Ecology

The CAN-SAR22 database was created to provide access to publicly available data on species at risk in Canada in a standardized format that can be used in a wide range of applied research contexts. The variables included in the database were chosen to provide a range of information available for species at risk with a particular focus on climate change to support the first publication using the database6. The database includes numerous data fields including extinction risk status, various biological and geographical attributes, threat assessments, date of listing, recovery actions, and a set of climate change impact and adaptation variables. CAN-SAR is a living database that can be updated as new information and reports become available, or as other targeted data extraction efforts become available23.In Canada, the listing process begins with an assessment of a wildlife species’ risk of extinction by the Committee on the Status of Endangered Wildlife in Canada (COSEWIC). A wildlife species can be either a species or a ‘designatable unit’, which includes subspecies, varieties, or other geographically or genetically distinct populations. Herein these are referred to collectively as ‘species’. COSEWIC is an independent body of experts who synthesize the best available information to date into a status report containing elements such as population size and trends, habitat availability, and threat assessments (Fig. 1)17. This report is then used as the basis for a status recommendation that is passed on to the Government of Canada, who makes the final decision on whether to legally list the species under Schedule 1 of SARA24. The species can be listed as ‘Special concern’, ‘Threatened’, ‘Endangered’, or ‘Extirpated’. If a species is listed as ‘Threatened’, ‘Endangered’ or ‘Extirpated’ then a recovery strategy is required, while for species listed as ‘Special concern’ a management plan must be created24. Recovery strategies must provide a description of the species’ needs, address identified threats, identify critical habitat (where applicable and to the extent possible), and include population and distribution objectives for the species’ recovery. Management plans include conservation measures for the species and its habitat24. Hereafter, we refer to recovery strategies and management plans collectively as ‘recovery documents’.Information included in the database was extracted from various sources and documents that are available from the online SAR Public Registry, including COSEWIC status reports and status appraisal summaries, and recovery documents (Fig. 1). A COSEWIC status appraisal summary is produced instead of a new status report when a species has been previously assessed and COSEWIC experts are confident that its status will not change (https://www.cosewic.ca/index.php/en-ca/assessment-process/status-appraisal-summary-process.html). It is considered an addendum to the existing status report; thus, we use ‘status report’ to refer to either a status report or a status appraisal summary and the previous status report. From the SAR Public Registry website we accessed information from 1146 documents for all 594 species listed under SARA Schedule 1 as of March 23, 2021, that were classified with the status of ‘Special concern’, ‘Threatened’, or ‘Endangered’. Some species have multiple documents of the same type because COSEWIC reassesses at risk species every 10 years or less and recovery strategies and management plans are reviewed every 5 years and updated as needed. As new documents have become available they have been added to the CAN-SAR database without overriding the previously existing document, which allows for tracking of changes in various data fields over time. Only documents between 2018 and 2021, inclusive, have an updated version due to our updating schedule.Data extractionVariables included in the CAN-SAR database were categorised as either directly transcribed or derived. Directly transcribed variables reflect information extracted from documents that require limited interpretation, such as scientific name or date of legal listing (Online-only Table 1). Derived variables reflect species’ attributes that required interpretation of text by data recorders (Online-only Table 1). The data dictionary (CAN-SAR_data_dictionary.xlsx) contains a description of each variable, including details of their extraction and synthesis22.Several derived variables were extracted from the status report technical summary section, including whether the species is endemic to Canada or North America, and whether the species’ range is continuous with the United States. Endemism was determined for each species at two spatial extents, Canada and North America, based on descriptions of their global distributions from status reports. Whether a Canadian species’ range is continuous with its conspecifics in the United States was interpreted from descriptions of geographic isolation in the distribution and rescue effect sections of the status reports.Variables related to species’ threats were derived from information in the status reports, recovery strategies and management plans. In 2012, COSEWIC initiated use of the IUCN threats classification system in status reports for some species; a ‘threats calculator’25. Threats calculators may also be included in recovery strategies and management plans. A threats calculator is a table included in the document that classifies threats into 11 general ‘level one’ classes and, more specific ‘level two’ subclasses (Table 1)26. Four variables (impact, severity, scope, and timing) for each level one and level two threats were scored independently and then combined into an overall impact score for each species. Impact is defined as the degree to which the species is threatened by the threat class; severity is the level of damage to the species from the threat class that is expected within ten years or three generations, whichever is longer; scope is the proportion of the species that is expected to be affected within ten years; and timing is the immediacy of the threat25. Threat-related variables were either transcribed directly from the threats calculator, or from the derived description of threats in the document if a threats calculator was not included.Table 1 Definitions of level one threat classes and names of level two threat classes following Version 1.1 of the IUCN threats classification system.Full size tableFor species where a threats calculator was included, we recorded whether each of the level one and level two threat classes were identified (i.e., considered a threat), and transcribed the scores for each of impact, scope, severity, and timing. Threat classes were considered identified if the impact was negligible, low, moderate, high, very high, unknown, or not calculated (outside assessment timeframe). Impact, scope, severity, and timing values were coded as ranked values of ‘0’: not a threat; ‘1’: neglible; ‘2’: low; ‘3’: moderate; ‘4’: high; ‘5’: very high; ‘-1’: unknown; ‘-2’: not calculated; or ‘NA’ where there were blank values. For exact ranking interpretations see CAN-SAR_data_dictionary22. For some species, the threats calculator was available from the COSEWIC Secretariat as a Microsoft Excel file, in which case threats information was extracted directly from the spreadsheet using R v 3.6.227. For species where a Microsoft Excel file was not available, threats calculator information was manually extracted from the status report.For species where a threats calculator was not included in the document, threats described in the text were classified into threat classes based on version 1.1 of the IUCN threats classification system (Table 1)26. Although a more recent version of the threats calculator exists, we applied version 1.1 classification to reflect the approach applied across the majority of species. Threats were considered identified if the threat was discussed as having any negative or potentially negative impact on the species. In cases where no threat calculator was available, the threat attributes of impact, scope, severity, and timing were scored as not applicable; ‘NA’.Several variables were derived to determine how climate change was addressed in status reports and recovery documents. Whether climate change was mentioned anywhere in the status report was determined by searching the document for the words climat*, warm, temperat*, and drought. If a document contained any of these search terms, we assessed the context for description of anthropogenic climate change impacts. In cases where the terms were not found, the threats section was checked for any other descriptions that were related to climate change; if none were found, climate change was recorded as not mentioned. When climate change was mentioned, we then determined if it was identified as a threat by interpreting whether it was described as having a negative or potentially negative impact on the species. If a threats calculator was included in the status report, climate change was considered a threat if the ‘Climate change and severe weather’ threat class had an impact that was more than negligible or if climate change was described outside the threats calculator as a threat or potential threat. We recorded whether the threat of climate change was unknown. This included instances where climate change was described as having unknown effects on the species, if ‘unknown’ was assigned to impact, scope, severity, or timing in the threats calculator, or if knowledge gaps related to climate change were identified. Finally, the impact of climate change relative to other threats was classified based on descriptions of threats in the status report. The relative impact of climate change was classified as ‘0’ if it was not a threat; ‘1’ if it was described as a minor, potential, possible, or other threat; ‘2’ if it was a significant threat but not the most important or if it was among the list of threats with no indication of relative importance; or ‘3’ if it was among the most important threats described.Additional derived variables extracted from recovery documents available on the SAR Public Registry included those related to critical habitat identification and recovery actions. For species with recovery strategies, we recorded whether critical habitat was described as identified, partially identified, or not identified. In cases where critical habitat was described as “identified to the extent possible”, it was marked as identified. We extracted information from recovery documents on what types of actions were recommended and whether the actions addressed the threat of climate change. Actions were categorized into four categories: outreach and stewardship, research and monitoring, habitat management, and population management (Table 2). Within each of the four categories, a set of 16 sub-types were recorded if any actions of that type were recommended or already completed. We also recorded action types and sub-types that specifically addressed climate change threats if climate change was listed as the threat addressed or the reason the action was necessary6.Table 2 Categories of actions specified in Recovery Strategies.Full size tableFive data recorders conducted the initial data extraction, synthesis, and interpretation. All recorders were trained on the definitions, interpretation, and general process of data extraction to ensure consistent extraction of all variables. Data extraction occurred in multiple stages and included an iterative set of verifications and assessments of the same species among recorders to ensure consistent and standardized interpretations. Once convergence of interpretations was achieved, each recorder was assigned a set of species/reports from which to extract information.Next stepsThe CAN-SAR database is intended to be a living database that can be updated by adding information from new documents or species as they become available, adding more historical documents, or extracting new information from all documents. The current set of species and associated information includes those listed on Schedule 1 of SARA (as of March 23rd 2021) as ‘Special concern’, ‘Threatened’, or ‘Endangered’. Examples of future data additions include integration of data from species assessed by COSEWIC that are not listed under Schedule 1 of SARA, adding fields that specify the criteria used to arrive at a risk status designation, and integration of data from action plans. We anticipate updating the database periodically, as time and resources allow, and we also encourage anyone interested in extending or expanding on the CAN-SAR database to communicate to discuss a collaboration. Integration of new datasets will require screening and validation to ensure adherence to data standards and consistent interpretations. In the longer term, we foresee the implementation of automatic updating of the CAN-SAR database for variables that do not require interpretation by using machine-readable formatted status and recovery documents.ApplicationsApplications of the CAN-SAR database reflect both opportunities to synthesise the data in novel ways and to expand the scope of the current database to include new data fields representing information contained in status assessments and recovery documents. The CAN-SAR database facilitates independent data analysis and synthesis efforts ranging from trend analysis of threats, identifying research and monitoring gaps, and assessing the effectiveness of recovery actions, which target various steps of the listing and recovery process. For example, the database provides a platform to extend existing climate change focused work6 to assess the prevalence of recommended climate change targeted recovery actions, such as translocations. With recent adoption of the ‘Pan-Canadian approach to transforming Species at Risk conservation in Canada’28, which emphasizes multi-species recovery planning approaches, there is an opportunity to assess patterns in key sectors, which include agriculture, forestry, and urban development, over time and by taxa and how they map to threats.With the integration of additional variables through future data extraction or integration efforts, the CAN-SAR database can be used to assess novel questions. For example, broadening recovery action categories to include those that reflect natural climate solutions can highlight where recovery efforts may provide co-benefits, thus achieving biodiversity conservation and climate change mitigation goals29. Specifically, habitat restoration actions for a forest-dependent species primarily threatened by habitat loss may lead to improved recovery outcomes while also resulting in carbon sequestration and improved climate change mitigation efforts. Tracking these types of actions in CAN-SAR could highlight both opportunities and gaps for the integration of climate smart conservation principles30 into species at risk recovery planning and the adoption of climate change adaption measures for species directly considered climate change threatened and those that are not6. More

