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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

Experimental designTo test whether an 8-year treatment of recurrent summer droughts would change biodiversity effects and species interactions of grassland plants when facing a subsequent drought event, we grew ambient- vs. drought-selected plants of 12 species in a glasshouse. The plants were grown from seeds collected from 40 plots (Supplementary Data 2) under 8-year treatments of yearly summer droughts vs. ambient precipitation in a biodiversity field experiment in Jena, Germany11,41.The Jena Experiment was established in 2002 using a common seed pool of 60 grassland species, with 80 (20times 20,{{{{{rm{m}}}}}}) large plots of species richness levels of 1, 2, 4, 8, 16, and 60 species40. Most of the species are perennial and capable of outcrossing (Supplementary Table 1). The Jena Drought Experiment11,41 was initiated in 2008. Two (1times 1,{{{{{rm{m}}}}}}) subplots were set within each large plot, designated as either drought treatment or ambient control. For the drought treatment, rainout shelters were set up to exclude natural rainfall in mid-summer for 6 weeks. The ambient control treatment got the same shelter construction but rain water was reapplied to not confound the results with artifacts from the shelter60. We repeatedly harvested the aboveground biomass per year, once before and once after the summer drought treatment11,41. The design of the Jena (Drought) Experiment did not allow the exclusion of cross pollination or gene flow between subplots or large plots in the field. Such gene flow may have reduced the possibility for genetic differentiation and for the observed effect sizes of the selection treatment23. We collected seeds from drought and control subplots throughout the 2016 growing season (Fig. 1). We obtained seeds of 17 species, but only used 12 of them, because the other five species had either few seeds or low germination rates. Seeds per species per selection treatment were collected from 4 to 23 (interquartile range: [8.50, 17.00]) maternal plants distributed across 2–10 (interquartile range: [4.75, 9.00]) large plots in Jena Experiment, in which the functional group richness ranged from 1 to 4 (Supplementary Data 2). The 12 plant species represented four functional groups (grass, small herb, tall herb, and legume) (Supplementary Table 1). The detailed classifications of the functional grouping can be found in the design of the Jena Experiment40. Eleven of the 12 species were perennial, and one was annual (Trifolium dubium). The average longevity of the perennial species in the Jena Experiment has been estimated at 3–4 years61, so that multiple generations and sexual reproduction cycles could occur during the 8-year drought treatment. Although each subplot was small, population sizes of each species were estimated to range from 100 to 1000 individuals m−2 in ambient and drought subplots at the beginning of the drought treatment in the field62.We germinated the seeds in Petri dishes and transplanted the seedlings into pots in February 2017 in a glasshouse (day temperature range 20–25 °C, night temperature range 15–21 °C, and humidity range 60–80%) at the University of Zurich, Switzerland. Seedlings were planted individually, in monocultures, or in 2-species mixtures in the glasshouse (Fig. 1). In the glasshouse experiment, both monocultures and mixtures contained four plants within a pot. The pots were (11times 11times 11.5) cm in size and filled with soil composed of 50% collected from a sugar-beet field, 25% sand and 25% perlite. We randomly assigned the pots into four blocks in the glasshouse. To test the effects of drought-induced selection on plant traits, we planted individual seedlings of the 12 species in a fifth block. Within the first 2 weeks, dead individuals were replaced, thereafter dead individuals were not replaced anymore. In total, we established 958 pots: 257 pots of mixtures, 217 pots of monocultures, and 484 pots of individual plants (244 pots of individuals in blocks 1–4, and 240 pots of individuals in block 5; Supplementary Methods). For mixtures, there were 21 species pairs (Supplementary Table 1). Species pairs composed of Crepis biennis or Lotus corniculatus had low numbers of replicates (Supplementary Table 1). However, including or excluding these communities produced qualitatively similar results. Thus, we present the results including these two species in this paper. We provide detailed explanations on the choices of species pairs and regarding the biodiversity treatment history in the Jena Experiment in Supplementary Methods.During a first phase of 3 months in the glasshouse (Fig. 1), pots were watered regularly (“before drought”). After 14–16 weeks, when most of the species had reached peak aboveground biomass, we harvested all individuals in each pot by cutting them 3 cm above the ground, allowing regrowth from the left plant bases (first harvest, “before drought”). The time span for the first harvest included both the time for trait measurements (section “Plant traits” below) and for the immediately following biomass harvest. We completed the biomass harvest of each block within 1–2 days. This allowed us to account for the larger time differences between blocks by fitting block effects in the statistical analyses. After the first harvest of each block, plants were watered regularly and allowed to