Ecology

Site selectionOur study focused on the mixed-conifer zone of the Sierra Nevada which is dominated by Ponderosa pine (Pinus ponderosa), Jeffrey pine (Pinus jeffreyi), Incense cedar (Calocedrus decurrens), White fir (Abies concolor) and California black oak (Quercus kelloggii). Common shrubs include Deer brush (Caenothus integerrimus), Mountain whitethorn (Caenothus cordulatus), Greenleaf manzanita (Arctostaphylos patula) and Prostrate ceanothus (Caenothus prostrates). Study sites in the Eldorado National Forest near Placerville, CA (38°45′N 120°20′W), were in and near the area burned during the King Fire (Fig. 2).We sampled sites in the mixed-conifer zone between 4000–6000 ft in three burn categories: unburned, low-to-moderate severity and high severity. We selected sites that occurred in similar pre-burn habitat (moderate to dense conifer forest) based on remotely-sensed vegetation class data from the California Wildlife Habitat Relationships program (CWHR)16. No site experienced wildfire or controlled burns in the preceding century23. Burn categories were based on remote sensing relative differenced Normalized Burn Ratio (RdNBR) canopy cover calibrated for the Sierra Nevada24. We validated these burn categories as meaningful and discrete with remote sensing (immediately post fire) and field data (3 years post-fire). Monitoring Trends in Burn Severity (MTBS) maps classify burn severity with Landsat reflectance imagery of pre-fire and post-fire conditions at 30-m resolution25. MTBS assigns pixels a value based on burn severity: 0 = outside the burn boundary; 1 = unburned-low severity within the fire perimeter; 2 = low severity; 3 = moderate severity; 4 = high severity). At each site, we determined the mean MTBS value of pixels in the small-mammal trapping grid (90 × 90 m) and a 225 m buffer (the largest home range diameter for small mammals we captured)26 extended on all sides. Immediately after the fire, the three burn categories differed significantly in tree cover and remote sensing scores of burn severity25 and are still different in tree surveys three years later.We sampled 27 sites, each 4 ha in size. To minimize site-specific influences, we paired sites across treatments to account for elevation, slope, pre-burn vegetation characteristics, pre-burn forest management, ownership, and soil type. Each burn category received nine sites. We blocked sites across burn severities (one site in each burn category per block). Site locations were chosen for accessibility, but all sites were at least 50 m from access roads and at least 200 m apart (sites were >1 km apart on average). We also excluded areas that experienced or were scheduled to experience salvage logging post fire. Occasionally, field conditions at a site location did not align with remotely sensed data classifications, a site was not large enough for homogeneous sampling plots or was dominated by slopes >30 degrees. In these cases, sites were moved to nearby locations that satisfied the site selection criteria.Sampling designIn this study we report the body size, abundance and biomass density of plants and free-living animals using a variety of methods in three different fire severities. Each site consisted of a 200 m × 200 m plot (4 ha) around which sampling methods were organized (Fig. 3). We collected data for organisms with 19 different sampling methods that were scaled to the abundance and body size of targeted organisms. Some methods were not performed at all sites, and some methods were pooled across sites (within treatments) due to low sampling success. Below, we explain each sampling method and detail any variation in its application across sites. To minimize seasonal effects, we sampled all sites in a block at the same time, over 4–5 continuous days, between late June and early September 2017. Weather was consistently hot and dry during this period with no pronounced variation. All animal survey procedures were approved by UC Davis IACUC (protocol number CA-17-451736) and carried out under CDFW permit #SC-3638.Species inclusionTo evaluate the effects of wildfire-burn severity on community structure we assembled a list of organisms and life stages encountered during our sampling of the Eldorado National Forest. Every animal in our list was broken into ontogenetic life stages. Plants were broken into constituent parts (e.g. leaves, roots, seeds) rather than stages. Every organism in our lists had at least one life stage observed by this study in the Eldorado National Forest. Not all life stages in the list were directly observed, many were inferred. These life stages were suspected to occur in the habitat but were not observed (or quantified) because (1) it was impractical (e.g. mycorrhizae), (2) our sampling methods did not capture them (e.g. larval insects in plant tissues) or (3) they could not be identified to species (e.g. arthropod eggs). Stages (not species) were excluded when they did not occur in the terrestrial habitat (e.g. aquatic larvae). Stages (or parts) were lumped when they could not be distinguished in terms of their resources or consumers (e.g. parasitoid wasp eggs, larvae and pupae). Unobserved stages were omitted from a burn category community if they were feeding (e.g. not eggs) but did not have any resources. All life stages, observed or not, received a body size estimate.This approach has several benefits. First, it fills life-cycle gaps without artificially inflating species richness (e.g. having a node labelled “bird eggs”). Second, unobserved life stages may not be major biomass contributors, but