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    Using satellite imagery to evaluate precontact Aboriginal foraging habitats in the Australian Western Desert

    ‘Foraging habitat suitability’ is a reference to the favorability of a patch of land for day-to-day subsistence. Here, suitability is an index value ascribed to each potential foraging patch (grid cell) captured in a raster image, based on terrain movement costs and the proximity of each patch to water and green vegetation. We constructed our foraging habitat suitability model using satellite-derived environmental data, digital terrain information and anthropological field data on foraging range (Fig. 3). The model’s environmental foundation is based on more than two decades of continuous near bi-weekly Landsat-5 satellite observations, allowing for the systematic detection and measurement of water recurrence and vegetation condition for every 30-x-30 m image pixel. This period of observation is long enough to observe multiple fluctuations in this highly variable environment and to not be restricted to a single short-term climatic state, such as a bushfire or drought. Therefore the time frame provides a reliable observation and measurement of maximum vegetation greenness, regardless of temporary drops in NDVI. Similarly, maximum extent and occurrence of surface water is systematically measured through long-term satellite observations, avoiding measurements only of phases of drought or irregular rainfall. For this reason our model focuses on maximal values to represent the best environmental conditions that would have been available for past foraging activities since the last glacial, based on the contemporary climatic regime.Figure 3A satellite derived model of foraging habitat suitability for the Australian Western Desert. Foraging habitat suitability is highly variable within IBRA boundaries and throughout the Western Desert. Several massive areas of low-ranked foraging habitats are evident throughout the region. IBRA codes and excavated rockshelter sites (lime green- numbered) are defined in the Fig. 1 caption. Map created in ESRI ArcGIS Desktop 10.5.1 (https://desktop.arcgis.com), linear stretch (1.0%) visualization. See “Methods” section for source raster information.Full size imageThe model also uses the ALOS World 3D 30 m digital elevation data product to quantify terrain ruggedness across the study area46. Terrain ruggedness is a geomorphometric measure of land surface ruggedness, where elevation variability is used to infer ease of traversal when walking between locations in the landscape. Terrain ruggedness is suggestive of potential energy expenditure, assuming that increasingly rugged terrains necessitate higher levels of physical activity and caloric intake. Here, we integrated measures of ruggedness with environmental satellite data, providing an indication of which patches of vegetation and water are most easily accessed in regards to minimum changes in elevation.Walking time to observed surface water is the final spatial parameter incorporated with the model. It is calculated using Tobler’s47 hiking algorithm and information on daily foraging practices. Historic anthropological data indicates Western Desert foraging activities typically operated for 4–6 h each day1,48, with foragers moving up to a day from ephemeral water sources in their food quest1. In accordance with these ethnographic statements we spatially delineated land areas where regular foraging activities may have occurred by first calculating the walking time from water, then weighting all areas that were less than 8 h walk from water more heavily in the input which went into our final suitability model. Since resources are said to be permanent in uplands4,5, we assume mountainous refugia were always suitable foraging habitats, so these refuge areas have been masked and removed from consideration (see mountain ranges in Fig. 3).Appropriate elements from all of the aforementioned satellite datasets were combined to produce our foraging habitat suitability model (Fig. 3). The ~ 30 m spatial resolution of the data facilitates the construction of a spatially-explicit, geographically broad, yet fine-grained ecological model to visually observe and critically appraise foraging habitat suitability at a variety of scales, offering new perspectives on regional human behavioral ecology. The model provides a continuous ranking of the relative foraging value for each landscape patch (or 30 m grid cell in this instance). Interpretation of patch values is based on the proposition that foragers know the conditions in all parts of the landscape they visit, and they organized their daily foraging movements in accordance with the factors outlined above.Our habitat suitability model illustrates the highly varied favorability of foraging patches across the Western Desert (Fig. 3), as calculated from data on natural resource distribution, terrain attributes, and daily foraging range. The model is conceptual, based on quantitative environmental variables that have been well documented to influence desert foraging activity. In regards to the model’s robustness, the input variables are equally weighted and statistically independent (see “Methods” section). The equal weighting reflects the concepts and assumptions of earlier research, particularly of existing landscape mapping, offering a coherent and consistent modelling approach. Advanced mathematical modelling, incorporating sensitivity analysis49,50, could be used to modify the weighted contribution of each variable, and such modelling will be the subject of future papers. Until more detailed knowledge of past forager land use and contemporary resources becomes available there is little benefit in arbitrarily substituting other input values in our model.The model comprises a matrix of nearly 1.3 billion data cells, each of which has been individually analyzed and ascribed each foraging patch a value indicative of potential habitat suitability. The computational power required to statistically analyze the dataset is massive, so to simplify computing and broadly characterize intraregional variation, we scaled up using nationally defined IBRA subregions. We used IBRA boundaries to group and rank the patch values into low, moderate, and high foraging habitat suitability classes and then calculated the land area occupied by each class (Fig. 4 and Table S1). Higher-ranked localities are well positioned in relation to suitable resources and easily traversed terrains. Lower-ranked patches are considered poorly-suited habitats due to their considerable distance from water and plant resources, and they are in comparatively rugged terrains. Areas deemed to have moderate foraging suitability have mixed accessibility to resources and variable terrain ruggedness.Figure 4Percentage of land area (km2) occupied by low, moderate, and high-ranked habitat suitability patches for the eleven largest IBRA bioregions of the Western Desert (Table S1). The histogram is ordered left to right based of the percentage of high-ranked foraging habitat within each bioregion. The percentages for the entire Western Desert are presented on the far right.Full size imageThe results show that during times of maximum water abundance and vegetation greenness, 36.6% of the Western Desert has high-ranked habitat suitability (Fig. 4 and Table S1). Moderately suitable areas constitute 48.9% and low-ranked patches encompass 13.1%. Breaking these findings down further, we calculated the ranked land areas for the eleven largest IBRA subregions ( > 10,000 km2) of the Western Desert (Fig. 4 and Table S1). At a broad bioregional level, intra-upland zones (Fig. 4; CER01) and desert plains (Fig. 4; GAS02, GVD01, and NUL01) offer a greater percentage of high-ranked foraging habitats. Bioregions dominated by dunefields have considerably less high ranked land areas compared to uplands, plains, and areas of low relief, although it is important to note that there is also considerable patchiness amongst suitable foraging areas in sandridge desert regions (Fig. 3). For instance, the centrally located Gibson Desert dunefield area (Fig. 4; GID02) has very little area of high-ranked habitat (10.8%), which is far less than other sandridge desert bioregions (Fig. 4; GSD02, LSD02, GVD02, GVD03, and GVD04) where high-ranked suitability areas range between 22.3 and 39.9%. Similarly, the Gibson Desert stony desert bioregion (Fig. 4; GID01), which is dominated by lateritic surface gravels, records only 25.5% high-ranked habitat areas. Thus, at a coarse-grained scale, it seems that some central core regions of the Western Desert are more environmentally hostile and offer less high-ranked foraging opportunities compared to more peripheral bioregions. This generality does not imply such areas were unutilized by desert peoples, but rather some areas were on average volatile and had low productivity.Foraging potential is highly varied amongst bioregions and land systemsWhen viewed at a fine-grained scale, our model clearly shows that there is an uneven gradient of suitable foraging habitats across the Western Desert, and foraging suitability trends are not pervasive throughout particular bioregions or land systems (Fig. 3). Away from montane uplands, water permanence is always temporary, and land systems with low topography, such as plains, stony plains, and sandridge desert, have highly varied foraging suitability, even when characterized in the best environmental conditions.The implications of this variation are important to understanding human ecology of the ethnohistoric period and the late Holocene archaeological record of the past 2000 years, when climatic conditions and landscapes were much like the present day36,40,42. Many scholars have noted that the historic desert peoples were familiar with the distribution of regional natural resources1,5,7. It has been argued that resource knowledge was articulated with socioeconomic strategies, and that groups routinely utilized all areas of the Western Desert during times of good rainfall and resource abundance. However, our suitability model reveals that there are large, expansive areas of the desert landscape that would have presented substantial challenges for survival, even in the best environmental circumstances (Fig. 3).Our model further suggests that low-ranked locations of foraging suitability were always below average productivity and were always comparatively unsuited as foraging habitats. To carry out that measure, we needed an independent indicator of land productivity, NDVI. We used satellite observations of maximum vegetation greenness to quantify how land productivity differs amongst low, moderate, and high ranked foraging habitats (Table S2). Variation in mean (µ) NDVI for each habitat class illustrates how land productivity differs within and amongst the most prominent Western Desert bioregions (Fig. 5). Given the below average NDVI of all low-ranked desert lowlands, we hypothesize that broad clusters of extremely unsuitable localities would be unlikely to provide adequate returns (Fig. 5), even when foragers were pursuing low-variance or lower quality resources51. Based on the distribution of low-ranked patches (Fig. 3), we agree with earlier research that the entire desert region was not equally economically viable for foraging, and that substantial tracts of land were not economically attractive to resident populations4,5,14,32. We also recognise that the distribution of massive-sized sub-optimal patches may be an important factor shaping the patterns of movement through the landscape, with foragers potentially preferring movement along high suitability corridors. However, unlike earlier research, our suitability model shows that unfavorable foraging areas are not correlated with large units of biogeography alone. Our model depicts the environmental variability of the Western Desert at a much higher resolution than its predecessors, revealing several massive land tracts where unfavorable foraging conditions occur (Fig. 3). If ethnographic patterns of land use were in place, we predict that many of these large areas would have been rarely utilized or perhaps some were purposefully avoided due to known deficiencies in the resource energy base12. This proposition is readily testable because it predicts that archaeological sites with poorly sorted, low densities of artefacts will be found in these places12. Defining the appropriate scale will be the key to testing our model, since we have demonstrated that broad biogeographic units are heterogenous and yet at a fine-grained scale, small areas of low suitability, which are often a local geographic feature (e.g., sand dune, bare rock outcrop, or erosional area), need not have been obstacles. Model testing will need an intermediate scale commensurate with daily foraging radii.Figure 5Boxplot of mean NDVI values and one standard deviation for low, moderate, and high-ranked suitability classes for the eleven largest IBRA subregions (a–k) of the Western Desert (l). Mean NDVI for individual bioregions and the spatial bounds of the Western Desert study area denoted as dashed black line and solid green line, respectively. IBRA subregion boxplot groups (a–k) are presented in order of increasing percentage of high-ranked foraging habitat, after Fig. 4. Table S2 offers the precise summarised NDVI values for each bioregion and suitability class.Full size imageAt present, the archaeological land use pattern of low-ranked foraging habitats is not something that is well-understood from the Western Desert, although periodic and short term use of impoverished, low productivity patches has been predicted12. Studies of contemporary Western Desert groups indicate that human-induced firing of the landscape enhances biodiversity and land productivity51,52,53,54, so it is possible that low productivity patches may have occasionally benefited from anthropogenic burning, especially in the past 1500 years51. However, research also suggests that cultural burning practices did not have widespread regional impacts51,52,53,54,55. Human influence on landscape modification is localized within day-range foraging areas around residential camps and frequently traversed pathways51,52,53,54. Low productivity patches away from residential camps were probably unlikely targets for either anthropogenic burning or foraging if higher-ranked patches were closer.Elsewhere, in the eastern Australian arid zone, periodic use of climatically harsh desert localities is known from archaeological sequences. While in some cases preservation may explain chronological discontinuities56, there is compelling evidence for irregular occupation in several desert areas10,57,58,59,60. For instance, in the western Strzelecki Desert broad portions of dunefield landscapes were periodically abandoned for centuries or even millennia57,60, while in semi-arid portions of southeastern Australia sequences of occupation were separated by decades or centuries of local/regional abandonment58,59. Fluctuations in local foraging suitability may well be a factor producing discontinuous land use across the Australian arid lands, and we suggest that in the Western Desert there were patches with chronologically varying foraging potential. The key test of this prediction would be to investigate whether archaeological sites in locations of fluctuating habitat suitability over time also display histories of discontinuous visitation. Such sites could be identified through local palaeoenvironmental records but we suggest that selections based on time-series analysis of vegetation greenness from the past few decades would be more readily used to establish samples and would facilitate comparison of archaeological sites in terms of local foraging suitability and NDVI values, as well as archaeological records of continuous or discontinuous visitation.Satellite data reveals a more nuanced understanding of land useAustralian archaeological research has relied heavily on biogeographic principles to distinguish the ‘barriers and boundaries’ of Aboriginal subsistence and settlement in the arid zone4,5,61. While equating particular land use practices with specific bioregional areas was initially useful for generalized conceptualizations of traditional foraging behaviors, the coarse analytical scale of earlier approaches is now problematic. Subsequent research has shown the dynamics of Aboriginal occupation and land usage in the Western Desert to be more complex and variable across spatial and temporal scales than originally conceived9,24,30,33. To gain a more nuanced understanding of past land use and foraging patterns, finer-scale methods of analysis are required.We used satellite imagery to tackle the issue of scale, allowing for a sharper and more spatially explicit examination of desert environments and landscapes. For example, as we focus at higher resolution on various areas of the Western Desert, our model clearly shows that foraging suitability is highly varied across all desert lowlands (Figs. 3, 4 and 6). In sandridge desert areas, proposed to have been a barrier at times in the past4, the model shows there are many well-watered and amply vegetated localities where good foraging is possible when rainfall is high and surface water is abundant (Fig. 6a). In this context, interdunal swales are hardly barriers to occupation because they can be lush with water, plant, and wildlife resources after local rain, and the energy expenditure required to walk along interdunal swales is low in comparison to the requirements needed to repeatedly scramble across a sea of loose sands and undulating dunes. Thus, it seems entirely plausible that resident groups could navigate and forage in many dunefield areas by following a well-resourced network of swales during times of good environmental conditions. The fine-grained nature of this observation opens up the possibility that many sandridge deserts were not necessarily broad barriers to occupation and that precontact land use behaviors varied in different dunefield contexts.Figure 6High resolution perspectives of various Western Desert landforms (e.g., sandridge, stony plain and sandy plain contexts) with generally higher-ranked and lower-ranked areas of foraging suitability. This figure illustrates the fine-grained scale of our habitat suitability model (Fig. 3), which has implications for better understanding localized land use behaviors. Juxtaposed areas, as mapped in Fig. 3 are: (a) Higher-ranked sandridge habitats vs. (b) lower-ranked sandridge land system. (c) Higher-ranked stony desert habitat vs. (d) lower-ranked stony desert areas. (e) Higher-ranked sandplain land systems vs. (f) lower-ranked plain habitats. Maps created in ESRI ArcGIS Desktop 10.5.1 (https://desktop.arcgis.com), linear stretch (1.0%) visualization. See “Methods” section for source raster information.Full size imageWe also highlight that the resource-rich swale pattern is not found in all dune systems (Fig. 6b), and it is plausible that some of these areas were periodic barriers to occupation, as previously suggested in more generalized ecological models4. There are substantial areas of sandridge desert, especially within central areas of the Western Desert (e.g., GID02), where survival would have always been extremely difficult, even during times of abundance (Fig. 6b). This variability is also expressed in stony desert contexts, where southern areas of the lateritic Gibson Desert (GID01) offer better habitat suitability (Fig. 6c) than the northern areas (Fig. 6d). On a fine scale, plain land systems also exhibit a wide range of habitat suitability, where high-ranked habitat suitability appears fairly widespread in some areas of the Nullabor Plain (NUL01; Fig. 6e), yet other areas of the plain were poorly-suited for foraging (Fig. 6f).In previous ecological models, stony desert and plain land systems are considered more favorable than sandridge desert4; however, as shown above, the modelled data clearly illustrate that there are substantial areas of plains and stony desert landscapes that vary considerably between high and low-ranked habitat suitability (Figs. 3, 4 and 6). The fine-grained scale of our model adds to a growing body of research5,9,24,30,33 that demonstrates how previous pan-continental characterizations of deserts as ‘corridors’ and ‘barriers’ for foragers oversimplify the link between human behavior and biogeography. When scrutinized at high resolution, extremely unsuitable foraging and very well-suited foraging areas can potentially occur in any area of the Western Desert, regardless of the biogeography or other physical characteristics. Thus, fine-grained ecological models allow for a more nuanced and spatially-explicit understanding of the past land use behaviors that led to the formation the desert archaeological record.Using environmental remote sensing to infer LGM habitat suitabilityThere is no doubt that the Western Desert environment has changed and evolved over time, through both natural and human-induced processes8,36,37,51. The region has undergone considerable environmental fluctuations over time, resulting in landform transformations (dune aggradation, in particular) and changes in vegetation cover. The long-term physical impact of these environmental changes clearly place limitations on how modern satellite data can be used to interpret deep-time patterns of occupation and land use. However, our model shows the likely distribution of low-ranked foraging habitats when climatic conditions were much drier than present.Ethnoarchaeological accounts depict resident populations as being low density and highly mobile, frequently moving and foraging across vast expanses of territory, thereby necessitating intermittent patterns of settlement1,2,3,4. Such a mobile strategy means that large swathes of the desert could not have been continuously occupied. Our habitat suitability model (Fig. 3) makes sense of the impermanent and mobile land use strategy seen historically. For example, we document several massive areas of the Western Desert where, in combination, surface terrains are physically challenging, the nearest proximity to surface water is greater than 2 days walk, and vegetation cover, density and condition is substandard, even in the best of documented environmental conditions! These exceptionally large areas were poorly-suited to foraging, and could not have been permanently occupied in the historical period. They would only have been visited rarely, perhaps only in atypical short-term climatic events, and may even have constrained forger movement between more favorable parts of their territories. Given current palaeoclimatic evidence we infer that in the Pleistocene these low ranked habitats would have been even more inhospitable to foragers than in recent times. During the LGM, the resource yields in such areas would have been more diminished than present, making conditions for survival even more difficult than today. Consequently, we predict that unless radically different economic strategies were being employed in the Pleistocene those areas would have been only rarely visited since the peak of the last glacial cycle, ~ 24–18 ka, even though adjacent desert areas may have supported regular or at least sporadic visitation.Our hypothesis is clear, detailed, and framed to be testable by archaeological fieldwork. The number of Western Desert sites with old archaeological sequences is growing, but the sample is small, site distribution is widely scattered, and none are located in the harsher core areas identified in this study (Figs. 1 and 3). Thus, it is evident that archaeological fieldwork in those impoverished landscapes as well as environmentally richer and more reliable landscapes is necessary to understand historical land use patterns and to make statements about earlier phases of regional occupation. Our work highlights how future models of forager land use across Australia’s desert regions can comprehend the environmental complexity and fine scale of resource variability in these vast, remote and diverse places. More