regrow until the 18th week from planting. This was followed by a second phase of 2 weeks without watering. Soil moisture decreased from more than 40% to less than 10% after 10 days since drought initiation. At the end of the second phase, that is after 20 weeks from planting, we made a second aboveground harvest at 3 cm above the ground (second harvest, “during drought”). During a third phase of 7 weeks, pots were watered regularly again for recovery until most plants reached a new aboveground biomass peak again. At the end of the third phase, that is after 27 weeks from planting, we harvested both above- and belowground plant biomass (third harvest, “after drought”). We checked and confirmed that most plants had reached the full-grown state and peak biomass before each harvest by monitoring their flowering. After each harvest, we cleaned and dried the harvested plant material at 70 °C for 48 h to obtain the dry biomass. We used the aboveground biomass as a proxy for productivity. Although clipping may affect plant responses to the experimental drought in the glasshouse, clipping had the advantage that all plants were “standardized” in height before the experimental drought, thus reducing carry-over effects of differential growth before the experimental drought.Additive partitioningWe used the additive partitioning approach (Eq. 1)17 to decompose the net biodiversity effect (NE) on aboveground biomass into the complementarity effect (CE) and the sampling effect (SE):$$triangle Y={Y}_{O}-{Y}_{E}=N,overline{triangle {RY}},{bar{M}}+N,{{{{{{rm{cov}}}}}}}left({{triangle }}{{{{{bf{RY}}}}}},,{{{{{bf{M}}}}}}right),$$
(1)
where (triangle Y) is the NE; ({Y}_{O}) is the observed yield (productivity) in a mixture; ({Y}_{E}) is the expected yield in the mixture, calculated from the observed yield in monocultures and their corresponding species proportions planted in the mixture, here 0.5; the two additive terms at the right side of the equation represent CE and SE, respectively; N is the number of species in the mixture, here 2. The partitioning is based on the observed and expected relative yield (RY) of species in the mixture. The expected RY of species in the mixture is the proportion planted. ∆({{{{{bf{RY}}}}}}) is the difference between observed and expected RY of species in the mixture; (overline{triangle {RY}}) is the average of ∆({{{{{bf{RY}}}}}}). A positive (overline{triangle {RY}}) indicates a positive CE; a positive covariation between monoculture yield (M), and ∆({{{{{bf{RY}}}}}}) indicates a positive SE. More details about the calculation can be found in Loreau and Hector17. We conducted the partitioning separately for each harvest, selection treatment, and block. We did not perform the partitioning for mixtures with zero biomass63. For monocultures with zero biomass in the second or third harvest, we kept the ones which had positive biomass in the previous harvest but excluded the ones which had zero biomass in the previous harvest. For example, when performing the partitioning for the second harvest, we kept the monocultures that had zero biomass in the second harvest but non-zero biomass in the first harvest; we excluded the monocultures that had zero biomass already in the first harvest. This was to assure that communities that died before the drought could not reappear during or after the drought, and communities that had died during the drought could not reappear after the drought.We used mixed-effects models to assess the influences of drought vs. ambient-selection treatments on biodiversity effects (NEs, CEs, and SEs) separately for each harvest (Fig. 2; Table 1). Block and selection treatment were set as fixed-effects terms, while species composition (identity of species pair) and its interaction with selection treatment were set as random-effects terms. This conservative approach was used to allow for generalizations across all possible species compositions, although a more liberal approach with species composition and its interactions as fixed-effects terms could also have been applied (see Schmid et al.64 for a discussion of defining terms as fixed- vs. random-effects terms, including a justification of preference for treating block as a fixed-effects term). We square-root transformed the CEs and SEs with sign reconstruction (({{{{{{rm{sign}}}}}}}(y)sqrt{y})) prior to analysis to improve the normality of residuals17. The mixed-effects model did not converge in the analysis with CE after the drought event. In this case, we used a general linear model, in which we fitted block, species composition, selection treatment, and species composition by selection treatment interaction in this order. Then we tested the significance of selection treatment using its interaction with species composition as an error term. This procedure is an alternative to mixed-effects models that estimate variance components for random-effects terms with maximum likelihood64.To test whether biodiversity effects on productivity differed from zero, we additionally tested the significance of NEs, CEs, and SEs separately for each selection treatment and harvest (Supplementary Table 3). We set block and species composition as fixed- and random-effects terms, respectively. The model corresponding to CE for ambient-selected plants during the drought event did not converge so that we fitted it with a general linear model, in which we tested the significance of the overall mean (intercept) using