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

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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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

Montzka SA, Dlugokencky EJ, Butler JH. Non-CO2 greenhouse gases and climate change. Nature 2011;476:43–50.CAS 
PubMed 
Article 

Google Scholar 
Masson-Delmotte V, Zhai P, Pirani A, Connors SL, Péan C, Berger S, et al. (eds). Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press; 2021. (in press).Wuebbles DJ. Nitrous oxide: no laughing matter. Science. 2009;326:56–7.CAS 
PubMed 
Article 

Google Scholar 
Kool DM, Dolfing J, Wrage N, van Groenigen JW. Nitrifier denitrification as a distinct and significant source of nitrous oxide from soil. Soil Biol Biochem. 2011;43:174–8.CAS 
Article 

Google Scholar 
Yoon S, Song B, Phillips RL, Chang J, Song MJ. Ecological and physiological implications of nitrogen oxide reduction pathways on greenhouse gas emissions in agroecosystems. FEMS Microbiol Ecol. 2019;95:fiz066.CAS 
PubMed 
Article 

Google Scholar 
Sanford RA, Wagner DD, Wu Q, Chee-Sanford JC, Thomas SH, Cruz-García C, et al. Unexpected nondenitrifier nitrous oxide reductase gene diversity and abundance in soils. Proc Natl Acad Sci USA. 2012;109:19709–14.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hallin S, Philippot L, Löffler FE, Sanford RA, Jones CM. Genomics and ecology of novel N2O-reducing microorganisms. Trends Microbiol. 2018;26:43–55.CAS 
PubMed 
Article 

Google Scholar 
Graf DR, Jones CM, Hallin S. Intergenomic comparisons highlight modularity of the denitrification pathway and underpin the importance of community structure for N2O emissions. PLoS One. 2014;9:e114118.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Roco CA, Bergaust LL, Bakken LR, Yavitt JB, Shapleigh JP. Modularity of nitrogen‐oxide reducing soil bacteria: linking phenotype to genotype. Environ Microbiol. 2017;19:2507–19.CAS 
PubMed 
Article 

Google Scholar 
Jones CM, Graf DR, Bru D, Philippot L, Hallin S. The unaccounted yet abundant nitrous oxide-reducing microbial community: A potential nitrous oxide sink. ISME J. 2013;7:417–26.CAS 
PubMed 
Article 

Google Scholar 
Frostegård Å, Vick SH, Lim NY, Bakken LR, Shapleigh JP. Linking meta-omics to the kinetics of denitrification intermediates reveals pH-dependent causes of N2O emissions and nitrite accumulation in soil. ISME J. 2022;16:26–37.PubMed 
Article 
CAS 

Google Scholar 
Simon J, Einsle O, Kroneck PMH, Zumft WG. The unprecedented nos gene cluster of Wolinella succinogenes encodes a novel respiratory electron transfer pathway to cytochrome c nitrous oxide reductase. FEBS Lett. 2004;569:7–12.CAS 
PubMed 
Article 

Google Scholar 
Foley J, De Haas D, Yuan Z, Lant P. Nitrous oxide generation in full-scale biological nutrient removal wastewater treatment plants. Water Res. 2010;44:831–44.CAS 
PubMed 
Article 

Google Scholar 
Zheng J, Doskey PV. Simulated rainfall on agricultural soil reveals enzymatic regulation of short-term nitrous oxide profiles in soil gas and emissions from the surface. Biogeochemistry. 2016;128:327–38.CAS 
Article 