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    Smoke from regional wildfires alters lake ecology

    1.He, T., Belcher, C. M., Lamont, B. B. & Lim, S. L. A 350-million-year legacy of fire adaptation among conifers. J. Ecol. 104, 352–363 (2016).Article 

    Google Scholar 
    2.Doerr, S. H. & Santín, C. Global trends in wildfire and its impacts: Perceptions versus realities in a changing world. Philos. Trans. R. Soc. B Biol. Sci. 371, 20150345 (2016).Article 

    Google Scholar 
    3.Hoegh-Guldberg, O. et al. Impacts of 1.5°C Global Warming on Natural and Human Systems. in Global Warming of 1.5°C. An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, (ed. Masson-Delmotte, V., P. Zhai, H.-O. Pörtner, D. Roberts, J. Skea, P.R. Shukla, A. Pirani, W. Moufouma-Okia, C. Péan, R. Pidcock, S. Connors, J.B.R. Matthews, Y. Chen, X. Zhou, M.I. Gomis, E. Lonnoy, T. Maycock, M. Tignor, and T. W.) 175–311 (2018).4.Dennison, P. E., Brewer, S. C., Arnold, J. D. & Moritz, M. A. Large wildfire trends in the western United States, 1984–2011. Geophys. Prospect. 41, 2928–2933 (2014).ADS 

    Google Scholar 
    5.Westerling, A. L. R. Increasing western US forest wildfire activity: Sensitivity to changes in the timing of spring. Philos. Trans. R. Soc. B Biol. Sci. 371, 20150178 (2016).Article 

    Google Scholar 
    6.Bailey, R. & Yeo, J. The Burning Issue (Marsh & McLennan Insights, 2019).
    Google Scholar 
    7.Province of British Columbia. 2018 Wildfire Season Summary. 2018 Wildfire Season Summary (2019). https://www2.gov.bc.ca/gov/content/safety/wildfire-status/about-bcws/wildfire-history/wildfire-season-summary?keyword=total&keyword=area&keyword=burned&keyword=by&keyword=wildfire&keyword=2018.8.Cal Fire. https://www.fire.ca.gov/incidents/2018/. https://www.fire.ca.gov/incidents/2018/ (2020). https://www.fire.ca.gov/incidents/2018/.9.McCullough, I. et al. Do lakes feel the burn? Ecological consequences of increasing exposure of lakes to fire in the continental US. Glob. Chang. Biol. https://doi.org/10.1111/gcb.14732 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    10.Westerling, A. L. et al. Climate change and growth scenarios for California wildfire. Clim. Change 109, 445–463 (2011).Article 

    Google Scholar 
    11.Nagy, C. R., Fusco, E., Bradley, B., Abatzoglou, J. T. & Balch, J. Human-related ignitions increase the number of large wildfires across U.S. Ecoregions. Fire 1, 1–14 (2018).
    Google Scholar 
    12.Balch, J. K. et al. Human-started wildfires expand the fire niche across the United States. Proc. Natl. Acad. Sci. U. S. A. 114, 2946–2951 (2017).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    13.Radeloff, V. C. et al. Rapid growth of the US wildland-urban interface raises wildfire risk. Proc. Natl. Acad. Sci. U. S. A. 115, 3314–3319 (2018).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    14.Wright, R. F. The Impact of Forest Fire on the Nutrient Influxes to Small Lakes in Northeastern Minnesota Author (s): Richard F . Wright Published by : Ecological Society of America Stable URL : http://www.jstor.org/stable/1936180 THE IMPACT OF FOREST FIRE ON THE NUT. 57, 649–663 (1976).15.Carignan, R., D’Arcy, P. & Lamontagne, S. Comparative impacts of fire and forest harvesting on water quality in Boreal Shield lakes. Can. J. Fish. Aquat. Sci. 57, 105–117 (2000).CAS 
    Article 

    Google Scholar 
    16.Tecle, A. & Neary, D. Water quality impacts of forest fires. J. Pollut. Eff. Control 03, (2015).17.Abney, R. B., Sanderman, J., Johnson, D., Fogel, M. L. & Berhe, A. A. Post-wildfire Erosion in mountainous terrain leads to rapid and major redistribution of soil organic carbon. Front. Earth Sci. 5, 1–16 (2017).Article 

    Google Scholar 
    18.Williamson, C. E. et al. Sentinel responses to droughts, wildfires, and floods: Effects of UV radiation on lakes and their ecosystem services. Front. Ecol. Environ. 14, 102–109 (2016).Article 

    Google Scholar 
    19.Goldman, C. R., Jassby, A. D. & De Amezaga, E. Forest fires, atmospheric deposition and primary productivity at Lake Tahoe, California-Nevada. Int. Vereinigung Theor. Angew. Limnol. Verhandlungen 24, 499–503 (1990).
    Google Scholar 
    20.Allen, E. W., Prepas, E. E., Gabos, S., Strachan, W. & Chen, W. Surface water chemistry of burned and undisturbed watersheds on the Boreal Plain: An ecoregion approach. J. Environ. Eng. Sci. 2, S73–S86 (2003).CAS 
    Article 