species composition as an error term. All statistical analyses were conducted in R 3.6.365. The mixed-effects models were conducted with asreml-R package 4.1.0.11066.Finally, we also tested whether the effects of drought selection on biodiversity effects (NEs, CEs, and SEs) in the glasshouse depended on the history of biodiversity treatment in the Jena Experiment. Most plants in the 2-species communities in the glasshouse originated from mixtures in the Jena Experiment (Supplementary Data 2; whether mixtures in the glasshouse composed of plants originating from monoculture field plots did not affect the effects of drought-selection on biodiversity effects on productivity (Supplementary Data 3)). To increase statistical power, we used functional group richness, ranging from 1 to 4, instead of species richness of the field plots as explanatory variable (Supplementary Methods). We fitted functional group richness either in linear (Supplementary Data 4) or log-linear (Supplementary Data 5) form. We did not detect significant effects of field treatment of functional group richness nor significant interactions between field treatment of functional group richness and the drought-selection history. Therefore, we excluded the history of biodiversity treatments in the field from further analyses.Biomass stability to the drought event in the glasshouseTo assess the temporal responses of community aboveground biomass to the drought event, we calculated three indices representing different facets of stability: biomass resistance, recovery, and resilience (see van Moorsel et al.43 for an example). We calculated resistance as the biomass ratio during vs. before the drought, recovery as the ratio after vs. during the drought and resilience as the ratio after vs. before the drought (see also Isbell et al.9). We log-transformed the indices (plus a half of the minimum positive value to allow taking logs of indices that were originally zero) prior to statistical analyses to improve the normality of residuals. Excluding index values that were originally zero produced qualitatively similar results.To assess the effects of drought-selection on biomass stability, we fitted mixed-effects models with block and selection treatment as fixed-effects terms, and species composition and its interaction with selection treatment as random-effects terms (Supplementary Fig. 3; Supplementary Table 4). We fitted the models separately for mixtures and monocultures. We included the log-transformed biomass at the first harvest as a covariate because biomass stability in response to droughts often depends on plant performance under ambient conditions.In the same way as net biodiversity effects on productivity were calculated for additive partitioning, we calculated biodiversity effects on biomass stability as the difference between each mixture and its corresponding monocultures. Then, we tested the influence of selection treatment on the biodiversity effects on biomass stability. Block and selection treatment were set as fixed-effects terms; species composition and its interaction with selection treatment were set as random-effects terms (Fig. 3; Supplementary Table 5). The log-transformed biomass at the first harvest was also included as a covariate43. To assess the significance of biodiversity effects on biomass stability for each selection treatment, we fitted another set of simplified models, with block and log-transformed biomass as fixed-effects terms, and species composition as random-effects term (Fig. 3).Neighbor interactionsWe assessed interactions between neighboring plants within pots using the metrics of neighbor interaction intensity with multiplicative symmetry (NIntM)44:$${NIn}{t}_{M}=2frac{triangle P}{{P}_{-N}+{P}_{+N}+left|triangle Pright|},$$
(2)
where ({P}_{-N}) and ({P}_{+N}) are the productivities without (individual plant) and with neighbors (monocultures or mixtures), respectively; (triangle P={P}_{+N}-{P}_{-N}). Negative values of NIntM indicate competition and positive values indicate facilitation. NIntM is bounded between –1 (competitive exclusion) and 1 (“obligate” facilitation). For monocultures, we first calculated the per-plant biomass as the ratio between total biomass and planting density, and then used the per-plant value to compare with the corresponding individuals (without neighbor) of the same species with the same selection treatment in the same block. Note that under the reciprocal yield law45, an individual grown alone in a pot should be four times larger than an individual grown with three others in a pot, resulting in a NIntM of –0.75. For 2-species mixtures, we calculated the per-plant biomass separately for each species and took the average NIntM of the two species to measure the interaction intensity of the mixture. We set zero biomass for dead plants in the calculation. Again, if mixtures would also follow the reciprocal yield law independent of species identity, then NIntM = –0.75 would be expected. Values greater than –0.75 indicate some sort of overyielding due to higher density or higher density and higher diversity.To assess how selection treatment modified interactions between plants, we tested the effects of selection treatment on neighbor interaction intensity separately for monocultures and mixtures. We included block and selection treatment as fixed-effects terms, species composition and its interaction with selection treatment as random-effects