Google Scholar 
Kern M, Simon J. Three transcription regulators of the Nss family mediate the adaptive response induced by nitrate, nitric oxide or nitrous oxide in Wolinella succinogenes. Environ Microbiol. 2016;18:2899–912.CAS 
PubMed 
Article 

Google Scholar 
Suenaga T, Riya S, Hosomi M, Terada A. Biokinetic characterization and activities of N2O-reducing bacteria in response to various oxygen levels. Front Microbiol. 2018;9:697.PubMed 
PubMed Central 
Article 

Google Scholar 
Kim DD, Park D, Yoon H, Yun T, Song MJ, Yoon S. Quantification of nosZ genes and transcripts in activated sludge microbiomes with novel group-specific qPCR methods validated with metagenomic analyses. Water Res. 2020;185:116261.CAS 
PubMed 
Article 

Google Scholar 
Yoon S, Nissen S, Park D, Sanford RA, Löffler FE. Nitrous oxide reduction kinetics distinguish bacteria harboring clade I NosZ from those harboring clade II NosZ. Appl Environ Microbiol. 2016;82:3793–800.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Yoon H, Song MJ, Kim DD, Sabba F, Yoon S. A serial biofiltration system for effective removal of low-concentration nitrous oxide in oxic gas streams: mathematical modeling of reactor performance and experimental validation. Environ Sci Technol. 2019;53:2063–74.CAS 
PubMed 
Article 

Google Scholar 
Suenaga T, Hori T, Riya S, Hosomi M, Smets BF, Terada A. Enrichment, isolation, and characterization of high-affinity N2O-reducing bacteria in a gas-permeable membrane reactor. Environ Sci Technol. 2019;53:12101–12.CAS 
PubMed 
Article 

Google Scholar 
Conthe M, Wittorf L, Kuenen JG, Kleerebezem R, van Loosdrecht MC, Hallin S. Life on N2O: Deciphering the ecophysiology of N2O respiring bacterial communities in a continuous culture. ISME J. 2018;12:1142–53.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Henry S, Bru D, Stres B, Hallet S, Philippot L. Quantitative detection of the nosZ gene, encoding nitrous oxide reductase, and comparison of the abundances of 16S rRNA, narG, nirK, and nosZ genes in soils. Appl Environ Microbiol. 2006;72:5181–9.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Qi C, Zhou Y, Suenaga T, Oba K, Lu J, Wang G, et al. Organic carbon determines nitrous oxide consumption activity of clade I and II nosZ bacteria: Genomic and biokinetic insights. Water Res. 2022;209:117910.CAS 
Article 

Google Scholar 
Gao Y, Mania D, Mousavi SA, Lycus P, Arntzen MØ, Woliy K, et al. Competition for electrons favours N2O reduction in denitrifying Bradyrhizobium isolates. Environ Microbiol. 2021;23:2244–59.CAS 
PubMed 
Article 

Google Scholar 
Song MJ, Choi S, Bae WB, Lee J, Han H, Kim DD, et al. Identification of primary effecters of N2O emissions from full-scale biological nitrogen removal systems using random forest approach. Water Res. 2020;184:116144.CAS 
PubMed 
Article 

Google Scholar 
Ahn JH, Kim S, Park H, Rahm B, Pagilla K, Chandran K. N2O emissions from activated sludge processes, 2008−2009: results of a national monitoring survey in the United States. Environ Sci Technol. 2010;44:4505–11.CAS 
PubMed 
Article 

Google Scholar 
Bollmann A, Conrad R. Influence of O2 availability on NO and N2O release by nitrification and denitrification in soils. Glob Chang Biol 1998;4:387–96.Article 

Google Scholar 
Morris RL, Schmidt TM. Shallow breathing: Bacterial life at low O2. Nat Rev Microbiol. 2013;11:205–12.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Marchant HK, Ahmerkamp S, Lavik G, Tegetmeyer HE, Graf J, Klatt JM, et al. Denitrifying community in coastal sediments performs aerobic and anaerobic respiration simultaneously. ISME J. 2017;11:1799–812.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Camejo PY, Oyserman BO, McMahon KD, Noguera DR. Integrated omic analyses provide evidence that a “Candidatus Accumulibacter phosphatis” strain performs denitrification under microaerobic conditions. mSystems. 2019;4:e00193–18.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Yoon S, Sanford RA, Löffler FE. Shewanella spp. use acetate as an electron donor for denitrification but not ferric iron or fumarate reduction. Appl Environ Microbiol. 2013;79:2818–22.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
van den Berg EM, Boleij M, Kuenen JG, Kleerebezem R, van Loosdrecht M. DNRA and denitrification coexist over a broad range of acetate/N-NO3− ratios, in a chemostat enrichment culture. Front Microbiol. 2016;7:1842.PubMed 
PubMed Central 
Article 

Google Scholar 
Sander R. Compilation of Henry’s law constants (version 4.0) for water as solvent. Atmos Chem Phys. 2015;15:4399–981.CAS 
Article 

Google Scholar 
Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME2. Nat Biotechnol. 2019;37:852–7.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Binder BJ, Liu YC. Growth rate regulation of rRNA content of a marine Synechococcus (cyanobacterium) strain. Appl Environ Microbiol. 1998;64:3346–51.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Shrestha PM, Rotaru AE, Aklujkar M, Liu F, Shrestha M, Summers ZM, et al. Syntrophic growth with direct interspecies electron transfer as the primary mechanism for energy exchange. Environ Microbiol Rep. 2013;5:904–10.CAS 
PubMed 
Article 

Google Scholar 
Ritalahti KM, Amos BK, Sung Y, Wu Q, Koenigsberg SS, Löffler FE. Quantitative PCR targeting 16S rRNA and reductive dehalogenase genes simultaneously monitors multiple Dehalococcoides strains. Appl Environ Microbiol. 2006;72:2765–74.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bolger AM, Lohse M, Usadel B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 2014;30:2114–20.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Nurk S, Meleshko D, Korobeynikov A, Pevzner PA. metaSPAdes: A new versatile metagenomic assembler. Genome Res. 2017;27:824–34.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hyatt D, Chen G-L, LoCascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: Prokaryotic gene recognition and translation initiation site identification. BMC Bioinform. 2010;11:1–11.Article 
CAS 

Google Scholar 
Buchfink B, Reuter K, Drost H-G. Sensitive protein alignments at tree-of-life scale using DIAMOND. Nat Methods. 2021;18:366–8.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kanehisa M, Sato Y, Morishima K. BlastKOALA and GhostKOALA: KEGG tools for functional characterization of genome and metagenome sequences. J Mol Biol. 2016;428:726–31.CAS 
PubMed 
Article 

Google Scholar 
Edgar RC. MUSCLE: Multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004;32:1792–97.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Stamatakis A. RAxML version 8: A tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 2014;30:1312–13.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Huang Y, Gilna P, Li W. Identification of ribosomal RNA genes in metagenomic fragments. Bioinformatics 2009;25:1338–40.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Miller CS, Baker BJ, Thomas BC, Singer SW, Banfield JF. EMIRGE: reconstruction of full-length ribosomal genes from microbial community short read sequencing data. Genome Biol. 2011;12:R44.CAS 
PubMed 
PubMed Central 
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. 2012;41:D590–6.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Wang Q, Garrity GM, Tiedje JM, Cole JR. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol. 2007;73:5261–7.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Li H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. Preprint arXiv:13033997. 2013.Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The sequence alignment/map format and SAMtools. Bioinformatics 2009;25:2078–9.PubMed 
PubMed Central 