    Google Scholar 
    21.Earl, S. R. & Blinn, D. W. Effects of wildfire ash on water chemistry and biota in south-western U.S.A. streams. Freshw. Biol. 48, 1015–1030 (2003).CAS 
    Article 

    Google Scholar 
    22.Overholt, E. P., Rose, K. C., Williamson, C. E., Fischer, J. M. & Cabrol, N. A. Behavioral responses of freshwater calanoid copepods to the presence of ultraviolet radiation: Avoidance and attraction. J. Plankton Res. 38, 16–26 (2015).Article 

    Google Scholar 
    23.Urmy, S. S. et al. Vertical redistribution of zooplankton in an oligotrophic lake associated with reduction in ultraviolet radiation by wildfire smoke. Geophys. Res. Lett. 43, 3746–3753 (2016).ADS 
    Article 

    Google Scholar 
    24.Williamson, C. E. et al. The interactive effects of stratospheric ozone depletion, UV radiation, and climate change on aquatic ecosystems. Photochem. Photobiol. Sci. 18, 717–746 (2019).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    25.Aguilera, R., Gershunov, A., Ilango, S. D., Guzman-Morales, J. & Benmarhnia, T. Santa ana winds of Southern California impact PM2.5 with and without smoke from wildfires. GeoHealth 4, 1–9 (2020).Article 

    Google Scholar 
    26.Liu, J. C. et al. Wildfire-specific fine particulate matter and risk of hospital admissions in urban and rural counties. Epidemiology 28, 77–85 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    27.Environmental Protection Agency. Air Quality Index, A Guide to Air Quality and Your Health. Encyclopedia of Quality of Life and Well-Being Research (2014).28.Melack, J. M., Sadro, S., Sickman, S. & Dozier, J. Lakes and Watersheds in the Sierra Nevada of California: Responses to Environmental Change. (University of California Press, 2020). https://doi.org/10.2307/j.ctv17hm9sr29.Goldman, C. R., Jassby, A. & Powell, T. Interannual fluctuations in primary production: Meteorological forcing at two subalpine lakes. Limnol. Oceanogr. 34, 310–323 (1989).ADS 
    CAS 
    Article 

    Google Scholar 
    30.Jassby, A. D., Powell, T. M. & Goldman, C. R. Interannual fluctuations in primary production: Direct physical effects and the trophic cascade at Castle Lake, California. Limnol. Oceanogr. 35, 1021–1038 (1990).ADS 
    CAS 
    Article 

    Google Scholar 
    31.Park, S., Brett, M. T., Müller-Solger, A. & Goldman, C. R. Climatic forcing and primary productivity in a subalpine lake: Interannual variability as a natural experiment. Limnol. Oceanogr. 49, 614–619 (2004).ADS 
    Article 

    Google Scholar 
    32.Winslow, L. et al. Package ‘ rLakeAnalyzer ’. Lake Physics Tools. (2019).33.Read, J. S. et al. Derivation of lake mixing and stratification indices from high-resolution lake buoy data. Environ. Model. Softw. 26, 1325–1336 (2011).Article 

    Google Scholar 
    34.Goldman, C. R. Primary productivity, nutrients, and transparency during the early onset of eutrophication in ultra-oligotrophic Lake Tahoe Califomia-Nevada. Limnol. Oceanogr. 33, 1321–1333 (1988).ADS 
    CAS 
    Article 

    Google Scholar 
    35.Marker, A. F. H. The use of acetone and methanol in the estimation of chlorophyll in the presence of phaeophytin. Freshw. Biol. 2, 361–385 (1972).Article 

    Google Scholar 
    36.Redfield, G. W. & Goldman, C. R. Diel vertical migration and dynamics of zooplankton biomass in the epilimnion of Castle Lake, California. Verhandlungen des Int. Verein Limnol. 20, 381–387 (1978).
    Google Scholar 
    37.Elser, J. J. et al. Factors associated with interannual and intraannual variation in nutrient limitation of phytoplankton growth in Castle Lake, California. Can. J. Fish. Aquat. Sci. 52, 93–104 (1995).Article 

    Google Scholar 
    38.Huovinen, P. S., Brett, M. T. & Goldman, C. R. Temporal and vertical dynamics of phytoplankton net growth in Castle Lake, California. J. Plankton Res. 21, 373–385 (1999).Article 

    Google Scholar 
    39.Maberly, S. C., King, L., Dent, M. M., Jones, R. I. & Gibson, C. E. Nutrient limitation of phytoplankton and periphyton growth in upland lakes. Freshw. Biol. 47, 2136–2152 (2002).Article 

    Google Scholar 
    40.R Core Team. A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. (2020).41.Zuur, A. F., Ieno, E. N., Walker, N., Saveliev, A. A. & Smith, G. M. Mixed effects models and extensions in ecology with R. (Springer, 2009).42.Lenth, R. V. emmeans: Estimated Marginal Means, aka Least-Squares Means. (2021). https://cran.r-project.org/package=emmeans.43.Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D. & R Core Team. nlme: Linear and Nonlinear Mixed Effects Models. (2020). https://cran.r-project.org/package=nlme.44.Anderson, M. J. Permutational Multivariate Analysis of Variance (PERMANOVA). Wiley StatsRef Stat. Ref. Online 1–15 (2017). https://doi.org/10.1002/9781118445112.stat0784145.Oksanen, J. F. et al. vegan: Community Ecology Package. (2019). https://cran.r-project.org/package=vegan%0A.46.Environmental Systems Research Institute. ArcGIS 10.8.1. (2020). https://www.esri.com/en-us/home.47.Inkscape Project. Inkscape. (2020). https://inkscape.org.48.Bachmann, R. W. & Goldman, C. R. Hypolimnetic heating in Castle Lake. California. Limnol. Oceanogr. 10, 233–239 (1965).ADS 
    Article 

    Google Scholar 
    49.Kochanski, A. K. et al. Modeling wildfire smoke feedback mechanisms using a coupled fire-atmosphere model with a radiatively active aerosol scheme. J. Geophys. Res. Atmos. 124, 9099–9116 (2019).ADS 
    Article 

    Google Scholar 
    50.David, A. T., Asarian, J. E. & Lake, F. K. Wildfire smoke cools summer river and stream water temperatures. Water Resour. Res. 54, 7273–7290 (2018).ADS 
    Article 

    Google Scholar 
    51.Moeller, R. Contribution of ultraviolet radiation (UV-A, UV-B) to photoinhibition of epilimnetic phytoplankton in lakes of differing UV transparency. Arch. Hydrobiol. Beihefte Ergebnisse Limnol. 43, 157–170 (1994).
    Google Scholar 
    52.Morris, D. P. & Hargreaves, B. R. The role of photochemical degradation of dissolved organic carbon in regulating the UV transparency of three lakes on the Pocono Plateau. Limnol. Oceanogr. 42, 239–249 (1997).ADS 
    CAS 
    Article 

    Google Scholar 
    53.Meyers, P. A. & Lallier-Vergès, E. Lacustrine sedimentary organic matter records of Late Quaternary paleoclimates. J. Paleolimnol. 21, 345–372 (1999).ADS 
    Article 

    Google Scholar 
    54.Lamb, A. L., Wilson, G. P. & Leng, M. J. A review of coastal palaeoclimate and relative sea-level reconstructions using d 13 C and C/N ratios in organic material. (2005). https://doi.org/10.1016/j.earscirev.2005.10.00355.Maxwell, T. M., Silva, L. C. R. & Horwath, W. R. Integrating effects of species composition and soil properties to predict shifts in montane forest carbon–water relations. Proc. Natl. Acad. Sci. U. S. A. 115, E4219–E4226 (2018).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    56.Bao, H., Niggemann, J., Luo, L., Dittmar, T. & Kao, S. J. Aerosols as a source of dissolved black carbon to the ocean. Nat. Commun. 8, 1–7 (2017).ADS 
    Article 
    CAS 

    Google Scholar 
    57.Zhang, Y. et al. Dissolved organic carbon in glaciers of the southeastern Tibetan Plateau: Insights into concentrations and possible sources. PLoS ONE 13, e0205414 (2018).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    58.Solomon, C. T. et al. Ecosystem consequences of changing inputs of terrestrial dissolved organic matter to lakes: Current knowledge and future challenges. Ecosystems 18, 376–389 (2015).Article 

    Google Scholar 
    59.Banse, K. Rates of growth, respiration and photosynthesis of unicellular algae as related to cell size—A review. J. Phycol. 12, 135–140 (1976).
    Google Scholar 
    60.Gao, K., Li, G., Helbling, E. W. & Villafañe, V. E. Variability of UVR effects on photosynthesis of summer phytoplankton assemblages from a tropical coastal area of the South China Sea. Photochem. Photobiol. 83, 802–809 (2007).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    61.Häder, D. P., Helbling, E. W., Williamson, C. E. & Worrest, R. C. Effects of UV radiation on aquatic ecosystems and interactions with climate change. Photochem. Photobiol. Sci. 10, 242–260 (2011).PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    62.Priscu, J. C. & Goldman, C. R. Seasonal dynamics of the deep-chlorophyll maximum in Castle Lake, California. Can. J. Fish. Aquat. Sci. 40, 208–214 (1983).CAS 
    Article 

    Google Scholar 
    63.Leach, T. H. et al. Patterns and drivers of deep chlorophyll maxima structure in 100 lakes: The relative importance of light and thermal stratification. Limnol. Oceanogr. 63, 628–646 (2018).ADS 
    CAS 
    Article 

    Google Scholar 
    64.Priscu, J. C. & Goldman, C. R. The effect of temperature on photosynthetic and respiratory electron transport system activity in the shallow and deep-living phytoplankton of a subalpine lake. Freshw. Biol. 14, 143–155 (1984).Article 

    Google Scholar 
    65.Modenutti, B. E. et al. Effect of volcanic eruption on nutrients, light, and phytoplankton in oligotrophic lakes. Limnol. Oceanogr. 58, 1165–1175 (2013).ADS 
    Article 

    Google Scholar 
    66.Horne, J. A. & Goldman, C. R. Zooplankton and zoobenthos. in Limnology 265–298 (McGraw-Hill Inc, 1994).67.Caldwell, T. J., Chandra, S., Feher, K., Simmons, J. B. & Hogan, Z. Ecosystem response to earlier ice break-up date: Climate-driven changes to water temperature, lake-habitat-specific production, and trout habitat and resource use. Glob. Chang. Biol. 26, 5475–5491 (2020).ADS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    68.Elser, J. J., Luecke, C., Brett, M. T. & Goldman, C. R. Effects of food web compensation after manipulation of rainbow trout in an oligotrophic lake. Ecology 76, 52–69 (1995).Article 

    Google Scholar 
    69.Cohen, J. H. & Forward Jr., R. B. Zooplankton diel vertical migration-a review of proximate control. in Oceanography and marine biology: An annual review (eds. Gibson, R. N., Atkinson, R. J. A. & Gordon, J. D. M.) 89–122 (Taylor & Francis, 2009).70.Williamson, C. E., Fischer, J. M., Bollens, S. M., Overholt, E. P. & Breckenridgec, J. K. Toward a more comprehensive theory of zooplankton diel vertical migration: Integrating ultraviolet radiation and water transparency into the biotic paradigm. Limnol. Oceanogr. 56, 1603–1623 (2011).ADS 
    Article 

    Google Scholar 
    71.Storz, U. C. & Paul, R. J. Phototaxis in water fleas (Daphnia magna) is differently influenced by visible and UV light. J. Comp. Physiol. Sens. Neural Behav. Physiol. 183, 709–717 (1998).Article 

    Google Scholar 
    72.National Interagency Fire Center. Total Wildland Fires and Acres (1983–2020). (2021). https://www.nifc.gov/fire-information/statistics/wildfires.73.MTBS. https://www.mtbs.gov/. MTBS (2020). https://www.mtbs.gov/. More