terms (Supplementary Fig. 4; Supplementary Table 6).We calculated the difference between the heterospecific interaction in a mixture and the conspecific interactions in its two corresponding monocultures. A positive value of this difference indicates a weaker heterospecific than conspecific competition (i.e., niche differentiation) or stronger heterospecific than conspecific facilitation, which may lead to a positive complementarity effect. We tested the effects of selection treatment on interaction difference for each harvest by fitting block and selection treatment as fixed-effects terms, and species composition and its interaction with selection treatment as random-effects terms (Fig. 4; Supplementary Table 8). We also tested the significance of the interaction difference for each selection treatment by fitting block and species composition as fixed- and random-effects term, respectively (Fig. 4; Supplementary Table 7).Plant traitsTo assess whether drought selection would change plant traits, we measured six traits (Supplementary Table 9) closely related to plant usages of water or carbon on plants in pots with one individual from blocks 1–5. We focused on the traits on individual plants without neighbor to evaluate the influence of selection treatment on traits without the impacts of plasticity induced by plant interactions. We measured leaf relative chlorophyll content, leaf area (LA), leaf mass per area (LMA) and leaf osmometric pressure before the drought; leaf stomatal conductance both before and during the drought; and dry biomass ratio between root and shoot after the drought (in the third harvest). Leaf relative chlorophyll content was measured for three mature, fully expanded leaves per plant by using a SPAD-502 Plus chlorophyll meter from Konica Minolta. LA was obtained by scanning 3–4 mature, fully expanded leaves per plant with a LI-3100C Area Meter from LI-COR. LMA was calculated as the ratio between leaf dry mass (oven-dried at 70 °C for 48 h, using the same leaves that for LA) and LA. Leaf osmotic potential at full hydration was considered as an important trait associated with plant tolerance to drought30. We measured leaf osmotic potential with freeze-thaw leaf pieces cut from 1 to 2 mature, fully expanded leaves per plant by using a Wescor vapor pressure osmometer VAPRO (Model 5520) according to the method by Bartlett, et al.30. Plants were fully hydrated 1 day before the leaf sampling for osmotic potential measurement. Leaf stomatal conductance is a measure of exchange rate of carbon dioxide and water vapor through the stomata67. It was measured for 3–5 healthy mature leaves per plant by using a SC-1 Leaf Porometer from Decagon Devices. For grass species, 3 blades were placed adjacent to each other to have a large enough area for the measurement of stomatal conductance. For stomatal conductance during the drought event, we measured the individual plants from block 5 only due to limited time during the drought phase. We harvested aboveground and belowground plant biomass separately for alive individuals at the end of the experiment (after the complete recovery from the drought). The oven-dried (70 °C for 48 h) aboveground and belowground biomass were used to calculate the biomass ratio between root and shoot. We took the average value of each trait of each plant for statistical analyses. Each trait was measured for each block in turn.We used linear mixed-effects models to assess the influence (generalized across species) of selection treatment on trait values (red lines in Supplementary Figs. 5–7). Block and selection treatment were set as fixed-effects terms; species and its interaction with selection treatment were set as random-effects terms. Alternatively, we set species, selection treatment and their interaction as fixed-effects terms to assess whether species responded differently to the selection treatment (Supplementary Table 9). To test whether effects of selection treatment on traits differed between the five trait groups (leaf relative chlorophyll content, leaf area, leaf mass per area, leaf osmometric pressure, and leaf stomatal conductance) measured before the drought event in the glasshouse, we conducted two alternative analyses. First, we performed a principal component analysis with all traits and retained the first two principal axes (PC1 and PC2), which accounted for 39.06% and 22.3% of the total variation, respectively. Then we used PC1 and PC2 as response variables in mixed-effect models, separately. We fitted the models with the same fixed- and random-effects terms as those using each separate trait as the response variable. Effects of selection treatment on PC1 or PC2 were not significant. Second, we pooled the five traits as a single response variable in a mixed-effect model (corresponding to multivariate analysis of variance). Block, trait group (a factor with five levels), selection treatment, and the interaction between trait group and selection treatment were set as fixed-effects terms; species and its interactions with trait group and selection treatment and their three-way interaction were set as random-effects terms. We did not detect significant effects of selection treatment nor its interaction with trait group. Therefore, we did not present the results associated with these multivariate analyses in this paper. LMA, LA, leaf osmotic potential, leaf stomatal conductance, and root-shoot biomass ratio were