Google Scholar 
Quinlan AR, Hall IM. BEDTools: A flexible suite of utilities for comparing genomic features. Bioinformatics 2010;26:841–2.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Nayfach S, Pollard KS. Toward accurate and quantitative comparative metagenomics. Cell 2016;166:1103–16.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Li B, Dewey CN. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinform. 2011;12:1–16.Article 

Google Scholar 
Li D, Liu C-M, Luo R, Sadakane K, Lam T-W. MEGAHIT: An ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 2015;31:1674–6.CAS 
PubMed 
Article 

Google Scholar 
Kang DD, Froula J, Egan R, Wang Z. MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities. PeerJ 2015;3:e1165.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Parks DH, Rinke C, Chuvochina M, Chaumeil P-A, Woodcroft BJ, Evans PN, et al. Recovery of nearly 8,000 metagenome-assembled genomes substantially expands the tree of life. Nat Microbiol 2017;2:1533–42.CAS 
PubMed 
Article 

Google Scholar 
Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;25:1043–55.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Olm MR, Brown CT, Brooks B, Banfield JF. dRep: A tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication. ISME J. 2017;11:2864–8.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Rodriguez-R LM, Gunturu S, Harvey WT, Rosselló-Mora R, Tiedje JM, Cole JR, et al. The Microbial Genomes Atlas (MiGA) webserver: taxonomic and gene diversity analysis of Archaea and Bacteria at the whole genome level. Nucleic Acids Res. 2018;46:W282–8.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Darling AE, Jospin G, Lowe E, Matsen FA IV, Bik HM, Eisen JA. PhyloSift: Phylogenetic analysis of genomes and metagenomes. PeerJ. 2014;2:e243.PubMed 
PubMed Central 
Article 

Google Scholar 
Talavera G, Castresana J. Improvement of phylogenies after removing divergent and ambiguously aligned blocks from protein sequence alignments. Syst Biol. 2007;56:564–77.CAS 
PubMed 
Article 

Google Scholar 
Salazar G, Paoli L, Alberti A, Huerta-Cepas J, Ruscheweyh H-J, Cuenca M, et al. Gene expression changes and community turnover differentially shape the global ocean metatranscriptome. Cell 2019;179:1068–83.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Milanese A, Mende DR, Paoli L, Salazar G, Ruscheweyh H-J, Cuenca M, et al. Microbial abundance, activity and population genomic profiling with mOTUs2. Nat Commun. 2019;10:1014.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Sunagawa S, Mende DR, Zeller G, Izquierdo-Carrasco F, Berger SA, Kultima JR, et al. Metagenomic species profiling using universal phylogenetic marker genes. Nat Methods. 2013;10:1196–9.CAS 
PubMed 
Article 

Google Scholar 
Yap CX, Henders AK, Alvares GA, Wood DL, Krause L, Tyson GW, et al. Autism-related dietary preferences mediate autism-gut microbiome associations. Cell 2021;184:5916–31.CAS 
PubMed 
Article 

Google Scholar 
Shan J, Sanford RA, Chee‐Sanford J, Ooi SK, Löffler FE, Konstantinidis KT, et al. Beyond denitrification: the role of microbial diversity in controlling nitrous oxide reduction and soil nitrous oxide emissions. Glob Chang Biol. 2021;27:2669–83.PubMed 
Article 

Google Scholar 
Jones CM, Spor A, Brennan FP, Breuil M-C, Bru D, Lemanceau P, et al. Recently identified microbial guild mediates soil N2O sink capacity. Nat Clim Chang. 2014;4:801–5.Kim J, Kim DD, Yoon S. Rapid isolation of fast-growing methanotrophs from environmental samples using continuous cultivation with gradually increased dilution rates. Appl Microbiol Biotechnol. 2018;102:5707–15.CAS 
PubMed 
Article 

Google Scholar 
Betlach MR, Tiedje JM. Kinetic explanation for accumulation of nitrite, nitric oxide, and nitrous oxide during bacterial denitrification. Appl Environ Microbiol. 1981;42:1074–84.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bueno E, Mesa S, Bedmar EJ, Richardson DJ, Delgado MJ. Bacterial adaptation of respiration from oxic to microoxic and anoxic conditions: Redox control. Antioxid Redox Signal. 2012;16:819–52.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Rauhamäki V, Bloch DA, Wikström M. Mechanistic stoichiometry of proton translocation by cytochrome cbb3. Proc Natl Acad Sci USA. 2012;109:7286–91.PubMed 
PubMed Central 
Article 

Google Scholar 
Borisov VB, Gennis RB, Hemp J, Verkhovsky MI. The cytochrome bd respiratory oxygen reductases. Biochim Biophys Acta – Bioenerg. 2011;1807:1398–413.CAS 
Article 

Google Scholar 
Lee A, Winther M, Priemé A, Blunier T, Christensen S. Hot spots of N2O emission move with the seasonally mobile oxic-anoxic interface in drained organic soils. Soil Biol Biochem. 2017;115:178–86.CAS 
Article 

Google Scholar 
Orellana L, Rodriguez-R L, Higgins S, Chee-Sanford J, Sanford R, Ritalahti K, et al. Detecting nitrous oxide reductase (nosZ) genes in soil metagenomes: method development and implications for the nitrogen cycle. MBio 2014;5:e01193–14.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ormeño-Orrillo E, Martínez-Romero E. A genomotaxonomy view of the Bradyrhizobium genus. Front Microbiol. 2019;10:1334.PubMed 
PubMed Central 
Article 

Google Scholar 
Tong W, Li X, Wang E, Cao Y, Chen W, Tao S, et al. Genomic insight into the origins and evolution of symbiosis genes in Phaseolus vulgaris microsymbionts. BMC Genom. 2020;21:186.CAS 
Article 

Google Scholar 
Conthe M, Lycus P, Arntzen MØ, da Silva AR, Frostegård Å, Bakken LR, et al. Denitrification as an N2O sink. Water Res. 2019;151:381–7.CAS 
PubMed 
Article 

Google Scholar 
Goldblatt C, Lenton TM, Watson AJ. Bistability of atmospheric oxygen and the Great Oxidation. Nature. 2006;443:683–6.CAS 
PubMed 
Article 

Google Scholar 
Brewer PG, Hofmann AF, Peltzer ET, Ussler W III. Evaluating microbial chemical choices: The ocean chemistry basis for the competition between use of O2 or NO3− as an electron acceptor. Deep Sea Res Part I Oceanogr Res Pap. 2014;87:35–42.CAS 
Article 