  • in

    The dynamics of evolutionary rescue from a novel pathogen threat in a host metapopulation

    1.Maslo, B. & Fefferman, N. H. A case study of bats and white-nose syndrome demonstrating how to model population viability with evolutionary effects. Conserv. Biol. 29, 1176–1185 (2015).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    2.Morris, W. F. & Doak, D. F. Quantitative Conservation Biology (Sinauer, Sunderland, 2002).
    Google Scholar 
    3.Liebhold, A. & Bascompte, J. The Allee effect, stochastic dynamics and the eradication of alien species. Ecol. Lett. 6, 133–140 (2003).Article 

    Google Scholar 
    4.Stephens, P. A. & Sutherland, W. J. Consequences of the Allee effect for behaviour, ecology and conservation. Trends Ecol. Evol. 14, 401–405 (1999).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    5.Nunney, L. & Elam, D. R. Estimating the effective population size of conserved populations. Conserv. Biol. 8, 175–184 (1994).Article 

    Google Scholar 
    6.Lande, R. & Barrowclough, G. Effective population size, genetic variation, and their use in population. Viable populations for conservation, 87 (1987).7.Frankham, R. Effective population size/adult population size ratios in wildlife: A review. Genet. Res. 66, 95–107 (1995).Article 

    Google Scholar 
    8.Tallmon, D. A., Luikart, G. & Waples, R. S. The alluring simplicity and complex reality of genetic rescue. Trends Ecol. Evol. 19, 489–496 (2004).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    9.Whiteley, A. R., Fitzpatrick, S. W., Funk, W. C. & Tallmon, D. A. Genetic rescue to the rescue. Trends Ecol. Evol. 30, 42–49 (2015).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    10.Jiao, J., Gilchrist, M. A. & Fefferman, N. H. The impact of host metapopulation structure on short-term evolutionary rescue in the face of a novel pathogenic threat. Glob. Ecol. Conserv. 23, 01174 (2020).
    Google Scholar 
    11.Hanski, I. Metapopulation Ecology (Oxford University Press, Oxford, 1999).
    Google Scholar 
    12.Mortier, F., Jacob, S., Vandegehuchte, M. L. & Bonte, D. Habitat choice stabilizes metapopulation dynamics by enabling ecological specialization. Oikos 128, 529–539 (2019).Article 

    Google Scholar 
    13.Jiao, J., Riotte-Lambert, L., Pilyugin, S. S., Gil, M. A. & Osenberg, C. W. Mobility and its sensitivity to fitness differences determine consumer–resource distributions. R. Soc. Open Sci. 7, 200247 (2020).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    14.Anderson, S. C., Moore, J. W., McClure, M. M., Dulvy, N. K. & Cooper, A. B. Portfolio conservation of metapopulations under climate change. Ecol. Appl. 25, 559–572 (2015).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    15.Case, T. J. Invasion resistance, species build-up and community collapse in metapopulation models with interspecies competition. Biol. J. Lin. Soc. 42, 239–266 (1991).Article 

    Google Scholar 
    16.Gyllenberg, M. & Hanski, I. Habitat deterioration, habitat destruction, and metapopulation persistence in a heterogenous landscape. Theor. Popul. Biol. 52, 198–215 (1997).CAS 
    PubMed 
    MATH 
    Article 
    PubMed Central 

    Google Scholar 
    17.Jiao, J., Pilyugin, S. S. & Osenberg, C. W. Random movement of predators can eliminate trophic cascades in marine protected areas. Ecosphere 7, e01421 (2016).Article 

    Google Scholar 
    18.Nee, S. & May, R. M. Dynamics of metapopulations: Habitat destruction and competitive coexistence. J. Anim. Ecol. 61, 37–40 (1992).Article 

    Google Scholar 
    19.Ying, Y., Chen, Y., Lin, L. & Gao, T. Risks of ignoring fish population spatial structure in fisheries management. Can. J. Fish. Aquat. Sci. 68, 2101–2120 (2011).Article 

    Google Scholar 
    20.Hess, G. Disease in metapopulation models: Implications for conservation. Ecology 77, 1617–1632 (1996).Article 

    Google Scholar 
    21.Daszak, P., Cunningham, A. A. & Hyatt, A. D. Emerging infectious diseases of wildlife: Threats to biodiversity and human health. Science 287, 443–449 (2000).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    22.Harding, K. C., Begon, M., Eriksson, A. & Wennberg, B. Increased migration in host–pathogen metapopulations can cause host extinction. J. Theor. Biol. 298, 1–7 (2012).MathSciNet 
    CAS 
    PubMed 
    MATH 
    Article 
    PubMed Central 

    Google Scholar 
    23.Dowling, A. J., Hill, G. E. & Bonneaud, C. Multiple differences in pathogen-host cell interactions following a bacterial host shift. Sci. Rep. 10, 1–12 (2020).Article 
    CAS 

    Google Scholar 
    24.Kuzmin, I. V. et al. Molecular inferences suggest multiple host shifts of rabies viruses from bats to mesocarnivores in Arizona during 2001–2009. PLoS Pathog 8, e1002786 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    25.Levine, R. S. et al. Supersuppression: Reservoir competency and timing of mosquito host shifts combine to reduce spillover of West Nile virus. Am. J. Trop. Med. Hyg. 95, 1174–1184 (2016).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    26.Langwig, K. E. et al. Context-dependent conservation responses to emerging wildlife diseases. Front. Ecol. Environ. 13, 195–202 (2015).Article 

    Google Scholar 
    27.Smith, K. F., Acevedo-Whitehouse, K. & Pedersen, A. B. The role of infectious diseases in biological conservation. Anim. Conserv. 12, 1–12 (2009).Article 

    Google Scholar 
    28.Xiao, Y., Tang, B., Wu, J., Cheke, R. A. & Tang, S. Linking key intervention timing to rapid decline of the COVID-19 effective reproductive number to quantify lessons from mainland China. Int. J. Infect. Dis. 97, 296–298 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    29.Cintrón-Arias, A., Castillo-Chávez, C., Betencourt, L., Lloyd, A. L. & Banks, H. T. The Estimation of the Effective Reproductive Number from Disease Outbreak Data. (North Carolina State University, Center for Research in Scientific Computation, 2008).30.Salpeter, E. E. & Salpeter, S. R. Mathematical model for the epidemiology of tuberculosis, with estimates of the reproductive number and infection-delay function. Am. J. Epidemiol. 147, 398–406 (1998).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    31.Grenfell, B. & Harwood, J. (Meta) population dynamics of infectious diseases. Trends Ecol. Evol. 12, 395–399 (1997).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    32.Millard, A. R., Roberts, C. A. & Hughes, S. S. Isotopic evidence for migration in Medieval England: The potential for tracking the introduction of disease. Soc. Biol. Human Affairs. 70, 9–13 (2005).
    Google Scholar 
    33.Chen, M. et al. The introduction of population migration to SEIAR for COVID-19 epidemic modeling with an efficient intervention strategy. Inf. Fusion 64, 252–258 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    34.Reed, K. D., Meece, J. K., Henkel, J. S. & Shukla, S. K. Birds, migration and emerging zoonoses: West Nile virus, Lyme disease, influenza A and enteropathogens. Clin. Med. Res. 1, 5–12 (2003).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    35.Roy, B. & Kirchner, J. Evolutionary dynamics of pathogen resistance and tolerance. Evolution 54, 51–63 (2000).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    36.Bliven, K. A. & Maurelli, A. T. Antivirulence genes: Insights into pathogen evolution through gene loss. Infect. Immun. 80, 4061–4070 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    37.Wild, G., Gardner, A. & West, S. A. Adaptation and the evolution of parasite virulence in a connected world. Nature 459, 983–986 (2009).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    38.Laine, A. L. Resistance variation within and among host populations in a plant–pathogen metapopulation: Implications for regional pathogen dynamics. J. Ecol. 92, 990–1000 (2004).Article 

    Google Scholar 
    39.Thrall, P. H. et al. Rapid genetic change underpins antagonistic coevolution in a natural host-pathogen metapopulation. Ecol. Lett. 15, 425–435 (2012).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    40.Juhas, M. Horizontal gene transfer in human pathogens. Crit. Rev. Microbiol. 41, 101–108 (2015).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    41.Soanes, D. & Richards, T. A. Horizontal gene transfer in eukaryotic plant pathogens. Annu. Rev. Phytopathol. 52, 583–614 (2014).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    42.Brunham, R. C., Plummer, F. A. & Stephens, R. S. Bacterial antigenic variation, host immune response, and pathogen-host coevolution. Infect. Immun. 61, 2273 (1993).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    43.Sasaki, A. Evolution of antigen drift/switching: Continuously evading pathogens. J. Theor. Biol. 168, 291–308 (1994).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    44.Lange, A. & Ferguson, N. M. Antigenic diversity, transmission mechanisms, and the evolution of pathogens. PLoS Comput. Biol. 5, 1000536 (2009).ADS 
    MathSciNet 
    Article 
    CAS 

    Google Scholar 
    45.Alizon, S., Hurford, A., Mideo, N. & Van Baalen, M. Virulence evolution and the trade-off hypothesis: History, current state of affairs and the future. J. Evol. Biol. 22, 245–259 (2009).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    46.Messenger, S. L., Molineux, I. J. & Bull, J. Virulence evolution in a virus obeys a trade off. Proc. R. Soc. Lond. B 266, 397–404 (1999).CAS 
    Article 

    Google Scholar 
    47.Alizon, S., de Roode, J. C. & Michalakis, Y. Multiple infections and the evolution of virulence. Ecol. Lett. 16, 556–567 (2013).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    48.Bull, J. J. & Lauring, A. S. Theory and empiricism in virulence evolution. PLoS Pathog 10, e1004387 (2014).49.Gray, M. J. & Chinchar, V. G. Ranaviruses: Lethal Pathogens of Ectothermic Vertebrates (Springer Science+ Business Media, New York, 2015).Book 

    Google Scholar 
    50.Dobbelaere, T., Muller, E. M., Gramer, L. J., Holstein, D. M. & Hanert, E. Coupled epidemio-hydrodynamic modeling to understand the spread of a deadly coral disease in Florida. Front. Mar. Sci. 7, 1016 (2020).Article 

    Google Scholar 
    51.Stoddard, S. T. et al. House-to-house human movement drives dengue virus transmission. Proc. Natl. Acad. Sci. USA 110, 994–999 (2013).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    52.Diekmann, O., Heesterbeek, J. & Roberts, M. G. The construction of next-generation matrices for compartmental epidemic models. J. R. Soc. Interface 7, 873–885 (2010).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    53.Smith, K. F., Sax, D. F. & Lafferty, K. D. Evidence for the role of infectious disease in species extinction and endangerment. Conserv. Biol. 20, 1349–1357 (2006).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    54.Anderson, R. M., Anderson, B. & May, R. M. Infectious Diseases of Humans: Dynamics and Control (Oxford University Press, Oxford, 1992).
    Google Scholar 
    55.O’Brien, S. J. et al. Genetic basis for species vulnerability in the cheetah. Science 227, 1428–1434 (1985).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    56.Ingvarsson, P. K. & Lundberg, S. The effect of a vector-borne disease on the dynamics of natural plant populations: A model for Ustilago violacea infection of Lychnis viscaria. J. Ecol. 81, 263–270 (1993).Article 