log-transformed to improve normality of residuals.We also measured leaf relative chlorophyll content, LA and LMA in mixtures before the drought event (Supplementary Table 10) to evaluate the influence of selection treatment on trait dissimilarity between interacting species within communities. We calculated the absolute trait distance between two species in each mixture both separately for each trait and jointly with the three traits. For multi-trait-based dissimilarity, we standardized each trait to mean zero and unit standard deviation and calculated the Euclidean trait distance in standardized three-dimensional trait space.We used linear mixed-effects models to assess the effects of selection treatment on trait dissimilarity in mixtures (Supplementary Table 10). Block and selection treatment were set as fixed-effects terms; species composition and its interaction with selection treatment were set as random-effects terms. The model for LA dissimilarity did not converge so that we fit it with a general linear model, in which we tested the significance of selection treatment using its interaction with species composition as an error term. For the models with LA, LMA, and the joint three traits as dependent variables, we removed one pot (B1P674) because the LA value of Alopecurus pratensis in this pot was extremely small (about 1/3 of the second minimum value of the same species in mixtures). However, including or excluding this pot produced qualitatively similar results.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

Fisheries overexploitation is a problem in all oceans and seas globally. Authorities and administrations in charge of assigning quotas have very little fine-grained information on the fish captures, and instead use large-scale, coarse data to assess the health level of fisheries. Thus, being able to cross-match fish species and sizes, to the sea regions they were captured from, can be helpful in this regard, providing finer-grained information.Previous attempts at assembling datasets for fish detection and classification exist, ranging from fish detection or counting in underwater images and video streams1,2,3, to counting on belts on trawler ships4, to classification in laboratory conditions5,6, or in underwater preprocessed images of single fish7,8,9, or single fish in free-form pictures10, as well as simultaneous detection and classification of several fish11,12. However, none of the works found in the literature addresses the topic of simultaneous instance segmentation and species classification, along with fish size estimation, in a fish market environment, as is the aim of this paper. Instance segmentation refers to the extraction of pixel-level masks for each individual object (in this case fish specimens), rather than bounding boxes (object detection), or class label masks (e.g. a single mask for all fish specimens of the same species, also referred to as semantic segmentation). Moreover, works in the literature use pictures taken in laboratory conditions (with a single fish per image, shown from the side), or in underwater conditions. Only French et al.4 uses pictures of fish catches on a belt, for counting purposes. Table 1 shows a summary of the datasets identified in the literature, along with their characteristics, including how the proposed dataset compares.Table 1 Summary of previous datasets found in the literature, and comparison to proposed dataset.Full size tableThe DeepFish project (website: http://deepfish.dtic.ua.es/) is aimed at providing fish species classification and size estimation for fish specimens arriving at fish markets, both for the automation of fish sales, and the retrieval of fine-grained information about the health of fisheries. For a period of six months (April to September 2021), images have been captured at the fish market in El Campello (Alicante, Spain). Images of market trays show a variety of fish species, including targeted as well as accidental captures from the ‘Cabo de la Huerta’, an important site for protection and preservation of marine habitats and biodiversity as defined by the European Comission Habitats Directive (92/43/EEC). From the pictures, a total of 59 different species are identified with 12 species having more than 100 specimens and 25 with more than 10 specimens, as shown in Table 2. There is a high imbalance of species captured due to the natural variation in fish species populations according to seasonality and other ecological factors (rarity of the species, i.e. total population count, etc). Due to some species showing sexual dimorphism (i.e. Symphodus tinca), this species is split into two separate class labels, leading to a different number of species, and class labels (59 species, but 60 class labels). The dataset presents a high temporal imbalance too. As shown in Fig. 1, the capture of new fish tray images was not evenly distributed during the six month study period. Several factors contributed to this: wholesale fish market operating days (e.g. no weekend data, holidays and stop periods, etc.), fish species variability (one of the aims was to be able to capture at least 100 specimens from several species, and seasonality meant some could not be available for capture in later months), as well as the time availability of research group members to attend the fish arrival, tray preparation and auctioning in the evenings.Table 2 Distribution of fish species in the dataset.Full size tableFig. 