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    Global relationships in tree functional traits

    Trait modelsOur analysis included 491,001 unique trait measurements across 18 traits, encompassing 13,189 tree species from 2313 genera, reflecting ~21% of all known tree species33 (Fig. 1). Traits were measured at 8683 locations across the globe and 373 distinct eco-regions (Supplementary Tables 1, 2), with georeferenced measurements capturing 15% of known tree species in Eurasia, 13% in South America, 9% in Oceania, and 6% in North America and Africa33. The raw data covered 22% of all trait-by-species combinations (Fig. 1b, Supplementary Fig. 2), nearly identical to other large-scale trait analyses across the entire plant kingdom5,17,30. Yet there was considerable variation in coverage across traits, with traits such as specific leaf area and leaf nitrogen measured on more than 60% of all species, versus traits such as crown diameter and conduit diameter, which captured fewer than 5% of species (Fig. 1b, Supplementary Fig. 2). Across all species, 423 had more than 10 unique traits measured, and two species (Picea abies and Pinus sylvestris) had measurements for all 18 traits. In general, there was highly consistent coverage across taxonomic orders and traits (Supplementary Fig. 1), with gymnosperms being slightly overrepresented (comprising 3.1 ± 6.8% of measurements in the database versus ~1% of all known tree species34,35, Fig. 1a), in part reflecting the wider geographic range of many gymnosperms relative to angiosperms36.To explore relationships in functional traits at the individual level, we used random-forest machine-learning models to estimate missing trait values for each individual tree as a function of its environment and phylogenetic history. We also conducted a second set of analyses where trait expression was estimated using phylogenetic information only, which allowed us to include additional non-georeferenced data (Fig. 1), while also quantifying the relative contribution of environmental information on trait expression (Supplementary Fig. 6). Following standard approaches5,15,29,30, all traits were log-transformed and standardized to allow for statistically robust comparisons. Environmental predictors included ten variables encompassing climate37,38,39,40, soil41, topographic42, and geological43 features. Phylogenetic history was incorporated via the first ten phylogenetic eigenvectors44,45 (see Methods). By including environmental information alongside phylogenetic information, this approach not only allowed us to impute species-level traits which have strong phylogenetic signals and weak environmental signals, as is traditionally done17,30 but also to robustly estimate traits which have a weak phylogenetic signal and are instead strongly sensitive to environmental conditions. Moreover, being a non-parametric approach, the random forest makes no a priori assumptions about how trait expression varies across phylogenetic groups or environments.Across all 18 traits, the best-fitting models explained 54 ± 14% of out-of-fit trait variation (VEcv, see Methods), ranging from 26% for stem diameter to 76% of the variation in leaf area (Supplementary Figs. 6, 7). This accuracy was quantified using buffered leave-one-out cross-validation to account for spatial and phylogenetic autocorrelation46, and thus serves as a conservative lower bound for species which are phylogenetically and environmentally distinct from the observations47. There was no significant relationship between out-of-fit cross-validation accuracy and sample size (R2 = 0.06, p = 0.33), highlighting the relatively broad taxonomic coverage for each trait (Fig. 1, Supplementary Fig. 1).Environmental variables and phylogenetic information had approximately equal explanatory power (relative importance of 0.51 vs 0.49 for environment vs. phylogeny), albeit with substantial variation across traits (Supplementary Fig. 9). The inclusion of environmental variables increased the explanatory power of the models by 35%, on average (Supplementary Fig. 6), with crown diameter, crown height, leaf density, and stem diameter exhibiting the largest relative increases (54%, 45%, 73%, and 26%, respectively), mirroring the fact that these traits have comparatively low phylogenetic signal relative to other traits (assessed via Pagel’s λ on the raw data, Fig. 4c). Seed dry mass was the only trait with a substantial increase in accuracy using the phylogeny-only model (25% improvement; Supplementary Fig. 6), reflecting the fact that seed dry mass had the strongest phylogenetic signal of all traits (Fig. 4c), and also because this trait has a substantial amount of additional non-georeferenced data that was included in the phylogeny-only models (Fig. 1b). Wood density was the only trait with nearly identical predictive power whether or not environmental information was included, whereas all other traits exhibited significantly reduced accuracy when environmental information was excluded (Supplementary Fig. 6).Relationships in tree trait expressionUsing the resulting trait models, we imputed missing trait values for every tree with at least one georeferenced trait measurement. For all traits except seed dry mass, we used the random-forest models accounting for environmental and phylogenetic information; for seed dry mass, we used the phylogeny-only model to estimate expression due to its substantially higher data availability and out-of-fit accuracy. For tree height, stem diameter, crown height, crown width, and root depth, we used quantile random forest48 to estimate the upper 90th percentile value for each species in its given location, thereby minimizing ontogenetic variation across a tree’s lifetime (see Methods). We used the resulting trait data to explore the dominant drivers of trait variation using species-weighted principal component analysis, accounting for an unequal number of observations across species.When considering all traits simultaneously, the first two axes of the resulting principal components (PC) capture 41% of the variation in overall trait expression (Fig. 2a; Supplementary Fig. 10; Supplementary Table 5). The first trait axis correlates most strongly with leaf thickness, specific leaf area, and leaf nitrogen (PC loadings of L = 0.77, 0.74, and 0.73, respectively). By capturing key aspects of the leaf-economic spectrum14, these traits reflect various physiological controls on leaf-level resource processing, tissue turnover and photosynthetic rates49. Thick leaves with low specific leaf area (SLA) can help minimize desiccation, frost damage, and nutrient limitation, but at the cost of reduced photosynthetic potential due to primary investment in structural resistance50. Accordingly, leaf nitrogen—a crucial component of Rubisco for photosynthesis51—trades off strongly with leaf thickness. This first axis thus captures the core distinction between “acquisitive” (fast) and “conservative” (slow) life-history strategies across the plant kingdom7,52, reflecting an organismal-level trade-off between the high photosynthetic potential in optimal conditions versus abiotic tolerance in suboptimal conditions. Nevertheless, leaf density—which is related to SLA and is a key feature of the leaf-economic spectrum—loads relatively weakly on this first trait axis compared to other leaf traits (L = −0.28 for axis 1, vs 0.20 for axis 2; Supplementary Table 5), highlighting important aspects of leaf structure that are not captured by this dominant trait axis53.Fig. 2: The dominant trait axes and relationships.Shown are the first two principal component axes capturing trait relationships across the 18 functional traits. a All tree species (n = 30,146 observations), b angiosperms only (n = 24,658), and c gymnosperms only (n = 5498). In a the three variables that load most strongly on each axis are shown in dark black lines, with the remaining variables shown in light grey. These same six variables are highlighted in b and c illustrating how the same relationships extend to angiosperms and gymnosperms (see Supplementary Figs. 10–12 for the full PCAs with all traits visible, and Supplementary Table 5 for the PC loadings).Full size imageThe second trait axis correlates most strongly with maximum tree height (PC loading of L = 0.77), crown height, (L = 0.75), and crown diameter (L = 0.88), highlighting the overarching importance of competition for light and canopy position in forests7 (Fig. 2a; Supplementary Fig. 10; Supplementary Table 5). Large trees and large crowns are critical for light access and for maximizing light interception down through the canopy54. Nevertheless, tall trees with deep crowns also experience greater susceptibility to disturbance and mechanical damage, primarily due to wind and weight25. Because of the massive carbon and nutrient costs required to create large woody structures55,56, larger trees are less viable in nutrient-limited or colder climates57, and in exposed areas with high winds or extreme weather events58. This second axis thus reflects a fundamental biotic/abiotic trade-off related to overall tree size, which is largely orthogonal to leaf-level nutrient-use and photosynthetic capacity.Despite substantial differences in wood and leaf structures between angiosperms and gymnosperms (e.g. vessels vs. tracheids), the two main relationships hold within, as well as across, angiosperms and gymnosperms (Fig. 2b, c; Supplementary Figs. 11, 12). Indeed, angiosperms and gymnosperms are subject to the same physical, mechanical, and chemical processes that determine the ability to withstand various biotic and abiotic pressures59.Collectively, these two primary trait axes capture two dominant ecological trade-offs that underpin tree survival in any given environment: (1) the ability to maximize leaf photosynthetic activity, at the cost of increased risk of leaf desiccation, and (2) the ability to compete for space and maximize light interception, at the cost of increased susceptibility to mechanical damage. By capturing two aspects of conservative-acquisitive life-history strategies, these two relationships closely mirror those seen when considering herbaceous species alongside woody species5,17. However, in line with our expectations, these two axes capture only ~40% of the variation in trait space, versus nearly ~75% of variation when considering only six traits across the entire plant kingdom5. Here, the first seven PC axes are needed to account for 75% of the variation across all 18 traits (Supplementary Table 5). Thus, while this analysis supports the universality of these two primary PC axes, it also demonstrates that the majority of trait variation in trees is unexplained by these two dimensions. As such, quantifying the full dimensionality of trait space by exploring multidimensional trait clusters is needed to better capture the wide breadth of tree form and function.Environmental predictors of trait relationshipsTo examine how environmental variation shapes trait expression across the globe, we next quantified the relationships between environmental conditions and the dominant trait axes. Using Shapley values60, we partitioned the relative influence of each environmental variable on the PC trait axes, controlling for all other variables in the model (see Methods).In line with previous analysis across the plant kingdom61, temperature variables were the strongest drivers of trait relationships (Fig. 3, Supplementary Figs. 17, 18), with annual temperature having the strongest influence both on leaf-economic traits (PC axis 1, Fig. 3c) and on tree-size traits (PC axis 2, Fig. 3d). Leaves face increased frost risk and reduced photosynthetic potential in colder conditions, such that ecological selection should favour thick leaves with low SLA over thin leaves with high SLA and high nutrient-use49. Trees in warm environments are more likely to experience strong biotic interactions, which should increase evolutionary and ecological selection pressures over time62,63, favouring tall species with large crowns that have high competitive ability and efficient light acquisition strategies. Annual temperature thus predominantly reflects the transition from gymnosperm- to angiosperm-dominated ecosystems, with this inflection point occurring at ~15 °C for both axes, demonstrating strong environmental convergence between the dominant axes of trait variation.Fig. 3: The relationship between environmental variables and trait axes.a, b The relative influence of the environmental variables on the two dominant PC axes. The ten variables are sorted by overall variable importance in the models (see Methods). Yellow points are observations which have high values of that environmental variable; blue values are the lowest. Points to the right of zero indicate a positive influence on the PC axis; points to the left indicate a negative influence (see also Supplementary Figs. 17, 18). c–h The relationships between environmental variables and PC axis values for the three variables in a with the strongest influence. Values above zero show a positive influence on PC axis values; values less than zero indicate a negative influence.Full size imageBeyond annual temperature, each trait axis demonstrated different relationships with climate, soil, and topographic variables (Fig. 3a, b, Supplementary Figs. 17, 18). Percent sand content had the second-highest influence on the first trait axis (Fig. 3e), supporting patterns seen across the entire plant kingdom17. Sand content is a strong proxy for soil moisture and soil-available nutrients such as phosphorous, and is therefore closely tied to leaf photosynthetic rates64. In contrast to previous work, however, we find that soil characteristics have correspondingly little effect on the second axis of trait variation (Fig. 3b; Supplementary Fig. 18). Instead, precipitation was the second strongest driver of tree height and crown size (Fig. 3f), with large trees with large crowns becoming consistently more frequent with increasing precipitation. These results highlight that, despite the primary importance of temperature, the main climate stressors to trees (e.g. xylem cavitation and embolism, fire regimes, and leaf desiccation) typically arise via interactions between temperature, soil nutrients, and water availability.For both axes, elevation was the third strongest driver of trait values (Fig. 3g, h), highlighting a critical component of tree functional biogeography that extends beyond climate and soil. Yet the effects of elevation on trait expression differed somewhat across the two axes. For the first axis related to leaf-economic traits, there is little influence at low elevations, followed by a sharp transition at ~2000 m towards gymnosperm-dominated species with thick leaves, low SLA, and low leaf N. For the second trait axis related to tree size, elevation instead has a strong positive influence on tree height and crown size at low elevations, which becomes increasingly less influential past ~500 m. Such results partly reflect the transition from angiosperm to gymnosperm-dominated stands at higher elevations (blue vs. red points, Fig. 3g, h), and potentially the role of environmentally mediated intraspecific variation in traits such as tree height65,66.These results demonstrate close alignment of the dominant trait PC axes across biogeographic regions. Despite the orthogonality of these axes in trait species, environmental conditions place similar constraints on both trait axes, particularly at the environmental extremes (e.g. warm, moist, low elevation vs. cold, dry, high elevation), leading to convergence of the dominant trait axes across environmental gradients.Trait clusters at the global scaleTo better explore the multidimensional nature of trait relationships that are not fully covered by the dominant two axes, we subsequently identified groups of traits that form tightly coupled clusters and which reflect distinct aspects of tree form and function.Our results show that these 18 traits can be grouped into eight trait clusters, each of which reflects a unique aspect of morphology, physiology, or ecology (Fig. 4a, Supplementary Fig. 23). The largest trait cluster (Fig. 4a, pink cluster) demonstrates wood/leaf integration of moisture regulation and photosynthetic activity via the inclusion of leaf area, stem conduit diameter, stomatal conductance, and leaf Vcmax (the maximum rate of carboxylation). Distinct from this cluster are the three traits loading most strongly on PC axis 1 (SLA, leaf thickness and leaf N; Fig. 4a, yellow), highlighting complementary aspects of the leaf-economic spectrum indicative of acquisitive vs. conservative resource use15. The role of leaf K and P in leaf nutrient economies are well established7,67, and yet these traits form a distinct cluster from the other leaf-economic traits (Fig. 4a, light blue) due to their relatively high correlation with tree height and crown size, particularly for leaf K, which loads almost equally on both trait axes (Fig. 4b, Supplementary Table 5).Fig. 4: Trait correlations and functional clusters.a Trait clusters with high average intra-group correlation. The upper triangle gives the species-weighted correlations incorporating intraspecific variation. The lower triangle gives the corresponding correlations among phylogenetic independent contrasts, which adjusts for pseudo-replication due to the non-independence of closely related species. The size of the circle denotes the relative strength of the correlation, with solid circles denoting positive correlations and open circles denoting negative correlations (see Supplementary Fig. 19 for the numeric values). b PC loadings for each trait and each of the first two principal component axes, illustrating which functional trait clusters align most strongly with the dominant axes of trait variation (see Supplementary Table 5 for the full set of PC loadings). c The species-level phylogenetic signal of each trait (Pagel’s λ), calculated using only the raw trait values.Full size imageTree height and crown size form their own distinct cluster (Fig. 4a, dark green), further supporting the inference that these traits reflect key aspects of tree form and function independent of the leaf-economic spectrum. Yet leaf area, despite being part of the cluster reflecting moisture regulation and photosynthetic activity, loads almost equally on PC axes 1 and 2 (Fig. 4b, Supplementary Table 5), highlighting that it serves as an intermediary between the two key aspects of tree size and leaf economics. It is a critical driver of moisture regulation and photosynthetic capacity, while also playing an important role in the light acquisition, leaf-turnover time, and competitive ability54,68.There are two additional two-trait clusters, both of which load relatively poorly on the two primary PC axes: (1) stem diameter and bark thickness (Fig. 4, dark blue), and (2) wood and leaf density (Fig. 4, light green). Bark thickness increases with tree size not only as a result of bark accumulation as trees age, but also due to the functional/metabolic needs of the plant69,70. From an ecological perspective, thick bark can be critical for defense against fire and pest damage (mainly a thick outer bark region), for storage and photosynthate transportation needs (mainly a thick inner bark region)71,72. Yet such relationships are strongly ecosystem-dependent, with tree size emerging as the dominant driver at the global scale70. In contrast, wood density and leaf density are strongly linked to slow/fast life-history strategies, where denser plant parts reduce growth rate and water transport6,15 but protect against pest damage, desiccation, and mechanical breakage6,50,56. As such, leaf density captures fundamentally unique aspects of leaf form and function relative to other leaf traits such as SLA53 (Fig. 4b, Supplementary Table 5), and our results support the inference that these translate into fundamentally different ecological strategies73. Collectively, these two-trait clusters each demonstrate unique and complementary mechanisms that insulate trees against various disturbances and extreme weather events, but at the cost of reduced growth, competitive ability, and productivity under optimal conditions (see Supplementary Notes).Lastly, two traits each comprise their own unique cluster: root depth and seed dry mass (Fig. 4a, purple and orange, respectively). Root growth is subject to a range of belowground processes (e.g. root herbivory, depth to bedrock), and our results confirm previous work demonstrating a clear disconnect between aboveground and belowground traits23,74,75. Root depth accordingly has a relatively weak phylogenetic signal (λ = 0.44, Fig. 4c) but a strong environmental signal (Supplementary Figs. 6, 9), reflecting distinct belowground constraints on trait expression23. In contrast, seed dry mass exhibits the strongest phylogenetic signal (λ = 0.98, Fig. 4c) and weakest environmental signal of any trait (Supplementary Figs. 6, 9), and it accordingly was the only trait where the phylogeny-only model performed substantially better (Supplementary Fig. 6). In line with previous work, seed dry mass has moderate correlations with various other traits underpinning leaf economics and tree size5,28 (e.g. ρ = 0.28, −0.22, and 0.22 for tree height, leaf K, and leaf density, using the raw data), yet it exhibits relatively weak correlation with most other traits, placing it in a distinct functional cluster. Reproductive traits are subject to unique evolutionary pressures26, indicative of different seed dispersal vectors (wind, water, animals) and various ecological stressors that uniquely affect seed viability and germination26. The emergence of root depth and seed dry mass as solo functional clusters thus supports the previous inference that belowground traits74 and reproductive traits26 reflect distinct aspects of tree form and function not fully captured by leaf or wood trait spectrums. More