    Google Scholar 
    57.Carlson, S. M., Cunningham, C. J. & Westley, P. A. Evolutionary rescue in a changing world. Trends Ecol. Evol. 29, 521–530 (2014).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    58.Gonzalez, A., Ronce, O., Ferriere, R. & Hochberg, M. E. (The Royal Society, 2013).59.Fine, P. E. Herd immunity: History, theory, practice. Epidemiol. Rev. 15, 265–302 (1993).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    60.Fontanet, A. & Cauchemez, S. COVID-19 herd immunity: Where are we?. Nat. Rev. Immunol. 20, 583–584 (2020).PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    61.Fine, P., Eames, K. & Heymann, D. L. “Herd immunity”: A rough guide. Clin. Infect. Dis. 52, 911–916 (2011).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    62.Barbarossa, M. V. & Röst, G. Immuno-epidemiology of a population structured by immune status: A mathematical study of waning immunity and immune system boosting. J. Math. Biol. 71, 1737–1770 (2015).MathSciNet 
    CAS 
    PubMed 
    MATH 
    Article 
    PubMed Central 

    Google Scholar 
    63.Hamami, D., Cameron, R., Pollock, K. G. & Shankland, C. Waning immunity is associated with periodic large outbreaks of mumps: A mathematical modeling study of Scottish data. Front. Physiol. 8, 233 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    64.Klepac, P. & Caswell, H. The stage-structured epidemic: Linking disease and demography with a multi-state matrix approach model. Thyroid Res. 4, 301–319 (2011).
    Google Scholar 
    65.Anderson, R. M. & May, R. M. Population biology of infectious diseases: Part I. Nature 280, 361–367 (1979).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    66.Colizza, V. & Vespignani, A. Epidemic modeling in metapopulation systems with heterogeneous coupling pattern: Theory and simulations. J. Theor. Biol. 251, 450–467 (2008).MathSciNet 
    PubMed 
    MATH 
    Article 
    PubMed Central 

    Google Scholar 
    67.Stone, C. M., Schwab, S. R., Fonseca, D. M. & Fefferman, N. H. Human movement, cooperation and the effectiveness of coordinated vector control strategies. J. R. Soc. Interface 14, 20170336 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    68.Jiao, J., Pilyugin, S. S., Riotte-Lambert, L. & Osenberg, C. W. Habitat-dependent movement rate can determine the efficacy of marine protected areas. Ecology 99, 2485–2495 (2018).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    69.Keeling, M. J., Rohani, P. & Grenfell, B. T. Seasonally forced disease dynamics explored as switching between attractors. Physica D 148, 317–335 (2001).ADS 
    MATH 
    Article 

    Google Scholar  More

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    Trade resolution further threatens Brazil’s amphibians

    In March, Brazil’s Ministry of Agriculture took an alarming step to boost trade of artisanal animal products across states (see go.nature.com/3by9). It added reptiles and amphibians — already the most threatened vertebrates on Earth — to the list permitting the capture of fishes, crustaceans and molluscs for human consumption.Brazil has the fastest rate of decline of amphibian populations in South America, owing to habitat loss and infectious diseases (B. C. Scheele et al. Science 363, 1459–1463; 2019). If the policy takes effect in its current form, trade of amphibians will increase — compounding the spread of lethal pathogens such as Batrachochytrium species and ranavirus.We urge the government to align its policy with the Convention on Biological Diversity and other international commitments that are backed by substantial scientific evidence. More

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    Soil bacterial community composition in rice–fish integrated farming systems with different planting years

    Soil properties in different rice farming systemsFive treatments were designed in the three selected rice fields, including (1) rice monoculture field (RM); (2) planting area in the 1st year of rice–fish field (OP); (3) aquaculture area in the 1st year of rice–fish field (OA); (4) planting area in the 5th year of rice–fish field (FP); (5) aquaculture area in the 5th year of rice–fish field (FA). The soil properties of the five treatments were shown in Table 1. The highest soil available nitrogen (AN) content was observed in FP and was significantly higher than that in RM, OP and OA. The highest soil available phosphorus (AP) content was observed in RM and was significantly higher than that in the other 4 treatments. The highest soil available potassium (AK) content was measured in the 1st year of rice–fish field (OP and OA), followed by RM and the 5th year of rice–fish field (FP and FA), and significant differences were observed among different rice fields. The highest soil organic matter (OM) content appeared in the 5th year of rice–fish field (FP and FA), and was only significantly higher than that in OA. In addition, the soil pH in the 1st year of rice–fish field (OP and OA) was significantly lower than that in RM and the 5th year of rice–fish field (FP and FA). In summary, significant differences of soil properties were observed among the different rice farming systems.Table 1 Soil properties in different rice systems and areas.Full size tableSoil bacterial community compositionA total of 1,346,468 sequences were obtained by 16S rRNA MiSeq sequencing analysis after basal quality control (reads containing ambiguous bases were discarded; only overlapping sequences longer than 10 bp were assembled; Operational taxonomic units (OTUs) were clustered with 97% similarity). These sequences were classified as 46 phyla, 800 genera and 5335 OTUs. As shown in Fig. 1, the dominant bacterial phyla across different treatments were Proteobacteria (26.06–29.41%) and Chloroflexi (20.07–27.99%), followed by Actinobacteria (7.22–20.87%), Acidobacteria (11.36–14.46%) and Nitrospirae (3.11–8.50%). Since the implementation of rice–fish farming regime, the soil bacterial community composition has greatly changed. For example, Actinobacteria abundance decreased from 20.87% in RM to 7.22% in FA, while Nitrospirae abundance greatly increased from 3.11% in RM to 8.50% in FA. Between different areas in a same rice–fish field (i.e. OP vs OA or FP vs FA), the bacterial community composition were similar. The PCoA analysis on OTU level also showed that different areas within the same rice–fish field had high similarity in bacterial community composition. In contrast, the bacterial community composition differed distinctly among different rice farming systems (Fig. 2). Bacterial alpha diversity indices, as evaluated by Shannon, Simpson, ACE and Chao1, were shown in Table 2. Student’s t-test was adopted to evaluate the difference among treatments. The results showed that the alpha indices of FP were significantly lower than other treatments, except for Simpson index.Figure 1The average relative abundances on phylum level of soil bacterial communities in different rice systems and areas.Full size imageFigure 2PCoA analysis on OTU level based on bray_curtis distance algorithm (significance among treatments were conducted with ANOSIM test, R = 0.4294, P = 0.0010).Full size imageTable 2 Alpha diversity indices of soil bacterial in different rice systems and areas.Full size tableBased on the Kruskal–Wallis test, the statistical differences among treatments were evaluated in the abundances of the top 15 phyla. The results showed that 5 phyla, including Actinobacteria, Nitrospirae, Bacteroidetes, Unclassified_k_norank and SBR1093 were observed significant differences among treatments, and the most significant phylum was Nitrospirae (Fig. 3). In order to trace the source of the significant differences, the Wilcoxon tests were conducted between every two rice cultivation patterns separately (Fig. 4). The results indicated that the significant differences were mainly derived from the comparison between RM and F_group (FP & FA), as well as the comparison between the O_group (OP & OA) and F_group. In the comparison between the RM and O_group, only the phylum Gemmatimonadetes was observed to have a significant difference. Furthermore, we also compared the differences of the top 15 phyla between planting area (P_group) and aquaculture area (A_group) within rice–fish fields, and the results showed no phyla observed with significant differences in the abundances.Figure 3The differences with significance of the top 15 phyla in different rice systems and areas (* indicates 0.01  More

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    Mapping marine debris encountered by albatrosses tracked over oceanic waters

    1.Cózar, A. et al. Plastic debris in the open ocean. Proc. Nat. Acad. Sci. USA 111, 10239–10244. https://doi.org/10.1073/pnas.1314705111 (2014).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    2.Lavers, J. L., Dicks, L., Dicks, M. R. & Finger, A. Significant plastic accumulation on the Cocos (Keeling) Islands, Australia. Sci. Rep. 9, 7102. https://doi.org/10.1038/s41598-019-43375-4 (2019).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    3.Cózar, A. et al. The arctic ocean as a dead end for floating plastics in the north atlantic branch of the thermohaline circulation. Sci. Adv. https://doi.org/10.1126/sciadv.1600582 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    4.Peeken, I. et al. Arctic sea ice is an important temporal sink and means of transport for microplastic. Nat. Commun. 9, 1505. https://doi.org/10.1038/s41467-018-03825-5 (2018).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    5.Woodall, L. C. et al. The deep sea is a major sink for microplastic debris. R. Soc. Open Sci. 1, 140317 (2014).ADS 
    Article 

    Google Scholar 
    6.Chiba, S. et al. Human footprint in the abyss: 30 year records of deep-sea plastic debris. Mar. Policy 96, 204–212. https://doi.org/10.1016/j.marpol.2018.03.022 (2018).Article 

    Google Scholar 
    7.Bergmann, M., Tekman, M. & Gutow, L. Sea change for plastic pollution. Nature 544, 297 (2017).ADS 
    CAS 
    Article 

    Google Scholar 
    8.Jambeck, J. R. et al. Plastic waste inputs from land into the ocean. Science 347, 768–771. https://doi.org/10.1126/science.1260352 (2015).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    9.Gall, S. C. & Thompson, R. C. The impact of debris on marine life. Mar. Pollut. Bull. 92, 170–179. https://doi.org/10.1016/j.marpolbul.2014.12.041 (2015).CAS 
    Article 
    PubMed 

    Google Scholar 
    10.Camphuysen, C. J. Northern Gannets Morus bassanus found dead in the Netherlands, 1970–2000. Atlantic Seabirds 3, 15–30 (2001).
    Google Scholar 
    11.Gregory, M. R. Environmental implications of plastic debris in marine settings–entanglement, ingestion, smothering, hangers-on, hitch-hiking and alien invasions. Phil. Trans. R. Soc. B 364, 2013–2025 (2009).Article 

    Google Scholar 
    12.Ryan, P. G. The effects of ingested plastic on seabirds: Correlations between plastic load and body condition. Environ. Pollut. 46, 119–125 (1987).CAS 
    Article 

    Google Scholar 
    13.Ryan, P. G. Effects of ingested plastic on seabird feeding: Evidence from chickens. Mar. Pollut. Bull. 19, 125–128 (1988).Article 

    Google Scholar 
    14.Pierce, K. E., Harris, R. J., Larned, L. S. & Pokras, M. A. Obstruction and starvation associated with plastic ingestion in a Northern Gannet Morus bassanus and a greater shearwater Puffinus gravis. Mar. Ornithol. 32, 187–189 (2004).
    Google Scholar 
    15.Ryan, P. G., Connell, A. D. & Gardner, B. D. Plastic ingestion and PCBs in seabirds: Is there a relationship?. Mar. Pollut. Bull. 19, 174–176 (1988).CAS 
    Article 

    Google Scholar 
    16.Lavers, J. L., Bond, A. L. & Hutton, I. Plastic ingestion by Flesh-footed Shearwaters (Puffinus carneipes): Implications for chick body condition and the accumulation of plastic-derived chemicals. Environ. Pollut. 187, 124–129. https://doi.org/10.1016/j.envpol.2013.12.020 (2014).CAS 
    Article 
    PubMed 

    Google Scholar 
    17.Tanaka, K. et al. In vivo accumulation of plastic-derived chemicals into seabird tissues. Curr. Biol. 30, 723-728.e3. https://doi.org/10.1016/j.cub.2019.12.037 (2020).CAS 
    Article 
    PubMed 

    Google Scholar 
    18.Teuten, E. L. et al. Transport and release of chemicals from plastics to the environment and to wildlife. Phil. Trans. R. Soc. B 364, 2027–2045 (2009).CAS 
    Article 

    Google Scholar 
    19.Tanaka, K., van Franeker, J. A., Deguchi, T. & Takada, H. Piece-by-piece analysis of additives and manufacturing byproducts in plastics ingested by seabirds: Implication for risk of exposure to seabirds. Mar. Pollut. Bull. 145, 36–41. https://doi.org/10.1016/j.marpolbul.2019.05.028 (2019).CAS 
    Article 
    PubMed 