1Temporal distribution of fish tray images captured. It can be observed that April (04) and May (05) were much more active than the rest of months. This is due to several contributing factors.Full size imageThe resulting DeepFish dataset introduced here contains annotated images from 1,291 fish market trays, with a total of 7,339 specimens (individual fish instances) which were labelled (species and mask) using a specially-adapted version of the Django labeller instance segmentation labelling tool13. Subsequently, another JSON file is generated, following the Microsoft Common Objects in Context (MS COCO) dataset format14, which can be directly fed to a neural network. This is done via a script that is also provided15. Figure 2 shows the distribution of individuals for the selected species within the dataset. Furthermore, Fig. 3 shows examples of the trays, with instance segmentation (ground truth silhouette, i.e. as an interpolation from human-provided points) along with species labelling (different colour shading).Fig. 2Graphical view of the distribution of fish species in the DeepFish dataset for species above 10 specimens. Note, Symphodus tinca is considered separately due to sexual dimorphism (211 male; 335 female samples).Full size imageFig. 3Examples of ground truth fish instance masks with class labelling, showing the 12 species (13 labels) with more than 100 specimens (in bold in Table 2).Full size imageFrom the point of view of research, this data is important for the classification of fish species, instance segmentation, as well as specimen size estimation (e.g. as a regression problem, or otherwise). From an end-results perspective, data automatically labelled with fish instance segmentation accompanied by species name and estimated size is useful to different stakeholders, namely: fishing authorities (to understand how much of each species is being caught per zone), maritime conservation (to calculate depletion of fisheries), but also managers of the markets themselves, as well as clients (digitized sales, e-commerce), etc.The usage of the provided data can be manifold, as it can be used for several problems, namely: object detection and classification, which involves finding objects (in this case fish specimens) providing a bounding box, and a class for each of these boxes; additionally, the data can also be used for semantic segmentation, which can provide a pixel-wise segmentation of the image providing labels (in this case species labels) to different pixel regions of the image; furthermore, also instance segmentation is possible, in which not just a single label for all instances of the same species is provided, but each specimen is provided with a mask (specimen segmentation), as well as a label (species). Furthermore, several measurements of each fish are provided, which can also be used to estimate their size, since they have been shown to be correlated with each other16. These are estimated from the calculated homography (given the tray size is known), given the burden of measuring each fish due to the large amount of specimens in the dataset. More

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Here we provide an overview of the key elements of our framework including describing the contact function that links economic activities to contacts, the SIRD (Susceptible-Infectious-Recovered-Dead) model, the dynamic economic model governing choices, and calibration. The core of our approach is a dynamic optimization model of individual behavior coupled with an SIRD model of infectious disease spread. Additional details are found in the SI.Contact functionWe model daily contacts as a function of economic activities (labor supply, measured in hours, and consumption demand, measured in dollars) creating a detailed mapping between contacts and economic activities. For example, all else equal, if a susceptible individual reduces their labor supply from 8 to 4 h, they reduce their daily contacts at work from 7.5 to 3.75. Epidemiological data is central to calibrating this mapping between epidemiology and economic behavior. Intuitively, the calibration involves calculating the mean number of disease-transmitting contacts occurring at the start of the epidemic and linking it to the number of dollars spent on consumption and hours of labor supplied before the recession begins.We use an SIRD transmission framework to simulate SARS-CoV-2 transmission for a population of 331 million interacting agents. This is supported by several studies (e.g.,77,78) that identify infectiousness prior to symptom onset. We consider three health types m ∈ {S, I, R} for individuals, corresponding to epidemiological compartments of susceptible (S), infectious (I), and recovered (R). Individuals of health type m engage in various economic activities ({A}_{i}^{m}), with i denoting the activities modeled. One of the ({A}_{i}^{m}) is assumed to represent unavoidable other non-economic activities, such as sleeping and commuting, which occur during the hours of the day not used for economic activities (see SI 2.3.1). Disease dynamics are driven by contacts between susceptible and infectious types, where the number of susceptible-infectious contacts per person is given by the following linear equation:$${{{{{{{{mathscr{C}}}}}}}}}^{SI}({{{{{{{bf{A}}}}}}}})=mathop{sum}limits_{i}{rho }_{i}{A}_{i}^{S}{A}_{i}^{I}$$
(1)