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    Animal behavior is central in shaping the realized diel light niche

    Benhamou, S. Of scales and stationarity in animal movements. Ecol. Lett. 17, 261–272 (2014).PubMed 
    Article 

    Google Scholar 
    Owen-Smith, N. Effects of temporal variability in resources on foraging behaviour. In Resource Ecology (eds. Prins, H. H. T. & Van Langevelde, F.) 159–181 (Springer Netherlands, 2008).Hutchinson, G. E. The multivariate niche. Cold Spring Harb. Symp. Quant. Biol. 22, 415–421 (1957).Article 

    Google Scholar 
    Kearney, M. Habitat, environment and niche: what are we modelling? Oikos 115, 186–191 (2006).Article 

    Google Scholar 
    Tauber, E., Last, K. S., Olive, P. J. W. & Kyriacou, C. P. Clock gene evolution and functional divergence. J. Biol. Rhythms 19, 445–458 (2004).CAS 
    PubMed 
    Article 

    Google Scholar 
    Pilorz, V., Helfrich-Förster, C. & Oster, H. The role of the circadian clock system in physiology. Pflug. Arch. – Eur. J. Physiol. 470, 227–239 (2018).CAS 
    Article 

    Google Scholar 
    Levy, O., Dayan, T., Porter, W. P. & Kronfeld-Schor, N. Time and ecological resilience: can diurnal animals compensate for climate change by shifting to nocturnal activity? Ecol. Monogr. 89, e01334 (2019).Article 

    Google Scholar 
    Gaynor, K. M., Hojnowski, C. E., Carter, N. H. & Brashares, J. S. The influence of human disturbance on wildlife nocturnality. Science 360, 1232–1235 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    Cox, D. T. C., Gardner, A. S. & Gaston, K. J. Diel niche variation in mammals associated with expanded trait space. Nat. Commun. 12, 1753 (2021).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Kronfeld-Schor, N. & Dayan, T. Partitioning of time as an ecological resource. Annu. Rev. Ecol. Evol. Syst. 34, 153–181 (2003).Article 

    Google Scholar 
    Kronfeld‐Schor, N. et al. On the use of the time axis for ecological separation: diel rhythms as an evolutionary constraint. Am. Nat. 158, 451–457 (2001).PubMed 
    Article 

    Google Scholar 
    Austin, R. W. & Petzold, T. J. Spectral dependence of the diffuse attenuation coefficient of light in ocean waters. OE OPEGAR 25, 253471 (1986).Article 

    Google Scholar 
    Bandara, K., Varpe, Ø., Wijewardene, L., Tverberg, V. & Eiane, K. Two hundred years of zooplankton vertical migration research. Biol. Rev. 96, 1547–1589 (2021).PubMed 
    Article 

    Google Scholar 
    Brierley, A. S. Diel vertical migration. Curr. Biol. 24, R1074–R1076 (2014).CAS 
    PubMed 
    Article 

    Google Scholar 
    Hays, G. C. A review of the adaptive significance and ecosystem consequences of zooplankton diel vertical migrations. In Migrations and Dispersal of Marine Organisms 163–170 (Springer, 2003).Aumont, O., Maury, O., Lefort, S. & Bopp, L. Evaluating the potential impacts of the diurnal vertical migration by marine organisms on marine biogeochemistry. Glob. Biogeochem. Cycles https://doi.org/10.1029/2018GB005886 (2018).Article 

    Google Scholar 
    Tarrant, A. M., McNamara-Bordewick, N., Blanco-Bercial, L., Miccoli, A. & Maas, A. E. Diel metabolic patterns in a migratory oceanic copepod. J. Exp. Mar. Biol. Ecol. 545, 151643 (2021).Article 

    Google Scholar 
    Cohen, J. H. & Forward, Jr. R. B. Zooplankton diel vertical migration—a review of proximate control. In Oceanography and Marine Biology (eds Gibson, R. N., Atkinson, R. J. A. & Gordon, J. D. M.) 89–122 (CRC Press, 2009).Benoit-Bird, K. J., Au, W. W. L. & Wisdoma, D. W. Nocturnal light and lunar cycle effects on diel migration of micronekton. Limnol. Oceanogr. 54, 1789–1800 (2009).Article 

    Google Scholar 
    Last, K. S., Hobbs, L., Berge, J., Brierley, A. S. & Cottier, F. Moonlight drives ocean-scale mass vertical migration of zooplankton during the Arctic Winter. Curr. Biol. 26, 244–251 (2016).CAS 
    PubMed 
    Article 

    Google Scholar 
    Omand, M. M., Steinberg, D. K. & Stamieszkin, K. Cloud shadows drive vertical migrations of deep-dwelling marine life. PNAS 118, e2022977118 (2021).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Strömberg, J.-O., Spicer, J. I., Liljebladh, B. & Thomasson, M. A. Northern krill, Meganyctiphanes norvegica, come up to see the last eclipse of the millennium? J. Mar. Biol. Assoc. UK 82, 919–920 (2002).Article 

    Google Scholar 
    Ludvigsen, M. et al. Use of an Autonomous Surface Vehicle reveals small-scale diel vertical migrations of zooplankton and susceptibility to light pollution under low solar irradiance. Sci. Adv. 4, eaap9887 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Häfker, N. S. et al. Circadian clock involvement in zooplankton diel vertical migration. Curr. Biol. 27, 2194–2201 (2017).PubMed 
    Article 
    CAS 

    Google Scholar 
    Chen, C. et al. Drosophila Ionotropic Receptor 25a mediates circadian clock resetting by temperature. Nature 527, 516–520 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    Epifanio, C. E. & Cohen, J. H. Behavioral adaptations in larvae of brachyuran crabs: a review. J. Exp. Mar. Biol. Ecol. 482, 85–105 (2016).Article 

    Google Scholar 
    Sorek, M. et al. Setting the pace: host rhythmic behaviour and gene expression patterns in the facultatively symbiotic cnidarian Aiptasia are determined largely by Symbiodinium. Microbiome 6, 83 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Hobbs, L., Banas, N. S., Cottier, F. R., Berge, J. & Daase, M. Eat or sleep: availability of winter prey explains mid-winter and spring activity in an Arctic Calanus population. Front. Mar. Sci. 7, 541564 (2020).Article 

    Google Scholar 
    Urmy, S. S., Horne, J. K. & Barbee, D. H. Measuring the vertical distributional variability of pelagic fauna in Monterey Bay. ICES J. Mar. Sci. 69, 184–196 (2012).Article 

    Google Scholar 
    Berge, J. et al. Arctic complexity: a case study on diel vertical migration of zooplankton. J. Plankton Res. 36, 1279–1297 (2014).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Berge, J. et al. In the dark: A review of ecosystem processes during the Arctic polar night. Prog. Oceanogr. https://doi.org/10.1016/j.pocean.2015.08.005 (2015).Article 

    Google Scholar 
    Pavlov, A. K. et al. The underwater light climate in Kongsfjorden and Its ecological implications. In The Ecosystem of Kongsfjorden, Svalbard (eds Hop, H. & Wiencke, C.) 137–170 (Springer International Publishing, 2019).Cohen, J. H. et al. Is ambient light during the high arctic polar night sufficient to act as a visual cue for Zooplankton? PLoS ONE 10, e0126247 (2015).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Veedin Rajan, V. B. et al. Seasonal variation in UVA light drives hormonal and behavioural changes in a marine annelid via a ciliary opsin. Nat. Ecol. Evol. 5, 204–218 (2021).PubMed 
    Article 

    Google Scholar 
    Vinayak, P. et al. Exquisite light sensitivity of Drosophila melanogaster cryptochrome. PLoS Genet. 9, e1003615 (2013).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Verasztó, C. et al. Ciliary and rhabdomeric photoreceptor-cell circuits form a spectral depth gauge in marine zooplankton. eLife 7, e36440 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Hobbs, L. et al. A marine zooplankton community vertically structured by light across diel to interannual timescales. Biol. Lett. 17, 20200810 (2021).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Daase, M., Eiane, K., Aksnes, D. L. & Vogedes, D. Vertical distribution of Calanus spp. and Metridia longa at four Arctic locations. Mar. Biol. Res. 4, 193–207 (2008).Article 

    Google Scholar 
    Irigoien, X., Conway, D. V. P. & Harris, R. P. Flexible diel vertical migration behaviour of zooplankton in the Irish Sea. Mar. Ecol. Prog. Ser. 267, 85–97 (2004).Article 

    Google Scholar 
    Frost, B. W. & Bollens, S. M. Variability of diel vertical migration in the marine planktonic copepod Pseudocalanus newmani in relation to its predators. Can. J. Fish. Aquat. Sci. 49, 1137–1141 (1992).Article 

    Google Scholar 
    Tarling, G. A., Jarvis, T., Emsley, S. M. & Matthews, J. B. L. Midnight sinking behaviour in Calanus finmarchicus: a response to satiation or krill predation? Mar. Ecol. Prog. Ser. 240, 183–194 (2002).Article 

    Google Scholar 
    Hays, G. C., Proctor, C. A., John, A. W. G. & Warner, A. J. Interspecific differences in the diel vertical migration of marine copepods: the implications of size, color, and morphology. Limnol. Oceanogr. 39, 1621–1629 (1994).Article 

    Google Scholar 
    Gastauer, S., Nickels, C. F. & Ohman, M. D. Body size- and season-dependent diel vertical migration of mesozooplankton resolved acoustically in the San Diego Trough. Limnol. Oceanogr. 67, 300–313 (2021).Article 

    Google Scholar 
    Hardy, A. C. & Bainbridge, R. Experimental observations on the vertical migrations of plankton animals. J. Mar. Biol. Assoc. UK 33, 409–448 (1954).Article 

    Google Scholar 
    Musilova, Z. et al. Vision using multiple distinct rod opsins in deep-sea fishes. Science 364, 588–592 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Gornik, S. G. et al. Photoreceptor diversification accompanies the evolution of Anthozoa. Mol. Biol. Evol. 38, 1744–1760 (2021).CAS 
    PubMed 
    Article 

    Google Scholar 
    Cohen, J. H. et al. Photophysiological cycles in Arctic krill are entrained by weak midday twilight during the Polar Night. PLoS Biol. 19, e3001413 (2021).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Kopperud, K. L. & Grace, M. S. Circadian rhythms of retinal sensitivity in the Atlantic tarpon, Megalops atlanticus. Bull. Mar. Sci. https://doi.org/10.5343/bms.2016.1045 (2017).Article 

    Google Scholar 
    Ohguro, C., Moriyama, Y. & Tomioka, K. The compound eye possesses a self-sustaining Circadian Oscillator in the Cricket Gryllus bimaculatus. Zool. Sci. 38, 82–89 (2020).Article 