they do make important contributions to food-web structure and population dynamics. Including life stages without abundance information is useful because it allows their inclusion in analyses of network structure, informs consumer-resource body-size ratios and allows comparisons to other food datasets organized around body size, but lacking abundance estimates.A comprehensive food web required the inclusion of non-living nodes like detritus which is an important resource for many consumers. Fire creates strong spatial structure in woody detritus, so we collected mass-density information on it and partitioned detritus into types (e.g., woody vs. leaf) and size classes. We did not collect mass-density information on carcasses but treated them similarly by partitioning them into logarithmic size classes to reflect their availability to different consumers (e.g., tick carcasses vs. deer carcasses). We have also included the biotic products honeydew and dung due to their importance to certain consumer groups in the system. We have not done so here but future efforts may wish to include nutrients as resources for plants to capture gradients and competition. Finally, analyses may wish to augment our lists by explicitly including fire as a consumer/herbivore as opposed to an external force shaping treatment effects.Below we describe the methods used to quantify the richness, abundance, and biomass density of these organisms in each of our burn categories. Unless noted, sampling effort did not vary with burn category.Species resolutionMost entries in the species list represent ontogenetic stages or constituent parts. With the exception of a few nodes (i.e. algae, moss, lichen, mycorrhizae, saprophytic bacteria, saprophytic fungi and nematodes) this ecological resolution is consistent throughout the list. Taxonomic information was assigned using the Global Biodiversity Information Facility database27, but taxonomic resolution varies. Vertebrates are identified at the species level. Invertebrates, while distinguished as morphospecies, were not identified below the family level in many cases. Despite not always being identified to the lowest taxonomic category, entries represent life stages of distinct species or groups, and are accompanied by all the taxonomic information we could provide at that level of resolution.Biomass density estimationEach species observed in our sampling methods received a biomass density estimate. Biomass density was estimated as the product of mean individual body mass and density. We estimated body-mass either by weighing individuals directly, or conservatively estimating their mass volumetrically. For the latter, we measured the length (tip of abdomen to tip of head), width (max width of body excluding appendages) and depth (max depth of body excluding appendages) of individual organisms and converted into an approximate volumetric shape (e.g. ellipsoid, cylinder, hemisphere, etc.)28,29. Mass was estimated by multiplying this volume by a tissue density of 1.1 g/mL30,31,32. Body sizes for stages that were not directly observed were inferred from published records and databases. Density estimates were derived from the sampling methods discussed below and varied with burn category. Mean and standard errors were derived from the nine replicate sites within each treatment, unless otherwise stated. Due to the brief sampling window at each site, these estimates should be regarded as point estimates rather than integrated over time.Plant samplingVegetation transectsTo estimate density and biomass of trees, shrubs, and ground cover, we conducted vegetation transects at each site. Transects were located within each 200 m × 200 m plot but were offset from other sampling methods by 10 m to avoid trampling. Transect direction was chosen at random and two transects were run in parallel. Each transect was 50 m in length but width (and distance between transects and sampling area) varied with sampling method as detailed below.Ground coverTo estimate plant ground cover, we surveyed 1 m2 quadrats at 5 m intervals on each 50 m transect. Summed, this gave a pooled ground cover sample area of 20 m2 at each site. Within each quadrat we estimated the percent cover and average height of grasses, forbs, woody litter, soft-loose and soft-rooted litter, shrubs, and trees. We identified small trees, shrubs and dominant forbs (i.e., forbs with the greatest cover at a site) to species. We then used cover area and height to estimate a volume for each species or group. Volumes were later multiplied by taxon-specific measurements of mass-to-volume ratios derived from vegetation box quadrats to obtain biomass estimates for each ground cover species or group.Canopy coverTo estimate canopy cover, above each of the 20 ground cover sampling locations, we identified each tree species overhead and estimated its absolute canopy cover. This gave a pooled canopy cover sample area of 20 m2 at each site. Canopy cover estimates were incorporated into arthropod biomass density estimates from fogging.TreesTo estimate tree density, we identified and measured all trees that exceeded 15 cm diameter-at-breast-height (DBH) within a 15 m wide band extending the length of each 50 m vegetation transect. Small trees (DBH 1 cm. We estimated shrub volume (length × width × height) and biomass density by combining volume and density estimates with taxon-specific measurements of mass-to-volume ratios derived from vegetation box quadrats. We estimated above ground biomass for small trees using species-specific DBH-to-biomass conversions33.