while similar in several respects to prior epi-econ models15,16,74, a methodological contribution is that ρi converts hours worked and dollars spent into contacts. For example, ρc has units of contacts per squared dollar spent at consumption activities, while ρl has units of contacts per squared hour worked.We also consider robustness to different functional forms in Fig. 6F, G as a reduced-form way to consider multiple consumption and labor activities with heterogeneous contact rates. Formally:$${{{{{{{{mathscr{C}}}}}}}}}^{SI}({{{{{{{bf{A}}}}}}}})=mathop{sum}limits_{i}{rho }_{i}{({A}_{i}^{S}{A}_{i}^{I})}^{alpha },$$
(2)
where α  > 1 (convex) corresponds to a contact function where higher-contact activities are easiest to reduce or individuals with more contacts are easier to isolate. α  More

Broadcasted social calls attracted hoary bats during both the spring and fall migration. Broadcasting conspecific social calls increased hoary bat capture rates at netting sites intentionally removed from normal capture locations. We had very low capture rates during control periods, because we intentionally placed nets in locations removed from flyways to reduce incidental captures. Moreover, capture rates of hoary bats tend to be low even in many locations where they are known to occur24,25, and capture rates of approximately one bat per hour in a single mist net suggest a very strong attraction response to broadcasted calls.Hoary bat activity, as measured by acoustic monitoring was not associated with increased capture rates in response to call broadcasting. However, subsequent research has shown that hoary bats periodically use higher frequency, inconspicuous calls, or do not constantly echolocate during the fall, which may mean acoustic monitoring did not effectively measure hoary bat activity in the vicinity of our trials26,27. We recorded substantially higher acoustic activity during the spring migration, which could represent either more hoary bats and/or bat activity, or a seasonal difference in echolocation or flight behavior such as differences in flight altitude27. It remains unknown if hoary bats use inconspicuous calls or fly in silence during spring migration or other times of year other than the fall when these inconspicuous echolocation behaviors were observed, and seasonally variable behavior could affect detectability or exposure to our playback trials in ways not captured by our acoustic activity covariate. In addition, while we did audibly hear social calls of hoary bats during the fall, we did not record any during fieldwork for this study, which may be an artifact or due to differences in social behavior, context, or number of hoary bats present in the area during our trials.We only captured one female during trials in New Mexico, and were unable to locate any females during the fall migration in coastal regions of California, despite high concentrations of males in the area during what is presumably the mating season. In New Mexico, during spring migration, females migrate through the study area before males28, with very little temporal overlap. As a result, we were unable to determine sex specific responses to call playback, however we have subsequently captured several female hoary bats and Ope’ape’a (Hawaiian hoary bat, L. semotus) using call playback during capture and radio-tracking studies (GAR, pers. obs.).It is difficult to elucidate the meaning of social calls based on the behaviors observed in the field. In bats, social call complexity often reflects social behavior complexity, with a range of uses including but not limited to attracting mates, locating pups within colonies, defending roosting or foraging territory, and attracting bats to roosts10. Attraction to conspecific call broadcasting could indicate positive social interactions (e.g., maintaining group cohesion or investigation) or agonistic behavior (e.g., hoary bats approaching to chase conspecific bats), as has been observed in other bat species29 and in hoary bats during the maternity season30. We did not observe any obvious instances of aggressive hoary bat interactions, and the social calls differ from hisses and clicks that hoary bats use defensively (Fig. 2). We would also audibly hear pairs of hoary bats calling in close proximity to each other, with no indication of aggressive or territorial responses, and these calls being low frequency and audible to humans means that they attenuate at greater distances than hoary bat echolocation calls.Aggressive or territorial interactions in many taxa are often driven by seasonally variable contexts, such as mating, defending food resources, or rearing of young. It may be unlikely that migrating hoary bats would expend energy defending territory during migration when they are utilizing roosts or foraging habitat for such limited periods of time (i.e., a few hours to a day). During active migration birds are often not territorial even when foraging at stopover sites31, and there may be benefits to maintaining group cohesion during migration including navigation and identification of favorable habitat. It is unknown if hoary bats utilize stopover sites for refueling during migration. However the silver-haired bat Lasionycteris noctivagans was found to utilize a migration stopover site in Long Point, Canada, where they opportunistically foraged for short periods of time (1 to 2 days32). Tracking studies would be required to determine temporal patterns of site usage by individual bats to examine