient ranges from − 1 to + 145. Therefore, the overall accuracy of the classification was determined by dividing the total number of correctly classified pixels by the total number of sampled ground truth data40. More

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    Biodiversity and climate COPs

    Restoring the connection between people and the rest of nature hinges on whole-system science, actions and negotiations.
    Those who think about and practise sustainability are constantly looking for holistic interpretations of the world and are trying to understand systemic relations, networks and connections. Biodiversity has all of these things. It shows how every species needs other species to exist and thrive. It shows that all living organisms are part of a sophisticated and fascinating system made up of myriads of links. And humans are undoubtedly a part of it.
    Credit: Pulsar Imagens / Alamy Stock PhotoIn the realm of sustainability, experts also ponder about time: how can life exist and thrive over time? Indeed, the above mentioned fascinating system evolves over time. And, over time, it has to adapt to unexpected change. It does that well when it is healthy, and less well when it is ill and constantly disturbed.For a long time, man-made impacts kept accumulating almost completely unchecked by societies, until the consequences for human well-being became untenable. Nowadays, environmental crises make the headlines regularly. They are nothing but the result of a broken connection between people and the rest of nature.Climate change is one major outcome of the broken human–rest of nature connection and has wide ramifications for both people and the planet. We now face imminent disaster, unequally across the world, yet addressing climate change remains an incredibly thorny task. Country representatives from most nations around the world meet regularly at the Conference of the Parties (COP) to the United Nations Framework Convention on Climate Change (UNFCC) — most recently at COP27, which was held in Egypt — to continue the debate on what actions are needed to move the climate agenda forward, all while disasters continue to hit the most vulnerable populations. The world has seen 27 COP meetings to the UNFCC so far; one wonders how many more meetings will be needed to see real change happen.Interestingly, country representatives also meet regularly to discuss biodiversity protection; biodiversity decline — the other major consequence of the broken human–rest of nature connection — is just as worrying, with severe and ramified implications that are still largely underappreciated by decision-makers. These gatherings are the COP meetings to the Convention on Biological Diversity (CBD). Last year, we wrote about the then forthcoming COP15 to the CBD (Nat. Sustain. 4, 189; 2021), the meeting in which the new conservation targets to be met by 2030 were to be agreed. We highlighted the extent to which experts worried that those new targets might not go far enough. The meeting was postponed more than once due to the COVID-19 pandemic, and it is finally happening on 7 December 2022, in Montreal, Canada. The world has already seen 15 COP meetings to the CBD, how many more meetings will be needed for the biodiversity crisis to be averted?But let’s go back to thinking about sustainability. Experts look for holistic visions of the world. Here is an interesting example of what holism means. Biodiversity decline and climate change are both the result of the broken connection between people and the rest of nature, they ultimately have the same, deep roots. They are mutually reinforcing phenomena: unhealthy biodiversity contributes to climate change, and climate change makes biodiversity ill. All this is bad news for human and planetary well-being. The climate–biodiversity conundrum, at least to some degree, has been recognized at a higher level — during COP27, leaders dedicated one day to biodiversity.Yet, given that these issues are highly interconnected and have the same origin, why is the world insisting on discussing them as separate agendas? Why are we still holding two separate COPs? How are these meetings going to promote any fruitful synergy? How will they lead people to reconnect with the rest of nature? Country representatives should be breaking silos, embracing holism and bringing these intertwined issues, and their multiple ramifications, to the same negotiating table.Nature Sustainability welcomes the long-awaited COP15 to the CBD and hopes that countries will agree on feasible yet ambitious 2030 targets to protect and enhance biodiversity. But most of all, we hope that all of the experts and leaders involved in addressing the environmental crises embrace holism to promote meaningful actions across the world aimed at restoring people’s connection with the rest of nature. We are eager to see progress to this end. In the meantime, the collection we started in March 2021 with Nature Ecology & Evolution has been updated to renew our support to the biodiversity community. More

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    Population fluctuations and synanthropy explain transmission risk in rodent-borne zoonoses