    Google Scholar 
    Brodrick, E. A., How, M. J. & Hemmi, J. M. Fiddler crab electroretinograms reveal vast circadian shifts in visual sensitivity and temporal summation in dim light. J. Exp. Biol. jeb.243693, https://doi.org/10.1242/jeb.243693 (2022).Kaartvedt, S., Røstad, A., Christiansen, S. & Klevjer, T. A. Diel vertical migration and individual behavior of nekton beyond the ocean’s twilight zone. Deep Sea Res. Part I: Oceanogr. Res. Pap. 103280, https://doi.org/10.1016/j.dsr.2020.103280 (2020).Flôres, D. E. F. L., Jannetti, M. G., Valentinuzzi, V. S. & Oda, G. A. Entrainment of circadian rhythms to irregular light/dark cycles: a subterranean perspective. Sci. Rep. 6, 34264 (2016).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Hays, G. C., Kennedy, H. & Frost, B. W. Individual variability in diel vertical migration of a marine copepod: why some individuals remain at depth when others migrate. Limnol. Oceanogr. 46, 2050–2054 (2001).Article 

    Google Scholar 
    Cohen, J. H. & Forward, R. B. Jr. Photobehavior as an inducible defense in the marine copepod Calanopia americana. Limnol. Oceanogr. 50, 1269–1277 (2005).Article 

    Google Scholar 
    Kvile, K. Ø., Altin, D., Thommesen, L. & Titelman, J. Predation risk alters life history strategies in an oceanic copepod. Ecology 102, e03214 (2021).PubMed 
    Article 

    Google Scholar 
    Spaak, P. & Ringelberg, J. Differential behaviour and shifts in genotype composition during the beginning of a seasonal period of diel vertical migration. Hydrobiologia 360, 177–185 (1997).Article 

    Google Scholar 
    Buskey, E. J. & Swift, E. Behavioral responses of oceanic zooplankton to simulated bioluminescence. Biol. Bull. 168, 263–275 (1985).Article 

    Google Scholar 
    Berndt, A. et al. A novel photoreaction mechanism for the circadian blue light photoreceptor Drosophila cryptochrome. J. Biol. Chem. 282, 13011–13021 (2007).CAS 
    PubMed 
    Article 

    Google Scholar 
    Franz-Badur, S. et al. Structural changes within the bifunctional cryptochrome/photolyase CraCRY upon blue light excitation. Sci. Rep. 9, 9896 (2019).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Biscontin, A. et al. Functional characterization of the circadian clock in the Antarctic krill, Euphausia superba. Sci. Rep. 7, 17742 (2017).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Piccolin, F. et al. Photoperiodic modulation of circadian functions in Antarctic krill Euphausia superba Dana, 1850 (Euphausiacea). J. Crustacean Biol. 38, 707–715 (2018).
    Google Scholar 
    Piccolin, F., Pitzschler, L., Biscontin, A., Kawaguchi, S. & Meyer, B. Circadian regulation of diel vertical migration (DVM) and metabolism in Antarctic krill Euphausia superba. Sci. Rep. 10, 16796 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Häfker, N. S., Teschke, M., Hüppe, L. & Meyer, B. Calanus finmarchicus diel and seasonal rhythmicity in relation to endogenous timing under extreme polar photoperiods. Mar. Ecol. Prog. Ser. 603, 79–92 (2018).Article 
    CAS 

    Google Scholar 
    Häfker, N. S. et al. Calanus finmarchicus seasonal cycle and diapause in relation to gene expression, physiology, and endogenous clocks. Limnol. Oceanogr. 63, 2815–2838 (2018).Article 

    Google Scholar 
    Hüppe, L. et al. Evidence for oscillating circadian clock genes in the copepod Calanus finmarchicus during the summer solstice in the high Arctic. Biol. Lett. 16, 20200257 (2020).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Dmitrenko, I. A. et al. Sea-ice and water dynamics and moonlight impact the acoustic backscatter diurnal signal over the eastern Beaufort Sea continental slope. Ocean Sci. 16, 1261–1283 (2020).CAS 
    Article 

    Google Scholar 
    Hobbs, L., Cottier, F. R., Last, K. S. & Berge, J. Pan-Arctic diel vertical migration during the polar night. Mar. Ecol. Prog. Ser. 605, 61–72 (2018).Article 

    Google Scholar 
    Chittka, L., Stelzer, R. J. & Stanewsky, R. Daily changes in ultraviolet light levels can synchronize the circadian clock of Bumblebees (Bombus terrestris). Chronobiol. Int. 30, 434–442 (2013).PubMed 
    Article 

    Google Scholar 
    Pauers, M. J., Kuchenbecker, J. A., Neitz, M. & Neitz, J. Changes in the colour of light cue circadian activity. Anim. Behav. 83, 1143–1151 (2012).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Mouland, J. W., Martial, F., Watson, A., Lucas, R. J. & Brown, T. M. Cones support alignment to an inconsistent world by suppressing mouse circadian responses to the blue colors associated with twilight. Curr. Biol. 29, 4260–4267.e4 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Walmsley, L. et al. Colour as a signal for entraining the mammalian circadian clock. PLoS Biol. 13, e1002127 (2015).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Ashley, N. T., Schwabl, I., Goymann, W. & Buck, C. L. Keeping time under the midnight sun: behavioral and plasma melatonin profiles of free-living lapland longspurs (Calcarius lapponicus) during the Arctic Summer. J. Exp. Zool. Part A: Ecol. Genet. Physiol. 319, 10–22 (2013).CAS 
    Article 

    Google Scholar 
    Nordtug, T. & Melø, T. B. Diurnal variations in natural light conditions at summer time in arctic and subarctic areas in relation to light detection in insects. Ecography 11, 202–209 (1988).Article 

    Google Scholar 
    Cohen, J. H. & Forward, R. B. Jr Diel vertical migration of the marine copepod Calanopia americana. II. Proximate role of exogenous light cues and endogenous rhythms. Mar. Biol. 147, 399–410 (2005).Article 

    Google Scholar 
    Maas, A. E., Blanco-Bercial, L., Lo, A., Tarrant, A. M. & Timmins-Schiffman, E. Variations in copepod proteome and respiration rate in association with diel vertical migration and circadian cycle. Biol. Bull. 000–000, https://doi.org/10.1086/699219 (2018).Berge, J. et al. Diel vertical migration of Arctic zooplankton during the polar night. Biol. Lett. 5, 69–72 (2009).PubMed 
    Article 

    Google Scholar 
    Dale, T. & Kaartvedt, S. Diel patterns in stage-specific vertical migration of Calanus finmarchicus in habitats with midnight sun. ICES J. Mar. Sci. 57, 1800–1818 (2000).Article 

    Google Scholar 
    Hut, R. A., van Oort, B. E. H. & Daan, S. Natural entrainment without dawn and dusk: the case of the European Ground Squirrel (Spermophilus citellus). J. Biol. Rhythms 14, 290–299 (1999).CAS 
    PubMed 
    Article 

    Google Scholar 
    Williams, C. T., Barnes, B. M., Yan, L. & Buck, C. L. Entraining to the polar day: circadian rhythms in arctic ground squirrels. J. Exp. Biol. 220, 3095–3102 (2017).PubMed 
    Article 

    Google Scholar 
    Daan, S. et al. Lab mice in the field: unorthodox daily activity and effects of a dysfunctional circadian clock allele. J. Biol. Rhythms 26, 118–129 (2011).PubMed 
    Article 

    Google Scholar 
    Gattermann, R. et al. Golden hamsters are nocturnal in captivity but diurnal in nature. Biol. Lett. 4, 253–255 (2008).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Green, E. W. et al. Drosophila circadian rhythms in seminatural environments: Summer afternoon component is not an artifact and requires TrpA1 channels. PNAS 112, 8702–8707 (2015).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Nagy, D. et al. A semi-natural approach for studying seasonal diapause in Drosophila melanogaster reveals robust photoperiodicity. J. Biol. Rhythms 33, 117–125 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    Prabhakaran, P. M., De, J. & Sheeba, V. Natural conditions override differences in emergence rhythm among closely related Drosophilids. PLoS ONE 8, e83048 (2013).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Ruf, F. et al. Natural Zeitgebers under temperate conditions cannot compensate for the loss of a functional circadian clock in timing of a vital behavior in Drosophila. J. Biol. Rhythms 0748730421998112, https://doi.org/10.1177/0748730421998112 (2021).Dollish, H. K., Kaladchibachi, S., Negelspach, D. C. & Fernandez, F.-X. The Drosophila circadian phase response curve to light: conservation across seasonally relevant photoperiods and anchorage to sunset. Physiol. Behav. 245, 113691 (2022).CAS 
    PubMed 
    Article 

    Google Scholar 
    Shaw, B., Fountain, M. & Wijnen, H. Control of daily locomotor activity patterns in Drosophila suzukii by the circadian clock, light, temperature and social interactions. J. Biol. Rhythms 34, 463–481 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    Chiesa, J. J., Aguzzi, J., García, J. A., Sardà, F. & de la Iglesia, H. O. Light intensity determines temporal niche switching of behavioral activity in deep-water Nephrops norvegicus (Crustacea: Decapoda). J. Biol. Rhythms 25, 277–287 (2010).PubMed 
    Article 

    Google Scholar 
    DeCoursey, P. J. Light-sampling behavior in photoentrainment of a rodent circadian rhythm. J. Comp. Physiol. 159, 161–169 (1986).CAS 
    Article 

    Google Scholar 
    Heard, E. Molecular biologists: let’s reconnect with nature. Nature 601, 9 (2022).CAS 
    PubMed 
    Article 

    Google Scholar 
    Deines, K. L. Backscatter estimation using Broadband acoustic Doppler current profilers. In Proc. IEEE Sixth Working Conference on Current Measurement 249–253 (1999).Darnis, G. et al. From polar night to midnight sun: diel vertical migration, metabolism and biogeochemical role of zooplankton in a high Arctic fjord (Kongsfjorden, Svalbard). Limnol. Oceanogr. 62, 1586–1605 (2017).CAS 
    Article 

    Google Scholar 
    Cottier, F. R., Tarling, G. A., Wold, A. & Falk-Petersen, S. Unsynchronized and synchronized vertical migration of zooplankton in a high arctic fjord. Limnol. Oceanogr. 51, 2586–2599 (2006).Article 

    Google Scholar 
    Johnsen, G. et al. All-sky camera system providing high temporal resolution annual time series of irradiance in the Arctic. Appl. Opt. 60, 6456–6468 (2021).PubMed 
    Article 

    Google Scholar 
    Pan, X. & Zimmerman, R. C. Modeling the vertical distributions of downwelling plane irradiance and diffuse attenuation coefficient in optically deep waters. J. Geophys. Res.: Oceans 115, C08016 (2010).
    Google Scholar 
    Hersbach, H. et al. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 146, 1999–2049 (2020).Article 

    Google Scholar 
    Tidau, S. et al. Marine artificial light at night: An empirical and technical guide. Methods Ecol. Evol. 12, 1588–1601 (2021).Article 

    Google Scholar 
    Mobley, C. D. Light and Water: Radiative Transfer in Natural Waters (Academic Press Inc, 1994).Kostakis, I. et al. Development of a bio-optical model for the Barents Sea to quantitatively link glider and satellite observations. Philos. Trans. R. Soc. A: Math., Phys. Eng. Sci. 378, 20190367 (2020).CAS 
    Article 

    Google Scholar 
    Buskey, E. J., Baker, K. S., Smith, R. C. & Swift, E. Photosensitivity of the oceanic copepods Pleuromamma gracilis and Pleuromamma xiphias and its relationship to light penetration and daytime depth distribution. Mar. Ecol. Prog. Ser. 55, 207–216 (1989).Article 

    Google Scholar  More

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    A catastrophic collapse for the ‘flying banana’ of the Kalahari

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    Spotted lanternfly predicted to establish in California by 2033 without preventative management