stopover behavior.As we had recorded most of our initial social calls during late summer and early fall when hoary bats mate21, we had originally hypothesized that these social calls were associated with mating behavior, which would have been consistent with observations in this study had we found both increased attraction during the fall, and less attraction to calls during the spring. However, social calls attracted hoary bats effectively during both the spring and fall migration. In addition, from acoustic recordings and capture observations in the field, hoary bats produced many social calls during the spring migration when only males were present. There is a possibility, due to our lack of understanding of the mating systems of hoary bats that some mating may continue into the spring. However the majority of taxonomic, physiological, and observational data suggests mating behavior ends by the spring migration19,33, and the majority of females are already pregnant when travelling through New Mexico28. While hoary bats may or may not use social calls as a component of mating behavior, social calls recorded during the spring likely serve purposes not associated with mating.Previous studies describe the hoary bat as solitary throughout most of the year, which would imply only brief social interactions limited to mating or association with offspring, and the many historical accounts of aggregations of hoary bats are thought to be related to mating behavior20,33,34. However the use of, and attraction to, social calls during both spring and fall migration supports that these calls are used for social interactions beyond mating behavior. Further research may determine if hoary bats use these social calls to maintain group cohesion during migration, and what, if any, relationships exist between individual hoary bats that appear to be migrating together. Baerwald and Barclay35 found that geographic and genetic relationships of hoary bats and silver-haired bat carcasses collected at wind turbines were not more closely related than expected by chance, which provides some evidence that groups of migrating hoary bats may not form based on kinship.Many studies hoping to elucidate the causes of fatalities at wind energy facilities have focused only on the fall migration period when bats are most often killed13,20,36. However hoary bats migrate during the spring as well, when they do not suffer high fatality rates. Investigating the spring migration presents a valuable baseline to compare behavioral changes and other factors that may place hoary bats or other impacted species at risk. If social behavior makes a major contribution to the risk of fatalities at wind energy developments, then social behavior should differ between spring and fall migration. We did not find a large difference in response to social calls between seasons. While this represents just an initial study into the social calling behavior of hoary bats during migration, it provides some conclusions to guide subsequent investigations: (1) detecting hoary bat social calls does not necessarily indicate mating behavior, and (2) researchers should be cautious in interpreting evidence of social interactions during the fall at wind energy sites as evidence of mating behavior as in the mating landmarks hypothesis22,37. Because it can separate out mating from other behavioral components, comparing spring and fall migration can benefit the investigation of social and other behaviors in hoary bats and other migratory species. Comparing flight behavior, diet, roost selection, hormonal and physiological changes, and further studies of social interactions including scent and, between the spring and fall migration will allow researchers to elucidate which behaviors change seasonally and which may underlie seasonal patterns of wind turbine fatalities. Additionally, exploring social attraction to audible sounds produced by turbines or other potential signals that could seasonally elicit social attraction could lead to additional insights.Hoary bats have proven challenging to capture and study in many locations across their range24, driven by their solitary tree roosting behavior and as they often fly out of the reach of mist nets or ground-based acoustic monitoring stations36,38. Using call broadcasting to increase capture rates can be a useful research tool, especially in locations where the habitat does not provide any ideal capture locations. Using this technique we have captured hoary bats on coastal sand dunes, in large open fields, and in groves of Eucalyptus trees adjacent to wind energy sites, all of which would normally yield low bat capture success without the use of lures. The ability to capture hoary bats more reliably is a great asset for research and conservation throughout the range of hoary bats.Our study tested the use of social call playback as a methodology to study the social behavior of hoary bats during migration, and the utility of using call playback as a research tool and acoustic lure for hoary bats. Increasing capture rates from conspecific social call playback during mating and non-mating season indicates social interactions during both migratory periods, despite the solitary roosting behavior of this species. Future studies to elucidate the behavioral function of these calls, and response during non-migratory seasons could refine our understanding of social behaviors of this elusive bat species. More