More stories

  • in

    A function-based typology for Earth’s ecosystems

    We developed the IUCN Global Ecosystem Typology in the following sequence of steps: design criteria; hierarchical structure and definition of levels; generic ecosystem assembly model; top-down classification of the upper hierarchical levels; iterative circumscription of the units and ecosystem-specific adaptations of the assembly model; full description of the units; and map compilation. Some iteration proved necessary, as the description and review process sometimes revealed a need for circumscribing additional units.Design criteria and other typologiesUnder the auspices of the IUCN Commission on Ecosystem Management, we developed six design principles to guide the development of a typology that would meet the needs for global ecosystem reporting, risk assessment, natural capital accounting and ecosystem management: (1) representation of ecological processes and ecosystem functions; (2) representation of biota; (3) conceptual consistency throughout the biosphere; (4) scalable structure; (5) spatially explicit units; and (6) parsimony and utility (see Supplementary Table 1.1 and Supplementary Information, Appendix 1 for definitions and rationale).We assessed 23 existing ecological classifications with global coverage of terrestrial, freshwater, and/or marine environments against these principles to determine their fitness for IUCN’s purpose (Supplementary Information, Appendix 1). These include general classifications of land, water or bioclimate, as well as classifications of units that conform with the definition of ecosystems adopted in the United Nations Convention on Biological Diversity45 or an equivalent definition in the IUCN Red List of Ecosystems30. We reviewed documentation on methods of derivation, descriptions of classification units and maps to assess each classification against the six design principles (Supplementary Table 1.2 for details).Typology structure and ecosystem assemblyWe developed the structure of the Global Ecosystem Typology and the generic ecosystem assembly model at a workshop attended by 48 terrestrial, freshwater and marine ecosystem experts at Kings College London, UK, in May 2017. Participants agreed that a hierarchical structure would provide an effective framework for integrating ecological processes and functional properties (Supplementary Table 1.1, design principle 1), and biotic composition (principle 2) into the typology, while also meeting the requirement for scalability (principle 4). Although neither function nor composition were intended to take primacy within the typology, we reasoned that a hierarchy representing functional features in the upper levels is likely to support generalizations and predictions by leveraging evolutionary convergence13. By contrast, a typology reflecting compositional similarities in its upperlevels is less likely to be stable owing to dynamism of species assemblages and evolving knowledge on species taxonomy and distributions. Furthermore, representation of compositional relationships at a global scale would require many more units in upper levels, and possibly more hierarchical levels. Therefore, we concluded that a hierarchical structure recognizing compositional variants at lower levels within broad functionally based groupings at upper levels would be more parsimonious and robust (principle 6) than one representing composition at upper levels and functions at lower levels.Workshop participants initially agreed that three hierarchical levels for ecosystem function and three levels for biotic composition could be sufficient to represent global variation across the whole biosphere. Participants developed the concepts of these levels into formal definitions (Supplementary Table 3.1), which were reviewed and refined during the development process.To ensure conceptual consistency of the typology and its units throughout the biosphere (principle 3), we drew from community assembly theory to develop a generic model of ecosystem assembly. The traditional community assembly model incorporates three types of filters (dispersal, the abiotic environment and biotic interactions) that determine which biota from a larger pool of potential colonists can occupy and persist in an area13. We extended this model to ecosystems by: (1) defining three groups of abiotic filters (resources, ambient environment and disturbance regimes) and two groups of biotic filters (biotic interactions and human activity); (2) incorporating evolutionary processes that shape characteristic biotic properties of ecosystems over time; (3) defining the outcomes of filtering and evolution in terms of all ecosystem properties including both ecosystem-level functions and species-level traits, rather than only in terms of species traits and composition; and (4) incorporating interactions and feedbacks among filters and selection agents and ecosystem properties to elucidate hypotheses about processes that influence temporal and spatial variability in the properties of ecosystems and their component biota. In community assembly, only a small number of filters are likely to be important in any given habitat13. In keeping with this proposition, we used the generic model to identify biological and physical features that distinguish functionally different groups of ecosystems from one another by focusing on different ecological drivers that come to the fore in structuring their assembly and shaping their properties.Hierarchical levelsThe top level of classification (Fig. 2 and Extended Data Tables 1–4) defines five core realms of the biosphere based on contrasting media that reflect ecological processes and functional properties: terrestrial; freshwaters and inland saline waters (hereafter freshwater); marine; subterranean; and atmospheric. Biome gradient concepts25 highlight continuous variation in ecosystem properties, which is represented in the typology by transitional realms that mark the interfaces between the five core realms (for example, floodplains (terrestrial–freshwater), estuaries (freshwater–marine), and so on). In Supplementary Information, Appendix 3 (pages 3–16) and Supplementary Table 3.1, we describe the five core realms and review the hypothesized assembly filters and ecosystem properties that distinguish different groups within them. The atmospheric realm is included for comprehensive coverage, but we deferred resolution of its lower levels because its biota is poorly understood, sparse, itinerant and represented mainly by dispersive life stages46.Functional biomes (level 2) are components of the biosphere united by one or more major assembly processes that shape key ecosystem functions and ecological processes, irrespective of taxonomic identity (Supplementary Information, Appendix 3, page 17). Our interpretation aligns broadly with ‘functional biomes’ described elsewhere24,25,47, extended here to reflect dominant assembly filters and processes across all realms, rather than the more restricted basis of climate-vegetation relationships that traditionally underpin biome definition on land. Hence, the 25 functional biomes (Supplementary Information, Appendix 4, pages 52–186 and https://global-ecosystems.org/) include some ‘traditional’ terrestrial biomes47, as well as lentic and lotic freshwater systems, pelagic and benthic marine systems, and anthropogenic functional biomes assembled and usually maintained by human activity48.Level 3 of the typology defines 110 ecosystem functional groups described with illustrated profiles in Supplementary Information, Appendix 4 (pages 52–186) and at https://global-ecosystems.org/. These are key units for generalization and prediction, because they include ecosystem types with convergent ecosystem properties shaped by the dominance of a common set of drivers (Supplementary Information, Appendix 3, pages 17–19). Ecosystem functional groups are differentiated along environmental gradients that define spatial and temporal variation in ecological drivers (Figs. 2 and 3 and Supplementary Figs. 3.2 and 3.4). For example, depth gradients of light and nutrients differentiate functional groups in pelagic ocean waters (Fig. 3c and Extended Data Table 4), influencing assembly directly and indirectly through predation. Resource gradients defined by flow regimes (influenced by catchment precipitation and evapotranspiration) and water chemistry, modulated by environmental gradients in temperature and geomorphology, differentiate functional groups of freshwater ecosystems25 (Fig. 3b and Extended Data Table 3). Terrestrial functional groups are distinguished primarily by gradients in water and nutrient availability and by temperature and seasonality (Fig. 3a and Extended Data Table 1), which mediate uptake of those resources and regulate competitive dominance and productivity of autotrophs. Disturbance regimes, notably fire, are important global drivers in assembly of some terrestrial ecosystem functional groups49.Three lower levels of the typology distinguish functionally similar ecosystems based on biotic composition. Our focus in this paper is on global functional relationships of ecosystems represented in the upper three levels of the typology, but the lower levels (Supplementary Information, Appendix 3, pages 19 and 20) are crucial for representing the biota in the typology, and facilitate the scaling up of information from established local-scale typologies that support decisions where most conservation action takes place. These lower levels are being developed progressively through two contrasting approaches with different trade-offs, strengths and weaknesses. First, level 4 units (regional ecosystem subgroups) are ecoregional expressions of ecosystem functional groups developed from the top-down by subdivisions based on biogeographic boundaries (for example, in ref. 50) that serve as simple and accessible proxies for biodiversity patterns51. Second, level 5 units (global ecosystem types) are also regional expressions of ecosystem functional groups, but unlike level 4 units they are explicitly linked to local information sources by bottom-up aggregation52 and rationalization of level 6 units from established subglobal ecological classifications. Subglobal classifications, such as those for different countries (see examples for Chile and Myanmar in Supplementary Tables 3.3 and 3.4), are often developed independently of one another, and thus may involve inconsistencies in methods and thematic resolution of units (that is, broadly defined or finely split). Aggregation of level 6 units to broader units at level 5 based on compositional resemblance is necessary to address inconsistencies among different subglobal classifications and produce compositionally distinctive units suitable for global or regional synthesis.Integrating local classifications into the global typology, rather than replacing them, exploits considerable efforts and investments to produce existing classifications, already developed with local expertise, accuracy and precision. By placing national and regional ecosystems into a global context, this integration also promotes local ownership of information to support local action and decisions, which are critical to ecosystem conservation and management outcomes (Supplementary Information, Appendix 3, page 20). These benefits of bottom-up approaches come at the cost of inevitable inconsistencies among independently developed classifications from different regions, a limitation avoided in the top-down approach applied to level 4.Circumscribing upper-level unitsWe formed specialist working groups (terrestrial/subterranean, freshwater and marine) to develop descriptions of the units within the upper levels of the hierarchy, subdividing realms into functional biomes, and biomes into ecosystem functional groups. We used definitions of the hierarchical levels (Supplementary Table 3.1) and the conceptual model of ecosystem assembly (Fig. 1) to maintain consistency in defining the units at each level during iterative discussions within and between the working groups.Working groups agreed on preliminary lists of functional biomes and ecosystem functional groups by considering variation in major drivers along ecological gradients (Figs. 2 and 3 and Supplementary Figs. 3.2 and 3.4) based on published literature, direct experience and expertise of working group members, and consultation with colleagues in their respective research networks. After the workshop, working groups sought recent global reviews of the candidate units and recent case studies of exemplars to shape descriptions of the major groups of ecosystem drivers and properties for each unit. Circumscriptions and descriptions of the units were reviewed and revised iteratively to ensure clear distinctions among units, with a total of 206 reviews of descriptive profiles undertaken by 60 specialists, a mean of 2.4 reviews per profile (Supplementary Table 5.1). The working groups concurrently adapted the generic model of ecosystem assembly (Fig. 1) to represent working hypotheses on salient drivers and ecosystem properties for each ecosystem functional group.Incorporating human influenceVery few of the ecological typologies reviewed in Supplementary Information, Appendix 1 integrate anthropogenic ecosystems in their classificatory frameworks. Anthropogenic influences create challenges for ecosystem classification, as they may modify defining features of ecosystems to a degree that varies from negligible to major transformation across different locations and times. We addressed this problem by distinguishing transformative outcomes of human activity at levels 2 and 3 of the typology from lesser human influences that may be represented either at levels 5 and 6, or through measurements of ecosystem integrity or condition that reflect divergence from reference states arising from human activity.Anthropogenic ecosystems grouped within levels 2 and 3 were thus defined as those created and sustained by intensive human activities, or arising from extensive modification of natural ecosystems such that they function very differently. These activities are ultimately driven by socio-economic and cultural-spiritual processes that operate across local to global scales of human organization. In many agricultural and aquacultural systems and some others, cessation of those activities may lead to transformation into ecosystem types with qualitatively different properties and organizational processes (see refs. 53,54 for cropland and urban examples, respectively). Indices such as human appropriation of net primary productivity55, combined with land-use maps56, offer useful insights into the distribution of some anthropogenic ecosystems, but further development of indices is needed to adequately represent others, particularly in marine, and freshwater environments. Beyond land-use classification and mapping approaches (Supplementary Information, Appendix 1, page 6), a more comprehensive elaboration of the intensity of human influence underpinning the diverse range of anthropogenic ecosystems requires a multidimensional framework incorporating land-use inputs, outputs, their interactions, legacies of earlier activity and changes in system properties17.Where less intense human activities occur within non-anthropogenic ecosystem types, we focused descriptions on low-impact reference states. Therefore, human activities are not shown as drivers in the assembly models for non-anthropogenic ecosystem groups, even though they may have important influences on the contemporary ecosystem distribution. This approach enables the degree and nature of human influence to be described and measured against these reference states using assessment methods such as the Red List of Ecosystems protocol30, with appropriate data on ecosystem change.Indicative distribution mapsFinally, to produce spatially explicit representations of the units at level 3 of the typology (principle 5), we sought published global maps (sources in Supplementary Table 4.1) that were congruent with the concepts of respective ecosystem functional groups. Where several candidate maps were available, we selected maps with the closest conceptual alignment, finest spatial resolution, global coverage, most recent data and longest time series. The purpose of maps for our study was to visualize global distributions. Prior to applications of map data to spatial analysis, we recommend critical review of methods and validation outcomes reported in each data source to ensure fitness for purpose (Supplementary Information, Appendix 4).Extensive searches of published literature and data archives identified high-quality datasets for some ecosystem functional groups (for example, T1.3 Tropical–subtropical montane rainforests; MT1.4 Muddy shorelines; M1.5 Sea ice) and datasets that met some of these requirements for a number of other ecosystem functional groups (see Supplementary Table 4.1 for details). Where evaluations by authors or reviewers identified limitations in available maps, we used global environmental data layers and biogeographic regionalizations as masks to adjust source maps and improve their congruence to the concept of the relevant functional group (for example, F1.2 Permanent lowland rivers). For ecosystem functional groups with no specific global mapping, we used ecoregions50,57,58 as biogeographic templates to identify broad areas of occurrence. We consulted ecoregion descriptions, global and regional reviews, national and regional ecosystem maps, and applied in situ knowledge of participating experts to identify ecoregions that contain occurrences of the relevant ecosystem functional group (for example, T4.4 Temperate woodlands) (see Supplementary Table 4.1 for details). We mapped ecosystem functional groups as major occurrences where they dominated a landscape or seascape matrix and minor occurrences where they were present, but not dominant in landscape–seascape mosaics, or where dominance was uncertain. Although these two categories in combination communicate more information about ecosystem distribution than binary maps, simple spatial overlays using minor occurrences are likely to inflate spatial statistics. The maps are progressively upgraded in new versions of the typology as explicit spatial models are developed and new data sources become available (see ref. 27 for a current archive of spatial data).The classification and descriptive profiles, including maps, for each functional biome and ecosystem functional group underwent extensive consultation, and targeted peer review and revision through a series of four phases described in Supplementary Information, Appendix 5 (pages 2–4). The reviewer comments and revisions from targeted peer review are documented in Supplementary Table 5.1. In all, more than 100 ecosystem specialists have contributed to the development of v2.1 of the typology.LimitationsUneven knowledge of Earth’s biosphere has constrained the delimitation and description of units within the typology. There is a considerable research bias across the full range of Earth’s ecosystems, with few formal research studies evaluating the relative influence of different ecosystem drivers in many of the functional groups, and abiotic assembly filters generally receiving more attention than biotic and dispersal filters. This poses challenges for developing standardized models of assembly for each ecosystem functional group. The models therefore represent working hypotheses, for which available evidence varies from large bodies of published empirical evidence to informal knowledge of ecosystem experts and their extensive research networks. Large numbers of empirical studies exist for some forest functional groups, savannas, temperate heathlands in Mediterranean-type climates, coral reefs, rocky shores, kelp forests, trophic webs in pelagic waters, small permanent freshwater lakes, and others (see references in the respective profiles (Supplementary Information, Appendix 4)). For example, Bond49 reviewed empirical and modelling evidence on the assembly and function of tropical savannas that make up three ecosystem functional groups, showing that they have a large global biophysical envelope that overlaps with tropical dry forests, and that their distribution and dynamics within that envelope is strongly influenced by top-down regulation via biotic filters (large herbivores and their predators) and recurrent disturbance regimes (fires). Despite the development of this critical knowledge base, savannas suffer from an awareness disparity that hinders effective conservation and management59. In other ecosystems, our assembly models rely more heavily on inferences and generalizations of experts drawn from related ecosystems, are more sensitive to interpretations of participating experts, and await empirical testing and adjustment as understanding improves. Empirical tests could examine hypothesized variation in ecosystem properties along gradients within and between ecosystem functional groups and should return incremental improvements on group delineation and description of assembly processes.High-quality maps at suitable resolution are not yet available for the full set of ecosystem functional groups, which limits current readiness for global analysis. The maps most fit for global synthesis are based on remote sensing and environmental predictors that align closely to the concept of their ecosystem functional group, incorporate spatially explicit ground observations and have low rates of omission and commission errors, ‘high’ spatial resolution (that is, rasters of 1 km2 (30 arcsec) or better), and time series of changes. Sixty of the maps currently in our archive27 aligned directly or mostly with the concept of their corresponding ecosystem functional group, while the remainder were based on indirect spatial proxies, and most were derived from polygon data or rasters of 30 arcsec or finer (Supplementary Table 4.1). Maps for 81 functional groups were based either on known records, or on spatial data validated by quantitative assessments of accuracy or efficacy. Therefore, we suggest that maps currently available for 60–80 of the 110 functional groups are potentially suitable for global spatial analysis of ecosystem distributions. Although, a significant advance on broad proxies such as ecoregions, the maps currently available for ecosystem functional groups would benefit from expanded application of recent advances in remote sensing, environmental datasets, spatial modelling and cloud computing to redress inequalities in reliability and resolution. The most urgent priorities for this work are those identified in Supplementary Table 4.1 as relying on indirect proxies for alignment to concept, qualitative evaluation by experts and coarse resolution ( >1 km2) spatial data.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