    Model structureWe used the PoPS (Pest or Pathogen Spread) Forecasting System11 version 2.0.0 to simulate the spread of SLF and calibrated the model (Fig. 6) using Approximate Bayesian Computation (ABC) with sequential Markov chain and a multivariate normal perturbation kernel18,19. We simulated the reproduction and dispersal of SLF groups (at the grid cell level) rather than individuals, as exact measures of SLF populations are not the goal of surveys conducted by USDA and state departments of agriculture. Reproduction was simulated as a Poisson process with mean β that is modified by local conditions. For example, if we have 5 SLF groups in a cell, a β value of 2.2, and a temperature coefficient of 0.7, our modified β value becomes 1.54 and we draw five numbers from a Poisson distribution with a λ value of 1.54. β and dispersal parameters were calibrated to fit the observed patterns of spread. For this application of PoPS, we replaced the long-distance kernel (α2) with a network dispersal kernel based on railroads, along which SLF and tree of heaven are commonly observed7. For each SLF group dispersing, if a railroad is in the grid cell with SLF, we used a Bernoulli distribution with mean of γ (probability of natural dispersal) to determine if an SLF group dispersed via the natural Cauchy kernel with scale (α) or along the rail network. This network dispersal kernel accounts for dispersal along railways if SLF is present in a cell containing a rail line. The network dispersal kernel added three new parameters to the PoPS model: a network file that contained the nodes and edges, minimum distance that each railcar travels, and the maximum distance that each railcar travels. Unlike typical network models, which simulate transport simply between nodes, our approach allows for SLF to disembark a railcar at any point along an edge, more closely mimicking their actual behavior. This network therefore captures the main pathway of SLF long-distance dispersal, i.e., along railways.Fig. 6: Model structure for spotted lanternfly (SLF, Lycorma delicatula).Unused modules in the PoPS model are gray in the equation. a The number of pests that disperse from a single host under optimal environmental conditions (β) is modified by the number of currently infested hosts (I) and environmental conditions in a location (i) at a particular time (t); environmental conditions include seasonality (X) and temperature (T) (see supplementary Fig. 3 for details on temperature). Dispersal is a function of gamma (γ), which is the probability of short-distance dispersal (alpha-1, α1) or long-distance via the rail network (N (dmin, dmax)). For the natural-distance Cauchy kernel, the direction is selected using 0-359 with 0 representing North. For the network kernel, the direction along the rail is selected randomly, and then travel continues in that direction until the drawn distance is reached. Once SLF has landed in a new location, its establishment depends on environmental conditions (X, T) and the availability of suitable hosts (number of susceptible hosts [S] divided by total number of potential hosts [N]). b We used a custom host map for tree of heaven (Ailanthus altissima) to determine the locations of susceptible hosts. The number of newly infested hosts (ψ) is predicted for each cell across the contiguous US.Full size imageSpotted lanternfly model calibrationWe used 2015–2019 data (over 300,000 total observations including both positive and negative surveys) provided by the USDA APHIS and the state Departments of Agriculture of Pennsylvania, New Jersey, Delaware, Maryland, Virginia, and West Virginia to calibrate model parameters (β, α1, γ, dmin, dmax). The calibration process starts by drawing a set of parameters from a uniform distribution. Simulated results for each model run are then compared to observed data within the year they were collected, and accuracy, precision, recall, and specificity are calculated for the simulation period. If each of these statistics is above 65% the parameter set is kept. This process repeats until 10,000 parameter sets are kept; then, the next generation of the ABC process begins: the mean of each accuracy statistic becomes the new accuracy threshold, and parameters are drawn from a multivariate normal distribution based on the means and covariance matrix of the first 10,000 kept parameters. This process repeats for a total of seven generations. Compared to the 2020 and 2021 observation data (over 100,000 total observations including both positive and negative surveys), the model performed well, with an accuracy of 84.4%, precision of 79.7%, recall of 91.55%, and specificity of 77.6%. In contrast, a model run using PoPS’ previous long-distance kernel (α2) instead of the network dispersal kernel had an accuracy of 76.5%, precision of 68.1%, recall of 92.68%, and specificity of 57.2%.We applied the calibrated parameters and their uncertainties (Fig. 7) to forecast the future spread of SLF, using the status of the infestation as of January 1, 2020 as a starting point and data for temperature and the distribution of SLF’s presumed primary host (tree of heaven, Ailanthus altissima) for the contiguous US at a spatial resolution of 5 km.Fig. 7: Parameter distributions.a Reproductive rate (β), b natural dispersal distance (α1), c percent natural dispersal (γ), d minimum distance (dmin), e maximum distance (dmax).Full size imageWeather dataOverwinter survival of SLF egg masses, and therefore spread, is sensitive to temperature (see ref. 2). To run a spread model in PoPS, all raw temperature values are first converted to indices ranging 0–1 to describe their impact on a species’ ability to survive and reproduce. We converted daily Daymet20 temperature into a monthly coefficient ranging 0–1 (Supplementary Fig. 1) and then rescaled from 1 to 5 km by averaging 1-km pixel values. We used weather data 1980–2019 and randomly drew from those historical data to simulate future weather conditions in our simulations, to account for uncertainty in future weather conditions.Tree of heaven distribution mappingSLF is known to feed on >70 species of mainly woody plants7, but tree of heaven is commonly viewed as necessary, or at least highly important, for SLF spread. Young nymphs are host generalists, but older nymphs and adults strongly prefer tree of heaven (in Korea21; in Pennsylvania, US22), and experiments in captivity23 and in situ9 have shown that adult survivorship is higher on the tree of heaven and grapevine than other host plants, likely due to the presence and proportion of sugar compounds important for SLF survival23. Secondary compounds found in tree of heaven also make adult SLF more unpalatable to avian predators24, and researchers have hypothesized that these protective compounds may be passed on to eggs21. For these reasons, tree of heaven is widely considered the primary host for SLF and linked to SLF spread1,25.We, therefore, used tree of heaven as the host in our spread forecast. We estimated the geographic range of tree of heaven using the Maximum Entropy (MaxEnt) model26,27. We chose to use niche modeling because tree of heaven has been in the US for over 200 years and is well past the early stage of invasion at which niche models perform poorly; instead, tree of heaven is well into the intermediate to equilibrium stage of invasion, when niche models perform well28. We obtained 19,282 presences for tree of heaven in the US from BIEN29,30 and EDDmaps31 and selected the most important variables from an initial MaxEnt model of all 19 WorldClim bioclimatic variables32. Our final climate variables were mean annual temperature, precipitation of the coldest quarter, and precipitation of the driest quarter. Given that tree of heaven is non-native and invasive in the US, prefers open and disturbed habitat, and is commonly found along roadsides and in urban landscapes33, we also included distance to major roads and railroads as an additional variable in our model, to account for the presence of disturbed habitat as well as approximate urbanization and anthropogenic degradation. For each 1-km cell in the extent, we calculated distance to the nearest road and nearest railroad using the US Census Bureau’s TIGER data set of primary roads and railroads34. We used our final MaxEnt model to generate the probability of the presence of tree of heaven for each 1-km cell, then reset all cells with a probability ≤0.2 to a value of 0 to minimize overprediction of the tree of heaven locations (because cells ≤0.2 contained less than 1% of the presences used to build the model). We rescaled the remaining probability values 0–1. We used 10% of the tree of heaven presence data to validate the model, which performed well: 95% of the validation data set locations had a probability of presence greater than 65%. We then rescaled the 1-km MaxEnt output to 5 km using the mean value of our 1-km cells, in order to reduce computational time.Forecasting spotted lanternflyWe used the Daymet temperature data and distribution of tree of heaven to simulate SLF spread with PoPS, assuming no further efforts to contain or eradicate either tree of heaven or SLF. We ran the spread simulation 10,000 times from 2020 to 2050 for the contiguous US. After running all 10,000 iterations, we created a probability of occurrence for each cell for each year by dividing the number of simulations in which a cell was simulated as being infested in that year by 10,000 (the total number of simulations). This gave us a probability of occurrence per year. We downscaled our probability of occurrence per year from 5 km to 1 km and set the probability to 0 in 1-km pixels with no tree of heaven occurrence.Data for mapping and comparisonWe compared our probability of occurrence map in 2050 to the SLF suitability map created by Wakie et al.1 using niche modeling to see how well the two modeling approaches would agree if SLF were allowed to spread unmanaged (Fig. 5). Wakie et al.1 categorized pixels below 8.359% as unsuitable, between 8.359% and 26.89% as low risk, between 26.89% and 51.99% as medium risk, and above 51.99% as high risk. To facilitate comparison, we used this same schema to categorize pixels as low, medium, or high probability of spread.We converted the yearly raster probability maps to county-level probabilities in order to examine the yearly risk to crops in counties. We performed this conversion using two methods: (1) the highest probability of occurrence in the county (Supplementary Movie 2) and (2) the mean probability of occurrence in the county (Fig. 1 and Supplementary Movie 1). The first method provides a simple, non-statistical estimate of the probability of SLF presence by assigning the county the value of the highest cell-level probability; the second accounts for all of the probabilities of the cells in the county and typically results in a higher county-level probability. We used USDA county-level production data10 for grapes, almonds, apples, walnuts, cherries, hops, peaches, plums, and apricots to determine the amount of production at risk each year (Fig. 2).Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

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    Indigenous knowledge reveals history of fire-prone California forest

    Controlled fires can be used to reduce the risk of wildfires.Credit: David Hoffmann/Alamy

    Indigenous oral accounts have helped scientists to reconstruct a 3,000-year history of a large fire-prone forest in California. The results suggest that parts of the forest are denser than ever before, and are at risk of severe wildfires1. The research is part of a growing effort to combine Indigenous knowledge with other scientific data to improve understanding of ecosystem histories.Wildfires are a substantial threat to Californian forests. Clarke Knight, a palaeo-ecosystem scientist at the US Geological Survey in Menlo Park, California, and her colleagues wanted to understand how Indigenous communities helped shape the forest by managing this risk in the state’s lush western Klamath Mountains. Specifically, they studied Indigenous peoples’ use of cultural burning — small, controlled fires that keep biomass low and reduce the risk of more widespread burning. The results are published in the Proceedings of the National Academy of Science.“When I was a little kid, my grandmother used to burn around the house,” says Rod Mendes, fire chief for the Yurok Tribe fire department, whose family is part of the Karuk Tribe of northern California. The Karuk and Yurok tribes have called the Klamath Mountains home for thousands of years. “She was just keeping the place clean. Native people probably did some of the first prescribed fire operations in history,” says Mendes.Understanding how Indigenous tribes used fire is essential for managing forests to reduce wildfire risk, says Knight. “We need to listen to Native people and learn and understand why they managed the landscape the way they did,” adds Mendes.Collaboration for corroborationTo map the region’s forest history, the team drew on historical accounts and oral histories from Karuk, Yurok and Hoopa Valley Tribe members collected by study co-author Frank Lake, a US Forest Service research ecologist in Arcata, California, and a Karuk descendant, as part of his PhD thesis in 2007. These accounts described the tribes’ fire and land use. For instance, members lit small fires to keep trails clear; this also reduced the amount of vegetation, preventing expansion of wildfires from lightning strikes. Larger fires, called broadcast burning, were used to improve visibility, hunting and nut-harvesting conditions in the forest. The effects of fire on the vegetation lasted for decades.Knight says that it was important to collaborate with the tribes given their knowledge of the region. The Karuk Resources Advisory Board approved a proposal for the study before it began. “In a way, it’s decolonizing the existing academic model that hasn’t been very inclusive of Indigenous histories,” says Lake.The researchers also analysed sediment cores collected near two low-elevation lakes in the Klamath Mountains that are culturally important to the tribes. Layers of pollen in the cores were used to infer the approximate tree density in the area at various times, and modelling helped date the cores so they could estimate how that density changed.The team also measured charcoal in the cores’ layers, which helped to map fluctuations in the amount of fire in the region. Burn scars on tree stumps pointed to specific instances of fire in between 1700-1900. Because the stumps’ rings serve as an ecological calendar, the researchers were able to compare periods of fire with corresponding tree-density data. They then pieced together how this density fluctuated with fire incidence. Although these empirical methods could not specifically confirm that the fires were lit by the tribes, patterns suggested when this was more probable, says Knight. For instance, increased burning in cool, wet periods, when fires caused by lightning were probably less common, suggested a human influence.Combining multiple lines of evidence, Knight and her team show that the tree density in this region of Klamath Mountains started to increase as the area was colonized, partly because the European settlers prevented Indigenous peoples from practising cultural burning. In the twentieth century, total fire suppression became a standard management practice, and fires of any kind were extinguished or prevented — although controlled burns are currently used in forest management. The team reports that in some areas, the tree density is higher than it has been for thousands of years, owing in part to fire suppression.Healthy forestA dense forest isn’t necessarily a healthy one, says Knight. The Douglas-fir, which dominate the low-land Klamath forests, are less fire resilient and more prone to calamitous wildfires. “This idea that we simply should let nature take its course is just not supported by this work,” she says. She adds that one of the study’s strengths is the multiple lines of evidence showing that past Indigenous burning helped to manage tree density.Fire ecologist Jeffrey Kane at the California State Polytechnic University Humboldt in Arcata says that the study’s findings of increased tree density are not surprising. He has made similar observations in the Klamath region. “There’s a lot more trees than were there just 120 years ago,” he says.Dominick DellaSala, chief scientist at forest-protection organization Wild Heritage in Talent, Oregon, points out that the results suggesting record tree densities cannot be applied to the entire Klamath region, owing to the limited range of the study’s lakeside data.Knight, however, says that the results can be extrapolated to other similar low-elevation lake sites that have similar vegetation types.More Indigenous voicesPalaeoecology studies are increasingly incorporating Indigenous knowledge — but there’s still a long way to go, says physical geographer Michela Mariani at the University of Nottingham, UK. In Australia, Mariani has also found that tree density began to increase after British colonization hampered cultural burning. “It’s very important that we now include Indigenous people in the discussion in fire management moving on,” Mariani says. “They have a deeper knowledge of the landscape we simply don’t have.”Including Indigenous voices in research is also crucial for decolonizing conventional scientific methods, Lake emphasizes. It “becomes a form of justice for those Indigenous people who have long been excluded, marginalized and not acknowledged”, he says. More

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    Hydrology, biogeochemistry and metabolism in a semi-arid mediterranean coastal wetland ecosystem

    Gibbs, J. P. Wetland loss and biodiversity conservation. Conserv. Biol. 14, 314–317 (2000).Article 

    Google Scholar 
    Turner, R. K. et al. Ecological-economic analysis of wetlands: Scientific integration for management and policy. Ecol. Econ. 35, 7–23 (2000).Article 

    Google Scholar 
    Zedler, J. B. & Kercher, S. Wetland resources: Status trends ecosystem services and restorability. Annu. Rev. Environ. Resour. 15, 39–74 (2005).Article 

    Google Scholar 
    Euliss, N. H., Smith, L. M., Wilcox, D. A. & Browne, B. A. Lining ecosystem processes with wetland management goals: Chartering a course for a sustainable future. Wetlands 28, 553–562 (2008).Article 

    Google Scholar 
    Costanza, R. et al. Changes in the global value of ecosystem services. Glob. Environ. Change. 26, 152–158 (2014).Article 

    Google Scholar 
    Macreadie, P. J. et al. The future of blue carbon. Nat. Commun. 10, 3998 (2019).ADS 
    Article 

    Google Scholar 
    RAMSAR. Wise use of wetlands, Ramsar Handbooks, 4th edition (2010).Kingsford, R. T., Basset, A. & Jackson, L. Wetlands: Conservation’s poor cousins. Aquat. Conserv. 26, 892–916 (2016).Article 

    Google Scholar 
    Beck, M. W., Heck, K. L. & Able, K. W. The Identification, Conservation, and Management of Estuarine and Marine Nurseries for Fish and Invertebrates: A better understanding of the habitats that serve as nurseries for marine species and the factors that create site-specific variability in nursery quality will improve conservation and management of these areas. Bioscience 51, 633–641 (2001).Article 

    Google Scholar 
    Canu, D. M. et al. Adressing sustainability of clam farming in the Venice Lagoon. Ecol. Soc. 16, 26 (2010).
    Google Scholar 
    Canu, D. M., Solidoro, C., Cossarini, G. & Giorgi, F. Effect of global change on bivalve rearing activity and the need for adaptive management. Clim. Res. 42, 13–26 (2011).Article 

    Google Scholar 
    Newton, A. et al. Anthropogenic pressures on Coastal Wetlands. Front. Ecol. Evol. 8, 144 (2020).Article 

    Google Scholar 
    Ayache, F. et al. Environmental characteristics landscape history and pressures on three coastal lagoons in the Southern Mediterranean Region: Merja Zerga (Morocco) Ghar El Melh (Tunisia) and Lake Manzala (Egypt). Hydrobiologia 622, 15–43 (2009).CAS 
    Article 

    Google Scholar 
    Solidoro, C. et al. Response of Venice Lagoon ecosystem to natural and anthropogenic pressures over the last 50 years. In Coastal Lagoons—Critical Habitats of Environmental Change (eds. Kennish, M. J. & Paerl, H. W.) 483–511 (2010).Newton, A. et al. Assessing quantifying and valuing the ecosystem services of coastal lagoons. J. Nat. Conserv. 44, 50–65 (2018).Article 