Understory plants
To estimate the volume and cover of understory vegetation we used three-dimensional box quadrats. These box quadrats consisted of a canvas-walled rectangular box with the floor panel removed, that was placed over the understory to be surveyed. The canvas walls prevent arthropods from escaping as they were also used in arthropod sampling. Each box quadrat measured 1 m × 0.5 m × 0.5 m, covering a ground area of 0.25 m2. At each site, we placed box quadrats at three locations representative of the dominant vegetation. This gave a pooled sample area of 0.75 m2 at each site. To generate a volume estimate, within each box we estimated the percent cover and maximum height of each plant species. Leaf litter was treated separately but also quantified. Then, using a Velcroed flap as access (designed to prevent bugs from escaping), we collected and weighed all the above ground plant biomass, separated by species and type (litter, branches, leaves, etc.). A subsample of material (up to 1 kg) from each plant species and type was retained and returned to the station to extract the associated arthropods via Berlese funnels (detailed below).
For each plant species measured in the vegetation box samples, we estimated a mass-to-volume ratio. We estimated the biomass of understory plants by combining our mass-to-volume ratios with volume estimates from ground cover transects. For taxa present in ground cover transects but not vegetation boxes, or with fewer than three measurements of mass-to-volume ratio, we pooled measurements from higher taxonomic levels. For example, if there was only one measurement of species A, but two measurements for a congener, species B, we used measurements for both species A and B to represent the mass-to-volume ratio of each. In this way, we estimated mean mass-to-volume ratios for every species-type encountered in the shrub and cover transects.
Coarse woody debrisTo estimate the mass density of woody detritus we quantified it according to the method detailed in Waddell (2002). Specifically, we used the center of the vegetation transect as a line-intercept for woody detritus. Woody debris was recorded if its longitudinal axis intersected the transect line, its diameter at the point of intersection was ≧ 12.5 cm; it was ≧ 1 m long and it was not decayed to the point of disintegration. For each piece of woody detritus, we measured the diameter at both ends, the length, and the stage of decay. Length was measured only for the portion exceeding 12.5 cm in diameter. We estimated the volume (m3) for each piece of debris:$$V{m}^{3}=left.frac{(pi /8)({D}_{s}^{2}+{D}_{L}^{2})l}{10,000}right)$$
(4)
({D}_{S}^{2}) is small diameter (cm), ({D}_{L}^{2}) is the large diameter (cm) and l is the length (m)34. We converted this estimate to a volume-density (m3 ha−1) estimate:$${m}^{3}h{a}^{-1}=left(frac{pi }{2;{L}_{t}}right)left(frac{V{m}^{3}}{{l}_{i}}right)f$$
(5)
Lt is the combined transect length (100 m), lt is the length of the individual piece, f is a conversion for area (10,000 m3 ha−1)34. Finally, we converted this is a dry-weight biomass density (kg ha−1):$$kg;h{a}^{-1}=({m}^{3}h{a}^{-1})left(frac{1000,kg}{{m}^{3}}right)SpGast D$$
(6)
SpG is a specific gravity estimate and D is the correction for the state of decay34. Specific gravity can be a species-specific estimate. Woody debris is difficult to identify to species so we applied a single specific gravity estimate to all debris weighted by the relative abundance of tree species in our surveys. We used a weighted specific gravity of 0.382, estimated by multiplying the mean specific gravity of Pinaceae (0.372) by their relative abundance (0.947) and adding that to the specific gravity of Quercus (0.566) multiplied by their relative abundance (0.053). We applied the weighted means to the decay corrections in Waddell (2002).Species of uncertain identificationSome plant identifications were difficult, particularly at burned sites, where some individuals were identified to genus or family. To assign species identities to individuals classified a higher taxonomic rank at a focal site, we first randomly selected a proxy site from the nine unburned sites. This proxy approach is possible because the composition of burned and unburned sites was similar before the fire. Proxy sites were randomly selected from among all the unburned sites because the spatial grouping didn’t lend itself to maintaining distinctions between blocks. Using the species abundance distribution of the focal plant taxon at the proxy site, we randomly assigned (with replacement) plant species identities to unidentified individual plants at the focal site. We repeated this random sampling 999 times, then estimated the resulting mean species abundance and bootstrapped standard errors at the treatment level.Invertebrate samplingGiven the diversity of invertebrates in the ecosystem, and the biases associated with each invertebrate sampling method, we employed several methods to quantitatively survey the entire community.Arthropods on understory plantsTo estimate the density of arthropods on understory plants we collected them along with plant material in the box quadrats. Up to 1 kg of plant material of each species was bagged inside box quadrats and transported to the field station for processing. Insects were extracted from the plant material in Berlese funnels that were hung in the shade at ambient temperatures and powered with 60 W frosted-incandescent bulbs. All plant samples were processed in funnels for 72 hours, after which they were checked twice a day (collecting jars changed). After no