    Google Scholar 
    20.Thiel, M. & Gutow, L. The ecology of rafting in the marine environment. I. The floating substrata. Oceanogr. Mar. Biol. Annu. Rev. 42, 181–264 (2005).
    Google Scholar 
    21.Kiessling, T., Gutow, L. & Thiel, M. Marine litter as habitat and dispersal vector. In: Bergmann M, Gutow L, Klages M, editors. Marine Anthropogenic Litter. p. 141–80 (2015).22.Day, R. H. & Shaw, D. G. Patterns of abundance of pelagic plastic and tar in the North Pacific Ocean, 1976–1985. Mar. Pollut. Bull. 18, 311–316 (1987).CAS 
    Article 

    Google Scholar 
    23.Pichel, W. G. et al. Marine debris collects within the North Pacific Subtropical Convergence Zone. Mar. Pollut. Bull. 54, 1207–1211 (2007).CAS 
    Article 

    Google Scholar 
    24.Yamashita, R. & Tanimura, A. Floating plastic in the Kuroshio Current area, western North Pacific Ocean. Mar. Pollut. Bull. 54, 485–488 (2007).CAS 
    Article 

    Google Scholar 
    25.Titmus, A. J. & Hyrenbach, K. D. Habitat associations of floating debris and marine birds in the North East Pacific Ocean at coarse and meso spatial scales. Mar. Pollut. Bull. 62, 2496–2506 (2011).CAS 
    Article 

    Google Scholar 
    26.Goldstein, M. C., Titmus, A. J. & Ford, M. Scales of spatial heterogeneity of plastic marine debris in the northeast pacific ocean. PLoS ONE 8, e80020 (2013).ADS 
    Article 

    Google Scholar 
    27.Eriksen, M. et al. Plastic pollution in the world’s oceans: More than 5 trillion plastic pieces weighing over 250,000 tons afloat at sea. PLoS ONE 9, e111913 (2014).ADS 
    Article 

    Google Scholar 
    28.IUCN. The IUCN Red List of Threatened Species. Version 2020–2. https://www.iucnredlist.org (2020).29.Lavers, J. L. & Bond, A. L. Ingested plastic as a route for trace metals in Laysan Albatross (Phoebastria immutabilis) and Bonin Petrel (Pterodroma hypoleuca) from Midway Atoll. Mar. Pollut. Bull. 110, 493–500. https://doi.org/10.1016/j.marpolbul.2016.06.001 (2016).CAS 
    Article 
    PubMed 

    Google Scholar 
    30.Roman, L., Hardesty, B. D., Hindell, M. A. & Wilcox, C. A quantitative analysis linking seabird mortality and marine debris ingestion. Sci. Rep. 9, 3202. https://doi.org/10.1038/s41598-018-36585-9 (2019).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    31.Jouventin, P. & Weimerskirch, H. Satellite tracking of wandering albatrosses. Nature 343, 746–748 (1990).ADS 
    Article 

    Google Scholar 
    32.Kappes, M. A. et al. Hawaiian albatrosses track interannual variability of marine habitats in the North Pacific. Prog. Oceanogr. 86, 246–260 (2010).ADS 
    Article 

    Google Scholar 
    33.Sakamoto, K. Q., Takahashi, A., Iwata, T. & Trathan, P. N. From the eye of the albatrosses: A bird-borne camera shows an association between albatrosses and a killer whale in the Southern Ocean. PLoS ONE 4, e7322 (2009).ADS 
    Article 

    Google Scholar 
    34.Fukuoka, T. et al. The feeding habit of sea turtles influences their reaction to artificial marine debris. Sci. Rep. 6, 28015. https://doi.org/10.1038/srep28015 (2016).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    35.Nishizawa, B. et al. Albatross-borne loggers show feeding on deep-sea squids: Implications for the study of squid distributions. Mar. Ecol. Prog. Ser. 592, 257–265 (2018).ADS 
    Article 

    Google Scholar 
    36.Hunt, G. L. Jr. & Schneider, D. Scale-dependent processes in the physical and biological environment of marine birds. In Seabirds: Feeding Ecology and Role in Marine Ecosystems (ed. Croxall, J. P.) 7–41 (Cambridge University Press, 1987).
    Google Scholar 
    37.Pinaud, D. & Weimerskirch, H. At-sea distribution and scale-dependent foraging behaviour of petrels and albatrosses: A comparative study. J. Anim. Ecol. 76, 9–19 (2007).Article 

    Google Scholar 
    38.Thiebot, J.-B., Nishizawa, B., Sato, F., Tomita, N. & Watanuki, Y. Albatross chicks reveal interactions of adults with artisanal longline fisheries within a short range. J. Ornithol. 159, 935–944 (2018).Article 

    Google Scholar 
    39.Froese, R. & Pauly, D. FishBase. World Wide Web electronic publication. www.fishbase.org, version (12/2019).40.Ryan, P. G. A simple technique for counting marine debris at sea reveals steep litter gradients between the Straits of Malacca and the Bay of Bengal. Mar. Pollut. Bull. 69, 128–136 (2013).CAS 
    Article 

    Google Scholar 
    41.Mitani, Y. et al. Marine debris observed in the North Pacific during Oshoro-maru cruise in 2012. Bull. Fish. Sci. Hokkaido Univ. 64, 25–29 (2014).
    Google Scholar 
    42.Hyrenbach, K. D. et al. Plastic ingestion by Black-footed albatross from Kure Atoll, Hawai’i: linking chick loads and parental at-sea distributions. Mar. Ornithol. 45, 225–236 (2017).
    Google Scholar 
    43.Nevitt, G. A., Losekoot, M. & Weimerskirch, H. Evidence for olfactory search in wandering albatross, Diomedea Exulans. Proc. Nat. Acad. Sci. USA 105, 4576–4581 (2008).ADS 
    CAS 
    Article 

    Google Scholar 
    44.Savoca, M. S., Wohlfeil, M. E., Ebeler, S. E. & Nevitt, G. A. Marine plastic debris emits a keystone infochemical for olfactory foraging seabirds. Sci. Adv. 2, e1600395 (2016).ADS 
    Article 

    Google Scholar 
    45.Santos, R. G., Andrades, R., Fardim, L. M. & Martins, A. S. Marine debris ingestion and Thayer’s law—The importance of plastic color. Environ. Pollut. 214, 585–588 (2016).CAS 
    Article 

    Google Scholar 
    46.Castro, J. J., Santiago, J. A. & Santana-Ortega, A. T. A general theory on fish aggregation to floating objects: An alternative to the meeting point hypothesis. Rev. Fish Biol. Fish. 11, 255–277 (2002).Article 

    Google Scholar 
    47.Harrison, C. S., Hida, T. S. & Seki, M. P. Hawaiian seabird feeding ecology. Wildl. Monogr. 85, 1–71 (1983).
    Google Scholar 
    48.Hunte, W., Oxenford, H. A. & Mahon, R. Distribution and relative abundance of flyingfish (Exocoetidae) in the eastern Caribbean. II. Spawning substrata, eggs and larvae. Mar. Ecol. Prog. Ser. 117, 25–37 (1995).ADS 
    Article 

    Google Scholar 
    49.Rapp, D. C., Youngren, S. M., Hartzell, P. & Hyrenbach, K. D. Community-wide patterns of plastic ingestion in seabirds breeding at French Frigate Shoals Northwestern Hawaiian Islands. Mar. Pollut. Bull. 123, 269–278 (2017).CAS 
    Article 

    Google Scholar 
    50.Douglas, D. & Peucker, T. Algorithms for the reduction of the number of points required to represent a digitized line or its caricature. Cannadian Cartogr. 10, 112–122 (1973).Article 

    Google Scholar 
    51.Edelhoff, H., Signer, J. & Balkenhol, N. Path segmentation for beginners: an overview of current methods for detecting changes in animal movement patterns. Move. Ecol. 4, 21 (2016).Article 

    Google Scholar 
    52.R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.r-project.org/index.html (2020). More

  • in

    Variable coastal hypoxia exposure and drivers across the southern California Current

    1.Díaz, R. J. Overview of hypoxia around the world. J. Environ. Qual. 30, 275–281 (2001).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    2.Laffoley, D. & Baxter, J. M. (eds) Ocean deoxygenation: Everyone’s problem. Causes, impacts, consequences and solutions (IUCN, International Union for Conservation of Nature, 2019).
    Google Scholar 
    3.Booth, J. A. T. et al. Patterns and potential drivers of declining oxygen content along the southern California coast. Limnol. Oceanogr. 59, 1127–1138 (2014).ADS 
    CAS 
    Article 

    Google Scholar 
    4.Gilbert, D., Rabalais, N. N., Díaz, R. J. & Zhang, J. Evidence for greater oxygen decline rates in the coastal ocean than in the open ocean. Biogeosciences 7, 2283–2296 (2010).ADS 
    CAS 
    Article 

    Google Scholar 
    5.Altieri, A. H. & Gedan, K. B. Climate change and dead zones. Glob. Change Biol. 21, 1395–1406 (2015).ADS 
    Article 

    Google Scholar 
    6.Breitburg, D. et al. Declining oxygen in the global ocean and coastal waters. Science (80-) 359, eaam7240 (2018).Article 
    CAS 

    Google Scholar 
    7.Keeling, R. E., Körtzinger, A. & Gruber, N. Ocean deoxygenation in a warming world. Ann. Rev. Mar. Sci. 2, 199–229 (2010).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    8.Levin, L. A. & Breitburg, D. L. Linking coasts and seas to address ocean deoxygenation. Nat. Clim. Change 5, 401–403 (2015).ADS 
    Article 

    Google Scholar 
    9.Rabalais, N. N., Turner, R. E., Díaz, R. J. & Justić, D. Global change and eutrophication of coastal waters. ICES J. Mar. Sci. 66, 1528–1537 (2009).Article 

    Google Scholar 
    10.Diaz, R. J. & Rosenberg, R. Spreading dead zones and consequences for marine ecosystems. Science (80-) 321, 926–929 (2008).ADS 
    CAS 
    Article 

    Google Scholar 
    11.Hofmann, A. F., Peltzer, E. T., Walz, P. M. & Brewer, P. G. Hypoxia by degrees: Establishing definitions for a changing ocean. Deep Res. Part I Oceanogr. Res. Pap. 58, 1212–1226 (2011).ADS 
    CAS 
    Article 

    Google Scholar 
    12.Rabalais, N. N. et al. Dynamics and distribution of natural and human-caused hypoxia. Biogeosciences 7, 585–619 (2010).ADS 
    CAS 
    Article 

    Google Scholar 
    13.Vaquer-Sunyer, R. & Duarte, C. M. Thresholds of hypoxia for marine biodiversity. Proc. Natl. Acad. Sci. 105, 15452–15457 (2008).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    14.Altieri, A. H. et al. Tropical dead zones and mass mortalities on coral reefs. Proc. Natl. Acad. Sci. U. S. A. 114, 3660–3665 (2017).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    15.Grantham, B. A. et al. Upwelling-driven nearshore hypoxia signals ecosystem and oceanographic changes in the northeast Pacific. Nature 429, 749–754 (2004).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    16.Kim, T. W., Barry, J. P. & Micheli, F. The effects of intermittent exposure to low-pH and low-oxygen conditions on survival and growth of juvenile red abalone. Biogeosciences 10, 7255–7262 (2013).ADS 
    Article 

    Google Scholar 
    17.Kolesar, S. E., Breitburg, D. L., Purcell, J. E. & Decker, M. B. Effects of hypoxia on Mnemiopsis leidyi, ichthyoplankton and copepods: Clearance rates and vertical habitat overlap. Mar. Ecol. Prog. Ser. 411, 173–188 (2010).ADS 
    CAS 
    Article 

    Google Scholar 
    18.Low, N. H. N. & Micheli, F. Lethal and functional thresholds of hypoxia in two key benthic grazers. Mar. Ecol. Prog. Ser. 594, 165–173 (2018).ADS 
    CAS 
    Article 