    Google Scholar 
    Newton, A. et al. An overview of ecological status vulnerability and future perspectives of European large shallow semi-enclosed coastal systems lagoons and transitional waters. Estuar. Coast. Shelf Sci. 140, 95–122 (2014).ADS 
    Article 

    Google Scholar 
    Béjaoui, B. et al. Random Forest model and TRIX used in combination to assess and diagnose the trophic status of Bizerte Lagoon, southern Mediterranean. Ecol. Indic. 71, 293–301 (2016).Article 

    Google Scholar 
    Ramdani, M. et al. North African wetland lakes: Characterization of nine sites included in the CASSARINA Project. Aquat. Ecol. 35, 281–302 (2001).Article 

    Google Scholar 
    Junk, W. J. et al. Current state of knowledge regarding the world’s wetlands and their future under global climate change: A synthesis. Aquat. Sci. 75, 151–167 (2013).CAS 
    Article 

    Google Scholar 
    Ouni, H. et al. Numerical modeling of hydrodynamic circulation in Ichkeul Lake-Tunisia. Energy Rep. 6, 208–213 (2020).Article 

    Google Scholar 
    Hollis, G. E. et al. Modeling and management of the internationally important wetland at Garaet Ichkeul Tunisia. Numéro 4 de IWRB special publication, International Waterfowl Research Bureau, ISSN 0962–6271 Volume 4 de International Waterfowl Research Bureau Slimbridge: IWRB special publication (ed. International Waterfowl Research Bureau) 1–121 (1986).Casagranda, C. & Boudouresque, C. F. A first quantification of the overall biomass, from phytoplankton to birds, of a Mediterranean brackish lagoon: Revisiting the ecosystem of Lake Ichkeul (Tunisia). Hydrobiologia 637, 73–85 (2010).Article 

    Google Scholar 
    Hamdi, N., Touihri, M. & Charfi, F. Diagnostic Écologique du Parc National Ichkeul (Tunisie) après la construction des barrages: Cas des oiseaux d’eau. Rev. Ecol-Terre Vie. 67, 41–62 (2012).
    Google Scholar 
    UNESCO. Biosphere Reserve Information Tunisia Ichkeul, UNESCO-MAB. Biosphere Reserves Directory. (2009a).UNESCO. Ichkeul National Park http://whc.unesco.org/en/list/8/ (2009b).RAMSAR. Convention and Wetlands International. Information Sheet on Ramsar Wetlands Tunisia Ichkeul, Ramsar Sites Information Service. (2009).Tamisier, A., et al. Modelling aquatic ecosystems: Benefits, costs and risks, for a field biologist. Ichkeul Lake, Tunisia, a case study. In Limnology and Aquatic birds, Monitoring, modeling and management (eds. Comin, F. A., Herrera, J. A. & Ramirez, J.) 185–203 (2001).Giordani, G. et al. Nutrient fluxes in transitional zones of the Italian coast. LOICZ Reports & Studies No. 28, ii+157 pages, LOICZ, Texel, the Netherlands. (2005).Thomson, A. J., Giannopoulos, G., Pretty, J., Baggs, E. M. & Richardson, D. J. Biological sources and sinks of nitrous oxide and strategies to mitigate emissions. Phil. Trans. R. Soc. B367, 1157–1168 (2012).Article 

    Google Scholar 
    Chen, N., Wu, J., Chen, Z., Lu, T. & Wang, L. Spatial-temporal variation of dissolved N2 and denitrification in an agricultural river network, southeast China. Agric. Ecosyst. Environ. 189, 1–10 (2014).ADS 
    CAS 
    Article 

    Google Scholar 
    Loeks, B. M. & Cotner, J. B. Upper Midwest lakes are supersaturated with N2. Proc. Natl. Acad. Sci. USA 117, 17063–17067 (2020).Article 

    Google Scholar 
    Thomann, R. V., DiToro, D. M., Winfield, R. P. & O’Connor, D. J. Mathematical modelling of phytoplankton in Lake Ontario. Part 1. Model development and verification. U.S. Environmental Protection Agency, EPA-660/3-75-005, Corvallis, Oreg. 77 (1975).DiToro, D. M. & Connolly, J. P. Mathematical models of water quality in large lakes. Part 2: Lake Erie. U.S. Environmental Protection Agency, Duluth, Minnesota. EPA-600/3-80-065. 231. (1980)Jacobsen, O. S. & Jorgensen, S. E. A submodel for nitrogen release from sediments. Ecol. Model. 1, 147–151 (1975).CAS 
    Article 

    Google Scholar 
    Jorgensen, S. E., Kamp-Neilsen, L. & Jacobsen, O. S. A submodel for anaerobic mud-water exchange of phosphate. Ecol. Model. 1, 133–146 (1975).Article 

    Google Scholar 
    Jorgensen, S. E. An Eutrophication model for a lake. Ecol. Model. 2, 147–165 (1976).Article 

    Google Scholar 
    Jorgensen, S. E., Mejer, H. & Friis, M. Examination of a Lake model. Ecol. Model. 4, 253–278 (1978).Article 

    Google Scholar 
    Chapelle, A., Mesnage, V., Mazouni, N., Deslous-Paoli, J. M. & Picot, B. Modélisation des cycles de l’azote et du phosphore dans les sédiments d’une lagune soumise à une exploitation conchylicole. Oceanol. Acta. 17, 609–620 (1994).CAS 

    Google Scholar 
    Raillard, O. & Ménesguen, A. An ecosystem box model for estimating the carrying capacity of a macrotidal shellfish system. Mar. Ecol. Prog. Ser. 115, 117–130 (1994).ADS 
    Article 

    Google Scholar 
    Kremer, H. H. et al. Land–ocean interactions in the coastal zone: Science plan and implementation strategy, IGBP Report 51, IHDP Report 18. International Geosphere-Biosphere Programme. (2005).Strobl, R., Zaldivar, C. J., Somma, F., Stips, A. & Garcia, G. E. Application of the LOICZ Methodology to the Mediterranean Sea EUR 23936 EN. Luxembourg (Luxembourg): OPOCE. JRC52454. (2009).Swaney, D. P. & Giordani, G (Eds.). Proceedings of the LOICZ Workshop on Biogeochemical Budget Methodology and Applications, Providence RI, November 9–10, 2007. LOICZ Reports and Studies no. 37. GKSS Research Centre, Geesthacht. http://www.loicz.org/imperia/md/content/loicz/print/rsreports/biogeochemical_budget_methodology_and_applications.pdf (2011).Swaney, D. P., Smith, S. V. & Wulff, F. The LOICZ Biogeochemical Modeling Protocol and its Application to Estuarine Ecosystems. In Teratise on Estuarine and Coastal Ecosystem Science, Academic Press, Elsevier (eds. Bauer, J. E. & Bianchi, T. S.) 136–159 (2011).Glaeser, B., Kannen, A. & Kremer, H. Introduction: The future of coastal areas. Challenges for planning practice and research. Gaia-Ecol. Perspect. Sci. Soc. 18, 145–149 (2009).
    Google Scholar 
    Glaeser, B., Bruckmeier, K., Glaser, M. & Krause, G. Social-ecological systems analysis in coastal and marine areas: A path toward integration of interdisciplinary knowledge. In Current Trends in Human Ecology. Cambridge Scholars Publishing (eds. Lopes, P. & Begossi, A.) 183–203 (2009b).Glaser, M. & Glaeser, B. The social dimension in the management of social ecological change. In Treatise on Estuarine and Coastal Science, Vol. 11: Integrated Management of Estuaries and Coasts. München: Elsevier (eds. Kremer, H. & Pinckney, J.) 59 (2011).Glaser, M. & Glaeser, B. Towards a framework for cross-scale and multi-level analysis of coastal and marine social-ecological systems dynamics. Reg. Environ. Change. 14, 2039–2052 (2014).Article 

    Google Scholar 
    Vybernaite-Lubiene, I. et al. Biogeochemical budgets of nutrients and metabolism in the curonian lagoon (Southeast Baltic Sea): Spatial and temporal variations. Water 14, 164 (2022).CAS 
    Article 

    Google Scholar 
    Yazidi, A., Saidi, S., Ben, M. N. & Darragi, F. Contribution of GIS to evaluate surface water pollution by heavy metals: Case of Ichkeul Lake (Northern Tunisia). J. Afr. Earth. Sci. 134, 166–173 (2017).ADS 
    CAS 
    Article 

    Google Scholar 
    Goudling, M. et al. Ecosystem-based management of Amazon fisheries and wetlands. Fish Fish. 20, 138–158 (2018).
    Google Scholar 
    Mitsch, W. J. & Gosselink, J. G. Wetlands 5th edn. (Wiley, 2015).
    Google Scholar 
    World Bank 2022.Affouri, H. & Sahraoui, O. The sedimentary organic matter from a Lake Ichkeul core (far northern Tunisia): Rock-Eval and biomarker approach. J. Afr. Earth. Sci. 129, 248–259 (2017).ADS 
    CAS 
    Article 

    Google Scholar 
    Vanderkelen, I., van Lipzig, N. P. M. & Thiery, A. Modelling the water balance of Lake Victoria (East Africa)–Part 1: Observational analysis. Hydrol. Earth Syst. Sci. 22, 1–17 (2018).Article 

    Google Scholar 
    Coe, M. T. & Foley, J. A. Human and natural impacts on the water resources of the Lake Chad basin. J. Geophys. Res. Atmos. 106, 3349–3356 (2001).ADS 
    Article 

    Google Scholar 
    Gao, H., Bohn, T. J., Podest, E., McDonald, K. C. & Lettenmaier, D. P. On the causes of the shrinking of Lake Chad. Environ. Res. Lett. 6, 34021 (2011).Article 

    Google Scholar 
    Prange, M., Wilke, T. & Wesselingh, F. P. The other side of sea level change. Commun. Earth Environ. 1, 69 (2020).ADS 
    Article 

    Google Scholar 
    Glausiusz, J. Environmental Science: New life for the DeaSea?. Nature 464, 1118–1120 (2010).CAS 
    Article 

    Google Scholar 
    Gronewold, A. D. & Stow, C. A. Water Loss from the Great Lakes. Science 343, 1084–1085 (2014).ADS 
    Article 

    Google Scholar 
    Mei, X., Dai, Z., Du, J. & Chen, J. Linkage between Three Gorges Dam impacts and the dramatic recessions in China’s largest freshwater lake, Poyang Lake. Sci. Rep. 5, 18197 (2015).ADS 
    CAS 
    Article 

    Google Scholar 
    Micklin, P. The aral sea disaster. Ann. Rev. Earth Planet. Sci. 35, 47–72 (2007).ADS 
    CAS 
    Article 

    Google Scholar 
    Feng, L., Han, X., Hu, C. & Chen, X. Four decades of wetland changes of the largest freshwater lake in China: Possible linkage to the Three Gorges Dam?. Remote Sens. Environ. 176, 43–55 (2016).ADS 
    Article 

    Google Scholar 
    Downing, J. A. et al. The global abundance and size distribution of lakes, ponds, and impoundments. Limnol. Oceanogr. 51, 2388–2397 (2006).ADS 
    Article 

    Google Scholar 
    Awange, J. L. et al. The falling lake victoria water level: GRACE, TRIMM and CHAMP satellite analysis of the lake basin. Water Resour. Manag. 22, 775–796 (2008).Article 

    Google Scholar 
    Carroll, M. L., Townshend, R. H. G., DiMiceli, C. M., Loboda, T. & Sohlberg, R. A. Shrinkage lakes of the Artic: Spatial relationships and trajectory of change. Geophys. Res. Lett. 38, 20406 (2011).ADS 
    Article 

    Google Scholar 
    Lefebvre, G. et al. Predicting the vulnerability of seasonally-flooded wetlands to climate change across the Mediterranean Basin. Sci. Total Environ. 692, 546–555 (2019).ADS 
    CAS 
    Article 

    Google Scholar 
    Touaylia, S., Ghannem, S., Toumi, H., Béjaoui, M. & Garrido, J. Assessment of heavy metals status in northern Tunisia using contamination indices: Case of the Ichkeul steams system. Int. J. Environ. Res. Public Health. 3, 209–217 (2016).
    Google Scholar 
    Aouissi, J., Benabdallah, S., Lili, C. Z. & Cudennec, C. Modelling water quality to improve agricultural practices and land management in a Tunisian catchment using soil and water assessment tool. J. Environ. Qual. 43, 18–25 (2014).Article 

    Google Scholar 
    Aouissi, J., Lili, C. Z., Benabdallah, S. & Cudennec, C. Assessing the hydrological impacts of agricultural changes upstream of the Tunisian World Heritage sea-connected Ichkeul Lake. Proc. Int. Assoc. Hydrol. Sci. 365, 61–65 (2015).
    Google Scholar 
    Fathalli, A. et al. Molecular and phylogenetic characterization of potentially toxic cyanobacteria in Tunisian freshwaters. Syst. Appl. Microbiol. 34, 303–310 (2011).CAS 
    Article 

    Google Scholar 
    Ouchir, N., Morin, S., Ben, A. L., Boughdiri, M. & Aydi, A. Periphytic diatom communities in tributaries around Lake Ichkeul, northern Tunisia: A preliminary assessment. Afr. J. Aquat. Sci. 42, 65–73 (2017).Article 

    Google Scholar 
    Chislock, M. F., Doster, E., Zitomer, R. A. & Wilson, A. E. Eutrophication: Causes, consequences, and controls in aquatic ecosystems. Nat. Educ. Knowl. 4, 10 (2013).
    Google Scholar 
    Paerl, H. W. & Huisman, J. Climate change: A catalyste for global expansion of harmful cyanobacteria blooms. Environ. Microb. Rep. 1, 27–37 (2009).CAS 
    Article 

    Google Scholar 
    Paerl, H. W., Nathan, S. H. & Calandrino, E. S. Controlling harmful cyanobacteria blooms in a world experiencing anthropogenic and climatic-induced change. Sci. Total Environ. 409, 1739–1745 (2011).ADS 
    CAS 
    Article 

    Google Scholar 
    O’Neil, J. M., Davis, T. M., Burford, M. A. & Gobler, C. J. The rise of harmful cyanobacteria blooms: The potential roles of eutrophication and climate change. Harmful Algae 14, 313–334 (2012).Article 