    Predictors of reservoir statusOur analyses include all known rodent reservoirs for zoonotic pathogens (282 species). These reservoirs harbour a total of 95 known zoonotic pathogens (34 viruses, 26 bacteria, 17 helminths, 12 protozoa and six fungi) employing all known modes of transmission (43 vector-borne, 32 close-contact, 28 non-close contact, and 13 using multiple transmission modes) (Supplementary Data 2). Compared to presumed non-reservoirs (species currently not known to harbour any zoonotic pathogens), we observed that reservoir rodents are strikingly synanthropic (Figs. 2, 3a, Table 1). Despite potential geographic biases, and the general possibility that synanthropic species are better studied compared to non-synanthropic species (see Sampling bias and Supplementary Figs. 1, 2), synanthropy emerged as a defining characteristic of nearly all (95%) currently known rodent reservoirs. Of the 155 synanthropic species, only six are considered as truly synanthropic, i.e., predominately, if not exclusively, occurring in or near human dwellings, while the remaining species only occasionally show synanthropic behaviour (Supplementary Data 1).Fig. 2: Predictors of reservoir status.Final structural equation model linking reservoir status of rodent species (n = 269) with their synanthropy and hunting status, population fluctuations (s-index, log-transformed), and adult body mass, controlling for their occurrence in a range of habitats and the number of studies available per species. One-sided (directional) arrows represent a causal influence originating from the variable at the base of the arrow, with the width of the arrow and associated value representing the standardised strength of the relationship. The small double-sided arrows and numbers next to each response (endogenous) variable represent the error variance.Full size imageFig. 3: Characteristics of reservoir and synanthropic rodents.a Reservoir rodents are predominately synanthropic (n = 436 with n (non-reservoir) = 154, n (reservoir) = 282). b Synanthropic rodents display high population fluctuations (high s-index) (n = 269) and c, occur in multiple artificial habitats (n = 269) (Tables 1–3). In a, estimated probability and 95% confidence intervals are shown and in b–c, estimated probability is shown and shaded areas show 95 % confidence intervals.Full size imageTable 1 Summary of best-fit generalized linear mixed effects model for reservoir status (n = 436)Full size tableCompared to non-reservoirs, we also found that rodent reservoirs are disproportionately exploited by humans (hunted for meat and fur). Seventy-two of the regularly hunted rodent species (n = 83) are reservoirs (87%), and hunted rodent species harbour on average five times the number of zoonotic pathogens than non-hunted species (Table 2).Table 2 Summary of rodent characteristics divided by rodent group with respect to hunting, reservoir status, and synanthropic behaviourFull size tableWe explored causal pathways using a structural equation model (SEM) linking synanthropy, reservoir status, and their hypothesized predictors. The final model, which we established a priori, had 17 free parameters and 21 degrees of freedom (n = 269). The model fit, based on the SRMR (standardized root mean squared residual) and the RMSEA (root mean squared error of approximation) indicated a good fit (see Methods). From the initially formulated full model, the pathways linking reservoir status to population fluctuations (s-index, Methods), occurrence in grasslands, number of artificial habitats a species occurs in, and number of studies found per species were not significant and thus removed from the final model (Supplementary Fig. 3). Similarly, pathways linking synanthropy and occurrence in grasslands were not significant and also removed. All reported coefficients for pathways are standardized to facilitate comparisons among the different relationships. The relationships and coefficients below all refer to those in the final model.The focal variable in the model was reservoir status, which was strongly and positively associated with synanthropy and had the highest estimated pathway coefficient (standardised estimate = 0.58, 95% CI 0.49–0.66, Fig. 2). Controlling for synanthropy, species were more likely to be a reservoir with increasing adult weight (0.13, 0.04–0.22). Species that occur in savanna were less likely to be reservoirs (−0.13, −0.22 to −0.04), while hunted species were more likely to be reservoirs (Fig. 2, 0.20, 0.11–0.30).Synanthropy was influenced by four habitat variables: a species was more likely to be synanthropic if it occurs in a higher number of artificial habitats (0.17, 0.04–0.31), and occurs in urban areas (0.14, 0.01–0.27), deserts (0.12, 0.01–0.23), or forests (0.13, 0.02–0.24). Notably, species with higher s-index, and thus larger population fluctuations, were more likely to be synanthropic (0.12, 0.01–0.22), and the s-index itself decreased as adult weight increased (−0.16, −0.27 to −0.04). Finally, hunted species were characterized by higher adult bodyweight (0.35, 0.25–0.44) (Fig. 2).The number of studies per species was positively associated with both a species’ synanthropic behaviour (0.29, 0.19–0.39) and its reservoir status (0.09, 0.00– 0.19), albeit with weaker evidence for the latter effect (p = 0.054) (Fig. 2),The confirmatory generalized linear mixed effects models (GLMMs) (Tables 1, 3), which control for correlation among species within the same family, showed that our SEM results were robust. Indeed, synanthropy was a significant predictor of reservoir status. These models underscore synanthropy as the most important predictor of reservoir status in our analysis (Table 1, Figs. 2–3).Table 3 Summary of best-fit generalized linear mixed effects model for synanthropic status (n = 269)Full size tablePopulation fluctuations affect transmission riskOur newly compiled data on the magnitude of population fluctuations enabled comparative investigations beyond theoretically straightforward predictions that transmission risk increases with reservoir abundance for density-dependent systems. We show that while strong population fluctuations (measured as the s-index) are found frequently in both reservoir and non-reservoir rodents (Table 2), synanthropic rodents exhibit much larger population fluctuations compared to non-synanthropic rodents (Table 2, Figs. 2–3). This pattern was apparent despite broad confidence intervals in the relationship between the s-index and the probability of being synanthropic (Fig. 3b, Tables 2, 3). Taken together, our results suggest that larger population fluctuations in reservoir species increase zoonotic transmission risk via synanthropic behaviours of rodents, thereby increasing the likelihood of zoonotic spillover infection to humans.Habitat generalism and habitat transformation increase transmission riskWe also find that reservoir species thrive in human-created (artificial) habitats (Fig. 3a, c, Tables 2–3), which reflects a general flexibility in their use of diverse habitat types compared to non-reservoir species (Fig. 4a, Table 2). In addition, the number of zoonotic pathogens harboured by a rodent species increased with habitat breadth (r436 = 0.34, p  More