    Google Scholar 
    19.Thomas, P. & Saydur Rahman, M. Extensive reproductive disruption, ovarian masculinization and aromatase suppression in Atlantic croaker in the northern Gulf of Mexico hypoxic zone. Proc. R. Soc. B Biol. Sci. 279, 28–38 (2011).Article 
    CAS 

    Google Scholar 
    20.Breitburg, D. Effects of hypoxia, and the balance between hypoxia and enrichment, on coastal fishes and fisheries. Estuaries 25, 767–781 (2002).Article 

    Google Scholar 
    21.Brown, J. H., Gillooly, J. F., Allen, A. P., Savage, V. M. & West, G. B. Toward a metabolic theory of ecology. Ecology 85, 1771–1789 (2004).Article 

    Google Scholar 
    22.Pörtner, H. O. & Knust, R. Climate change affects marine fishes through the oxygen limitation of thermal tolerance. Science (80-) 315, 95–97 (2007).ADS 
    Article 
    CAS 

    Google Scholar 
    23.Vaquer-Sunyer, R. & Duarte, C. M. Temperature effects on oxygen thresholds for hypoxia in marine benthic organisms. Glob. Change Biol. 17, 1788–1797 (2011).ADS 
    Article 

    Google Scholar 
    24.Breitburg, D. L., Hondorp, D. W., Davias, L. A. & Diaz, R. J. Hypoxia, nitrogen, and fisheries: Integrating effects across local and global landscapes. Ann. Rev. Mar. Sci. 1, 329–349 (2009).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    25.Booth, J. A. T. et al. Natural intrusions of hypoxic, low pH water into nearshore marine environments on the California coast. Cont. Shelf. Res. 45, 108–115 (2012).ADS 
    Article 

    Google Scholar 
    26.Walter, R. K., Woodson, C. B., Leary, P. R. & Monismith, S. G. Connecting wind-driven upwelling and offshore stratification to nearshore internal bores and oxygen variability. J. Geophys. Res. Ocean 119, 3517–3534 (2014).ADS 
    Article 

    Google Scholar 
    27.Boch, C. A. et al. Local oceanographic variability influences the performance of juvenile abalone under climate change. Sci. Rep. 8, 1–12 (2018).CAS 
    Article 

    Google Scholar 
    28.DiMarco, S. F., Chapman, P., Walker, N. & Hetland, R. D. Does local topography control hypoxia on the eastern Texas–Louisiana shelf?. J. Mar. Syst. 80, 25–35 (2010).Article 

    Google Scholar 
    29.Leary, P. R. et al. “Internal tide pools” prolong kelp forest hypoxic events. Limnol. Oceanogr. 62, 2864–2878 (2017).ADS 
    CAS 
    Article 

    Google Scholar 
    30.Walter, R. K., Brock Woodson, C., Arthur, R. S., Fringer, O. B. & Monismith, S. G. Nearshore internal bores and turbulent mixing in southern Monterey Bay. J. Geophys. Res. Ocean 117, 1–13 (2012).
    Google Scholar 
    31.Long, W. C. & Seitz, R. D. Trophic interactions under stress: Hypoxia enhances foraging in an estuarine food web. Mar. Ecol. Prog. Ser. 362, 59–68 (2008).ADS 
    Article 

    Google Scholar 
    32.Kwiatkowski, L. & Orr, J. C. Diverging seasonal extremes for ocean acidification during the twenty-first centuryr. Nat. Clim. Chang. 8, 141–145 (2018).ADS 
    CAS 
    Article 

    Google Scholar 
    33.Safaie, A. et al. High frequency temperature variability reduces the risk of coral bleaching. Nat. Commun. 9, 1–12 (2018).Article 
    CAS 

    Google Scholar 
    34.Woodson, C. B. The fate and impact of internal waves in nearshore ecosystems. Ann. Rev. Mar. Sci. 10, 421–441 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    35.Woodson, C. B. et al. Harnessing marine microclimates for climate change adaptation and marine conservation. Conserv. Lett. 12(2), 1–9 (2018).
    Google Scholar 
    36.Micheli, F. et al. Evidence that marine reserves enhance resilience to climatic impacts. PLoS ONE 7, e40832 (2012).
    ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    37.Cox, K. W. California abalones, family haliotidae. Fish. Bull. 118 28–32 (1962).

    Google Scholar 
    38.Frieder, C. A., Nam, S. H., Martz, T. R. & Levin, L. A. High temporal and spatial variability of dissolved oxygen and pH in a nearshore California kelp forest. Biogeosciences 9, 3917–3930 (2012).ADS 
    CAS 
    Article 

    Google Scholar 
    39.Mayol, E., Ruiz-Halpern, S., Duarte, C. M., Castilla, J. C. & Pelegrí, J. L. Coupled CO2 and O2-driven compromises to marine life in summer along the Chilean sector of the Humboldt Current System. Biogeosciences 9, 1183–1194 (2012).ADS 
    CAS 
    Article 

    Google Scholar 
    40.Orellana-Cepeda, E., Granados-Machuca, C. & Serrano-Esquer, J. Ceratium furca: One possible cause of mass mortality of cultured Blue-Fin Tuna at Baja California, Mexico. Harmful Algae 2002, 514–516 (2004).
    Google Scholar 
    41.Bograd, S. J. et al. Oxygen declines and the shoaling of the hypoxic boundary in the California Current. Geophys. Res. Lett. 35, 1–6 (2008).Article 
    CAS 

    Google Scholar 
    42.Bernardi, G., Findley, L. & Rocha-Olivares, A. Vicariance and dispersal across Baja California in disjunct marine fish populations. Evolution (N Y) 57, 1599–1609 (2003).
    Google Scholar 
    43.Haupt, A. J., Micheli, F. & Palumbi, S. R. Dispersal at a snail’s pace: Historical processes affect contemporary genetic structure in the exploited wavy top snail (Megastraea undosa). J. Hered. 104, 327–340 (2013).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    44.Al Najjar, M. W. Nearshore Processes of a Coastal Island: Physical Dynamics and Ecological Implications (Stanford University, 2019).
    Google Scholar 
    45.Hughes, B. B. et al. Climate mediates hypoxic stress on fish diversity and nursery function at the land-sea interface. Proc. Natl. Acad. Sci. U. S. A. 112, 8025–8030 (2015).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    46.Sydeman, W. J. et al. Climate change and wind intensification in coastal upwelling ecosystems. Science (80-) 345, 77–80 (2014).ADS 
    CAS 
    Article 

    Google Scholar 
    47.Fulton, S. et al. From fishing fish to fishing data: The role of Artisanal Fishers in Conservation and Resource Management in Mexico. In Viability and Sustainability of Small-Scale Fisheries in Latin America and The Caribbean (eds Salas, S. et al.) 151–175 (Springer International Publishing, 2019).
    Google Scholar 
    48.Chang, W., Cheng, J., Allaire, J. J., Xie, Y. & McPherson, J. shiny: Web Application Framework for R. R package version 1.4.0.2. https://cran.r-project.org/package=shiny (2020).49.R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ (2020).50.Eerkes-Medrano, D., Menge, B. A., Sislak, C. & Langdon, C. J. Contrasting effects of hypoxic conditions on survivorship of planktonic larvae of rocky intertidal invertebrates. Mar. Ecol. Prog. Ser. 478, 139–151 (2013).ADS 
    Article 

    Google Scholar 
    51.Low, N. H. N. & Micheli, F. Short- and long-term impacts of variable hypoxia exposures on kelp forest sea urchins. Sci. Rep. 10, 1–9 (2020).CAS 
    Article 

    Google Scholar 
    52.Bograd, S. J. et al. Phenology of coastal upwelling in the California Current. Geophys. Res. Lett. 36, 1–5 (2009).Article 

    Google Scholar 
    53.Nam, S., Kim, H. J. & Send, U. Amplification of hypoxic and acidic events by la Nia conditions on the continental shelf off California. Geophys. Res. Lett. 38, 1–5 (2011).Article 
    CAS 

    Google Scholar 
    54.Rogers-Bennett, L. et al. Dinoflagellate bloom coincides with marine invertebrate mortalities in Northern California. Harmful Algae News 46, 10–11 (2012).
    Google Scholar 
    55.Chan, F. et al. Persistent spatial structuring of coastal ocean acidification in the California Current System. Sci. Rep. 7, 1–8 (2017).Article 
    CAS 

    Google Scholar 
    56.Montgomery, D. W., Simpson, S. D., Engelhard, G. H., Birchenough, S. N. R. & Wilson, R. W. Rising CO2 enhances hypoxia tolerance in a marine fish. Sci. Rep. 9, 1–10 (2019).CAS 
    Article 

    Google Scholar 
    57.Boch, C. A. et al. Effects of current and future coastal upwelling conditions on the fertilization success of the red abalone (Haliotis rufescens). ICES J. Mar. Sci. 74, 1125–1134 (2017).Article 

    Google Scholar 
    58.Gobler, C. J. & Baumann, H. Hypoxia and acidification in marine ecosystems: Coupled dynamics and effects on
    ocean life. Biol. Lett. 12, 20150976 (2016).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar  More

  • in

    Arbuscular mycorrhizal trees influence the latitudinal beta-diversity gradient of tree communities in forests worldwide

    1.Myers, J. A. & LaManna, J. A. The promise and pitfalls of beta-diversity in ecology and conservation. J. Veg. Sci. 27, 1081–1083 (2016).Article 

    Google Scholar 
    2.Socolar, J. B., Gilroy, J. J., Kunin, W. E. & Edwards, D. P. How should beta-diversity inform biodiversity conservation? Trends Ecol. Evol. 31, 67–80 (2016).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    3.Xing, D. L. & He, F. L. Environmental filtering explains a U-shape latitudinal pattern in regional beta-deviation for eastern North American trees. Ecol. Lett. 22, 284–291 (2019).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    4.Anderson, M. J. et al. Navigating the multiple meanings of beta diversity: a roadmap for the practicing ecologist. Ecol. Lett. 14, 19–28 (2011).ADS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    5.Baselga, A. Partitioning the turnover and nestedness components of beta diversity. Glob. Ecol. Biogeogr. 19, 134–143 (2010).Article 

    Google Scholar 
    6.Menegotto, A., Dambros, C. S. & Netto, S. A. The scale-dependent effect of environmental filters on species turnover and nestedness in an estuarine benthic community. Ecology 100, e02721 (2019).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    7.Whittaker, R. H. Vegetation of the Siskiyou mountains, Oregon and California. Ecol. Monogr. 30, 279–338 (1960).Article 

    Google Scholar 
    8.Hubbell, S. P. The unified neutral theory of biodiversity and biogeography. (Princeton University Press, 2001).9.Nekola, J. C. & White, P. S. The distance decay of similarity in biogeography and ecology. J. Biogeogr. 26, 867–878 (1999).Article 

    Google Scholar 
    10.da Silva, P. G., Lobo, J. M., Hensen, M. C., Vaz-de-Mello, F. Z. & Hernandez, M. I. M. Turnover and nestedness in subtropical dung beetle assemblages along an elevational gradient. Divers Distrib. 24, 1277–1290 (2018).Article 

    Google Scholar 
    11.Wang, X. G. et al. Ecological drivers of spatial community dissimilarity, species replacement and species nestedness across temperate forests. Glob. Ecol. Biogeogr. 27, 581–592 (2018).Article 

    Google Scholar 
    12.McFadden, I. R. et al. Temperature shapes opposing latitudinal gradients of plant taxonomic and phylogenetic beta diversity. Ecol. Lett. 22, 1126–1135 (2019).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    13.Qian, H., Chen, S., Mao, L. & Ouyang, Z. Drivers of β‐diversity along latitudinal gradients revisited. Glob. Ecol. Biogeogr. 22, 659–670 (2013).Article 

    Google Scholar 
    14.Xu, W. B., Chen, G. K., Liu, C. R. & Ma, K. P. Latitudinal differences in species abundance distributions, rather than spatial aggregation, explain beta-diversity along latitudinal gradients. Glob. Ecol. Biogeogr. 24, 1170–1180 (2015).Article 