    Google Scholar 
    Ben, S. F. et al. Pesticides in Ichkeul Lake-Bizerte Lagoon Watershed in Tunisia: Use, occurrence, and effects on bacteria and free-living marine nematodes. Environ. Sci. Pollut. Res. 23, 36–48 (2016).Article 

    Google Scholar 
    Bourhane, Z. et al. Microbial diversity alteration reveals biomarkers of contamination in soil-river-lake continuum. J. Hazard. Mater. 421, 126789 (2022).CAS 
    Article 

    Google Scholar 
    Kolzau, S. et al. Seasonal patterns of nitrogen and phosphorus limitation in four German lakes and the predictability of limitation status from Ambient nutrient concentrations. PLoS ONE 9, e96065 (2014).ADS 
    Article 

    Google Scholar 
    Abidi, M., Ben, A. R. & Gueddari, M. Assessment of the trophic status of the South Lagoon of Tunis (Tunisia, Mediterranean Sea); a Geochemical and Statistical Approaches. J. Chem. (2018).Saunders, D. L. & Kaffl, J. Denitrification rates in the sediments of Lake Memphremagog, Canda–USA. Water Res. 35, 1897–1904 (2001).CAS 
    Article 

    Google Scholar 
    Davidson, E. A. & Seitzinger, S. The enigma of progress in denitrification research. Ecol. Appl. 16, 2057–2063 (2006).Article 

    Google Scholar 
    Medina-Galvan, J. et al. Comparing the biogeochemical functioning of two arid subtropical coastal lagoons: The effect of wastewater discharges. Ecosyst. Health Sustain. 7, 1 (2021).Article 

    Google Scholar 
    Piehler, M. F. & Smyth, A. R. Habitat-specific distinctions in estuarine denitrification affect both ecosystem function and services. Ecosphere. 2, 1–17 (2011).ADS 
    Article 

    Google Scholar 
    Loeks-Johson, B. M. & Cotner, J. B. Upper Midwest lakes are supersaturated with N2. Proc. Natl. Acad. Sci. U S A. 117, 17063–17067 (2020).Reddy, K. R., Patrick, W. H. & Lindau, C. W. Nitrification-denitrification at the plant root sediment interface in Wetlands. Limnol. Oceanogr. 34, 1004–1013 (1989).ADS 
    CAS 
    Article 

    Google Scholar 
    Adrian, A. et al. Lakes as sentinels of climate change. Limnol. Oceanogr. 54, 2283–2297 (2009).ADS 
    Article 

    Google Scholar 
    Seo, C. D. & DeLaune, R. D. Fungal and bacterial mediated denitrification in wetlands: Influence of sediment redox condition. Water Res. 44, 2441–2450 (2010).CAS 
    Article 

    Google Scholar 
    Montzka, S. A., Dlugokencky, I. J. & Butler, J. H. Non-CO2 greenhouse gases and climate change. Nature 476, 43–50 (2011).CAS 
    Article 

    Google Scholar 
    Sferratore, A., Billen, G. & Garnier, J. The S Modeling nutrient (N, P, Si ) budget in the Seine watershed: Application of the River Strahler model using data from local to global scale resolution Modeling nutrient (N, P, Si) budget in the Seine watershed: Application of the River Strahler model using data from local to global scale resolution. Glob. Biogeochem. Cycles. 19, 20 (2005).Article 

    Google Scholar 
    Béjaoui, B. et al. 3D modeling of phytoplankton seasonal variation and nutrient budget in a Southern Mediterranean Lagoon. Mar. Pollut. Bull. 114, 962–976 (2017).Article 

    Google Scholar 
    Shaiek, M., Fassatoui, C. & Romdhane, M. S. Past and present fish species recorded in the estuarine Lake Ichkeul, northern Tunisia. Afr. J. Aquat. Sci. 41, 171–180 (2016).Article 

    Google Scholar 
    INM. Données climatiques de la région de Bizerte. Institut National de Météorologie, Tunis, Tunisie. (2017).Rodier, J. et al. L’analyse de l’eau, Eaux naturelles, eaux résiduaires, eau de mer, Dunod Paris. (1996).Lorenzen, C. J. Determination of chlorophyll and pheopigments by spectrophotometric equations. Limnol. Oceanogr. 12, 343–346 (1967).ADS 
    CAS 
    Article 

    Google Scholar 
    Parsons, T. R., Maita, Y. & Lalli, C. M. A manual of chemical and biological methods for seawater analysis. Geol. Mag. 122, 570–570 (1980).
    Google Scholar 
    Redfield, A. C. The biological control of chemical factors in the environment. Sci. Prog. 11, 150–170 (1960).CAS 

    Google Scholar 
    Gordon, D. C. et al. LOICZ biogeochemical modelling guidelines. LOICZ Rep and Stud. 5, 1–96 (1996).
    Google Scholar 
    Seitzinger, S. P. Denitrification in freshwater and coastal marine ecosystems: Ecological and geochemical significance. Limnol. Oceanogr. 33, 702–724 (1988).ADS 
    CAS 

    Google Scholar 
    Atkinson, M. J. & Smith, S. V. C:N: P ratios of benthic marine plants. Limnol. Oceanogr. 28, 568–574 (1983).ADS 
    CAS 
    Article 

    Google Scholar 
    APHA (American Public Health Association) Standard Methods for the Examination of Water and Wastewater. 18th Edition, American Public Health Association (APHA), American Water Works Association (AWWA) and Water Pollution Control Federation (WPCF), Washington DC (1992). More

  • in

    Repeated introduction of micropollutants enhances microbial succession despite stable degradation patterns

    Schwarzenbach RP, Escher BI, Fenner K, Hofstetter TB, Johnson CA, Von Gunten U, et al. The challenge of micropollutants in aquatic systems. Science (80-). 2006;313:1072–7.Article 

    Google Scholar 
    Deblonde T, Cossu-Leguille C, Hartemann P. Emerging pollutants in wastewater: a review of the literature. Int J Hyg Environ Health. 2011;214:442–8.Article 

    Google Scholar 
    Wang M, Cernava T. Overhauling the assessment of agrochemical-driven interferences with microbial communities for improved global ecosystem integrity. Environ Sci Ecotechnol. 2020;4:100061.Article 

    Google Scholar 
    Luo Y, Guo W, Ngo HH, Nghiem LD, Hai FI, Zhang J, et al. A review on the occurrence of micropollutants in the aquatic environment and their fate and removal during wastewater treatment. Sci Total Environ. 2014;473–474:619–41.Article 

    Google Scholar 
    Wang Z, Zhang XH, Huang Y, Wang H. Comprehensive evaluation of pharmaceuticals and personal care products (PPCPs) in typical highly urbanized regions across China. Environ Pollut. 2015;204:223–32.Article 

    Google Scholar 
    Eggen RIL, Hollender J, Joss A, Schärer M, Stamm C. Reducing the discharge of micropollutants in the aquatic environment: the benefits of upgrading wastewater treatment plants. Environ Sci Technol. 2014;48:7683–9.Article 

    Google Scholar 
    Vila-Costa M, Cerro-Gálvez E, Martínez-Varela A, Casas G, Dachs J. Anthropogenic dissolved organic carbon and marine microbiomes. ISME J. 2020;14:2646–8.Article 

    Google Scholar 
    da Silva GCX, Medeiros de Abreu CH, Ward ND, Belúcio LP, Brito DC, Cunha HFA, et al. Environmental impacts of dam reservoir filling in the East Amazon. Front Water. 2020;2:11.Article 

    Google Scholar 
    Kuroda K, Murakami M, Oguma K, Muramatsu Y, Takada H, Takizawa S. Assessment of groundwater pollution in Tokyo using PPCPs as sewage markers. Environ Sci Technol. 2012;46:1455–64.Article 

    Google Scholar 
    Liu WR, Zhao JL, Liu YS, Chen ZF, Yang YY, Zhang QQ, et al. Biocides in the Yangtze River of China: spatiotemporal distribution, mass load and risk assessment. Environ Pollut. 2015;200:53–63.Article 

    Google Scholar 
    Roberts J, Kumar A, Du J, Hepplewhite C, Ellis DJ, Christy AG, et al. Pharmaceuticals and personal care products (PPCPs) in Australia’s largest inland sewage treatment plant, and its contribution to a major Australian river during high and low flow. Sci Total Environ. 2016;541:1625–37.Article 

    Google Scholar 
    Rodea-Palomares I, Gonzalez-Pleiter M, Gonzalo S, Rosal R, Leganes F, Sabater S, et al. Hidden drivers of low-dose pharmaceutical pollutant mixtures revealed by the novel GSA-QHTS screening method. Sci Adv. 2016;2:1–12.Article 

    Google Scholar 
    Yang X, Chen F, Meng F, Xie Y, Chen H, Young K, et al. Occurrence and fate of PPCPs and correlations with water quality parameters in urban riverine waters of the Pearl River Delta, South China. Environ Sci Pollut Res. 2013;20:5864–75.Article 

    Google Scholar 
    Cerro-Gálvez E, Dachs J, Lundin D, Fernández-Pinos MC, Sebastián M, Vila-Costa M. Responses of coastal marine microbiomes exposed to anthropogenic dissolved organic carbon. Environ Sci Technol. 2021;55:9609–21.Article 

    Google Scholar 
    Martinez-Varela A, Cerro-Gálvez E, Auladell A, Sharma S, Moran MA, Kiene RP, et al. Bacterial responses to background organic pollutants in the northeast subarctic Pacific Ocean. Environ Microbiol. 2021;23:4532–46.Article 

    Google Scholar 
    Bob A, Shen D, Li S, Zhang L, Rashid A, Sun Q, et al. Strong impact of micropollutants on prokaryotic communities at the horizontal but not vertical scales in a subtropical reservoir, China. Sci Total Environ. 2020;721:137767.Article 

    Google Scholar 
    Tlili A, Corcoll N, Arrhenius Å, Backhaus T, Hollender J, Creusot N, et al. Tolerance patterns in stream biofilms link complex chemical pollution to ecological impacts. Environ Sci Technol. 2020;54:10745–53.Article 

    Google Scholar 
    Chalew TEA, Halden RU. Environmental exposure of aquatic and terrestrial biota to triclosan and triclocarban. J Am Water Resour Assoc. 2009;45:4–13.Article 

    Google Scholar 
    Zhang W, Yin K, Chen L. Bacteria-mediated bisphenol A degradation. Appl Microbiol Biotechnol. 2013;97:5681–9.Article 

    Google Scholar 
    Staples CA, Dorn PB, Klecka GM, O’Block ST, Harris LR. A review of the environmental fate, effects, and exposures of bisphenol A. Chemosphere. 1998;36:2149–73.Article 

    Google Scholar 
    Choi YJ, Lee LS. Aerobic soil biodegradation of bisphenol (BPA) alternatives bisphenol S and bisphenol AF compared to BPA. Environ Sci Technol. 2017;51:13698–704.Article 

    Google Scholar 
    McMurry LM, Oethinger M, Levy SB. Triclosan targets lipid synthesis [4]. Nature. 1998;394:531–2.Article 

    Google Scholar 
    Cabana H, Jiwan JLH, Rozenberg R, Elisashvili V, Penninckx M, Agathos SN, et al. Elimination of endocrine disrupting chemicals nonylphenol and bisphenol A and personal care product ingredient triclosan using enzyme preparation from the white rot fungus Coriolopsis polyzona. Chemosphere. 2007;67:770–8.Article 

    Google Scholar 
    Hu A, Ju F, Hou L, Li J, Yang X, Wang H, et al. Strong impact of anthropogenic contamination on the co-occurrence patterns of a riverine microbial community. Environ Microbiol. 2017;19:4993–5009.Article 

    Google Scholar 
    Boyd TJ, Smith DC, Apple JK, Hamdan LJ, Osburn CL, Montgomery MT. Evaluating PAH biodegradation relative to total bacterial carbon demand in coastal ecosystems: Are PAHs truly recalcitrant? In: Van Dijk T. (ed). Microbial Ecology Research Trends. Nova Science Publishers, 2008. pp 1–38.Okere UV, Cabrerizo A, Dachs J, Ogbonnaya UO, Jones KC, Semple KT. Effects of pre-exposure on the indigenous biodegradation of 14C-phenanthrene in Antarctic soils. Int Biodeterior Biodegrad. 2017;125:189–99.Article 

    Google Scholar 
    Coll C, Bier R, Li Z, Langenheder S, Gorokhova E, Sobek A. Association between aquatic micropollutant dissipation and river sediment bacterial communities. Environ Sci Technol. 2020;54:14380–92.Article 

    Google Scholar 
    Bender EA, Case TJ, Gilpin ME. Perturbation experiments in community ecology: Theory and practice. Ecology. 1984;65:1–13.Shade A, Peter H, Allison SD, Baho DL, Berga M, Bürgmann H, et al. Fundamentals of microbial community resistance and resilience. Front Microbiol. 2012;3:1–19.Article 

    Google Scholar 
    Buerger S, Spoering A, Gavrish E, Leslin C, Ling L, Epstein SS. Microbial scout hypothesis, stochastic exit from dormancy, and the nature of slow growers. Appl Environ Microbiol. 2012;78:3221–8.Article 

    Google Scholar 
    Lee SH, Sorensen JW, Grady KL, Tobin TC, Shade A. Divergent extremes but convergent recovery of bacterial and archaeal soil communities to an ongoing subterranean coal mine fire. ISME J. 2017;11:1447–59.Article 

    Google Scholar 
    Lennon JT, den Hollander F, Wilke-Berenguer M, Blath J. Principles of seed banks and the emergence of complexity from dormancy. Nat Commun. 2021;12:1–16.Article 

    Google Scholar 
    Philippot L, Griffiths BS, Langenheder S. Microbial community resilience across ecosystems and multiple disturbances. Microbiol Mol Biol Rev. 2021;85:e00026–20.Article 

    Google Scholar 
    Hu A, Li S, Zhang L, Wang H, Yang J, Luo Z, et al. Prokaryotic footprints in urban water ecosystems: a case study of urban landscape ponds in a coastal city, China. Environ Pollut. 2018;242:1729–39.Article 

    Google Scholar 
    Im J, Löffler FE. Fate of bisphenol A in terrestrial and aquatic environments. Environ Sci Technol. 2016;50:8403–16.Article 