Google Scholar 
Bianchi D, Dunne JP, Sarmiento JL, Galbraith ED. Data‐based estimates of suboxia, denitrification, and N2O production in the ocean and their sensitivities to dissolved O2. Global Biogeochem Cycles 2012;26:GB2009.Stolper DA, Revsbech NP, Canfield DE. Aerobic growth at nanomolar oxygen concentrations. Proc Natl Acad Sci USA. 2010;107:18755–60.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zakem E, Follows M. A theoretical basis for a nanomolar critical oxygen concentration. Limnol Oceanogr. 2017;62:795–805.Article 

Google Scholar 
Liengaard L, Nielsen LP, Revsbech NP, Priemé A, Elberling B, Enrich-Prast A, et al. Extreme emission of N2O from tropical wetland soil (Pantanal, South America). Front Microbiol. 2013;3:433.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Shcherbak I, Robertson GP. Nitrous oxide (N2O) emissions from subsurface soils of agricultural ecosystems. Ecosystems. 2019;22:1650–63.CAS 
Article 

Google Scholar 
Qu Z, Bakken LR, Molstad L, Frostegård Å, Bergaust LL. Transcriptional and metabolic regulation of denitrification in Paracoccus denitrificans allows low but significant activity of nitrous oxide reductase under oxic conditions. Environ Microbiol. 2016;18:2951–63.CAS 
PubMed 
Article 

Google Scholar 
Desloover J, Roobroeck D, Heylen K, Puig S, Boeckx P, Verstraete W, et al. Pathway of nitrous oxide consumption in isolated Pseudomonas stutzeri strains under anoxic and oxic conditions. Environ Microbiol. 2014;16:3143–52.CAS 
PubMed 
Article 

Google Scholar  More

Game, E. T. et al. Pelagic protected areas: The missing dimension in ocean conservation. Trends Ecol. Evol. 24, 360–369 (2009).PubMed 
Article 

Google Scholar 
McCauley, D. J. et al. Marine defaunation: Animal loss in the global ocean. Science 347, 1255641–1255647 (2015).PubMed 
Article 
CAS 

Google Scholar 
Paleczny, M., Hammill, E., Karpouzi, V. & Pauly, D. Population trend of the world’s monitored seabirds, 1950–2010. PLoS ONE 10, e0129342 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Croxall, J. P. et al. Seabird conservation status and threats: A global assessment of priorities. Bird Conserv. Int. 22, 1–34 (2012).Article 

Google Scholar 
Dias, M. P. et al. Threats to seabirds: A global assessment. Biol. Conserv. 237, 525–537 (2019).Article 

Google Scholar 
Trathan, P. N. et al. Pollution, habitat loss, fishing, and climate change as critical threats to penguins. Conserv. Biol. 29, 31–41 (2014).PubMed 
Article 

Google Scholar 
Boersma, D. et al. Applying science to pressing conservation needs for penguins. Conserv. Biol. 34, 103–112 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Ropert-Coudert, Y. et al. Happy feet in a hostile world? The future of penguins depends on proactive management of current and expected threats. Front. Mar. Sci. 6, 248 (2019).Article 

Google Scholar 
Maestro, M., Pérez-Cayeiro, M. L., Chica-Ruiz, J. A. & Reyes, H. Marine protected areas in the 21st century: Current situation and trends. Ocean Coast. Manag. 171, 28–36 (2019).Article 

Google Scholar 
Hays, G. C. et al. Key questions in marine megafauna movement ecology. Trends Ecol. Evol. 31, 463–475 (2016).PubMed 
Article 

Google Scholar 
Boyd, C. et al. Spatial scale and the conservation of threatened species. Conserv. Lett. 1, 37–43 (2008).Article 

Google Scholar 
Marra, P. P., Cohen, E. B., Loss, S. R., Rutter, J. E. & Tonra, C. M. A call for full annual cycle research in animal ecology. Biol. Lett. 11, 20150552 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Kürten, N. et al. High individual repeatability of the migratory behaviour of a long-distance migratory seabird. Mov. Ecol. 10, 5 (2022).PubMed 
PubMed Central 
Article 

Google Scholar 
Weimerskirch, H. et al. Lifetime foraging patterns of the wandering albatross: Life on the move!. J. Exp. Mar. Biol. Ecol. 450, 68–78 (2014).Article 

Google Scholar 
Trebilco, R., Gales, R., Baker, G. B., Terauds, A. & Sumner, M. D. At sea movement of Macquarie Island giant petrels: Relationships with marine protected areas and Regional Fisheries Management Organisations. Biol. Conserv. 141, 2942–2958 (2008).Article 

Google Scholar 
Clay, T. A. et al. A comprehensive large-scale assessment of fisheries bycatch risk to threatened seabird populations. J. Appl. Ecol. 56, 1882–1893 (2019).Article 

Google Scholar 
Meier, R. E. et al. Tracking, feather moult and stable isotopes reveal foraging behaviour of a critically endangered seabird during the non-breeding season. Divers. Distrib. 23, 130–145 (2017).Article 

Google Scholar 
Frankish, C. K., Phillips, R. A., Clay, T. A., Somveille, M. & Manica, A. Environmental drivers of movement in a threatened seabird: Insights from a mechanistic model and implications for conservation. Divers. Distrib. 26, 1315–1329 (2020).Article 

Google Scholar 
Ratcliffe, N. et al. Changes in prey fields increase the potential for spatial overlap between gentoo penguins and a krill fishery within a marine protected area. Divers. Distrib. 27, 552–563 (2021).Article 

Google Scholar 
Grémillet, D. et al. Persisting worldwide seabird-fishery competition despite seabird community decline. Curr. Biol. 28, 4009–4013 (2018).PubMed 
Article 
CAS 

Google Scholar 
Bogdanova, M. I. et al. Multi-colony tracking reveals spatio-temporal variation in carry-over effects between breeding success and winter. Mar. Ecol. Prog. Ser. 578, 167–181 (2017).Article 
ADS 

Google Scholar 
van Bemmelen, R. et al. Flexibility in otherwise consistent non-breeding movements of a long-distance migratory seabird, the long-tailed skua. Mar. Ecol. Prog. Ser. 578, 197–211 (2017).Article 
ADS 

Google Scholar 
Robinson, W. M. L., Butterworth, D. S. & Plagányi, É. E. Quantifying the projected impact of the South African sardine fishery on the Robben Island penguin colony. ICES J. Mar. Sci. 72, 1882–1883 (2015).Article 

Google Scholar 
Sherley, R. B. et al. Bottom-up effects of a no-take zone on endangered penguin demographics. Biol. Lett. 11, 20150237 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Studholme, K. R., Hipfner, J. M., Domalik, A. D., Ivrson, S. J. & Crossin, G. T. Year-round tracking reveals multiple migratory tactics in a sentinel North Pacific seabird, Cassin’s auklet. Mar. Ecol. Prog. Ser. 619, 169–185 (2019).Article 
ADS 

Google Scholar 
Salton, M., Saraux, C., Dann, P. & Chiaradia, A. Carry-over body mass effect from winter to breeding in a resident seabird, the little penguin. R. Soc. Open Sci. 2, 140390 (2015).PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
Barbraud, C. et al. Density dependence, prey accessibility and prey depletion by fisheries drive Peruvian seabird population dynamics. Ecography 41, 1092–1102 (2018).Article 