  • in

    Early-season plant-to-plant spatial uniformity can affect soybean yields

    Sites description and field operationsA total of six field studies were conducted in two different regions over two seasons. Four studies (two dryland and two irrigated) were in Kansas, United States (dryland: 39°4′30″ N, − 96°44′43″ W, irrigated: 39°4′25″N, − 96°43′12″ W) during the 2019 and 2020 growing seasons (hereafter referred to as USDry19, USIrr19, USDry20, and USIrr20 studies). The remaining two studies (dryland) were in Entre Rios, Argentina (31°50′49″ S; 60°32′16″ W) during the 2018/2019 and 2019/2020 growing seasons (hereafter referred to as Arg19 and Arg20 studies). The soils were Fluventic Hapludolls [silt loam, 40% sand, 13% clay, 47% silt, organic matter (OM) 1.7%, 7.7 pH, 31.1 ppm P (Bray−1)] at the US dryland studies, and Pachic Argiudolls [silty clay loam, 10.1% sand, 30.6% clay and 59.3% silt, OM 3.2%, 6.8 pH, 34.7 ppm P (Bray−1)] at the US irrigated studies. At the Argentinian studies soil was a Vertic Argiudoll in 2019 [silty clay loam to clay loam, 3.9% sand, 27.6% clay, 67.9% silt, OM 2.65%, 7.2 pH, 12.5 ppm P (Bray−1)] and an Acuic Argiudoll in 2020 [silt loam to silty-clay-loam, 5.6% sand, 28.6% clay, 65.8% silt, OM 3.33%].The US dryland and irrigated studies were sown on June 4, 2019, and May 20, 2020. In 2019, the dryland study was replanted on June 29 due to poor emergence after the first sowing. The studies in Argentina were sown on December 5 in 2018 and November 20 in 2019. At all six studies, plots were kept free of weeds, pests, and diseases through recommended chemical control.The genotypes used in the US were P40A47X (MG 4.0) and P39A58X (MG 3.9) (Corteva Agriscience, Johnston, IA, USA) in 2019 and 2020, respectively. Both varieties are tolerant to glyphosate and dicamba herbicides (RR2X) and have low lodging probability. For the northeast region of Kansas, recommended sowing dates range from May 15 to June 15 along with MG 421. In addition, recommended seeding rates are between 270 and 355 thousand seeds ha−1 for low-yielding environments and 190 to 285 thousand seeds ha−1 for medium- and high-yielding environments13. In Argentina, the genotype AW5815IPRO (MG 5.8, Bayer, Leverkusen, Germany) was used both in 2020 and 2021, it is tolerant to glyphosate and sulfonylureas, and has low lodging probability. Recommended sowing dates for Entre Rios considering soybeans as a single crop range from October 20 to December 10, and MG usually range from 4 to 6; lastly, seeding rate recommendations are between 200 and 250 thousand seeds ha−1 in the region22.Study designThe studies carried out in the US were arranged as a split plot design with three replicates in both 2019 and 2020. In 2019, the main plot treatment factor was planter type with two levels [John Deere (Moline, Illinois, US) Max Emerge planter (ME, 12 rows), and John Deere Exact Emerge Planter (EE, 16 rows)], and the split-plot treatment factor was seeding rate with two levels (160 and 321 thousand seeds ha−1). In 2020 the main plot treatment factor was also planter type with two levels (ME and EE), and the split-plot treatment factor was seeding rate with four levels (160, 215, 270 and 321 thousand seeds ha−1). Planting speed was 7 km h−1 in both studies and years, plots were 24 and 32 rows wide when planted with ME and EE, respectively, with 0.76 m row spacing. Plot length was 80 m in the dryland studies and 160 m in the irrigated studies. The studies in Argentina were arranged as a single replicate of each seeding rate (100, 230, 360 and 550 thousand seeds ha−1) in both years. Planting speed was 5.5 km h−1 in both years, and plots were 10 rows wide with 0.52 m row spacing and 350 m in length.All treatment factors in US studies were evaluated with the overall goal of producing substantial variation in the variable of interest, plant-to-plant spatial uniformity, rather than to make an inference of their effect on yield. The Argentinian studies were only used for selection of stand uniformity variables due to the single replicate. Plant spatial uniformity variables were first fitted using the data from US studies (details below), and then the best explanatory metrics were selected to re-fit the relationships combining both data sets from US and Argentina. Finally, sowing dates, maturity groups, and seeding rates evaluated in this study at both locations (Arg and US) were aligned with those recommended for each region.Data collection and spacing uniformity variablesTwo segments of 2 m in length were established early in the season inside each plot. At the V5 (US studies) and R1 (Arg studies) soybean development stage23, the cumulative distance of the plants within each segment was measured and then used to calculate multiple derived variables. Plant spacing (cm) was calculated as the average distance between neighboring plants. In addition, the distance from a plant to each neighboring plant was classified as shorter or longer than the plant spacing (named nearest and farthest neighbor distance, respectively). Achieved versus Target Evenness Index (ATEI, dimensionless) was calculated as the ratio between the observed plant spacing and the theoretical plant spacing (TPS, cm), where TPS is the expected plant spacing derived from a specific seeding rate and row width (Eq. 1).$$ATEI = frac{Spacing;(cm) }{{TPS;(cm)}}$$
    (1)
    The ATEI index was designed to account for the proximity of the observed plant spacing to the TPS. Values closer to 1 indicate that the plant spacing is close to the TPS and values that are below or above 1 indicate that the plant spacing is lower or higher than the TPS, respectively; thereby departing from an ideal plant spacing. Hence, ATEI values greater than 1 depict both (i) non-uniform plant-to-plant spacing distribution and (ii) plant densities below the target (seeding rate). To further understand the meaning of ATEI, the relative density (rd) was calculated as the ratio between plant density (based on the number of plants in the 2 m segment) and seeding rate.To account for the unevenness of distance from a plant to both neighboring plants within the row, we used the Evenness Index (EI, dimensionless), calculated as the ratio between the distance to the nearest neighbor (cm) and the plant spacing (cm) of a given plant (Eq. 2). The Evenness Index values range from 0 to 1, a value closer to 1 indicates that a plant is equidistantly spaced to both of its neighboring plants within the row, if zero then those plants are occupying the same position (as doubles). It is important to note that EI does not provide information on the spacing (in distance, cm) or how close the spacing is compared to the TPS, but only describes the unevenness distance of a plant to its neighboring plants within a row.$$Evenness ;Index; (EI) = frac{nearest; neighbor ;(cm)}{{Spacing; (cm)}}$$
    (2)
    In addition, the distance from a plant to its preceding neighboring plant, and the TPS were used to classify the position of each plant into one of eight classes (Fig. 1). Plants were classified in classes ranging from “double” (preceding plant distance  Double-skip) as a function of seeding rate, planter type and their interaction (fixed effects), and block nested in site-year (random effect) (Tables 1 and 2). Independent models for each of the 4 US studies were built assessing the effects of planter type, seeding rate, and their interaction (fixed effects), and seeding rate nested in planter type, and in block (random effects) on the same variables previously mentioned (Supplementary Table 1). The models were run using the lmer function from lme4 package in R (R Core Team, 2021). In addition, the US and Arg studies were combined to evaluate the effect of site-year on yield, plant density, and all stand uniformity variables (Supplementary Fig. 1) using the lm function from package stats. Means separation were performed using Fisher’s LSD (Least Significance Difference) test (alpha = 0.05) with emmeans function from package emmeans.Table 1 Effect of planter type, seeding rate, and their interaction on variables from plant position classification for all US studies. References: percentage of perfectly spaced plants (Perfect), percentage of plants misplaced by 66% (Mis 66), percentage of plants misplaced by 33% (Mis 33), percentage of double plants (Double), percentage of short skips plants (Short-skip), percentage of long skip plants (Long-skip), percentage of double skips plants (Double-skip), and percentage of greater than double skip plants ( > Double-skip).Full size tableTable 2 Effect of planter type, seeding rate, and their interaction on yield and stand uniformity variables for all US studies. References: Spacing between plants standard deviation (Spacing sd), achieved versus targeted evenness index mean and standard deviation (ATEI and ATEI sd, respectively), and evenness index mean and standard deviation (EI and EI sd, respectively).Full size tableCommunity-scale data from the four US studies were combined and fitted to bivariate linear regression models with yield as the response variable and each of the stand spatial uniformity variables as the explanatory variable. Significant models (alpha = 0.05) were further evaluated by calculating the coefficient of determination (R2) and root mean squared error (RMSE) (Fig. 2). Models with the lower RMSE and higher R2 were selected as those that best captured the effect of non-uniform stands on soybean yield. After variables were selected, both US and Arg data sets were combined and the linear regressions between the selected variables and yield were re-fitted to assess the consistency of the relationships when an independent data set was included. Community-scale yield from US and Arg studies was modelled as a function of the selected stand uniformity variable, country (US and Arg), and their interaction (fixed effects) (Fig. 3). The spatial uniformity metric showing the most consistent relationship for both US and Arg studies (i.e., non-significant interaction between stand uniformity metric and country), was selected to continue the analysis. The bivariate linear regression models were run with function lm.Figure 2Relationship between stand uniformity variables and soybean yield for US studies. ATEI mean and sd achieved versus targeted evenness index mean and standard deviation, EI mean and sd evenness index mean and standard deviation, Perfect percentage of perfectly spaced plants, R2 coefficient of determination, RMSE root mean square error. All stand uniformity variables presented a significant slope at alpha = 0.05.Full size imageFigure 3Relationship of spacing standard deviation (Spacing sd, cm) and achieved versus targeted evenness index standard deviation (ATEI sd) to soybean yield. Different colors and line types denote different countries (Argentina, Arg—full line, red points; United States, US—dashed line, blue points). R2 coefficient of determination, RMSE root mean square error.Full size imageDifferent environmental conditions and seeding rate levels may modify the effect of plant spatial uniformity on yield. To explore this, each of the studies from Arg and US were separated into low- (USDry19 and ArgDry20, mean of 2.7 Mg ha−1), medium- (USIrr19, USDry20 and ArgDry19, mean of 3.0 Mg ha−1), and high- (USIrr20, mean of 4.3 Mg ha−1) yield environments based on the effect of site-year on yield (Supplementary Fig. 1). Additionally, the tested seeding rates were separated in low ( 300 thousand seeds ha−1) levels based on the current optimal seeding rate for medium yielding environments (235 thousand seeds ha−1, 4 Mg ha−1)13 and the extreme values proposed by Suhre et al.11 (148 and 445 thousand seeds ha−1). This classification was used to model yield as a function of (i) the selected stand uniformity metric, yield environment, and their interaction, and (ii) the selected stand uniformity metric, seeding rate levels, and their interaction. These models were tested to obtain a robust conclusion on the overall effect of yield environment and seeding rate levels, and their interactions (all treated as fixed effects) with plant-to-plant spatial uniformity relative to the response variable, soybean yield. The Akaike information criteria (AIC) was used to compare the full (with interactions) relative to the reduced models (single effects).Ethics declarationsExperimental research and field studies on plants including the collection of plant material, complied with relevant institutional, national, and international guidelines and legislation. More

  • in

    Global hotspots for soil nature conservation

    Bardgett, R. D. & van der Putten, W. H. Belowground biodiversity and ecosystem functioning. Nature 515, 505–511 (2014).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Guerra, C. A. et al. Tracking, targeting, and conserving soil biodiversity. Science 371, 239–241 (2021).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Wall, D. H. et al. (eds) Soil Ecology and Ecosystem Services (Oxford University Press, 2012).Jansson, J. K. & Hofmockel, K. S. Soil microbiomes and climate change. Nat. Rev. Microbiol. 18, 35–46 (2020).CAS 
    PubMed 
    Article 