    Google Scholar 
    Sun Q, Li M, Ma C, Chen X, Xie X, Yu CP. Seasonal and spatial variations of PPCP occurrence, removal and mass loading in three wastewater treatment plants located in different urbanization areas in Xiamen, China. Environ Pollut. 2016;208:371–81.Article 

    Google Scholar 
    Sun Q, Wang Y, Li Y, Ashfaq M, Dai L, Xie X, et al. Fate and mass balance of bisphenol analogues in wastewater treatment plants in Xiamen City, China. Environ Pollut. 2017;225:542–9.Article 

    Google Scholar 
    Sun Q, Li Y, Chou PH, Peng PY, Yu CP. Transformation of bisphenol A and alkylphenols by ammonia-oxidizing bacteria through nitration. Environ Sci Technol. 2012;46:4442–8.Article 

    Google Scholar 
    Zaayman M, Siggins A, Horne D, Lowe H, Horswell J. Investigation of triclosan contamination on microbial biomass and other soil health indicators. FEMS Microbiol Lett. 2017;364:1–6.Article 

    Google Scholar 
    Xie J, Zhao N, Zhang Y, Hu H, Zhao M, Jin H. Occurrence and partitioning of bisphenol analogues, triclocarban, and triclosan in seawater and sediment from East China Sea. Chemosphere. 2022;287:132218.Article 

    Google Scholar 
    Yamazaki E, Yamashita N, Taniyasu S, Lam J, Lam PKS, Moon HB, et al. Bisphenol A and other bisphenol analogues including BPS and BPF in surface water samples from Japan, China, Korea and India. Ecotoxicol Environ Saf. 2015;122:565–72.Article 

    Google Scholar 
    Kalyuzhny M, Shnerb NM. Dissimilarity-overlap analysis of community dynamics: opportunities and pitfalls. Methods Ecol Evol. 2017;8:1764–73.Article 

    Google Scholar 
    Wang J, Pan F, Soininen J, Heino J, Shen J. Nutrient enrichment modifies temperature-biodiversity relationships in large-scale field experiments. Nat Commun. 2016;7:1–9.
    Google Scholar 
    Hildebrand F, Tito RY, Voigt AY, Bork P, Raes J. Correction to: LotuS: an efficient and user-friendly OTU processing pipeline [Microbiome, 2, (2014), 30]. Microbiome. 2014;2:1–7.Article 

    Google Scholar 
    Edgar RC. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat Methods. 2013;10:996–8.Article 

    Google Scholar 
    Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 2013;41:590–6.Article 

    Google Scholar 
    Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. QIIME allows analysis of high- throughput community sequencing data Intensity normalization improves color calling in SOLiD sequencing. Nat Methods. 2010;7:335–6.Article 

    Google Scholar 
    Klappenbach JA, Saxman PR, Cole JR, Schmidt TM. Rrndb: the ribosomal RNA operon copy number database. Nucleic Acids Res. 2001;29:181–4.Article 

    Google Scholar 
    Wu L, Yang Y, Chen S, Zhao M, Zhu Z, Yang S, et al. Long-term successional dynamics of microbial association networks in anaerobic digestion processes. Water Res. 2016;104:1–10.Article 

    Google Scholar 
    Stegen JC, Lin X, Fredrickson JK, Chen X, Kennedy DW, Murray CJ, et al. Quantifying community assembly processes and identifying features that impose them. ISME J. 2013;7:2069–79.Article 

    Google Scholar 
    Stegen JC, Lin X, Fredrickson JK, Konopka AE. Estimating and mapping ecological processes influencing microbial community assembly. Front Microbiol. 2015;6:1–15.Article 

    Google Scholar 
    Webb CO, Ackerly DD, Kembel SW. Phylocom: software for the analysis of phylogenetic community structure and trait evolution. Bioinformatics. 2008;24:2098–2100.Article 

    Google Scholar 
    Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:1–21.Article 

    Google Scholar 
    Letunic I, Bork P. Interactive Tree of Life (iTOL) v4: recent updates and new developments. Nucleic Acids Res. 2019;47:2–5.Article 

    Google Scholar 
    Anderson MJ. Permutation tests for univariate or multivariate analysis of variance and regression. Can J Fish Aquat Sci. 2001;58:626–39.Article 

    Google Scholar 
    Oksanen AJ, Blanchet FG, Friendly M, Kindt R, Legendre P, Mcglinn D, et al. Vegan: community ecology package. Encyclopedia of Food and Agricultural Ethics. 2019; 2395–6.Bashan A, Gibson TE, Friedman J, Carey VJ, Weiss ST, Hohmann EL, et al. Universality of human microbial dynamics. Nature. 2016;534:259–62.Article 

    Google Scholar 
    Vila JCC, Liu YY, Sanchez A. Dissimilarity–overlap analysis of replicate enrichment communities. ISME J. 2020;14:2505–13.Article 

    Google Scholar 
    Ahlmann-Eltze C, Patil I. ggsignif: significance Brackets for ‘ggplot2’. R package version 0.6.1. 2021.Callahan BJ, McMurdie PJ, Holmes SP. Exact sequence variants should replace operational taxonomic units in marker-gene data analysis. ISME J. 2017;11:2639–43.Article 

    Google Scholar 
    Glassman SI, Martiny JBH. Broadscale ecological patterns are robust to use of exact. mSphere. 2018;3:e00148–18.Article 

    Google Scholar 
    Lindström ES, Östman Ö. The importance of dispersal for bacterial community composition and functioning. PLoS One. 2011;6:e25883.Article 

    Google Scholar 
    Shen D, Langenheder S, Jürgens K. Dispersal modifies the diversity and composition of active bacterial communities in response to a salinity disturbance. Front Microbiol. 2018;9:2188.Article 

    Google Scholar 
    Zhou NA, Lutovsky AC, Andaker GL, Gough HL, Ferguson JF. Cultivation and characterization of bacterial isolates capable of degrading pharmaceutical and personal care products for improved removal in activated sludge wastewater treatment. Biodegradation. 2013;24:813–27.Article 

    Google Scholar 
    Thelusmond JR, Strathmann TJ, Cupples AM. Carbamazepine, triclocarban and triclosan biodegradation and the phylotypes and functional genes associated with xenobiotic degradation in four agricultural soils. Sci Total Environ. 2019;657:1138–49.Article 

    Google Scholar 
    Danzl E, Sei K, Soda S, Ike M, Fujita M. Biodegradation of bisphenol A, bisphenol F and bisphenol S in seawater. Int J Environ Res Public Health. 2009;6:1472–84.Article 

    Google Scholar 
    Zaborowska M, Wyszkowska J, Borowik A. Soil microbiome response to contamination with Bisphenol A, Bisphenol F and Bisphenol S. Int J Mol Sci. 2020;21:3529.Article 

    Google Scholar 
    Freilich S, Zarecki R, Eilam O, Segal ES, Henry CS, Kupiec M, et al. Competitive and cooperative metabolic interactions in bacterial communities. Nat Commun. 2011;2:587–9.Article 

    Google Scholar 
    Pacheco AR, Moel M, Segrè D. Costless metabolic secretions as drivers of interspecies interactions in microbial ecosystems. Nat Commun. 2019;10:103.Article 

    Google Scholar 
    Oh S, Choi D, Cha C-J. Ecological processes underpinning microbial community structure during exposure to subinhibitory level of triclosan. Sci Rep. 2019;9:4598.Article 

    Google Scholar 
    Hagberg A, Gupta S, Rzhepishevska O, Fick J, Burmølle M, Ramstedt M. Do environmental pharmaceuticals affect the composition of bacterial communities in a freshwater stream? A case study of the Knivsta river in the south of Sweden. Sci Total Environ. 2021;763:142991.Article 

    Google Scholar 
    Gao H, LaVergne JM, Carpenter CMG, Desai R, Zhang X, Gray K, et al. Exploring co-occurrence patterns between organic micropollutants and bacterial community structure in a mixed-use watershed. Environ Sci Process Impacts. 2019;21:867–80.Article 

    Google Scholar 
    Wolff D, Krah D, Dötsch A, Ghattas AK, Wick A, Ternes TA. Insights into the variability of microbial community composition and micropollutant degradation in diverse biological wastewater treatment systems. Water Res. 2018;143:313–24.Article 

    Google Scholar 
    Bajić D, Vila JCC, Blount ZD, Sánchez A. On the deformability of an empirical fitness landscape by microbial evolution. Proc Natl Acad Sci USA. 2018;115:11286–91.Article 

    Google Scholar 
    Nemergut DR, Schmidt SK, Fukami T, O’Neill SP, Bilinski TM, Stanish LF, et al. Patterns and Processes of Microbial Community Assembly. Microbiol Mol Biol Rev. 2013;77:342–56.Article 

    Google Scholar 
    Zhou J, Ning D. Stochastic community assembly: does it matter in microbial ecology? Microbiol Mol Biol Rev. 2017;81:1–32.Article 

    Google Scholar 
    Vellend M. Conceptual synthesis in community ecology. Q Rev Biol. 2010;85:183–206.Article 

    Google Scholar 
    Svoboda P, Lindström ES, Ahmed Osman O, Langenheder S. Dispersal timing determines the importance of priority effects in bacterial communities. ISME J. 2018;12:644–6.Article 

    Google Scholar 
    Bernstein HC. Reconciling ecological and engineering design principles for building microbiomes. mSystems. 2019;4:1–5.Article 

    Google Scholar 
    Borchert E, Hammerschmidt K, Hentschel U, Deines P. Enhancing microbial pollutant degradation by integrating eco-evolutionary principles with environmental biotechnology. Trends Microbiol. 2021;29:908–18.Article 

    Google Scholar 
    Rocca JD, Muscarella ME, Peralta AL, Izabel-Shen D, Simonin M. Guided by microbes: applying community coalescence principles for predictive microbiome engineering. mSystems. 2021;6:e00538–21.Article 

    Google Scholar 
    Nemergut DR, Knelman JE, Ferrenberg S, Bilinski T, Melbourne B, Jiang L, et al. Decreases in average bacterial community rRNA operon copy number during succession. ISME J. 2016;10:1147–56.Article 

    Google Scholar 
    Frost LS, Leplae R, Summers AO, Toussaint A, Edmonton A. Mobile genetic elements: the agents of open source evolution. Nat Rev Microbiol. 2005;3:722–32.Ullastres A, Merenciano M, Guio L, Gonz J. Transposable elements: a toolkit for stress and environmental adaptation in bacteria. Stress Environ Regul Gene Expr Adapt Bact. 2016;1:137–45.
    Google Scholar 
    Chang CY, Vila JCC, Bender M, Li R, Mankowski MC, Bassette M, et al. Engineering complex communities by directed evolution. Nat Ecol Evol. 2021;5:1011–23.Article 

    Google Scholar  More

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    Pingers are effective in reducing net entanglement of river dolphins

    Lal Mohan, R. S., Dey, S. C., Bairagi, S. P. & Roy, S. On a survey of the Ganges River dolphin Platanista gangetica of Bramaputra River, Assam. J. Bombay Nat. Hist. Soc. 94, 483–495 (1997).
    Google Scholar 
    Sinha, R.K., et al. Status and distribution of the Ganges susu (Platanista gangetica) in Ganges River system of India and Nepal in Biology and conservation of freshwater cetaceans in Asia (eds. Reeves, R. R., Smith, B. D. & Kasuya, T). 42–48 (Switzerland: Occasional Paper of the IUCN Species Survival Commission, 2000)Sinha, R. K. & Kannan, K. Ganges River dolphin: an overview of biology, ecology, and conservation status in India. Ambio. 43,1029–1046 (2014).
    Google Scholar 
    Anderson, J. Anatomical and Zoological Researches: Comprising an Account of the Zoological Results of the Two Expeditions to Western Yunnan in 1868 and 1875; and A Monograph of the Two Cetacean Genera, Platanista and Orcella-Vol. 1 (Text). Vol. 1 (Bernard Quaritch, 1878).Herald, E. S. et al. Blind river dolphin: first side-swimming cetacean. Science 166, 1408–1410 (1969).ADS 
    CAS 

    Google Scholar 
    Herald, E. S. Field and aquarium study of the blind River Dolphin (Platanista Gangetica) (California Academy of Sciences San Fransico Steinhart Aquarium, 1969).Pilleri, G., Zbinden, K., Gihr, M. & Kraus, C. Sonar clicks, directionality of the emission field and echolocating behaviour of the Indus dolphin (Platanista indi, Blyth, 1859). Invest. Cetacea Brain Anat. Inst. Berne Switzerl. 13–43 (1976).Jensen, F. H. et al. Clicking in shallow rivers: short-range echolocation of Irrawaddy and Ganges river dolphins in a shallow, acoustically complex habitat. PLoS ONE 8, e59284 (2013).ADS 
    CAS 

    Google Scholar 
    Pence, E.A. Monofilament gill net acoustic study. (National Mammal Laboratory, Contract 40-ABNF-5-1988,1986)Jefferson, T. A., Würsig, B. & Fertl, D. Cetacean Detection and Responses to Fishing Gear in Marine Mammal Sensory Systems (eds. Thomas, J.A., Kastelein, R.A. & Supin, A.Y.) 663–684 (Springer, 1992)
    Google Scholar 
    Mansur, E. F., Smith, B. D., Mowgli, R. M. & Diyan, M. A. A. Two incidents of fishing gear entanglement of Ganges River dolphins (Platanista gangetica gangetica) in waterways of the Sundarbans mangrove forest, Bangladesh. Aquat. Mamm. 34, 362 (2008).
    Google Scholar 
    Sinha, R. K. An alternative to dolphin oil as a fish attractant in the Ganges River system: conservation of the Ganges River dolphin.
    Biol. Conserv. 107, 253–257 https://doi.org/10.1016/S0006-3207(02)00058-7 (2002).Article 