Google Scholar 
Grémillet, D. et al. Starving seabirds: Unprofitable foraging and its fitness consequences in Cape gannets competing with fisheries in the Benguela upwelling ecosystem. Mar. Biol. 163, 1–11 (2016).Article 

Google Scholar 
Cook, A. S. C. P., Dadam, D., Mitchell, I., Ross-Smith, V. H. & Robinson, R. A. Indicators of seabird reproductive performance demonstrate the impact of commercial fisheries on seabird populations in the North Sea. Ecol. Indic. 38, 1–11 (2014).Article 

Google Scholar 
Thiebot, J.-B. et al. Adjustment of pre-moult foraging strategies in Macaroni Penguins Eudyptes chrysolophus according to locality, sex and breeding status. Ibis 156, 511–522 (2014).Article 

Google Scholar 
Brasso, R. L. et al. Unique pattern of molt leads to low intraindividual variation in feather mercury concentrations in penguins. Environ. Toxicol. Chem. 32, 2331–2334 (2013).CAS 
PubMed 
Article 

Google Scholar 
Cooper, J. Moult of the black-footed penguin. Int. Zoo Yearb. 18, 22–27 (1978).Article 

Google Scholar 
Cherel, Y., Charrassin, J. & Challet, E. Energy and protein requirements for molt in the king penguin Aptenodytes patagonicus. Am. J. Physiol. 266, R1182–R1188 (1994).CAS 
PubMed 
Article 

Google Scholar 
Brown, C. R. Energetic cost of moult in macaroni penguins (Eudyptes chrysolophus) and rockhopper penguins (E. chrysocome). J. Comp. Physiol. B 155, 515–520 (1985).Article 

Google Scholar 
Dehnhard, N. et al. Survival of rockhopper penguins in times of global climate change. Aquat. Conserv. Mar. Freshw. Ecosyst. 23, 777–789 (2013).
Google Scholar 
Rebstock, G. & Boersma, D. Oceanographic conditions in wintering grounds affect arrival date and body condition in breeding female Magellanic penguins. Mar. Ecol. Prog. Ser. 601, 253–267 (2018).Article 
ADS 

Google Scholar 
Green, J. A., Boyd, I. L., Woakes, A. J., Warren, N. L. & Butler, P. J. Evaluating the prudence of parents: Daily energy expenditure throughout the annual cycle of a free-ranging bird, the macaroni penguin Eudyptes chrysolophus. J. Avian Biol. 40, 529–538 (2009).Article 

Google Scholar 
Crawford, R. J. M., Makhado, A. B., Upfold, L. & Dyer, B. M. Mass on arrival of rockhopper penguins at Marion Island correlated with breeding success. Afr. J. Mar. Sci. 30, 185–188 (2008).Article 

Google Scholar 
Crawford, R. J. M. et al. Food habits of an endangered seabird indicate recent poor forage fish availability off western South Africa. ICES J. Mar. Sci. 76, 1344–1352 (2019).
Google Scholar 
Okes, N. C. et al. Competition for shifting resources in the southern Benguela upwelling: Seabirds versus purse-seine fisheries. Biol. Conserv. 142, 2361–2368 (2009).Article 

Google Scholar 
Campbell, K. J. et al. Local forage fish abundance influences foraging effort and offspring condition in an endangered marine predator. J. Appl. Ecol. 56, 1751–1760 (2019).Article 

Google Scholar 
Grémillet, D. et al. Spatial match-mismatch in the Benguela upwelling zone: Should we expect chlorophyll and sea-surface temperature to predict marine predator distributions?. J. Appl. Ecol. 45, 610–621 (2008).Article 
CAS 

Google Scholar 
Sherley, R. B. et al. Metapopulation tracking juvenile penguins reveals an ecosystem-wide ecological trap. Curr. Biol. 27, 1–6 (2017).Article 
CAS 

Google Scholar 
Sherley, R. B. et al. Influence of local and regional prey availability on breeding performance of African penguins Spheniscus demersus. Mar. Ecol. Prog. Ser. 473, 291–301 (2013).Article 
ADS 

Google Scholar 
Cury, P. M. et al. Global seabird response to forage fish depletion—One-third for the birds. Science 334, 1703–1706 (2011).CAS 
PubMed 
Article 
ADS 

Google Scholar 
Crawford, R. J. M. et al. Collapse of South Africa’s penguins in the early 21st century. Afr. J. Mar. Sci. 33, 139–156 (2011).Article 

Google Scholar 
Sherley, R. B. et al. The conservation status and population decline of the African penguin deconstructed in space and time. Ecol. Evol. 10, 8506–8516 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Weller, F. et al. A system dynamics approach to modelling multiple drivers of the African penguin population on Robben Island, South Africa. Ecol. Model. 277, 38–56 (2014).Article 

Google Scholar 
Pichegru, L. Increasing breeding success of an Endangered penguin: Artificial nests or culling predatory gulls?. Bird Conserv. Int. 23, 296–308 (2013).Article 

Google Scholar 
Weller, F. et al. System dynamics modelling of the Endangered African penguin populations on Robben and Dyer islands, South Africa. Ecol. Model. 327, 44–56 (2016).Article 

Google Scholar 
Pichegru, L. et al. Overlap between vulnerable top predators and fisheries in the Benguela upwelling system: Implications for marine protected areas. Mar. Ecol. Prog. Ser. 391, 199–208 (2009).Article 
ADS 

Google Scholar 
Sherley, R. B. et al. Bayesian inference reveals positive but subtle effects of experimental fishery closures on marine predator demographics. Proc. R. Soc. B 285, 20172443 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Pichegru, L., Grémillet, D., Crawford, R. J. M. & Ryan, P. G. Marine no-take zone rapidly benefits endangered penguin. Biol. Lett. 6, 498–501 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Weller, F. et al. Penguins’ perilous conservation status calls for complementary approach based on sound ecological principles: Reply to Butterworth et al. (2015). Ecol. Model. 337, 1–3 (2016).Article 

Google Scholar 
Butterworth, D. S., Plagányi, E. E., Robinson, W. M. L., Moosa, N. & de Moor, C. L. Penguin modelling approach queried. Ecol. Model. 316, 78–80 (2015).Article 

Google Scholar 
Pichegru, L. et al. Sex-specific foraging behaviour and a field sexing technique for Endangered African penguins. Endanger. Species Res. 19, 255–264 (2013).Article 

Google Scholar 
Roberts, J. African Penguin (Spheniscus demersus) Distribution During the Non-breeding Season: Preparation for, and Recovery from, a Moulting Fast (University of Cape Town, 2016).
Google Scholar 
Dias, M. P. et al. Identification of marine Important Bird and Biodiversity Areas for penguins around the South Shetland Islands and South Orkney Islands. Ecol. Evol. 8, 10520–10529 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Lascelles, B. G. et al. Applying global criteria to tracking data to define important areas for marine conservation. Divers. Distrib. 22, 422–431 (2016).Article 

Google Scholar 
Department of Forestry, Fisheries and Environment, T. National data and information report for marine spatial planning: Knowledge baseline for marine spatial planning in South Africa. (2021).Kirkman, S. P. et al. Evaluating the evidence for ecological effectiveness of South Africa’s marine protected areas. Afr. J. Mar. Sci. 43, 389–412 (2021).Article 