    Google Scholar 
    de Vries, F. T. et al. Soil food web properties explain ecosystem services across European land use systems. Proc. Natl Acad. Sci. USA 110, 14296–14301 (2013).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Adhikari, K. & Hartemink, A. E. Linking soils to ecosystem services—a global review. Geoderma 262, 101–111 (2016).ADS 
    CAS 
    Article 

    Google Scholar 
    Pereira, P., Bogunovic, I., Muñoz-Rojas, M. & Brevik, E. C. Soil ecosystem services, sustainability, valuation and management. Curr. Opin. Environ. Sci. Health 5, 7–13 (2018).Article 

    Google Scholar 
    Wall, D. H., Nielsen, U. N. & Six, J. Soil biodiversity and human health. Nature 528, 69–76 (2015).Delgado-Baquerizo, M. et al. The proportion of soil-borne pathogens increases with warming at the global scale. Nat. Clim. Chang. 10, 550–554 (2020).ADS 
    Article 

    Google Scholar 
    Rillig, M. C. et al. The role of multiple global change factors in driving soil functions and microbial biodiversity. Science 366, 886–890 (2019).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Guerra, C. A. et al. Global vulnerability of soil ecosystems to erosion. Landsc. Ecol. 35, 823–842 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Geisen, S., Wall, D. H. & van der Putten, W. H. Challenges and opportunities for soil biodiversity in the Anthropocene. Curr. Biol. 29, R1036–R1044 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    Jung, M. et al. Areas of global importance for conserving terrestrial biodiversity, carbon and water. Nat. Ecol. Evol. 5, 1499–1509 (2021).PubMed 
    Article 

    Google Scholar 
    Xu, H. et al. Ensuring effective implementation of the post-2020 global biodiversity targets. Nat. Ecol. Evol. 5, 411–418 (2021).PubMed 
    Article 

    Google Scholar 
    Díaz, S. et al. (eds). Summary for Policymakers of the Global Assessment Report on Biodiversity and Ecosystem Services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES, 2019); https://zenodo.org/record/3553579#.YyhIsXbMK70Phillips, H. R. P. et al. Global distribution of earthworm diversity. Science 366, 480–485 (2019).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    van den Hoogen, J. et al. Soil nematode abundance and functional group composition at a global scale. Nature 572, 194–198 (2019).ADS 
    PubMed 
    Article 

    Google Scholar 
    Delgado-baquerizo, M. et al. A global atlas of the dominant bacteria found in soil. Science 325, 320–325 (2018).ADS 
    Article 

    Google Scholar 
    Tedersoo, L. et al. Global diversity and geography of soil fungi. Science 346, 1256688 (2014).PubMed 
    Article 

    Google Scholar 
    Xu, X., Thornton, P. E. & Post, W. M. A global analysis of soil microbial biomass carbon, nitrogen and phosphorus in terrestrial ecosystems: global soil microbial biomass C, N and P. Glob. Ecol. Biogeogr. 22, 737–749 (2013).Article 

    Google Scholar 
    Djukic, I. et al. Early stage litter decomposition across biomes. Sci. Total Environ. 628–629, 1369–1394 (2018).Guerra, C. A. et al. Global projections of the soil microbiome in the Anthropocene. Glob. Ecol. Biogeogr. 30, 987–999 (2021).PubMed 
    Article 

    Google Scholar 
    Cameron, E. K. et al. Global mismatches in aboveground and belowground biodiversity. Conserv. Biol. 33, 1187–1192 (2019).PubMed 
    Article 

    Google Scholar 
    El Moujahid, L. et al. Effect of plant diversity on the diversity of soil organic compounds. PLoS One 12, e0170494 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Guerra, C. A. et al. Blind spots in global soil biodiversity and ecosystem function research. Nat. Commun. 11, 3870 (2020).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Fierer, N. & Jackson, R. B. The diversity and biogeography of soil bacterial communities. Proc. Natl Acad. Sci. USA 103, 626–631 (2006).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Tedersoo, L. et al. Regional-scale in-depth analysis of soil fungal diversity reveals strong pH and plant species effects in Northern Europe. Front. Microbiol. 11, 1953 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Popp, A. et al. Land-use futures in the shared socio-economic pathways. Glob. Environ. Change 42, 331–345 (2017).Article 

    Google Scholar 
    Dornelas, M. et al. Assemblage time series reveal biodiversity change but not systematic loss. Science 344, 296–299 (2014).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Egoh, B., Reyers, B., Rouget, M., Bode, M. & Richardson, D. M. Spatial congruence between biodiversity and ecosystem services in South Africa. Biol. Conserv. 142, 553–562 (2009).Article 

    Google Scholar 
    Jürgens, N. et al. The BIOTA Biodiversity Observatories in Africa—a standardized framework for large-scale environmental monitoring. Environ. Monit. Assess. 184, 655–678 (2012).PubMed 
    Article 

    Google Scholar 
    Wyborn, C. & Evans, M. C. Conservation needs to break free from global priority mapping. Nat. Ecol. Evol. 5, 1322–1324 (2021).PubMed 
    Article 

    Google Scholar 
    Hautier, Y. et al. Local loss and spatial homogenization of plant diversity reduce ecosystem multifunctionality. Nat. Ecol. Evol. 2, 50–56 (2018).PubMed 
    Article 

    Google Scholar 
    Zhou, Z., Wang, C. & Luo, Y. Meta-analysis of the impacts of global change factors on soil microbial diversity and functionality. Nat. Commun. 11, 3072 (2020).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Eisenhauer, N., Schulz, W., Scheu, S. & Jousset, A. Niche dimensionality links biodiversity and invasibility of microbial communities. Funct. Ecol. 27, 282–288 (2013).Article 

    Google Scholar 
    Wagg, C., Bender, S. F., Widmer, F. & van der Heijden, M. G. A. Soil biodiversity and soil community composition determine ecosystem multifunctionality. Proc. Natl Acad. Sci. USA 111, 5266–5270 (2014).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Haines-Young, R. H. & Potschin, M. B. in Ecosystems Ecology: A New Synthesis (eds Raffaelli, D. G. & Frid, C. L. J.) Ch. 6 (2012).Smith, L. C. et al. Large‐scale drivers of relationships between soil microbial properties and organic carbon across Europe. Glob. Ecol. Biogeogr. 30, 2070–2083 (2021).Article 

    Google Scholar 
    Keesstra, S. et al. The superior effect of nature based solutions in land management for enhancing ecosystem services. Sci. Total Environ. 610-611, 997–1009 (2018).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Le Provost, G. et al. Contrasting responses of above- and belowground diversity to multiple components of land-use intensity. Nat. Commun. 12, 3918 (2021).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Tanneberger, F. et al. The power of nature‐based solutions: how peatlands can help us to achieve key EU sustainability objectives. Adv. Sustain. Syst. 5, 2000146 (2021).CAS 
    Article 

    Google Scholar 
    Johnston, A. et al. Observed and predicted effects of climate change on species abundance in protected areas. Nat. Clim. Chang. 3, 1055–1061 (2013).ADS 
    Article 

    Google Scholar 
    Hannah, L. et al. Protected area needs in a changing climate. Front. Ecol. Environ. 5, 131–138 (2007).Article 

    Google Scholar 
    Gallardo, B. et al. Protected areas offer refuge from invasive species spreading under climate change. Glob. Chang. Biol. 23, 5331–5343 (2017).ADS 
    PubMed 
    Article 

    Google Scholar 
    O’Neill, B. C. et al. The roads ahead: narratives for shared socioeconomic pathways describing world futures in the 21st century. Glob. Environ. Change 42, 169–180 (2017).Article 

    Google Scholar 
    Fedele, G., Donatti, C. I., Bornacelly, I. & Hole, D. G. Nature-dependent people: mapping human direct use of nature for basic needs across the tropics. Glob. Environ. Change 71, 102368 (2021).Visconti, P. et al. Protected area targets post-2020. Science 364, 239–241 (2019).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Allan, J. R. et al. The minimum land area requiring conservation attention to safeguard biodiversity. Science 376, 1094–1101 (2022).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Maestre, F. T. et al. Plant species richness and ecosystem multifunctionality in global drylands. Science 335, 214–218 (2012).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Delgado-Baquerizo, M. et al. Changes in belowground biodiversity during ecosystem development. Proc. Natl Acad. Sci. USA. 116, 6891–6896 (2019).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Mace, G. M. Whose conservation? Science 345, 1558–1560 (2014).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Amaral-Zettler, L. A., McCliment, E. A., Ducklow, H. W. & Huse, S. M. A method for studying protistan diversity using massively parallel sequencing of V9 hypervariable regions of small-subunit ribosomal RNA genes. PLoS One 4, e6372 (2009).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Stoeck, T. et al. Multiple marker parallel tag environmental DNA sequencing reveals a highly complex eukaryotic community in marine anoxic water. Mol. Ecol. 19, 21–31 (2010).CAS 
    PubMed 
    Article 

    Google Scholar 
    Ramirez, K. S. et al. Biogeographic patterns in below-ground diversity in New York City’s Central Park are similar to those observed globally. Proc. Biol. Sci. 281, 20141988 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336 (2010).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).CAS 
    PubMed 
    Article 

    Google Scholar 
    Edgar, R. C. & Flyvbjerg, H. Error filtering, pair assembly and error correction for next-generation sequencing reads. Bioinformatics 31, 3476–3482 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    Edgar, R. C. UNOISE2: improved error-correction for Illumina 16S and ITS amplicon sequencing. Preprint at bioRxiv https://doi.org/10.1101/081257 (2016).Tedersoo, L. et al. Towards understanding diversity, endemicity and global change vulnerability of soil fungi. Preprint at bioRxiv https://doi.org/10.1101/2022.03.17.484796 (2022).Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013).CAS 
    PubMed 
    Article 

    Google Scholar 
    Delgado-Baquerizo, M. et al. Global homogenization of the structure and function in the soil microbiome of urban greenspaces. Sci. Adv. 7, eabg5809 (2021).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Phillips, H. R. P., Heintz-Buschart, A. & Eisenhauer, N. Putting soil invertebrate diversity on the map. Mol. Ecol. 29, 655–657 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Xiong, W. et al. A global overview of the trophic structure within microbiomes across ecosystems. Environ. Int. 151, 106438 (2021).PubMed 
    Article 

    Google Scholar 
    Drummond, A. J. et al. Evaluating a multigene environmental DNA approach for biodiversity assessment. Gigascience 4, 46 (2015).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Oliverio, A. M., Gan, H., Wickings, K. & Fierer, N. A DNA metabarcoding approach to characterize soil arthropod communities. Soil Biol. Biochem. 125, 37–43 (2018).CAS 
    Article 

    Google Scholar 
    Horton, D. J., Kershner, M. W. & Blackwood, C. B. Suitability of PCR primers for characterizing invertebrate communities from soil and leaf litter targeting metazoan 18S ribosomal or cytochrome oxidase I (COI) genes. Eur. J. Soil Biol. 80, 43–48 (2017).CAS 
    Article 

    Google Scholar 
    Delgado-Baquerizo, M. et al. Multiple elements of soil biodiversity drive ecosystem functions across biomes. Nat. Ecol. Evol. 4, 210–220 (2020).PubMed 
    Article 

    Google Scholar 
    Carter, M. R. & Gregorich, E. G. (eds) Soil Sampling and Methods of Analysis (CRC Press, 2007).Sparks, D. L. et al. (eds) Methods of Soil Analysis, Part 3: Chemical Methods (Wiley, 2020).Nguyen, N. H. et al. FUNGuild: an open annotation tool for parsing fungal community datasets by ecological guild. Fungal Ecol. 20, 241–248 (2016).Article 

    Google Scholar 
    Bell, C. W. et al. High-throughput fluorometric measurement of potential soil extracellular enzyme activities. J. Vis. Exp. 81, e50961 (2013).Wang, L. et al. Diversifying livestock promotes multidiversity and multifunctionality in managed grasslands. Proc. Natl Acad. Sci. USA. 116, 6187–6192 (2019).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Durán, J., Delgado-Baquerizo, M., Rodríguez, A., Covelo, F. & Gallardo, A. Ionic exchange membranes (IEMs): a good indicator of soil inorganic N production. Soil Biol. Biochem. 57, 964–968 (2013).Article 

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

    Google Scholar 
    Friedman, J. H. Greedy function approximation: a gradient boosting machine. Ann. Stat. 29, 1189–1232 (2001).MathSciNet 
    MATH 
    Article 

    Google Scholar 
    Sharma, N. XGBoost. The Extreme Gradient Boosting for Mining Applications (GRIN Verlag, 2018).Chen, T. & Guestrin, C. XGBoost: a scalable tree boosting system. In Proc. 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 785–794 (Association for Computing Machinery, 2016).Wilson. ParBayesianOptimization: Parallel Bayesian Optimization of Hyperparameters. R version 1 https://CRAN.R-project.org/package=ParBayesianOptimization (2021).Hastie, T., Friedman, J. & Tibshirani, R. The Elements of Statistical Learning (Springer, 2001).Jackson, D. A. & Chen, Y. Robust principal component analysis and outlier detection with ecological data. Environmetrics 15, 129–139 (2004).Article 

    Google Scholar 
    Breiman, L. Bagging predictors. Mach. Learn. 24, 123–140 (1996).MATH 
    Article 

    Google Scholar 
    Breiman, L., Friedman, J., Stone, C. J. & Olshen, R. A. Classification and Regression Trees (Routledge, 1984).Ord, J. K. & Getis, A. Local spatial autocorrelation statistics: distributional issues and an application. Geogr. Anal. 27, 286–306 (2010).Article 

    Google Scholar 
    Getis, A. & Ord, J. K. The analysis of spatial association by use of distance statistics. Geogr. Anal. 24, 189–206 (2010).Article 

    Google Scholar 
    Prasannakumar, V., Vijith, H., Charutha, R. & Geetha, N. Spatio-temporal clustering of road accidents: GIS based analysis and assessment. Procedia Soc. Behav. Sci. 21, 317–325 (2011).Article 

    Google Scholar 
    Lin, G. Comparing spatial clustering tests based on rare to common spatial events. Comput. Environ. Urban Syst. 28, 691–699 (2004).Article 

    Google Scholar 
    Araújo, M. B. et al. Standards for distribution models in biodiversity assessments. Sci. Adv. 5, eaat4858 (2019).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Rousseeuw, P. J. & van Zomeren, B. C. Unmasking multivariate outliers and leverage points. J. Am. Stat. Assoc. 85, 633–639 (1990).Article 

    Google Scholar 
    Hempel, S., Frieler, K., Warszawski, L., Schewe, J. & Piontek, F. A trend-preserving bias correction—the ISI-MIP approach. Earth Syst. Dyn. 4, 219–236 (2013).ADS 
    Article 