    Google Scholar 
    Qureshi, Q. et al. Development of conservation action plan for river dolphin. 228 (Wildlife Institute of India, Dehradun, Uttarakhand, 2018).Kolipakam, V. et al. Evidence for the continued use of river dolphin oil for bait fishing and traditional medicine: implications for conservation. Heliyon 6, e04690 (2020).
    Google Scholar 
    Wakid, A. Initiative to reduce the fishing pressures in and around identified habitats of endangered Gangetic dolphin in Brahmaputra River system. (Assam, 2010).Braulik, G.T. & Smith, B.D. Platanista gangetica (amended version of 2017
    assessment). The IUCN Red List of Threatened Species, e.T41758A151913336. https://doi.org/10.2305/IUCN.UK.2017-3.RLTS.T41758A151913336.en (2019).Dawson, S. M., Northridge, S., Waples, D. & Read, A. J. To ping or not to ping: the use of active acoustic devices in mitigating interactions between small cetaceans and gillnet fisheries. Endanger. Species Res. 19, 201–221 (2013)
    Google Scholar 
    Reeves, R. R., McClellan, K. & Werner, T. B. Marine mammal bycatch in gillnet and other entangling net fisheries, 1990 to 2011. Endanger. Species Res. 20, 71–97 (2013).
    Google Scholar 
    Moore, M. J. et al. Fatally entangled right whales can die extremely slowly in OCEANS 2006. 1–3 (IEEE, 2006).Meÿer, M.A. et al. Trends and interventions in large whale entanglement along the South African coast. Afr. J. Mar. Sci. 33, 429–439 (2011).
    Google Scholar 
    Knowlton, A. R., Hamilton, P. K., Marx, M. K., Pettis, H. M. & Kraus, S. D. Monitoring North Atlantic right whale Eubalaena glacialis entanglement rates: a 30 year retrospective. Mar. Ecol. Prog. Ser. 466, 293–302 (2012).ADS 

    Google Scholar 
    Knowlton, A. R. et al. Effects of fishing rope strength on the severity of large whale entanglements. Conserv. Biol. 30, 318–328 (2016).
    Google Scholar 
    Pace, R. M. III., Cole, T. V. & Henry, A. G. Incremental fishing gear modifications fail to significantly reduce large whale serious injury rates. Endanger. Species Res. 26, 115–126 (2014).
    Google Scholar 
    Salvador, G., Kenney, J. & Higgins, J. 2008 Supplement to the Large whale gear research summary. NOAA/Fisheries Northeast Regional Office, Protected Resources Division, Gloucester, MA (2008).van der Hoop, J. M. et al. Assessment of management to mitigate anthropogenic effects on large whales. Conserv. Biol. 27, 121–133 (2013).
    Google Scholar 
    Hamilton, S. & Baker, G. B. Technical mitigation to reduce marine mammal bycatch and entanglement in commercial fishing gear: lessons learnt and future directions. Rev. Fish Biol. Fish. 29, 223–247 (2019).
    Google Scholar 
    Bordino, P., Mackay, A. I., Werner, T. B., Northridge, S. & Read, A. Franciscana bycatch is not reduced by acoustically reflective or physically stiffened gillnets. Endanger. Species Res. 21, 1–12 (2013).
    Google Scholar 
    Dawson, S. M. Incidental catch of Hector’s dolphin in inshore gillnets. Mar. Mamm. Sci. 7, 283–295 (1991).
    Google Scholar 
    Mooney, T. A., Nachtigall, P. E. & Au, W. W. Target strength of a nylon monofilament and an acoustically enhanced gillnet: predictions of biosonar detection ranges. Aquat. Mamm. 30, 220–226 (2004).
    Google Scholar 
    Northridge, S., Sanderson, D., Mackay, A. & Hammond, P. Analysis and mitigation of cetacean bycatch in UK fisheries. Final Report
    to DEFRA, Project MF0726, Sea Mammal Research Unit, School of Biology, University of St. Andrews (2003).Mangel, J. C. et al. Illuminating gillnets to save seabirds and the potential for multi-taxa bycatch mitigation. R. Soc. Open Sci. 5, 180254 (2018).ADS 

    Google Scholar 
    Stephenson, P. C. & Wells, S. Evaluation of the effectiveness of reducing dolphin catches with pingers and exclusion grids in the Pilbara trawl fishery. (Department of Fisheries, Western Australia, 2006).Santana-Garcon, J. et al. Risk versus reward: Interactions, depredation rates, and bycatch mitigation of dolphins in demersal fish trawls. Can. J. Fish. Aquat. Sci. 75, 2233–2240 (2018).
    Google Scholar 
    Carretta, J., Barlow, J. & Enriquez, L. Acoustic pingers eliminate beaked whale bycatch in a gill net fishery. Mar. Mamm. Sci. 24, 956–961 (2008).
    Google Scholar 
    Bordino, P. et al. Reducing incidental mortality of Franciscana dolphin Pontoporia blainvillei with acoustic warning devices attached to fishing nets. Mar. Mamm. Sci. 18, 833–842 (2002).
    Google Scholar 
    Khan, U. & Willems, D. Report of the Trinational workshop on the Irrawaddy Dolphin, 1st to 4th December 2020. 41 (WWF, Pakistan & Netherlands, 2021).Deori, S. et al. PINGERS: can be the eyes of blind ganges dolphins (Platanista Gangetica Gangetica, Roxburgh 1801). J. Sci. Trans. Environ. Technov. 11, 169–178 (2018).
    Google Scholar 
    Kraus, S. D. The once and future ping: challenges for the use of acoustic deterrents in fisheries. Mar. Technol. Soc. J. 33, 90 (1999).
    Google Scholar 
    Mate, B. R. & Harvey, J. T. Acoustical deterrents in marine mammal conflicts with fisheries. a workshop held February 17–18, 1986 at Newport, Oregon. NTIS, SPRINGFIELD, VA(USA) (1987).Favaro, L., Gnone, G. & Pessani, D. Postnatal development of echolocation abilities in a bottlenose dolphin (Tursiops truncatus): Temporal organization. Zoo Biol. 32, 210–215 (2013).
    Google Scholar 
    Dey, M., Krishnaswamy, J., Morisaka, T. & Kelkar, N. Interacting effects of vessel noise and shallow river depth elevate metabolic stress in Ganges river dolphins. Sci. Rep. 9, 15426. https://doi.org/10.1038/s41598-019-51664-1 (2019).ADS 

    Google Scholar 
    Kastelein, R. A. et al. Effects of acoustic alarms, designed to reduce small cetacean bycatch in gillnet fisheries, on the behaviour of North Sea fish species in a large tank. Mar. Environ. Res. 64, 160–180 (2007).CAS 

    Google Scholar 
    Kraus, S. et al. Acoustic alarms reduce porpoise mortality. Nature 388, 525 (1997).ADS 
    CAS 

    Google Scholar 
    Roberts, B. L. & Read, A. J. Field assessment of C-POD performance in detecting echolocation click trains of bottlenose dolphins (Tursiops truncatus). Mar. Mamm. Sci. 31, 169–190 (2015).
    Google Scholar 
    Wickham, H. ggplot2: elegant graphics for data analysis. (Springer-Verlag, New York, 2009).RStudio Team. RStudio: Integrated Development for R. RStudio, PBC, Boston, MA. http://www.rstudio.com/ (2021).Crawley, M. J. Statistics: An Introduction using R (Wiley, 2005).MATH 

    Google Scholar 
    Perrin, W. F., Donovan, G.P. & Barlow, J. Report of the workshop on mortality of cetaceans in passive fishing nets and traps. Rep. Int. Whal. Commn. 1–71 (Cambridge: IWC, 1994).Read, A. J., Drinker, P. & Northridge, S. Bycatch of marine mammals in US and global fisheries. Conserv. Biol. 20, 163–169 (2006).
    Google Scholar 
    Reeves, R. & Leatherwood, S. Action plan for the conservation of cetaceans: dolphins, porpoises, and whales. IUCN/SSC Cetacean Specialist Group (IUCN Cambridge, 1998).Smith, B. D. & Braulik, G. Susu and Bhulan : Platanista gangetica gangetica and P. g. minor in Encyclopedia of Marine Mammals. 1135–1139 (Academic Press Ltd – Elsevier Science Ltd, 2009).Wakid, A. Status and distribution of the endangered Gangetic dolphin (Platanista gangetica gangetica) in the Brahmaputra River within India in 2005. Curr. Sci., 97, 1143–1151 (2009).
    Google Scholar 
    D’agrosa, C., Lennert-Cody, C. E. & Vidal, O. Vaquita bycatch in Mexico’s artisanal gillnet fisheries: driving a small population to extinction. Conserv. Biol. 14, 1110–1119 (2000).
    Google Scholar 
    Jaramillo-Legorreta, A. et al. Saving the vaquita: immediate action, not more data. Conserv. Biol., 21, 1653–1655 (2007).
    Google Scholar 
    Turvey, S. T. et al. First human-caused extinction of a cetacean species?. Biol. Lett. 3, 537–540 (2007).
    Google Scholar 
    Bashir, T., Khan, A., Gautam, P. & Behera, S. K. Abundance and prey availability assessment of Ganges River dolphin (Platanista gangetica gangetica) in a stretch of Upper Ganges River, India. Aquat. Mamm. 36, 19–26 (2010).
    Google Scholar 
    Braulik, G.T. & Smith, B.D. Platanista gangetica. The IUCN Red List of Threatened Species, e.T41758A50383612. https://doi.org/10.2305/IUCN.UK.2017-3.RLTS.T41758A50383612.en (2017).Hastie, G. D., Wilson, B., Wilson, L., Parsons, K. M. & Thompson, P. M. Functional mechanisms underlying cetacean distribution patterns: hotspots for bottlenose dolphins are linked to foraging. Mar. Biol. 144, 397–403 (2004).
    Google Scholar 
    Smith, A. M. & Smith, B. D. Review of status and threats to river cetaceans and recommendations for their conservation. Environ. Rev. 6, 189–206 (1998).
    Google Scholar 
    Wedekin, L., Daura-Jorge, F., Piacentini, V. & Simões-Lopes, P. Seasonal variations in spatial usage by the estuarine dolphin, Sotalia guianensis (van Bénéden, 1864)(Cetacea; Delphinidae) at its southern limit of distribution. Brazil. J. Biol. 67, 1–8 (2007).CAS 

    Google Scholar 
    Omeyer, L. et al. Assessing the effects of banana pingers as a bycatch mitigation device for harbour porpoises (Phocoena phocoena). Front. Mar. Sci. 285 (2020).Barlow, J. & Cameron, G. A. Field experiments show that acoustic pingers reduce marine mammal bycatch in the California drift gill net fishery. Mar. Mamm. Sci. 19, 265–283 (2003).
    Google Scholar 
    Amano, M., Kusumoto, M., Abe, M. & Akamatsu, T. Long-term effectiveness of pingers on a small population of finless porpoises in Japan. Endanger. Species Res. 32, 35–40 (2017).
    Google Scholar 
    Clay, T. A., Alfaro-Shigueto, J., Godley, B. J., Tregenza, N. & Mangel, J. C. Pingers reduce the activity of Burmeister’s porpoise around small-scale gillnet vessels. Mar. Ecol. Prog. Ser. 626, 197–208 (2019).ADS 

    Google Scholar 
    Kyhn, L. A. et al. Pingers cause temporary habitat displacement in the harbour porpoise Phocoena phocoena. Mar. Ecol. Prog. Ser. 526, 253–265 (2015).ADS 

    Google Scholar 
    Sugimatsu, H. et al. Study of acoustic characteristics of Ganges river dolphin calf using echolocation clicks recorded during long-term in-situ observation in 2012 OCEANS. 1–7 (IEEE, 2012).Ayadi, A., Ghorbel, M. & Bradai, M. N. Do pingers reduce interactions between bottlenose dolphins and trammel nets around the Kerkennah Islands (Central Mediterranean Sea)?. Cahiers Biol. Mar. 54, 375–383 (2013).
    Google Scholar 
    Carretta, J. V. & Barlow, J. Long-term effectiveness, failure rates, and “dinner bell” properties of acoustic pingers in a gillnet fishery. Mar. Technol. Soc. J. 45, 7–19 (2011).
    Google Scholar 
    Read, A. J., Waples, D. M., Urian, K. W. & Swanner, D. Fine-scale behaviour of bottlenose dolphins around gillnets. Proc. R. Soc. Lond. Ser. B Biol. Sci. 270, S90–S92 (2003).
    Google Scholar 
    Olesiuk, P. F., Nichol, L. M., Sowden, M. J. & Ford, J. K. Effect of the sound generated by an acoustic harassment device on the relative abundance and distribution of harbor porpoises (Phocoena phocoena) in Retreat Passage, British Columbia. Mar. Mamm. Sci. 18, 843–862 (2002).
    Google Scholar 
    Cox, T. M., Read, A. J., Solow, A. & Tregenza, N. Will harbour porpoises (Phocoena phocoena) habituate to pingers?. J. Cetacean Res. Manag. 3, 81–86 (2001).
    Google Scholar 
    Bruno, C. A. et al. Acoustic deterrent devices as mitigation tool to prevent dolphin-fishery interactions in the Aeolian Archipelago (Southern Tyrrhenian Sea, Italy). Mediterr. Mar. Sci. 22, 408–421 (2021).
    Google Scholar 
    Enger, P. S. Frequency discrimination in teleosts—central or peripheral in Hearing and sound communication in fishes (eds. Tavolga, W. N. et al.) 243–255 (Springer-Verlag, New York, 1981).
    Google Scholar 
    Halvorsen, M. B., Casper, B. M., Matthews, F., Carlson, T. J. & Popper, A. N. Effects of exposure to pile-driving sounds on the lake sturgeon, Nile tilapia and hogchoker. Proc. R. Soc. B Biol. Sci. 279, 4705–4714 (2012).
    Google Scholar 
    Ladich, F. Sound communication in fishes and the influence of ambient and anthropogenic noise. Bioacoustics 17, 34–38 (2008).
    Google Scholar 
    McCauley, R. D., Fewtrell, J. & Popper, A. N. High intensity anthropogenic sound damages fish ears. J. Acoust. Soc. Am. 113, 638–642 (2003).ADS 

    Google Scholar 
    Slabbekoorn, H. et al. A noisy spring: the impact of globally rising underwater sound levels on fish. Trends Ecol. Evol. 25, 419–427 (2010).
    Google Scholar 
    Gazo, M., Gonzalvo, J. & Aguilar, A. Pingers as deterrents of bottlenose dolphins interacting with trammel nets. Fish. Res. 92, 70–75 (2008).
    Google Scholar 
    Waples, D. M. et al. A field test of acoustic deterrent devices used to reduce interactions between bottlenose dolphins and a coastal gillnet fishery. Biol. Conserv. 157, 163–171 (2013).
    Google Scholar 
    Leaper, R. & Calderan, S. Review of methods used to reduce risks of cetacean bycatch and entanglements. CMS Tech. Ser. 38 (UNEP/CMS Secretariat, Bonn, Germany, 2018). More