Google Scholar 
Harris, L. R. et al. Practical marine spatial management of ecologically or biologically significant marine areas: Emerging lessons from evidence-based planning and implementation in a developing-world context. Front. Mar. Sci. 9, 831678 (2022).Article 

Google Scholar 
Whitehead, T. O., Kato, A., Ropert-Coudert, Y. & Ryan, P. G. Habitat use and diving behaviour of macaroni Eudyptes chrysolophus and eastern rockhopper E. chrysocome filholi penguins during the critical pre-moult period. Mar. Biol. 163, 19 (2016).Article 

Google Scholar 
Warwick-Evans, V., Downie, R., Santos, M. & Trathan, P. N. Habitat preferences of Adélie Pygoscelis adeliae and Chinstrap Penguins Pygoscelis antarctica during pre-moult in the Weddell Sea (Southern Ocean). Polar Biol. 42, 703–714 (2019).Article 

Google Scholar 
Green, C.-P. et al. The role of allochrony in influencing interspecific differences in foraging distribution during the non-breeding season between two congeneric crested penguin species. PLoS ONE 17, e0262901 (2022).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Pütz, K., Ingham, R. J. & Smith, J. G. Satellite tracking of the winter migration of Magellanic Penguins Spheniscus magellanicus breeding in the Falkland Islands. Ibis 142, 614–622 (2000).Article 

Google Scholar 
Pütz, K. et al. Post-moult movements of sympatrically breeding Humboldt and Magellanic Penguins in south-central Chile. Glob. Ecol. Conserv. 7, 49–58 (2016).Article 

Google Scholar 
Pütz, K., Ingham, R. J., Smith, J. G. & Lüthi, B. H. Winter dispersal of rockhopper penguins Eudyptes chrysocome from the Falkland Islands and its implications for conservation. Mar. Ecol. Prog. Ser. 240, 273–284 (2002).Article 
ADS 

Google Scholar 
Thiebot, J.-B., Cherel, Y., Trathan, P. N. & Bost, C. A. Coexistence of oceanic predators on wintering areas explained by population-scale foraging segregation in space or time. Ecology 93, 122–130 (2012).PubMed 
Article 

Google Scholar 
Thiebot, J.-B., Bost, C.-A., Poupart, T. A., Filippi, D. & Waugh, S. M. Extensive use of the high seas by Vulnerable Fiordland Penguins across non-breeding stages. J. Ornithol. 161, 1033–1043 (2020).Article 

Google Scholar 
Mattern, T. et al. Marathon penguins—Reasons and consequences of long-range dispersal in Fiordland penguins/Tawaki during the pre-moult period. PLoS ONE 13, e0198688 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Bost, C.-A., Thiebot, J.-B., Pinaud, D., Cherel, Y. & Trathan, P. N. Where do penguins go during the inter-breeding period? Using geolocation to track the winter dispersion of the macaroni penguin. Biol. Lett. 5, 473–476 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Baylis, A. M. M., Tierney, M., Orben, R. A., González de la Peña, D. & Brickle, P. Non-breeding movements of gentoo penguins at the Falkland Islands. Ibis 163, 507–518 (2021).Article 

Google Scholar 
Orgeret, F. et al. Exploration during early life: Distribution, habitat and orientation preferences in juvenile king penguins. Mov. Ecol. 7, 29 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Thiebot, J. B., Lescroël, A., Barbraud, C. & Bost, C. A. Three-dimensional use of marine habitats by juvenile emperor penguins Aptenodytes forsteri during post-natal dispersal. Antarct. Sci. 25, 536–544 (2013).Article 
ADS 

Google Scholar 
Pütz, K. et al. Post-fledging dispersal of king penguins (Aptenodytes patagonicus) from two breeding sites in the South Atlantic. PLoS ONE 9, e97164 (2014).PubMed 
PubMed Central 
Article 
ADS 
CAS 

Google Scholar 
Birt, V., Birt, T., Goulet, D., Cairns, D. & Montevecchi, W. Ashmole’s halo: Direct evidence for prey depletion by a seabird. Mar. Ecol. Prog. Ser. 40, 205–208 (1987).Article 
ADS 

Google Scholar 
Furness, R. W. & Birkhead, T. R. Seabird colony distributions suggest competition for food supplies during the breeding season. Nature 311, 655–656 (1984).Article 
ADS 

Google Scholar 
Carpenter-Kling, T. et al. Foraging in a dynamic environment: Response of four sympatric sub-Antarctic albatross species to interannual environmental variability. Ecol. Evol. 10, 11277–11295 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Kowalczyk, N. D., Reina, R. D., Preston, T. J. & Chiaradia, A. Environmental variability drives shifts in the foraging behaviour and reproductive success of an inshore seabird. Oecologia 178, 967–979 (2015).PubMed 
Article 
ADS 

Google Scholar 
Machovsky-Capuska, G. E. et al. The nutritional nexus: Linking niche, habitat variability and prey composition in a generalist marine predator. J. Anim. Ecol. 87, 1286–1298 (2018).PubMed 
Article 

Google Scholar 
Hays, G. C. et al. Translating marine animal tracking data into conservation policy and management. Trends Ecol. Evol. 34, 459–473 (2019).PubMed 
Article 

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

Google Scholar 
Bost, C. A. et al. Large-scale climatic anomalies affect marine predator foraging behaviour and demography. Nat. Commun. 6, 8220 (2015).CAS 
PubMed 
Article 
ADS 

Google Scholar 
Brown, C. J. et al. Effects of climate-driven primary production change on marine food webs: Implications for fisheries and conservation. Glob. Chang. Biol. 16, 1194–1212 (2010).Article 
ADS 

Google Scholar 
Beever, E. A. et al. Behavioral flexibility as a mechanism for coping with climate change. Front. Ecol. Environ. 15, 299–308 (2017).Article 

Google Scholar 
McInnes, A. M., Ryan, P. G., Lacerda, M. & Pichegru, L. Targeted prey fields determine foraging effort thresholds of a marine diver: Important cues for the sustainable management of fisheries. J. Appl. Ecol. 56, 2206–2215 (2019).Article 

Google Scholar 
van Eeden, R., Reid, T., Ryan, P. G. & Pichegru, L. Fine-scale foraging cues for African penguins in a highly variable marine environment. Mar. Ecol. Prog. Ser. 543, 257–271 (2016).Article 
ADS 

Google Scholar 
Coetzee, J. C., van der Lingen, C. D., Hutchings, L. & Fairweather, T. P. Has the fishery contributed to a major shift in the distribution of South African sardine?. ICES J. Mar. Sci. 65, 1676–1688 (2008).Article 

Google Scholar 
Blamey, L. K. et al. Ecosystem change in the southern Benguela and the underlying processes. J. Mar. Syst. 144, 9–29 (2015).Article 

Google Scholar 
Roy, C., Van Der Lingen, C. D., Coetzee, J. C. & Lutjeharms, J. R. E. Abrupt environmental shift associated with changes in the distribution of Cape anchovy Engraulis encrasicolus spawners in the southern Benguela. Afr. J. Mar. Sci. 29, 309–319 (2007).Article 