    Google Scholar 
    Lawrence, D. M. et al. The Land Use Model Intercomparison Project (LUMIP) contribution to CMIP6: rationale and experimental design. Geosci. Model Dev. 9, 2973–2998 (2016).ADS 
    Article 

    Google Scholar 
    Kim, H. et al. A protocol for an intercomparison of biodiversity and ecosystem services models using harmonized land-use and climate scenarios. Geosci. Model Dev. 11, 4537–4562 (2018).Dufresne, J.-L. et al. Climate change projections using the IPSL-CM5 Earth System Model: from CMIP3 to CMIP5. Clim. Dyn. 40, 2123–2165 (2013).Article 

    Google Scholar 
    Hurtt, G. C. et al. Harmonization of land-use scenarios for the period 1500–2100: 600 years of global gridded annual land-use transitions, wood harvest, and resulting secondary lands. Clim. Change 109, 117 (2011).ADS 
    Article 

    Google Scholar 
    Hurtt, G. C. et al. Harmonization of global land use change and management for the period 850–2100 (LUH2) for CMIP6. Geosci. Model Dev. 13, 5425–5464 (2020).ADS 
    CAS 
    Article 

    Google Scholar 
    Riahi, K. et al. The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: an overview. Glob. Environ. Change 42, 153–168 (2017).Article 

    Google Scholar 
    O’Neill, B. C. et al. A new scenario framework for climate change research: the concept of shared socioeconomic pathways. Clim. Change 122, 387–400 (2014).ADS 
    Article 

    Google Scholar 
    Newbold, T. et al. Global effects of land use on local terrestrial biodiversity. Nature 520, 45–50 (2015).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Powers, R. P. & Jetz, W. Global habitat loss and extinction risk of terrestrial vertebrates under future land-use-change scenarios. Nat. Clim. Chang. 9, 323–329 (2019).ADS 
    Article 

    Google Scholar  More

  • in

    Coral community data Heron Island Great Barrier Reef 1962–2016

    Study site and field data collectionPermanent 1 m2 photoquadrats were established on Heron Reef in 1962/63, using 9 mm diameter mild steel (rebar) pegs, which were replaced over time. From the 1990’s, replacement pegs were stainless steel for greater longevity. Four sites were established, the protected (south) crest, inner flat, exposed (north) crest and exposed pools. Co-ordinates for each site are presented in Table 1, the layout shown in Fig. 2, and sites have been well described previously5,6. At each census, a 1 m2 frame divided into a 5 × 5 grid using string was placed over the pegs, and the quadrat photographed from directly above at low tide. From 1963 until 2003, a 35 mm camera and colour slide film were used. The camera was attached to a tripod affixed to the 1 m2 frame, and captured around 2/3 of the quadrat. The frame (and camera) were then rotated 180 degrees to capture the remainder of the quadrat. After 2003, a hand-held digital camera was used, with the entire quadrat being captured in a single image. Concurrent with each census, mud maps of each quadrat were hand drawn in the field, and all colonies identified in situ by someone with expertise in coral taxonomy.Table 1 Coordinates of the study sites on Heron Island Reef (WGS84).Full size tableFig. 2Quadrat layouts for each of the four sites respectively, noting that the north crest and north ridge have been treated as a single north crest site in previous publications. Underlining indicates original 1962/63 quadrats. Other quadrats were added in or after 2008, as indicated in the text. Contiguous quadrats are pictured bordering each other. Spacing between separate quadrats or groups of quadrats is not shown to scale. Note that up until 2005, NRNW was known as NR. The acronyms in each quadrat represent its name.Full size imageAt the protected (south) crest, a set of six contiguous quadrats were established in 1963 in a 2 × 3 arrangement parallel to the waterline, and about 420 m southeast of the island. This site is exposed at low tide, and was photographed once all water had drained off it. Images of quadrats A, C & E (the shoreward row) from 1963 to 2012 have been fully processed, and the data have been through QA/QC. Data after 2012 exist as images only. These quadrats form the basis of previous analyses1,4,5,6 for this site. Photographs are available for quadrats B, D & F, but apart from 2003–2010, have not been processed. In 2010, an additional two quadrats were established either side of the original six, leading to a 2 × 5 arrangement. Again, only imagery is available for these additional quadrats.At the inner flat, two pairs of contiguous quadrats were established in 1962, 44 m apart, about 70 m south of the island. This site is covered by ~10 cm of water at low tide, so could only be photographed on a still day. Imagery for this site is only available to 2012, after which the marker stakes appear to have been removed in a cleanup of the area. Images for one quadrat in each pair have been processed, but have not been subject to full QA/QC.At the exposed (north) crest main site, a set of four contiguous quadrats was established about 1100 m northeast of the island in 1963. An additional single quadrat (north ridge) was established 326 m to the east. Images from 1963 to 2012 have been fully processed, and the data have been through QA/QC. Data after 2012 exist as images only. In 2005, the single north ridge quadrat was expanded to 4 m2, and in 2008, both subsites were expanded to six quadrats in a 2 × 3 arrangement. These additional quadrats have been digitised up to 2012, but have not been through full QA/QC.The exposed pools are two individual quadrats about 5 m apart about 30 m north of the eastern (north ridge) exposed crest site. These are on the edge of a natural pool, and range from ~5–50 cm deep at low tide, and so could only be photographed on a calm day. Imagery for this site is only available until 2005, after which the marker stakes could not be relocated. Images from 1963 to 1998 have been processed, but have not been through full QA/QC.Retrieval of coral composition data from the photoquadratsProcessing of the images involved scanning the colour slides to produce digital images, and then orthorectifying each image to a 1 m2 basemap in ArcGIS (ESRI Ltd). The corners of the frame, and the holes for the string grid, were used as control points for the orthorectification. For images that originated as colour slides, each half of the quadrat was individually orthorectified to the same basemap, producing a single image of the entire quadrat (see Fig. 3). While contiguous quadrats were orthorectified individually, they were done so against a basemap containing all quadrats in the group, meaning that the resulting images can be easily merged to create a single image of the group. The outlines of all visible coral colonies ( >~1 cm2), and other benthic organisms such as algae and clams, were then digitised in ArcGIS to create a single shapefile for each quadrat for each year. Each colony was represented as an individual feature within the shapefile, and was assigned a unique colony number and species based on the mud maps drawn in the field. Colony numbers were consistent across years, allowing individual colonies to be tracked over time. If a colony underwent fission, the original colony number was retained for each, with the addition of a unique identifier after a decimal point. For example, if colony 35 split in two, the resultant colonies were identified as 35.1 and 35.2. If 35.2 later split again, the resultant colonies were identified as 35.2.1 and 35.2.2. If the colony overlapped the edge of the quadrat, only the area within the quadrat was digitised, and a flag was applied to indicate that only part of the colony was included (edgestatus = 1 in the data). Upon completion of digitisation, ArcGIS was used to calculate the area and perimeter of all colonies. While multiple census were conducted in 1963, 1971 and 1983, only a single census in each year has been processed. There are currently no plans to undertake further digitisation or QA/QC of this data set.Fig. 3Example orthorectified and stitched (prior to 2001) images from the NCNE quadrat, showing the effects of a cyclone that removed all colonies in 1972, and slow recovery over subsequent decades.Full size image More

  • in

    Biological invasions as a selective filter driving behavioral divergence

    Pecl, G. T. et al. Biodiversity redistribution under climate change: impacts on ecosystems and human well-being. Science 355, (2017).IPBES. Global assessment report on biodiversity and ecosystem services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. (IPBES secretariat, 2019). https://doi.org/10.5281/zenodo.3831673.Elton, C. S. The Ecology of Invasions by Animals and Plants. (University of Chicago Press, 1958).Lockwood, J. L., Hoopes, M. F. & Marchetti, M. P. Invasion Ecology. (Wiley-Blackwell, 2013).O’Dowd, D. J., Green, P. T. & Lake, P. S. Invasional “meltdown” on an oceanic island. Ecol. Lett. 6, 812–817 (2003).
    Google Scholar 
    Doherty, T. S., Glen, A. S., Nimmo, D. G., Ritchie, E. G. & Dickman, C. R. Invasive predators and global biodiversity loss. Proc. Natl Acad. Sci. 113, 11261–11265 (2016).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Spatz, D. R. et al. Globally threatened vertebrates on islands with invasive species. Sci. Adv. 3, (2017).Pimentel, D. et al. Economic and environmental threats of alien plant, animal, and microbe invasions. Agriculture, Ecosyst. Environ. 84, 1–20 (2001).
    Google Scholar 
    Hoffmann, B. D. & Broadhurst, L. M. The economic cost of managing invasive species in Australia. NeoBiota 31, 1–18 (2016).
    Google Scholar 
    Kolar, C. S. & Lodge, D. M. Progress in invasion biology: predicting invaders. Trends Ecol. Evolution 16, 199–204 (2001).
    Google Scholar 
    Jeschke, J. M. & Strayer, D. L. Invasion success of vertebrates in Europe and North America. Proc. Natl Acad. Sci. 102, 7198–7202 (2005).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Lovell, R. S. L., Blackburn, T. M., Dyer, E. E. & Pigot, A. L. Environmental resistance predicts the spread of alien species. Nat. Ecol. Evolution 5, 322–329 (2021).
    Google Scholar 
    Blackburn, T. M. et al. A proposed unified framework for biological invasions. Trends Ecol. Evolution 26, 333–339 (2011).
    Google Scholar 
    Chapple, D. G., Simmonds, S. M. & Wong, B. B. M. Can behavioral and personality traits influence the success of unintentional species introductions? Trends Ecol. Evolution 27, 57–64 (2012).
    Google Scholar 
    Chapple, D. G. & Wong, B. B. M. The role of behavioural variation across different stages of the introduction process. in Biological Invasions and Animal Behaviour (eds. Weis, Judith, S. & Sol, Daniel.) 7–25 (Cambridge University Press, 2016).Holway, D. & Suarez, A. Animal behavior: an essential component of invasion biology. Trends Ecol. Evolution 14, 328–330 (1999).CAS 

    Google Scholar 
    Felden, A. et al. Behavioural variation and plasticity along an invasive ant introduction pathway. J. Anim. Ecol. 87, 1653–1666 (2018).PubMed 

    Google Scholar 
    D’Amore, D. M., Popescu, V. D. & Morris, M. R. The influence of the invasive process on behaviours in an intentionally introduced hybrid, Xiphophorus helleri-maculatus. Anim. Behav. 156, 79–85 (2019).
    Google Scholar 
    Perkins, T. A., Boettiger, C. & Phillips, B. L. After the games are over: life‐history trade‐offs drive dispersal attenuation following range expansion. Ecol. Evolution 6, 6425–6434 (2016).
    Google Scholar 
    Phillips, B. L., Brown, G. P., Travis, J. M. J. & Shine, R. Reid’s Paradox revisited: the evolution of dispersal kernels during range expansion. Am. Naturalist 172, S34–S48 (2008).
    Google Scholar 
    Shine, R., Brown, G. P. & Phillips, B. L. An evolutionary process that assembles phenotypes through space rather than through time. Proc. Natl Acad. Sci. 108, 5708–5711 (2011).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Lindström, T., Brown, G. P., Sisson, S. A., Phillips, B. L. & Shine, R. Rapid shifts in dispersal behavior on an expanding range edge. Proc. Natl Acad. Sci. 110, 13452–13456 (2013).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Heger, T. & Jeschke, J. M. The enemy release hypothesis as a hierarchy of hypotheses. Oikos 123, 741–750 (2014).
    Google Scholar 
    Colautti, R. I., Ricciardi, A., Grigorovich, I. A. & MacIsaac, H. J. Is invasion success explained by the enemy release hypothesis? Ecol. Lett. 7, 721–733 (2004).
    Google Scholar 
    Wilson, J. R. U., Dormontt, E. E., Prentis, P. J., Lowe, A. J. & Richardson, D. M. Something in the way you move: dispersal pathways affect invasion success. Trends Ecol. Evolution 24, 136–144 (2009).
    Google Scholar 
    Wilson, S. & Swan, G. A complete guide to reptiles of Australia. (New Holland Publishers, 2021).Chapple, D. G., Miller, K. A., Kraus, F. & Thompson, M. B. Divergent introduction histories among invasive populations of the delicate skink (Lampropholis delicata): has the importance of genetic admixture in the success of biological invasions been overemphasized? Diversity Distrib. 19, 134–146 (2013).
    Google Scholar 
    Chapple, D., Knegtmans, J., Kikillus, H. & van Winkel, D. Biosecurity of exotic reptiles and amphibians in New Zealand: building upon Tony Whitaker’s legacy. J. R. Soc. N.Z. 46, 66–84 (2016).
    Google Scholar 
    Chapple, D. G., Whitaker, A. H., Chapple, S. N. J., Miller, K. A. & Thompson, M. B. Biosecurity interceptions of an invasive lizard: Origin of stowaways and human-assisted spread within New Zealand. Evolut. Appl. 6, 324–339 (2013).
    Google Scholar 
    Tingley, R., Thompson, M. B., Hartley, S. & Chapple, D. G. Patterns of niche filling and expansion across the invaded ranges of an Australian lizard. Ecography 39, 270–280 (2016).
    Google Scholar 
    Chapple, D. G. et al. Biology of the invasive delicate skink (Lampropholis delicata) on Lord Howe Island. Aust. J. Zool. 62, 498–506 (2014).
    Google Scholar 
    Moule, H. et al. A matter of time: temporal variation in the introduction history and population genetic structuring of an invasive lizard. Curr. Zool. 61, 456–464 (2015).CAS 

    Google Scholar 
    Chapple, D. G., Simmonds, S. M. & Wong, B. B. M. Know when to run, know when to hide: can behavioral differences explain the divergent invasion success of two sympatric lizards? Ecol. Evolution 1, 278–289 (2011).
    Google Scholar 
    Cromie, G. L. & Chapple, D. G. Impact of tail loss on the behaviour and locomotor performance of two sympatric Lampropholis skink species. PLoS ONE 7, e34732 (2012).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Brand, J. A. et al. Rapid shifts in behavioural traits during a recent fish invasion. Behav. Ecol. Sociobiol. 75, 134 (2021).
    Google Scholar 
    Myles-Gonzalez, E., Burness, G., Yavno, S., Rooke, A. & Fox, M. G. To boldly go where no goby has gone before: boldness, dispersal tendency, and metabolism at the invasion front. Behav. Ecol. 26, 1083–1090 (2015).
    Google Scholar 
    Pintor, L. M., Sih, A. & Bauer, M. L. Differences in aggression, activity and boldness between native and introduced populations of an invasive crayfish. Oikos 117, 1629–1636 (2008).
    Google Scholar 
    Mueller, J. C. et al. Selection on a behaviour-related gene during the first stages of the biological invasion pathway. Mol. Ecol. 26, 6110–6121 (2017).MathSciNet 
    CAS 
    PubMed 