    Google Scholar 
    15.Kraft, N. J. et al. Disentangling the drivers of β diversity along latitudinal and elevational gradients. Science 333, 1755–1758 (2011).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    16.Griffiths, D. Connectivity and vagility determine beta diversity and nestedness in North American and European freshwater fish. J. Biogeogr. 44, 1723–1733 (2017).Article 

    Google Scholar 
    17.Soininen, J., Heino, J. & Wang, J. J. A meta-analysis of nestedness and turnover components of beta diversity across organisms and ecosystems. Glob. Ecol. Biogeogr. 27, 96–109 (2018).Article 

    Google Scholar 
    18.LaManna, J. A., Belote, R. T., Burkle, L. A., Catano, C. P. & Myers, J. A. Negative density dependence mediates biodiversity-productivity relationships across scales. Nat. Ecol. Evol. 1, 1107–1115 (2017).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    19.van der Heijden, M. G. A., Martin, F. M., Selosse, M. A. & Sanders, I. R. Mycorrhizal ecology and evolution: the past, the present, and the future. New Phytol. 205, 1406–1423 (2015).PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    20.Brundrett, M. C. Mycorrhizal associations and other means of nutrition of vascular plants: understanding the global diversity of host plants by resolving conflicting information and developing reliable means of diagnosis. Plant Soil 320, 37–77 (2009).CAS 
    Article 

    Google Scholar 
    21.Gibert, A., Tozer, W. & Westoby, M. Plant performance response to eight different types of symbiosis. New Phytol. 222, 526–542 (2019).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    22.Veresoglou, S. D., Rillig, M. C. & Johnson, D. Responsiveness of plants to mycorrhiza regulates coexistence. J. Ecol. 106, 1864–1875 (2018).Article 

    Google Scholar 
    23.Delavaux, C. S. et al. Mycorrhizal fungi influence global plant biogeography. Nat. Ecol. Evol. 3, 424–429 (2019).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    24.Barcelo, M., van Bodegom, P. M. & Soudzilovskaia, N. A. Climate drives the spatial distribution of mycorrhizal host plants in terrestrial ecosystems. J. Ecol. 107, 2564–2573 (2019).Article 

    Google Scholar 
    25.Steidinger, B. S. et al. Climatic controls of decomposition drive the global biogeography of forest-tree symbioses. Nature 571, E8–E8 (2019).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    26.Bennett, J. A. et al. Plant-soil feedbacks and mycorrhizal type influence temperate forest population dynamics. Science 355, 181–184 (2017).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    27.Johnson, D. J., Clay, K. & Phillips, R. P. Mycorrhizal associations and the spatial structure of an old-growth forest community. Oecologia 186, 195–204 (2018).ADS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    28.Hargreaves, A. L., Germain, R. M., Bontrager, M., Persi, J. & Angert, A. L. Local adaptation to biotic interactions: a meta-analysis across latitudes. Am. Nat. 195, 395–411 (2020).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    29.Liu, X. B. et al. Partitioning of soil phosphorus among arbuscular and ectomycorrhizal trees in tropical and subtropical forests. Ecol. Lett. 21, 713–723 (2018).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    30.Jacquemyn, H., De Kort, H., Vanden Broeck, A. & Brys, R. Immigrant and extrinsic hybrid seed inviability contribute to reproductive isolation between forest and dune ecotypes of Epipactis helleborine (Orchidaceae). Oikos 127, 73–84 (2018).Article 

    Google Scholar 
    31.Osborne, O. G. et al. Arbuscular mycorrhizal fungi promote coexistence and niche divergence of sympatric palm species on a remote oceanic island. New Phytol. 217, 1254–1266 (2018).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    32.Myers, J. A. et al. Beta-diversity in temperate and tropical forests reflects dissimilar mechanisms of community assembly. Ecol. Lett. 16, 151–157 (2013).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    33.Jankowski, J. E., Ciecka, A. L., Meyer, N. Y. & Rabenold, K. N. Beta diversity along environmental gradients: implications of habitat specialization in tropical montane landscapes. J. Anim. Ecol. 78, 315–327 (2009).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    34.McCarthy-Neumann, S. & Ibáñez, I. Tree range expansion may be enhanced by escape from negative plant–soil feedbacks. Ecology 93, 2637–2649 (2012).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    35.Peay, K. G. The mutualistic niche: mycorrhizal symbiosis and community dynamics. Annu. Rev. Ecol., Evol. Syst. 47, 143–164 (2016).Article 

    Google Scholar 
    36.Wang, Z. H., Fang, J. Y., Tang, Z. Y. & Shi, L. Geographical patterns in the beta diversity of China’s woody plants: the influence of space, environment and range size. Ecography 35, 1092–1102 (2012).Article 

    Google Scholar 
    37.Liang, M. X. et al. Soil fungal networks maintain local dominance of ectomycorrhizal trees. Nat. Commun. 11, 2636 (2020).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    38.Segnitz, R. M., Russo, S. E., Davies, S. J. & Peay, K. G. Ectomycorrhizal fungi drive positive phylogenetic plant-soil feedbacks in a regionally dominant tropical plant family. Ecology 101, e03083 (2020).39.Chen, L. et al. Differential soil fungus accumulation and density dependence of trees in a subtropical forest. Science 366, 124–128 (2019).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    40.Brundrett, Mark, Murase, Gracia & K, B. Comparative anatomy of roots and mycorrhizae of common Ontario trees. Can. J. Bot. 68, 551–578 (1990).Article 

    Google Scholar 
    41.Liu, Y. & He, F. L. Incorporating the disease triangle framework for testing the effect of soil-borne pathogens on tree species diversity. Funct. Ecol. 33, 1211–1222 (2019).MathSciNet 
    Article 

    Google Scholar 
    42.LaManna, J. A. et al. Plant diversity increases with the strength of negative density dependence at the global scale. Science 356, 1389–1392 (2017).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    43.Johnson, D. J., Beaulieu, W. T., Bever, J. D. & Clay, K. Conspecific negative density dependence and forest diversity. Science 336, 904–907 (2012).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    44.Crawford, K. M. et al. When and where plant-soil feedback may promote plant coexistence: a meta-analysis. Ecol. Lett. 22, 1274–1284 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    45.Liu, X. B., Etienne, R. S., Liang, M. X., Wang, Y. F. & Yu, S. X. Experimental evidence for an intraspecific Janzen-Connell effect mediated by soil biota. Ecology 96, 662–671 (2015).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    46.Chu, C. J. et al. Direct and indirect effects of climate on richness drive the latitudinal diversity gradient in forest trees. Ecol. Lett. 22, 245–255 (2019).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    47.Gavito, M. E. & Azcon-Aguilar, C. Temperature stress in arbuscular mycorrhizal fungi: a test for adaptation to soil temperature in three isolates of Funneliformis mosseae from different climates. Agr. Food Sci. 21, 2–11 (2012).Article 

    Google Scholar 
    48.Hetrick, B. D. & Bloom, J. The influence of temperature on colonization of winter wheat by vesicular-arbuscular mycorrhizal fungi. Mycologia 76, 953–956 (1984).Article 

    Google Scholar 
    49.Anderson-Teixeira, K. J. et al. CTFS-ForestGEO: a worldwide network monitoring forests in an era of global change. Glob. Change Biol. 21, 528–549 (2015).ADS 
    Article 

    Google Scholar 
    50.Condit, R. Tropical forest census plots: methods and results from Barro Colorado Island, Panama and a comparison with other plots. (Springer-Verlag andRG. Landes Company, 1998).51.Stillhard, J. et al. Stand inventory data from the 10-ha forest research plot in Uholka: 15 yr of primeval beech forest development. Ecology 100, e02845 (2019).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    52.Marion, Z. H., Fordyce, J. A. & Fitzpatrick, B. M. Pairwise beta diversity resolves an underappreciated source of confusion in calculating species turnover. Ecology 98, 933–939 (2017).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    53.Bennett, J. R. & Gilbert, B. Contrasting beta diversity among regions: how do classical and multivariate approaches compare? Glob. Ecol. Biogeogr. 25, 368–377 (2016).Article 

    Google Scholar 
    54.Legendre, P. & De Caceres, M. Beta diversity as the variance of community data: dissimilarity coefficients and partitioning. Ecol. Lett. 16, 951–963 (2013).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    55.Baselga, A. Separating the two components of abundance-based dissimilarity: balanced changes in abundance vs. abundance gradients. Methods Ecol. Evol. 4, 552–557 (2013).Article 

    Google Scholar 
    56.De Cáceres, M. et al. The variation of tree beta diversity across a global network of forest plots. Glob. Ecol. Biogeogr. 21, 1191–1202 (2012).Article 

    Google Scholar 
    57.Yen, J. D. L., Fleishman, E., Fogarty, F. & Dobkin, D. S. Relating beta diversity of birds and butterflies in the Great Basin to spatial resolution, environmental variables and trait-based groups. Glob. Ecol. Biogeogr. 28, 328–340 (2019).Article 

    Google Scholar 
    58.Craven, D., Knight, T. M., Barton, K. E., Bialic-Murphy, L. & Chase, J. M. Dissecting macroecological and macroevolutionary patterns of forest biodiversity across the Hawaiian archipelago. Proc. Natl Acad. Sci. USA 116, 16436–16441 (2019).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    59.Brundrett, M. & Tedersoo, L. Misdiagnosis of mycorrhizas and inappropriate recycling of data can lead to false conclusions. New Phytol. 221, 18–24 (2019).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    60.Soudzilovskaia, N. A. et al. FungalRoot: global online database of plant mycorrhizal associations. New Phytol. 227, 955–966 (2020).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    61.Furniss, T. J., Larson, A. J. & Lutz, J. A. Reconciling niches and neutrality in a subalpine temperate forest. Ecosphere 8 (2017).62.Jucker, T. et al. Canopy structure and topography jointly constrain the microclimate of human-modified tropical landscapes. Glob. Change Biol. 24, 5243–5258 (2018).ADS 
    Article 

    Google Scholar 
    63.Legendre, P. et al. Partitioning beta diversity in a subtropical broad-leaved forest of China. Ecology 90, 663–674 (2009).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    64.Robert J., H. raster: Geographic data analysis and modeling. R package version 2.6-7 (2017). .65.Alahuhta, J. et al. Global variation in the beta diversity of lake macrophytes is driven by environmental heterogeneity rather than latitude. J. Biogeogr. 44, 1758–1769 (2017).Article 

    Google Scholar 
    66.Cribari-Neto, F. & Zeileis, A. Beta regression in R. J. Stat. Softw. 34, 1–24 (2010).Article 

    Google Scholar 
    67.Jump, A. S., Matyas, C. & Penuelas, J. The altitude-for-latitude disparity in the range retractions of woody species. Trends Ecol. Evol. 24, 694–701 (2009).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    68.Oksanen, J. et al. vegan: Community ecology package. R package version 2.5-2 (2018). .69.Gilbert, B. & Bennett, J. R. Partitioning variation in ecological communities: do the numbers add up? J. Appl Ecol. 47, 1071–1082 (2010).Article 

    Google Scholar 
    70.Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).MATH 
    Article 

    Google Scholar 
    71.R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (2019). .72.Baselga, A., Orme, D., Villeger, S., De Bortoli, J. & Leprieur, F. Partitioning beta diversity into turnover and nestedness components. R package version 1.5.0 (2019). .73.Harrell Jr, F. E. & Dupont, C. Hmisc: Harrell miscellaneous. R package version 4.2-3 (2019). .74.Liaw, A. & Wiener, M. Classification and regression by randomForest. R News. 2, 18–22 (2002).
    Google Scholar 
    75.Archer, E. rfPermute: estimate permutation p-values for random forest importance metrics. R package version 2.1.6 (2018). . More