Google Scholar 
McInnes, A. M. et al. Small pelagic fish responses to fine-scale oceanographic conditions: Implications for the endangered African penguin. Mar. Ecol. Prog. Ser. 569, 187–203 (2017).CAS 
Article 
ADS 

Google Scholar 
Barange, M., Hampton, I. & Roel, B. A. Trends in the abundance and distribution of anchovy and sardine on the South African continental shelf in the 1990s, deduced from acoustic surveys. S. Afr. J. Mar. Sci. 21, 367–391 (1999).Article 

Google Scholar 
Hutchings, L. et al. Spawning on the edge: Spawning grounds and nursery areas around the southern African coastline. Mar. Freshw. Res. 53, 307–318 (2002).Article 

Google Scholar 
Verheye, H. M., Hutchings, L., Huggett, J. A. & Painting, S. J. Mesozooplankton dynamics in the Benguela ecosystem, with emphasis on the herbivorous copepods. S. Afr. J. Mar. Sci. 12, 561–584 (1992).Article 

Google Scholar 
Hutchings, L., Jarre, A., Lamont, T., van den Berg, M. & Kirkman, S. P. St Helena Bay (southern Benguela) then and now: Muted climate signals, large human impact. Afr. J. Mar. Sci. 34, 559–583 (2012).Article 

Google Scholar 
Goschen, W. S. & Schumann, E. H. Upwelling and the occurrence of cold water around Cape Recife, Algoa Bay, South Africa. S. Afr. J. Mar. Sci. 16, 57–67 (1995).Article 

Google Scholar 
Hutchings, L. et al. The Benguela Current: An ecosystem of four components. Prog. Oceanogr. 83, 15–32 (2009).Article 
ADS 

Google Scholar 
Goschen, W. S., Schumann, E. H., Bernard, K. S., Bailey, S. E. & Deyzel, S. H. P. Upwelling and ocean structures off Algoa Bay and the south-east coast of South Africa. Afr. J. Mar. Sci. 34, 525–536 (2012).Article 

Google Scholar 
van der Lingen, C. D. Diet of sardine Sardinops sagax in the southern Benguela upwelling ecosystem. S. Afr. J. Mar. Sci. 24, 301–316 (2002).Article 

Google Scholar 
van der Lingen, C. D., Hutchings, L. & Field, J. G. Comparative trophodynamics of anchovy Engraulis encrasicolus and sardine Sardinops sagax in the southern Benguela: Are species alternations between small pelagic fish trophodynamically mediated?. Afr. J. Mar. Sci. 28, 465–477 (2006).Article 

Google Scholar 
Wright, K. L. B., Pichegru, L. & Ryan, P. G. Penguins are attracted to dimethyl sulphide at sea. J. Exp. Biol. 214, 2509–2511 (2011).PubMed 
Article 

Google Scholar 
Hagen, C. et al. Evaluating the state of knowledge on fishing exclusions around major African Penguin colonies. (2014).Fort, J. et al. Multicolony tracking reveals potential threats to little auks wintering in the North Atlantic from marine pollution and shrinking sea ice cover. Divers. Distrib. 19, 1322–1332 (2013).Article 

Google Scholar 
Reiertsen, T. K. et al. Prey density in non-breeding areas affects adult survival of black-legged kittiwakes Rissa tridactyla. Mar. Ecol. Prog. Ser. 509, 289–302 (2014).Article 
ADS 

Google Scholar 
Fayet, A. L. et al. Ocean-wide drivers of migration strategies and their influence on population breeding performance in a declining seabird. Curr. Biol. 27, 3871–3878 (2017).CAS 
PubMed 
Article 

Google Scholar 
Desprez, M., Jenouvrier, S., Barbraud, C., Delord, K. & Weimerskirch, H. Linking oceanographic conditions, migratory schedules and foraging behaviour during the non-breeding season to reproductive performance in a long-lived seabird. Funct. Ecol. 32, 2040–2053 (2018).Article 

Google Scholar 
Randall, R. M. & Randall, B. The annual cycle of the Jackass Penguin Spheniscus demersus at St Croix Island, South Africa. In Proc. Symp. Birds Sea Shore 427–450 (1981).Wolfaardt, A. C., Underhill, L. G. & Visagie, J. Breeding and moult phenology of African penguins Spheniscus demersus at Dassen Island. Afr. J. Mar. Sci. 31, 119–132 (2009).Article 

Google Scholar 
Crawford, R. J. M. et al. Molt of the African penguin, Spheniscus demersus, in relation to its breeding season and food availability. Acta Zool. Sin. 52, 444–447 (2006).
Google Scholar 
Randall, R. M. Biology of the Jackass Penguin Spheniscus demersus (L.) at St Croix, South Africa (Univeristy of Port Elizabeth, 1983).
Google Scholar 
Harding, C. T. Tracking African Penguins (Spheniscus demersus) Outside of the Breeding Season: Regional Effects and Fishing Pressure During the Pre-moult Period (University of Cape Town, 2013).
Google Scholar 
Wilson, R. P. The Jackass Penguin (Spheniscus demersus) as a pelagic predator. Mar. Ecol. Prog. Ser. 25, 219–227 (1985).Article 
ADS 

Google Scholar 
Freitas, C. argosfilter: Argos locations filter. (2012).Worton, B. J. Kernel methods for estimating the utilization distribution in home-range studies. Ecology 70, 164–168 (1989).Article 

Google Scholar 
Calenge, C. The package ‘adehabitat’ for the R software: A tool for the analysis of space and habitat use by animals. Ecol. Modell. 197, 516–519 (2006).Article 

Google Scholar 
Vander Wal, E. & Rodgers, A. R. An individual-based quantitative approach for delineating core areas of animal space use. Ecol. Model. 224, 48–53 (2012).Article 

Google Scholar 
Dinno, A. dunn.test: Dunn’s Test of Multiple Comparisons Using Rank Sums (2017).Bhattacharyya, A. On a measure of divergence between two multinomial populations. Indian J. Stat. 7, 401–406 (1946).MathSciNet 
MATH 

Google Scholar 
Beal, M. et al. track2KBA: An R package for identifying important sites for biodiversity from tracking data. Methods Ecol. https://doi.org/10.1111/2041-210X.13713 (2021).Article 

Google Scholar 
Donald, P. F. et al. Important Bird and Biodiversity Areas (IBAs): The development and characteristics of a global inventory of key sites for biodiversity. Bird Conserv. 29, 177–198 (2019).Article 

Google Scholar 
Handley, J. M. et al. Evaluating the effectiveness of a large multi-use MPA in protecting Key Biodiversity Areas for marine predators. Divers. Distrib. 26, 715–729 (2020).Article 

Google Scholar 
Strimas-Mackey, M. smoothr: Smooth and tidy spatial features. R package version 0.2.2. https://CRAN.R-project.org/package=smoothr (2018).Department of Forestry Fisheries and the Environment, T. South Africa Marine Protected Area Zonations (SAMPAZ_OR_2021_Q3). https://egis.environment.gov.za/data_egis/data_dow (2021).R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing https://www.r-project.org/ (2021). More

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