    Google Scholar 
    Snell-Rood, E. C. An overview of the evolutionary causes and consequences of behavioural plasticity. Anim. Behav. 85, 1004–1011 (2013).
    Google Scholar 
    Niemelä, P. T., Niehoff, P. P., Gasparini, C., Dingemanse, N. J. & Tuni, C. Crickets become behaviourally more stable when raised under higher temperatures. Behav. Ecol. Sociobiol. 73, 81 (2019).
    Google Scholar 
    Polverino, G. et al. Psychoactive pollution suppresses individual differences in fish behaviour. Proc. R. Soc. B: Biol. Sci. 288, 20202294 (2021).
    Google Scholar 
    Royauté, R., Garrison, C., Dalos, J., Berdal, M. A. & Dochtermann, N. A. Current energy state interacts with the developmental environment to influence behavioural plasticity. Anim. Behav. 148, 39–51 (2019).
    Google Scholar 
    Michelangeli, M., Chapple, D. G., Goulet, C. T., Bertram, M. G. & Wong, B. B. M. Behavioral syndromes vary among geographically distinct populations in a reptile. Behav. Ecol. 30, 393–401 (2019).
    Google Scholar 
    Nicolaus, M., Tinbergen, J. M., Ubels, R., Both, C. & Dingemanse, N. J. Density fluctuations represent a key process maintaining personality variation in a wild passerine bird. Ecol. Lett. 19, 478–486 (2016).PubMed 

    Google Scholar 
    Lapiedra, O., Schoener, T. W., Leal, M., Losos, J. B. & Kolbe, J. J. Predator-driven natural selection on risk-taking behavior in anole lizards. Science 360, 1017–1020 (2018).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Gruber, J., Brown, G., Whiting, M. J. & Shine, R. Geographic divergence in dispersal-related behaviour in cane toads from range-front versus range-core populations in Australia. Behav. Ecol. Sociobiol. 71, 38 (2017).
    Google Scholar 
    Gruber, J., Brown, G., Whiting, M. J. & Shine, R. Is the behavioural divergence between range-core and range-edge populations of cane toads (Rhinella marina) due to evolutionary change or developmental plasticity? R. Soc. Open Sci. 4, 170789 (2017).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Morgan, D., Waas, J. R. & Innes, J. Do territorial and non-breeding Australian Magpies Gymnorhina tibicen influence the local movements of rural birds in New Zealand? Ibis 148, 330–342 (2006).
    Google Scholar 
    O’leary, R. A. & Jones, D. N. Foraging by suburban Australian magpies during dry conditions. Corella 26, 53–54 (2002).
    Google Scholar 
    Wright, T. F., Eberhard, J. R., Hobson, E. A., Avery, M. L. & Russello, M. A. Behavioral flexibility and species invasions: the adaptive flexibility hypothesis. Ethol. Ecol. Evolution 22, 393–404 (2010).
    Google Scholar 
    Dingemanse, N. J. & Wolf, M. Between-individual differences in behavioural plasticity within populations: causes and consequences. Anim. Behav. 85, 1031–1039 (2013).
    Google Scholar 
    Ducatez, S., Sol, D., Sayol, F. & Lefebvre, L. Behavioural plasticity is associated with reduced extinction risk in birds. Nat. Ecol. Evolution 4, 788–793 (2020).
    Google Scholar 
    Cole, E. F. & Quinn, J. L. Personality and problem-solving performance explain competitive ability in the wild. Proc. R. Soc. B: Biol. Sci. 279, 1168–1175 (2012).
    Google Scholar 
    Webster, M. M., Ward, A. J. W. & Hart, P. J. B. Individual boldness affects interspecific interactions in sticklebacks. Behav. Ecol. Sociobiol. 63, 511–520 (2009).
    Google Scholar 
    McGhee, K. E., Pintor, L. M. & Bell, A. M. Reciprocal behavioral plasticity and behavioral types during predator-prey interactions. Am. Naturalist 182, 704–717 (2013).
    Google Scholar 
    Ioannou, C. C., Payne, M. & Krause, J. Ecological consequences of the bold–shy continuum: the effect of predator boldness on prey risk. Oecologia 157, 177–182 (2008).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Moran, N. P., Wong, B. B. M. & Thompson, R. M. Weaving animal temperament into food webs: implications for biodiversity. Oikos 126, 917–930 (2017).
    Google Scholar 
    Bellard, C., Cassey, P. & Blackburn, T. M. Alien species as a driver of recent extinctions. Biol. Lett. 12, 20150623 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    Moule, H., Michelangeli, M., Thompson, M. B. & Chapple, D. G. The influence of urbanization on the behaviour of an Australian lizard and the presence of an activity–exploratory behavioural syndrome. J. Zool. 298, 103–111 (2016).
    Google Scholar 
    Michelangeli, M., Wong, B. B. M. & Chapple, D. G. It’s a trap: sampling bias due to animal personality is not always inevitable. Behav. Ecol. 27, 62–67 (2016).
    Google Scholar 
    Michelangeli, M., Melki-Wegner, B., Laskowski, K., Wong, B. B. M. & Chapple, D. G. Impacts of caudal autotomy on personality. Anim. Behav. 162, 67–78 (2020).
    Google Scholar 
    Shine, R. Locomotor speeds of gravid lizards: Placing “costs of reproduction” within an ecological context. Funct. Ecol. 17, 526–533 (2003).
    Google Scholar 
    Naimo, A. C., Jones, C., Chapple, D. G. & Wong, B. B. M. Has an invasive lizard lost its antipredator behaviours following 40 generations of isolation from snake predators? Behav. Ecol. Sociobiol. 75, 131 (2021).
    Google Scholar 
    Brand, J. A. et al. Population differences in the effect of context on personality in an invasive lizard. Behav. Ecol. 32, 1363–1371 (2021).
    Google Scholar 
    Goulet, C. T., Thompson, M. B., Michelangeli, M., Wong, B. B. M. & Chapple, D. G. Thermal physiology: a new dimension of the pace‐of‐life syndrome. J. Anim. Ecol. 86, 1269–1280 (2017).PubMed 

    Google Scholar 
    Michelangeli, M., Goulet, C. T., Kang, H. S., Wong, B. B. M. & Chapple, D. G. Integrating thermal physiology within a syndrome: locomotion, personality and habitat selection in an ectotherm. Funct. Ecol. 32, 970–981 (2018).
    Google Scholar 
    Bell, A. M. Randomized or fixed order for studies of behavioral syndromes? Behav. Ecol. 24, 16–20 (2013).PubMed 

    Google Scholar 
    Friard, O. & Gamba, M. BORIS: a free, versatile open-source event-logging software for video/audio coding and live observations. Methods Ecol. Evolution 7, 1325–1330 (2016).
    Google Scholar 
    R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.r-project.org/. (2019).Bürkner, P. C. brms: an R package for Bayesian multilevel models using Stan. J. Stat. Softw. 80, 1–28 (2017).
    Google Scholar 
    Munson, A. A., Michelangeli, M. & Sih, A. Stable social groups foster conformity and among-group differences. Anim. Behav. 174, 197–206 (2021).
    Google Scholar 
    Royauté, R. & Dochtermann, N. A. Comparing ecological and evolutionary variability within datasets. Behav. Ecol. Sociobiol. 75, 127 (2021).
    Google Scholar 
    Dalos, J., Royauté, R., Hedrick, A. V. & Dochtermann, N. A. Phylogenetic conservation of behavioural variation and behavioural syndromes. J. Evolut. Biol. 35, 311–321 (2022).
    Google Scholar 
    Miller, K. A., Duran, A., Melville, J., Thompson, M. B. & Chapple, D. G. Sex-specific shifts in morphology and colour pattern polymorphism during range expansion of an invasive lizard. J. Biogeogr. 44, 2778–2788 (2017).
    Google Scholar 
    Michelangeli, M., Chapple, D. G. & Wong, B. B. M. Are behavioural syndromes sex specific? Personality in a widespread lizard species. Behav. Ecol. Sociobiol. 70, 1911–1919 (2016).
    Google Scholar 
    Vehtari, A., Gelman, A. & Gabry, J. Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Stat. Comput. 27, 1413–1432 (2017).MathSciNet 
    MATH 

    Google Scholar 
    Nakagawa, S. & Schielzeth, H. Repeatability for Gaussian and non-Gaussian data: a practical guide for biologists. Biol. Rev. 85, 935–956 (2010).PubMed 

    Google Scholar 
    Chapple, D. G. et al. Data from Chapple et al. “Biological invasions as a selective filter driving behavioral divergence”. Monash University. Dataset. https://doi.org/10.26180/18851036.v2 (2022). More

  • in

    Pollinator biological traits and ecological interactions mediate the impacts of mosquito-targeting malathion application

    Garibaldi, L. A. et al. Stability of pollination services decreases with isolation from natural areas despite honey bee visits. Ecol. Lett. 14(10), 1062–1072 (2011).PubMed 
    Article 

    Google Scholar 
    Kremen, C. et al. Pollination and other ecosystem services produced by mobile organisms: A conceptual framework for the effects of land-use change. Ecol. Lett. 10(4), 299–314 (2007).PubMed 
    Article 

    Google Scholar 
    Kluser, S. & Peduzzi, P. Global pollinator decline: A literature review. Preprint at http://archive-ouverte.unige.ch/unige 32258 (2007).Potts, S. G. et al. Global pollinator declines: Trends, impacts and drivers. Trends Ecol. Evol. 25(6), 345–353 (2010).PubMed 
    Article 

    Google Scholar 
    Rhodes, C. J. Pollinator decline—an ecological calamity in the making?. Sci. Prog. 101(2), 121–160 (2018).PubMed 
    Article 

    Google Scholar 
    Huang, H. & D’Odorico, P. Critical transitions in plant-pollinator systems induced by positive inbreeding-reward-pollinator feedbacks. Iscience 23(2), 100819 (2020).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Krishnan, N. et al. Assessing field-scale risks of foliar insecticide applications to monarch butterfly (Danaus plexippus) larvae. Environ. Toxicol. Chem. 39(4), 923–941 (2020).CAS 
    PubMed 
    Article 

    Google Scholar 
    Bargar, T. A., Hladik, M. L. & Daniels, J. C. Uptake and toxicity of clothianidin to monarch butterflies from milkweed consumption. PeerJ 8, e8669 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Emmel, T. C. & Tucker, J. C. In Mosquito Control Pesticides: Ecological Impacts and Management Alternatives (eds Emmel, T. C. & Tucker, J. C.) 105 (Scientific Publishers, 1991).Johnson, R. M., Ellis, M. D., Mullin, C. A. & Frazier, M. Pesticides and honey bee toxicity–USA. Apidologie 41(3), 312–331 (2010).CAS 
    Article 

    Google Scholar 
    Olaya-Arenas, P., Scharf, M. E. & Kaplan, I. Do pollinators prefer pesticide-free plants? An experimental test with monarchs and milkweeds. J. Appl. Ecol. 57(10), 2019–2030 (2020).CAS 
    Article 

    Google Scholar 
    Berryman, A. A. What causes population cycles of forest Lepidoptera?. Trends Ecol. Evol. 11(1), 28–32 (1996).CAS 
    PubMed 
    Article 

    Google Scholar 
    Elkinton, J. & Boettner, G. Benefits and harm caused by the introduced generalist tachinid, Compsilura concinnata North America. Biol. Control 57(2), 277–288 (2012).
    Google Scholar 
    Beschta, R. L. & Ripple, W. J. Riparian vegetation recovery in Yellowstone: The first two decades after wolf reintroduction. Biol. Conserv. 198, 93–103 (2016).Article 

    Google Scholar 
    Oberhauser, K. et al. Lacewings wasps and fliesoh my insect enemies take a bite out of monarchs. In Monarchs in a Changing World: Biology and Conservation of an iconic insect (eds Oberhauser, K. S. et al.) 71–82 (Cornell University Press, 2015).Chapter 

    Google Scholar 
    Zalucki, M. P., Clarke, A. R. & Malcolm, S. B. Ecology and behavior of first instar larval Lepidoptera. Annu. Rev. Entomol. 47(1), 361–393 (2002).CAS 
    PubMed 
    Article 

    Google Scholar 
    Hermann, S. L., Blackledge, C., Haan, N. L., Myers, A. T. & Landis, D. A. Predators of monarch butterfly eggs and neonate larvae are more diverse than previously recognised. Sci. Rep. 9(1), 1–9 (2019).CAS 
    Article 

    Google Scholar 
    McCoshum, S. M., Andreoli, S. L., Stenoien, C. M., Oberhauser, K. S. & Baum, K. A. Species distribution models for natural enemies of monarch butterfly (Danaus plexippus) larvae and pupae: Distribution patterns and implications for conservation. J. Insect Conserv. 20(2), 223–237 (2016).Article 

    Google Scholar 
    Geest, E. A., Wolfenbarger, L. L. & McCarty, J. P. Recruitment, survival and parasitism of monarch butterflies (Danaus plexippus) in milkweed gardens and conservation areas. J. Insect Conserv. 23(2), 211–224 (2019).Article 

    Google Scholar 
    Stenoien, C. et al. Monarchs in decline: A collateral landscape-level effect of modern agriculture. Insect Sci. 25(4), 528–541 (2018).PubMed 
    Article 

    Google Scholar 
    Crone, E. E., Pelton, E. M., Brown, L. M., Thomas, C. C. & Schultz, C. B. Why are monarch butterflies declining in the west? Understanding the importance of multiple correlated drivers. Ecol. Appl. 29(7), e01975 (2019).PubMed 
    Article 

    Google Scholar 
    Brower, L. P. et al. Effect of the 2010–2011 drought on the lipid content of monarchs migrating through Texas to overwintering sites in Mexico. In The Monarchs in a Changing World: Biology and Conservation of an Iconic Butterfly (eds Oberhauser, K. S. et al.) 117–129 (Cornell University Press, 2015).
    Google Scholar 
    Thogmartin, W. E. et al. Monarch butterfly population decline in North America: Identifying the threatening processes. R. Soc. Open Sci. 4(9), 170760 (2017).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Olaya-Arenas, P. & Kaplan, I. Quantifying pesticide exposure risk for monarch caterpillars on milkweeds bordering agricultural land. Front. Ecol. Evol. https://doi.org/10.3389/fevo.2019.00223 (2019).
    Article 

    Google Scholar 
    Olaya-Arenas, P., Hauri, K., Scharf, M. E. & Kaplan, I. Larval pesticide exposure impacts monarch butterfly performance. Sci. Rep. 10(1), 1–12 (2020).Article 

    Google Scholar 
    Cameron, S. A. et al. Patterns of widespread decline in North American bumble bees. PNAS 108(2), 662–667 (2011).ADS 
    MathSciNet 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Epstein, L. Fifty years since silent spring. Annu. Rev. Phytopathol. 52, 377–402 (2014).CAS 
    PubMed 
    Article 

    Google Scholar 
    Rayor, L. S. Effects of monarch larval host plant chemistry and body size on Polistes wasp predation. In The Monarch Butterfly Biology and Conservation (eds Oberhauser, K. S. & Solensky, M. J.) 39–46 (Cornell University Press, 2004).
    Google Scholar 
    Baker, A. M. & Potter, D. A. Invasive paper wasp turns urban pollinator gardens into ecological traps for monarch butterfly larvae. Sci. Rep. 10(1), 1–7 (2020).Article 

    Google Scholar 
    Castellanos, I. & Barbosa, P. Dropping from host plants in response to predators by a polyphagous caterpillar. J. Lepid. Soc. 65(4), 270–272 (2011).
    Google Scholar 
    Kessler, S. C. et al. Bees prefer foods containing neonicotinoid pesticides. Nature 521(7550), 74–76 (2015).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Liao, L.-H., Wu, W.-Y. & Berenbaum, M. R. Behavioral responses of honey bees (Apis mellifera) to natural and synthetic xenobiotics in food. Sci. Rep. 7(1), 1–8 (2017).Article 

    Google Scholar 
    Musser, R. O. et al. Caterpillar saliva beats plant defences. Nature 416(6881), 599–600 (2002).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Schmidt, J. & Smith, J. Host examination walk and oviposition site selection of Trichogramma minutum: Studies on spherical hosts. J. Insect Behav. 2(2), 143–171 (1989).Article 

    Google Scholar 
    Ramos, R. S. et al. Investigation of the lethal and behavioral effects of commercial insecticides on the parasitoid wasp Copidosoma truncatellum. Chemosphere 191, 770–778 (2018).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Chareonviriyaphap, T. et al. Pesticide avoidance behavior in Anopheles albimanus, a malaria vector in the Americas. J. Am. Mosq. Control Assoc. 13(2), 171–183 (1997).CAS 
    PubMed 

    Google Scholar 
    Nansen, C., Baissac, O., Nansen, M., Powis, K. & Baker, G. Behavioral avoidance-will physiological insecticide resistance level of insect strains affect their oviposition and movement responses?. PLoS ONE 11(3), e0149994 (2016).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Martini, X., Kincy, N. & Nansen, C. Quantitative impact assessment of spray coverage and pest behavior on contact pesticide performance. Pest Manag. Sci. 68(11), 1471–1477 (2012).CAS 
    PubMed 
    Article 

    Google Scholar 
    Bull, D. & Coleman, R. Effects of pesticides on Trichogramma spp. Southwest. Entomol. Suppl. 8, 156–168 (1985).CAS 

    Google Scholar 
    Thubru, D., Firake, D. & Behere, G. Assessing risks of pesticides targeting lepidopteran pests in cruciferous ecosystems to eggs parasitoid, Trichogramma brassicae (Bezdenko). Saudi J. Biol. Sci. 25(4), 680–688 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    Selwood, K. & Zimmer, H. Refuges for biodiversity conservation: A review of the evidence. Biol. Conserv. 245, 108502 (2020).Article 

    Google Scholar 
    Chmiel, J. A., Daisley, B. A., Pitek, A. P., Thompson, G. J. & Reid, G. Understanding the effects of sublethal pesticide exposure on honey bees: A role for probiotics as mediators of environmental stress. Front. Ecol. Evol. 8, 22 (2020).Article 

    Google Scholar 
    Chittka, L., Williams, N., Rasmussen, H. & Thomson, J. Navigation without vision: Bumblebee orientation in complete darkness. Proc. R. Soc. B 266(1414), 45–50 (1999).PubMed Central 
    Article 

    Google Scholar 
    Young, M. W. & Kay, S. A. Time zones: A comparative genetics of circadian clocks. Nat. Rev. Genet. 2(9), 702–715 (2001).CAS 
    PubMed 
    Article 

    Google Scholar 
    Mallet, J. Gregarious roosting and home range in Heliconius butterflies. Natl. Geogr. Res. 2(2), 198–215 (1986).
    Google Scholar 
    Chang, Y.-M. et al. Roosting site usage, gregarious roosting and behavioral interactions during roost-assembly of two Lycaenidae butterflies. Zool. Stud. 59, e10 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    Vulinec, K. Collective security aggregation by insects as a defence. In Insect Defences. Adaptive Mechanisms of Prey and Predators (eds Evans, D. L. & Schmidt, J. O.) 251–288 (State University of New York, 1990).
    Google Scholar 
    Salcedo, C. Environmental elements involved in communal roosting in Heliconius butterflies (Lepidoptera: Nymphalidae). Environ. Entomol. 39(3), 907–911 (2010).PubMed 
    Article 

    Google Scholar 
    Giordano, B. V., McGregor, B. L., Runkel, A. E. IV. & Burkett-Cadena, N. D. Distance diminishes the effect of deltamethrin exposure on the monarch butterfly, Danaus plexippus. J. Am. Mosq. Control Assoc. 36(3), 181–188 (2020).PubMed 
    Article 

    Google Scholar 
    Matzrafi, M. Climate change exacerbates pest damage through reduced pesticide efficacy. Pest Manag. Sci. 75(1), 9–13 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    Hewitt, A. Spray drift: Impact of requirements to protect the environment. Crop Prot. 19(8–10), 623–627 (2000).Article 

    Google Scholar 
    Nail, K. R., Stenoien, C. & Oberhauser, K. S. Immature monarch survival: Effects of site characteristics, density and time. Ann. Entomol. Soc. 108(5), 680–690 (2015).Article 

    Google Scholar 
    Payne, C. C. & Mertens, P. P. Cytoplasmic polyhedrosis viruses. In The Reoviridae (ed. Joklik, K.) 425–504 (Springer, 1983).Chapter 

    Google Scholar 
    Zalucki, M. P. et al. It’s the first bites that count: Survival of first-instar monarchs on milkweeds. Austral. Ecol. 26(5), 547–555 (2001).Article 

    Google Scholar 
    Salvato, M. Influence of mosquito control chemicals on butterflies (Nymphalidae, Lycaenidae, Hesperiidae) of the lower Florida keys. J. Lepid. Soc. 55(1), 8–14 (2001).
    Google Scholar 
    Frey, D. F. & Leong, K. L. Can microhabitat selection or differences in ‘catchability’ explain male-biased sex ratios in overwintering populations of monarch butterflies?. Anim. Behav. 45(5), 1025 (1993).Article 

    Google Scholar 
    Macgregor, C. J. & Scott-Brown, A. S. Nocturnal pollination: An overlooked ecosystem service vulnerable to environmental change. Emerg. Top. Life Sci. 4(1), 19–32 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar  More

  • in

    Fitness costs associated with a GABA receptor mutation conferring dieldrin resistance in Aedes albopictus

    Agnew P, Berticat C, Bedhomme S, Sidobre C, Michalakis Y (2004) Parasitism increases and decreases the costs of insecticide resistance in mosquitoes. Evolution 58:579–586CAS 
    PubMed 
    Article 

    Google Scholar 
    Ahmad NA, Endersby-Harshman NM, Mohd Mazni NR, Mohd Zabari NZA, Amran SNS, Ridhuan Ghazali MK et al. (2020) Characterization of sodium channel mutations in the Dengue vector mosquitoes Aedes aegypti and Aedes albopictus within the context of ongoing Wolbachia releases in Kuala Lumpur, Malaysia. Insects 11:529PubMed Central 
    Article 

    Google Scholar 
    Alout H, Ndam NT, Sandeu MM, Djégbe I, Chandre F, Dabiré RK et al. (2013) Insecticide resistance alleles affect vector competence of Anopheles gambiae s.s. for Plasmodium falciparum field isolates. PLoS ONE 8:e63849CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Andreasen MH, ffrench-Constant RH (2002) In situ hybridization to the Rdl locus on polytene chromosome 3L of Anopheles stephensi. Med Vet Entomol 16:452–455CAS 
    PubMed 
    Article 

    Google Scholar 
    Assogba BS, Djogbénou LS, Milesi P, Berthomieu A, Perez J, Ayala D et al. (2015) An ace-1 gene duplication resorbs the fitness cost associated with resistance in Anopheles gambiae, the main malaria mosquito. Sci Rep. 5:14529CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Assogba BS, Milesi P, Djogbénou LS, Berthomieu A, Makoundou P, Baba-Moussa LS et al. (2016) The ace-1 locus is amplified in all resistant Anopheles gambiae mosquitoes: fitness consequences of homogeneous and heterogeneous duplications. PloS Biol 14:e2000618PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Atyame CM, Alout H, Mousson L, Vazeille M, Diallo M, Weill M et al. (2019) Insecticide resistance genes affect Culex quinquefasciatus vector competence for West Nile virus. Proc Biol Sci 286:20182273CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Auteri M, La Russa F, Blanda V, Torina A (2018) Insecticide resistance associated with kdr mutations in Aedes albopictus: an update on worldwide evidences. Biomed Res Int 2018:e3098575Article 

    Google Scholar 
    Berticat C, Boquien G, Raymond M, Chevillon C (2002) Insecticide resistance genes induce a mating competition cost in Culex pipiens mosquitoes. Genet Res 79:41–47Berticat C, Duron O, Heyse D, Raymond M (2004) Insecticide resistance genes confer a predation cost on mosquitoes, Culex pipiens. Genet Res 83:189–196CAS 
    PubMed 
    Article 

    Google Scholar 
    Bhatia SC, Deobhankar RB (1963) Reversion of dieldrin-resistance in the field population of A. culicifacies in Maharashtra State (erstwhile Bombay State), India. Indian J Malariol 17:339–351CAS 
    PubMed 

    Google Scholar 
    Bonizzoni M, Gasperi G, Chen X, James AA (2013) The invasive mosquito species Aedes albopictus: current knowledge and future perspectives. Trends Parasitol 29:460–468PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Bourguet D, Guillemaud T, Chevillon C, Raymond M (2004) Fitness costs of insecticide resistance in natural breeding sites of the mosquito Culex pipiens. Evolution 58:128–135PubMed 
    Article 

    Google Scholar 
    Brooke BD, Hunt RH, Coetzee M (2000) Resistance to dieldrin + fipronil assorts with chromosome inversion 2La in the malaria vector Anopheles gambiae. Med Vet Entomol 14:190–194CAS 
    PubMed 
    Article 

    Google Scholar 
    Buckingham SD, Biggin PC, Sattelle BM, Brown LA, Sattelle DB (2005) Insect GABA receptors: splicing, editing, and targeting by antiparasitics and insecticides. Mol Pharm 68:942–951CAS 
    Article 

    Google Scholar 
    Chen H, Li K, Wang X, Yang X, Lin Y, Cai F et al. (2016) First identification of kdr allele F1534S in VGSC gene and its association with resistance to pyrethroid insecticides in Aedes albopictus populations from Haikou City, Hainan Island, China. Infect Dis Poverty 5:31PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Davari B, Vatandoost H, Oshaghi MA, Ladonni H, Enayati AA, Shaeghi M et al. (2007) Selection of Anopheles stephensi with DDT and dieldrin and cross-resistance spectrum to pyrethroids and fipronil. Pestic Biochem Physiol 89:97–103CAS 
    Article 

    Google Scholar 
    Delatte H, Paupy C, Dehecq JS, Thiria J, Failloux AB, Fontenille D (2008) Aedes albopictus, vector of Chikungunya and Dengue viruses in Reunion Island: biology and control. Parasite 15:3–13CAS 
    PubMed 
    Article 

    Google Scholar 
    Deng J, Guo Y, Su X, Liu S, Yang W, Wu Y et al. (2021) Impact of deltamethrin-resistance in Aedes albopictus on its fitness cost and vector competence. PLoS Negl Trop Dis 15:e0009391CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Djogbénou L, Weill M, Hougard J-M, Raymond M, Akogbéto M, Chandre F (2007) Characterization of insensitive acetylcholinesterase (ace-1R) in Anopheles gambiae (Diptera: Culicidae): resistance levels and dominance. J Med Entomol 44:805–810PubMed 

    Google Scholar 
    Du W, Awolola TS, Howell P, Koekemoer LL, Brooke BD, Benedict MQ et al. (2005) Independent mutations in the Rdl locus confer dieldrin resistance to Anopheles gambiae and An. arabiensis. Insect Mol Biol 14:179–183CAS 
    PubMed 
    Article 

    Google Scholar 
    Duron O, Labbé P, Berticat C, Rousset F, Guillot S, Raymond M et al. (2006) High Wolbachia density correlates with cost of infection for insecticide resistant Culex pipiens mosquitoes. Evolution 60:303–314CAS 
    PubMed 
    Article 

    Google Scholar 
    ffrench-Constant RH, Rocheleau TA, Steichen JC, Chalmers AE (1993) A point mutation in a Drosophila GABA receptor confers insecticide resistance. Nature 363:449–451CAS 
    PubMed 
    Article 

    Google Scholar 
    ffrench-Constant RH, Anthony N, Aronstein K, Rocheleau T, Stilwell G (2000) Cyclodiene insecticide resistance: from molecular to population genetics. Annu Rev Entomol 45:449–466CAS 
    PubMed 
    Article 

    Google Scholar 
    Fox J, Weisberg S (2019) An R companion to applied regression, 3rd edn. SAGE, Thousand Oaks California, https://socialsciences.mcmaster.ca/jfox/Books/Companion/
    Google Scholar 
    Freeman JC, Smith LB, Silva JJ, Fan Y, Sun H, Scott JG (2021) Fitness studies of insecticide resistant strains: lessons learned and future directions. Pest Manag Sci 77:3847–3856CAS 
    PubMed 
    Article 

    Google Scholar 
    Gratz NG (2004) Critical review of the vector status of Aedes albopictus. Med Vet Entomol 18:215–227CAS 
    PubMed 
    Article 

    Google Scholar 
    Grau-Bové X, Tomlinson S, O’Reilly AO, Harding NJ, Miles A, Kwiatkowski D et al. (2020) Evolution of the insecticide target Rdl in African Anopheles is driven by interspecific and interkaryotypic introgression. Mol Biol Evol 37:2900–2917PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Grigoraki L, Lagnel J, Kioulos I, Kampouraki A, Morou E, Labbé P et al. (2015) Transcriptome profiling and genetic study reveal amplified carboxylesterase genes implicated in temephos resistance, in the Asian tiger mosquito Aedes albopictus. PLoS Negl Trop Dis 9:e0003771PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Hamon J, Garret-Jones C (1962) Insecticide-resistance in major vectors of malaria, and its operational importance. Bull World Health Organ, Geneva
    Google Scholar 
    Hartley CJ, Newcomb RD, Russell RJ, Yong CG, Stevens JR, Yeates DK et al. (2006) Amplification of DNA from preserved specimens shows blowflies were preadapted for the rapid evolution of insecticide resistance. Proc Natl Acad Sci USA 103:8757–8762CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Hemingway J, Ranson H (2000) Insecticide resistance in insect vectors of human disease. Annu Rev Entomol 45:371–391CAS 
    PubMed 
    Article 

    Google Scholar 
    Hemingway J, Hawkes NJ, McCarroll L, Ranson H (2004) The molecular basis of insecticide resistance in mosquitoes. Insect Biochem Mol Biol 34:653–665CAS 
    PubMed 
    Article 

    Google Scholar 
    Hosie AM, Baylis HA, Buckingham SD, Sattelle DB (1995) Actions of the insecticide fipronil, on dieldrin-sensitive and -resistant GABA receptors of Drosophila melanogaster. Br J Pharm 115:909–912CAS 
    Article 

    Google Scholar 
    Ishak IH, Riveron JM, Ibrahim SS, Stott R, Longbottom J, Irving H et al. (2016) The Cytochrome P450 gene CYP6P12 confers pyrethroid resistance in kdr-free Malaysian populations of the Dengue vector Aedes albopictus. Sci Rep. 6:24707CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Kasai S, Ng LC, Lam-Phua SG, Tang CS, Itokawa K, Komagata O et al. (2011) First detection of a putative knockdown resistance gene in major mosquito vector, Aedes albopictus. Jpn J Infect Dis 64:217–221CAS 
    PubMed 
    Article 

    Google Scholar 
    Kliot A, Ghanim M (2012) Fitness costs associated with insecticide resistance. Pest Manag Sci 68:1431–1437CAS 
    PubMed 
    Article 

    Google Scholar 
    Kolaczinski J, Curtis C (2001) Laboratory evaluation of fipronil, a phenylpyrazole insecticide, against adult Anopheles (Diptera: Culicidae) and investigation of its possible cross-resistance with dieldrin in Anopheles stephensi. Pest Manag Sci 57:41–45CAS 
    PubMed 
    Article 

    Google Scholar 
    Kraemer MU, Sinka ME, Duda KA, Mylne AQ, Shearer FM, Barker CM et al. (2015) The global distribution of the arbovirus vectors Aedes aegypti and Ae. albopictus. Elife 4:e08347PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Labbé P, David J-P, Alout H, Milesi P, Djogbénou L, Pasteur N et al. (2017) 14 – Evolution of resistance to insecticide in disease vectors. In: Tibayrenc M (ed) Genetics and Evolution of Infectious Diseases, Second Edition. Elsevier, London, p 313–339Chapter 

    Google Scholar 
    Latreille AC, Milesi P, Magalon H, Mavingui P, Atyame CM (2019) High genetic diversity but no geographical structure of Aedes albopictus populations in Réunion Island. Parasit Vectors 12:597PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Lebon C, Alout H, Zafihita S, Dehecq JS, Weill M, Tortosa P et al. (2022) Spatio-temporal dynamics of a dieldrin resistance gene in Aedes albopictus and Culex quinquefasciatus populations from Reunion Island. J Insect Sci 22:4PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Lebon C, Soupapoule K, Wilkinson DA, Goff GL, Damiens D, Gouagna LC (2018) Laboratory evaluation of the effects of sterilizing doses of γ-rays from Caesium-137 source on the daily flight activity and flight performance of Aedes albopictus males. PLoS ONE 13:e0202236PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Li Y, Xu J, Zhong D, Zhang H, Yang W, Zhou G et al. (2018) Evidence for multiple-insecticide resistance in urban Aedes albopictus populations in southern China. Parasit Vectors 11:4PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Low VL, Vinnie-Siow WY, Lim YAL, Tan TK, Leong CS, Chen CD et al. (2015) First molecular genotyping of A302S mutation in the gamma aminobutyric acid (GABA) receptor in Aedes albopictus from Malaysia. Trop Biomed 32:554–556CAS 
    PubMed 

    Google Scholar 
    McKenzie BA, Wilson AE, Zohdy S (2019) Aedes albopictus is a competent vector of Zika virus: a meta-analysis. PLoS ONE 14:e0216794CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Milesi P, Pocquet N, Labbé P (2013) BioRssay: A R script for bioassay analyses. http://www.isem.univ-montp2.fr/recherche/equipes/genomique-de-ladaptation/personnel/labbepierrick/Moyes CL, Vontas J, Martins AJ, Ng LC, Koou SY, Dusfour I et al. (2017) Contemporary status of insecticide resistance in the major Aedes vectors of arboviruses infecting humans. PLoS Negl Trop Dis 11:e0005625PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Ozoe Y, Kita T, Ozoe F, Nakao T, Sato K, Hirase K (2013) Insecticidal 3-benzamido-N-phenylbenzamides specifically bind with high affinity to a novel allosteric site in housefly GABA receptors. Pestic Biochem Physiol 107:285–292CAS 
    PubMed 
    Article 

    Google Scholar 
    Paupy C, Ollomo B, Kamgang B, Moutailler S, Rousset D, Demanou M et al. (2009) Comparative role of Aedes albopictus and Aedes aegypti in the emergence of Dengue and Chikungunya in central Africa. Vector Borne Zoonotic Dis 10:259–266Article 

    Google Scholar 
    Peakall R, Smouse PE (2012) GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research—an update. Bioinformatics 28:2537–2539CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Platt N, Kwiatkowska RM, Irving H, Diabaté A, Dabire R, Wondji CS (2015) Target-site resistance mutations (kdr and RDL), but not metabolic resistance, negatively impact male mating competiveness in the malaria vector Anopheles gambiae. Heredity 115:243–252CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    R Core Team (2019) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, https://www.R-project.org/
    Google Scholar 
    Ranson H, Burhani J, Lumjuan N, Black WCI (2010) Insecticide resistance in Dengue vectors. TropIKA.net [online] 1. http://journal.tropika.net/scielo.php?script=sci_arttext&pid=S2078-86062010000100003&lng=en&nrm=iso. Accessed 03 March 2022Raymond M, Berticat C, Weill M, Pasteur N, Chevillon C (2001) Insecticide resistance in the mosquito Culex pipiens: what have we learned about adaptation? Genetica 112–113:287–296PubMed 
    Article 

    Google Scholar 
    Renault P, Solet J-L, Sissoko D, Balleydier E, Larrieu S, Filleul L et al. (2007) A major epidemic of Chikungunya virus infection on Réunion Island, France, 2005–2006. Am J Trop Med Hy 77:727–731Article 

    Google Scholar 
    Rowland M (1991a) Behaviour and fitness of γHCH/dieldrin resistant and susceptible female Anopheles gambiae and An. stephensi mosquitoes in the absence of insecticide. Med Vet Entomol 5:193–206CAS 
    PubMed 
    Article 

    Google Scholar 
    Rowland M (1991b) Activity and mating competitiveness of γHCH/dieldrin resistant and susceptible male and virgin female Anopheles gambiae and An. stephensi mosquitoes, with assessment of an insecticide-rotation strategy. Med Vet Entomol 5:207–222CAS 
    PubMed 
    Article 

    Google Scholar 
    Russell VL (2021) Emmeans: Estimated Marginal Means, aka Least-Squares Means. R package version 1.5.5.1. https://CRAN.R-project.org/package=emmeansSu X, Guo Y, Deng J, Xu J, Zhou G, Zhou T et al. (2019) Fast emerging insecticide resistance in Aedes albopictus in Guangzhou, China: alarm to the Dengue epidemic. PLoS Negl Trop Dis 13:e0007665CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Tantely ML, Tortosa P, Alout H, Berticat C, Berthomieu A, Rutee A et al. (2010) Insecticide resistance in Culex pipiens quinquefasciatus and Aedes albopictus mosquitoes from La Réunion Island. Insect Biochem Mol Biol 40:317–324CAS 
    PubMed 
    Article 

    Google Scholar 
    Taskin BG, Dogaroglu T, Kilic S, Dogac E, Taskin V (2016) Seasonal dynamics of insecticide resistance, multiple resistance, and morphometric variation in field populations of Culex pipiens. Pestic Biochem Physiol 129:14–27CAS 
    PubMed 
    Article 

    Google Scholar 
    Taylor‐Wells J, Brooke BD, Bermudez I, Jones AK (2015) The neonicotinoid imidacloprid, and the pyrethroid deltamethrin, are antagonists of the insect Rdl GABA receptor. J Neurochem 135:705–713PubMed 
    Article 

    Google Scholar 
    Therneau T (2015) A Package for Survival Analysis in S. R package version 2.38. https://CRAN.R-project.org/package=survivalThompson M, Shotkoski F, ffrench-Constant R (1993) Cloning and sequencing of the cylodienne insecticide resistance from the yellow fewer Aedes aegypti. FEBS Lett 325:187–190CAS 
    PubMed 
    Article 

    Google Scholar 
    Tsetsarkin KA, Vanlandingham DL, McGee CE, Higgs S (2007) A single mutation in Chikungunya virus affects vector specificity and epidemic potential. PLoS Pathog 3:e201PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Vontas J, Kioulos E, Pavlidi N, Morou E, della Torre A, Ranson H (2012) Insecticide resistance in the major Dengue vectors Aedes albopictus and Aedes aegypti. Pestic Biochem Physiol 104:126–131CAS 
    Article 

    Google Scholar 
    Wondji CS, Dabire RK, Tukur Z, Irving H, Djouaka R, Morgan JC (2011) Identification and distribution of a GABA receptor mutation conferring dieldrin resistance in the malaria vector Anopheles funestus in Africa. Insect Biochem Mol Biol 41:484–491CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Xu J, Bonizzoni M, Zhong D, Zhou G, Cai S, Li Y et al. (2016) Multi-country survey revealed prevalent and novel F1534S mutation in voltage-gated sodium channel (VGSC) gene in Aedes albopictus. PLoS Negl Trop Dis 10:e0004696PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Yang C, Huang Z, Li M, Feng X, Qiu X (2017) RDL mutations predict multiple insecticide resistance in Anopheles sinensis in Guangxi, China. Malar J 16:482PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Zhou X, Yang C, Liu N, Li M, Tong Y, Zeng X et al. (2019) Knockdown resistance (kdr) mutations within seventeen field populations of Aedes albopictus from Beijing China: first report of a novel V1016G mutation and evolutionary origins of kdr haplotypes. Parasit Vectors 12:180PubMed 
    PubMed Central 
    Article 

    Google Scholar  More

  • in

    Towards an absolute light pollution indicator

    DefinitionWe present here a new statistical approach to measure and characterize light pollution. The objective is to define an indicator which is not limited to clear sky measurements and does not require a precise calibration of a photometer. The key attributes of the indicator are the following:

    It requires the automated acquisition of a large number of zenithal NSB measures when the Sun is below (-18^{circ }) and the Moon below (-5^{circ });

    The acquisitions must at least cover a period of 6 months in order to record a wide range of possible weather conditions from perfectly clear to totally overcast skies. The objective is to obtain a significant sample of every type of cloud conditions (e.g. cloud density and ceiling altitude) as well as a good characterization of the average clear sky ;

    It is based on the analysis of the zenithal NSB measure dispersion which is directly linked to the level of light pollution a site experiences.

    As presented above in Fig. 2, the NSB density histograms, which are assembled from a large number of NSB measures, display a higher density zone which denotes a characteristic clear sky level that we name nominal NSB in the scope of the indicator calculation. On both sides of the clear sky level (above and below), NSB measures are distributed in a way that reflect the zenithal night sky luminance in cloudy conditions: NSB measures above the clear sky level mean that the light pollution is amplified by clouds while those below the clear sky level indicate a darker environment where clouds mask light pollution from distant sources as well as natural light sources. The calculation of the indicator is based on the evaluation of the NSB measure dispersion on both sides of the nominal NSB (i.e. characteristic clear sky level). Since there can have strong variations of artificial light emitted into the environment at the beginning and end of the night (decrease then increase of human activity, extinction of public lighting, etc.), the range of NSB measures retained for calculating the indicator is restricted to a portion in the middle of the night, typically 2 h.Figure 8 shows a typical NSB density histogram for a site which is quite severely impacted by light pollution. It covers a 2 h time range between 23:00 UTC and 01:00 UTC and one can easily see that the zone above the nominal NSB is much higher and denser than the one below, i.e. cloud conditions create more often a brighter environment than a darker one and with a greater amplitude.Figure 8NSB density histogram where the nominal NSB that represents the most common clear sky conditions is identified. It delimits two areas, the NSB bright dispersion above the nominal NSB and the NSB dark dispersion below.Full size imageBased on the determination of the nominal NSB, a quantitative indicator, called NDR for NSB Dispersion Ratio, is calculated in the following way:$$begin{aligned} NDR = (N_b cdot MAD_b) / (N_d cdot MAD_d) end{aligned}$$where (N_b) is the number of measures above the nominal NSB (brighter sky), (N_d) is the number of measures below the nominal NSB (darker sky), (MAD_b) is the median absolute deviation of the measures in the bright dispersion zone (above the nominal NSB) and (MAD_d) is the median absolute deviation of the measures in the dark dispersion zone (below the nominal NSB). The median absolute deviation is a statistical tool used to measure the variability of a data set, which is exactly what we try to achieve with the two NSB extensions above and below the nominal NSB. It is formally defined as (MAD = median(|X_i – tilde{{mathbf {X}}}|)) where (X_i) in our case represents an NSB value and (tilde{{mathbf {X}}}) is (median(X_i)). The median absolute deviation is a better choice than the usual standard deviation to measure the spread of NSB measures since the data does not follow a normal distribution.In order to make the determination of the NSB Dispersion Ratio stronger from a statistical standpoint, we use a bootstrapping with replacement resampling method on the set of night portions used to compute the indicator. Assuming we have N night portions at our disposal, we randomly select a sample of N items in this set of night portions knowing that a given item can appear multiple times in the sample (hence the bootstrapping with replacement). The NDR value is then computed for the considered sample. This process is repeated 1000 times and the average NDR value if eventually computed. This average value represents the actual NDR indicator of the considered site.The NDR indicator takes into account both the number of NSB values on each side of the nominal NSB and the dispersion of these values. This is what makes it relevant as an indicator of light pollution which encompasses all kinds of meteorological conditions experienced at a particular site. On that aspect, it is therefore not an astronomical light pollution indicator since it is not focused on clear sky conditions. On the opposite, it requires to have a significant number of NSB measures in all sorts of cloudy conditions so that a valid NDR indicator can be derived.A key aspect of the NDR calculation methodology is to determine the level of the nominal NSB, i.e. the typical clear sky level, since it will be used to differentiate the NSB measures that go in each of the two sets to calculate the bright and dark dispersions. As we have seen earlier in the article, such a determination can be biased by natural light sources that raise or lower clear sky NSB at different times of the night. This can result into a “blurry” high density zone which makes the determination of the nominal NSB difficult or even impossible depending on the observation period. Based on the quantitative estimate of the different natural light sources presented above, the most important bias to address is the contribution of the Galactic plane. This contribution must be eliminated for all the NSB measures which are used to calculate the NDR indicator. In order to do that, Noxi, the Ninox processing software developed by DarkSkyLab, calculates for each NSB measure the corresponding Galactic plane and star fields contribution using the galactic coordinates of the zenith and integrating the combined flux of all stars in the field of view using the UCAC4 astrometry and photometry star catalogue. It is not possible to cancel the contribution of the airglow due to its unpredictable nature, but since it only appears in rare occasions, it is not seen as a problem and is ignored. Regarding the contribution of the zodiacal light, it is considered as minimal at the zenith and it is also ignored.As an example, Fig. 9 shows on the left an NSB density histogram where the Galactic plane bias has not been corrected in the data, and on the right the same data but with the Galactic plane bias corrected. It is easy to see that in the latter the nominal NSB is much easier to determine, providing a more accurate reference level to calculate the NDR indicator. Once the Galactic plane bias has been corrected, the nominal NSB is determined as the highest density zone of the NSB histogram. It must be noted that, as of today, all the NSB measures are corrected from the Galactic plane bias without regards to the presence of clouds or high levels of light pollution. This results into an additional source of inaccuracy that will be addressed in the future through the implementation of two heuristics within the Noxi software:

    1.

    A first heuristic will determine if a night portion is considered as having a clear sky or not so that the Galactic bias correction is applied only if the sky is clear. In order to do that, we have developed an indicator called the NSS (for Night Sky Stability). To determine the NSS for a full night of measures or just a night portion, we fit the NSB curve with a degree 10 polynomial and we then compute the difference between each NSB measure and it polynomial counterpart. As a result, we obtain a set of residuals. The variance of all the residuals defines the NSS for the considered NSB dataset. Below a given value, the sky is considered as clear knowing that the NSS indicator has been calibrated on several NSB data sets for which the corresponding weather conditions are known;

    2.

    A second heuristic will allow us to weight the Galactic bias correction to be applied to NSB measures according to their value. For non-polluted skies with high values of NSB, the full Galactic bias correction will be applied while below a certain NSB threshold (for instance 21 mag(_{mathrm{SQM}})/arcsec(^{2}) which corresponds roughly to the brightest parts of the Milky Way) no correction will be applied.

    Figure 9NSB density histograms of the same data set with no correction of the Galactic plane bias applied on the left and a full correction applied on the right.Full size imageThe NDR indicator is unitless since it is the ratio of two quantities with the same unit (mag(_{mathrm{SQM}})/arcsec(^{2})). For the data set presented in Fig. 9, the NDR value which is obtained is 25 (which is justified by the fact that the bright extension in the density histogram is much higher and denser than the dark extension). This denotes a quite high level of light pollution despite the fact that the nominal NSB is at a level of 21.6 mag(_{mathrm{SQM}})/arcsec(^{2}). This highlights the fact that there is not always a strict correlation between the typical clear sky NSB obtained for a given site and its NDR indicator, i.e. the presence of clouds decreases the NSB more than we could have expected just by knowing the clear sky NSB. On that respect, the NDR ratio brings more information that the clear sky NSB alone.In addition to provide an indicator which is representative of light pollution in all possible atmospheric conditions, the NDR provides a tool to compare locations in a more meaningful way than just using a set of standalone NSB evaluations. First it is not dependent of an inter-calibration between different systems and second its statistical nature makes it more robust when it comes to perform comparisons.NDR into practiceThe NDR indicator has been calculated for several different sites by DarkSkyLab during various projects in France that involved NSB measuring sessions in the field. To demonstrate some of the results that have been obtained, Fig. 10 provides the density histograms of 4 different sites which have quite different light pollution profiles.Figure 10NSB density histograms of 4 different sites used to compute the NDR indicator. The nominal NSB (which corresponds to the most common clear sky conditions) is noted with a white tick mark next to the vertical axis. Relative levels of the bright and dark dispersion terms (((N_b cdot MAD_b)) and ((N_d cdot MAD_d))) are noted respectively with an orange tick mark and a green tick mark. The computed values of the NDR indicator and nominal NSB are provided in the top-left corner of each figure.Full size imageTo build these diagrams, only the measures acquired during a few hours in the middle of the nights have been used to ensure the maximum stability of the NSB curves and avoid lighting extinctions that create large gaps in NSB profiles. The Galactic plane bias is corrected on all plots and the same NSB scale is used in order to perform comparisons between the 4 sites. One can notice that the number of measures and nights for the 4 sites are quite different. However, they are all sufficient to derive a meaningful value of the NDR indicator using the bootstrapping with replacement resampling method described above, but it is clear that the more NSB measures used, the more accurate the NDR indicator.Table 2 summarizes the NDR indicators as well as the nominal NSB for the 4 sites which are sorted in the order of decreasing NDR indicator values.Table 2 Summary of the nominal NSB and NDR indicators of the 4 different sites.Full size tableOne can see that the NDR indicator values are not strictly correlated to the nominal NSB values, e.g. despite the fact that the nominal NSB of site (a) is slightly better than the one of site (b), the NDR indicator value is much larger for site (a) than for site (b). This can be explained if we consider the specificities of each site:

    Cervières (a) is a small village in the Haut-Forez area, France, which is surrounded by large cities (Lyon, Saint-Etienne and Clermont-Ferrand at a distance between 50 to 80 km) and a closer mid-size city (Roanne at 30 km). At the top of that, the town of Noirétable and a large highway rest area are just 2 km away without any nocturnal extinction applied (as opposed to the village of Cervières itself for which public lighting is turned off from 23:00 to 05:00 local time). These conditions are favourable to the presence of a constant light pollution background which has a negative impact on the zenithal NSB measures in most cloudy conditions (distant large cities for high elevation clouds and Noirétable and the highway rest area for lower elevation clouds). Only rare cloud conditions actually protect the site from the effect of mid-distance light sources. In clear sky conditions, however, the fact that there is no close light sources provides reasonably good NSB levels;

    The Copernic Association Observatory (b) is located 6 km from the large town of Gap in the mountain area of Hautes-Alpes in the south of France. There is no significant short distant light sources but in many cloud conditions the contribution of Gap has a very negative impact on the zenithal luminance. However, due to the fact that the observatory is at a higher altitude on the hills surrounding the city of Gap, there are cloud conditions that make the site darker. In clear sky conditions, the proximity of Gap does not permit a quality better than that of a rural sky;

    The Astrièves Observatory (c) is located near the center of the small town of Gresse-en-Vercors in the Parc Naturel Régional du Vercors. There is a full nocturnal extinction of the village for a large part of the night resulting in a good sky quality in clear sky conditions. The large city of Grenoble is at a distance of 30 km in a valley at the north-east, and the two locations are separated by a few mountains which efficiently help masking the light pollution as soon as the cloud ceiling is below a certain altitude, resulting into a dark environment. On the opposite, high elevation clouds reflect the light from Grenoble and increase the zenithal luminance;

    Eourres (d) is a small and isolated village located 20 km west of Sisteron in the department of Hautes-Alpes, France, which is surrounded by mountains. There is no significant light sources closer than those of Sisteron and this results into a very good night sky quality with, most of the times, a very dark environment in cloudy conditions.

    Figure 11 provides a graphical representation of the NDR indicator values for the 4 sites. On the NDR scale, the value 1 indicates that the bright and dark dispersion terms (respectively ((N_b cdot MAD_b)) and ((N_d cdot MAD_d))) are equal, which means there is a balance between dark and bright conditions at the zenith on the considered site with reference to the most common clear sky level.Figure 11Summary of the NDR indicators obtained for the 4 sites. The diagram uses 1 as the pivotal value to delineate sites according to the two bright and dark dispersion terms ((N_b cdot MAD_b)) and ((N_d cdot MAD_d)).Full size imageThe NDR can theoretically vary between 0 (totally dark site) and several hundreds (extremely bright site) but in practice the best sites can reach NDR indicator values down to 0.3 in the best preserved locations and up to 200 for very large and polluted cities.Robustness of the NDR indicatorIt is important to evaluate how the NDR indicator is dependant on the number of measures used to compute it and to figure out what would be the minimum number of night sessions required to obtain a meaningful NDR indicator value at a given site. To achieve that, we have used the data from two of the four sites presented above (the two which have the largest number or recorded nights: Cervières with 424 nights and the Astrièves Observatory with 373 nights). The 1000-step bootstrapping procedure has been repeatedly executed on each data set with a regularly decreasing sample of nights: starting from the full number of nights, a decrement of 10 nights is applied at each step until only 20 nights are remaining. At every bootstrap step, each sample is composed of n nights randomly chosen among the N available ones knowing that any night can be selected several times.Figure 12 shows the NDR indicator values that have been obtained for each of the two sites as a function of the night sample considered. The 95% confidence interval is plotted against each NDR indicator value (it is preferred to the standard deviation since the NSB distribution in the data sets is not normal). In the right plot of Fig. 12, the last confidence interval for the 24 night sample is too wide to fit in the y-axis NDR range (the top value is 195).Figure 12Results of the NDR resampling on the two data sets of Cervières and Astrièves Observatory. The horizontal axis is the number of nights considered into the night sample and the vertical axis provides the NDR indicator obtained for each sampling set through a 1000-iteration bootstrapping with replacement calculation.Full size imageDiscussion on the required number of nightsWe can see in Fig. 12 that the NDR indicator and the confidence interval remain stable down to 200 nights. Below this threshold, the NDR starts to become unstable with growing confidence intervals. Based on this data, we can estimate that the minimum number of nights required to compute a robust NDR indicator is 200 (therefore between 7 and 8 months since there are periods around the full moons where there is no night portions recorded).However, depending on the measuring session objectives, the NDR indicator can be considered as accurate enough even when using a smaller number of nights. If the goal is simply to get a first estimate of the light pollution level at a given site, we can consider that 90 nights (a little more than 3 months of measures) are enough. On the opposite, if we want to perform a comparison between several sites for evaluating the impact of light pollution on a particular species, we might want to perform at least 200 nights of measurement to get a better accuracy for the NDR indicator. The experience from DarkSkyLab through many NSB measuring sessions is that 3 to 4 months of measures are required to get a meaningful density histogram, hence an accurate enough NDR indicator, so that a site can be sufficiently characterized from a light pollution perspective. Such a measuring period usually guarantees that the clear sky nominal NSB is well defined and that various cloud conditions have been observed. This estimate is sustained by the results obtained in Fig. 12.Value of the NDR indicator for ecological researchThe study of the impact of light pollution on biodiversity is currently in full expansion, amplifying a political and citizen demand for the reclamation of the night2,32,33.We identify three main contributions of the NDR indicator for ecological research. First, it overcomes the limits of an old problem of communication in terms of measurement units between disciplines and potentially limits the use of units without real meaning from a biodiversity point of view34,35. Secondly, the use of the NDR indicator limits the common biases linked to a characterization of the effects of anthropogenic light which is too limited in time and space35. Indeed, the life history traits of species are not only shaped by the intensity of light emitted into the nocturnal environment but also by its variation over time34,35,36. Currently, the characterization of light pollution is too often limited in time and space, which can lead to misinterpretation37. Thirdly, the NDR indicator provides ecological researchers with a unit of measurement that integrates a sufficiently long time step to study the impact of light pollution on the evolutionary processes at work in the life of species and particularly on population dynamics and animal behavior36,38,39.Limitations and future improvements of the NDR indicatorThe main limitation of the NDR indicator resides in the possible difficulty to identify a well defined value for the nominal NSB, i.e. the NSB value that represents the most common clear sky conditions of a given site. For the most part, this is due to the contribution of the Galactic plane to the zenithal sky brightness and, to a lower extent, to the contribution of other natural light sources (dense star fields, airglow and zodiacal light). The residual spread of NSB measures is due to changing atmospheric conditions at various time scales, but, for this particular contribution, we can expect a statistical compensation to eliminate a systematic associated bias.At the moment, the contribution of the Galactic plane and star fields is canceled into the NSB measures by calculating in the Noxi software the integrated flux of all the stars that belong to the field of view (using the UCAC4 star catalogue). However, this approach has proven some limitations, especially in the southern hemisphere where the Galactic center goes through the zenith and is particularly bright. A probable explanation for that lack of predictability is the fact that the Galactic plane contains diffuse sources such as nebulae which are not accounted for into the star catalogues and which actually cannot be ignored. To address this issue, DarkSkyLab has the project to create a brightness map of the Galactic plane with a square degree resolution or better so that the contribution of all sources can be correctly accounted for.In addition to better correcting the Galactic plane bias, an improvement must be made with regards to the NSB measures that need to be corrected. At the moment, all NSB measures are corrected from the Galactic plane bias without regards to the presence of clouds or high levels of light pollution. So a first heuristic must be implemented to only apply the bias correction to clear sky NSB measures. An other heuristic must also be developed to reduce the correction applied as a function of the NSB level.A third limitation of the NDR indicator is related to a possible lack of cloudy conditions at some sites (e.g. in the Atacama desert in Chile with more than 320 clear nights per year), the reason simply being that the NDR indicator requires the presence of clouds to differentiate the bright and dark extensions into the NSB density histograms. This means that the NDR indicator can hardly be used for such astronomy-oriented sites which experience rare cloudy conditions. More