additional arthropods had been collected for 24 hours samples were removed. Arthropods were placed in ethanol for later identification. After identification, arthropod densities were estimated as the product of the arthropod-to-volume ratio for each plant and the total volume of each plant estimated from transects. We collected 4543 individual arthropods from 170 morphospecies associated with plant material from box quadrats, processed in Berlese funnels.Soil-dwelling arthropodsTo estimate the density of soil-dwelling arthropods we collected soil cores. Alongside each box, we collected four cores, each 10 cm in diameter. Two shallow cores were sunk to a depth of 5 cm, giving a combined (6 replicates total) sample area of 0.0471 m2 at each site. Shallow cores, which targeted small arthropods (e.g. collembola, diplura, acari), were collected and transported back to the field station for processing in Berlese funnels. Soil samples were processed in Berlese funnels in the same manner as plant material. Two deep cores were sunk to a depth of 15 cm, giving a combined (6 replicates total) sample area of 0.0471 m2 at each site. Deep cores targeted large invertebrates (e.g. lumbricidae, myriapoda) and were processed by hand in the field. Any large invertebrates encountered were placed in ethanol for later identification. After identification, densities were estimated as the quotient of counts and area sampled. We collected 690 individual arthropods from 57 morphospecies from soil cores.Arthropods on hard substrateTo estimate the density of arthropods like wasps and spiders on tree trunks, rock and logs we visually surveyed these hard substrates. In three locations at each site, we surveyed all hard substrates within a cylinder 5 m in diameter and 2 m in height. The total sample area at each site was 58.9 m2. All invertebrates larger than 2 cm in length were collected and fixed in ethanol for later identification. After identification, hard substrate densities were estimated as the quotient of counts and sample area. Hard substrate surveys yielded 616 individual arthropods from 74 morphospecies.Understory arthropodsTo estimate the arthropod densities associated with understory shrubs at each site we supplemented box quadrats with sweep net surveys. We used nets with a 38 cm diameter opening (Bioquip) to sweep the same 5 m diameter area as the hard substrate surveys. Three replicates at each site gave a total sample area of 58.9 m2. Net sweeps were performed at a constant pace of 2 arcs per meter. All invertebrates collected were fixed in ethanol for later identification. After identification, arthropod densities were estimated as the quotient of counts and sample area. Sweep net surveys yielded 706 individual arthropods from 168 morphospecies.Canopy arthropodsTo survey arboreal arthropods, we used a thermal fogger to loft insecticidal fog into the tree canopy. While this method can sample a large area, we were hampered by permit and accessibility issues at some sites. As a result, we sampled a representative subset of 12 sites (6 unburned, 3 high severity, and 3 low-to-moderate severity) with canopy fogging. Chemical fogging remains the most widely used method for sampling canopy arthropods35,36,37. It is notoriously difficult to assess canopy arthropods with any other method35. We used an IGEBA TF-35 Thermal Fogger to disperse a pyrethrum-based insecticide (EverGreen Crop Protection EC 60-6) into the forest canopy. During each fog we dispersed 4 L of solution (a 7% concentration of insecticide in a water-based carrier-dispersant) in 10 minutes over two representative trees and one live shrub at each site. Live trees were sampled at unburned sites, dead trees were sampled at high severity sites, and one dead and one live tree were sampled at low-to-moderate severity sites. Fogging was done under low-wind conditions allowing the thermal fogger and temperature gradients to lift the fog into the canopy. White 2.25 m2 tarps were placed beneath the fogged area to collect insects. The number of tarps used varied with the size and shape of the canopy. After an optimal elapsed drop-time of 120 minutes37, arthropods were collected from tarps and placed in ethanol for later identification. To estimate site-level arthropod abundance using canopy fogging, we estimated the density per square meter of tarp for each morphospecies and habitat type (living tree, dead tree, or shrub), then multiplied these densities by the total area covered by each habitat type. The area covered by each fogged habitat type was obtained from canopy cover transects (trees) and shrub transects (shrubs). Mean and standard errors for density estimates were estimated using the 3 (or 6) sites per treatment. Canopy fogging yielded 16,353 individual arthropods from 489 morphospecies.Strong-flying insectsTo estimate the diversity and relative abundance of strong-flying arthropods, we utilized blacklight traps (Miniature Downdraft Blacklight (UV) Trap Model 912). We supplemented our sampling with blacklight traps, because strong flying arthropods (e.g. vespid wasps) may not be adequately sampled by fogging or sweep netting. Black lights were deployed at sites prior to dusk on the third night of bat acoustic surveys (see below). Each site received one trap and black lights were not deployed on rainy or windy nights. Samples were collected the following morning. To preserve their integrity for later identification, lepidoptera samples were removed and frozen, other flying insects were fixed in ethanol. Black light traps collected 5,508 individuals from 279 morphospecies.Black light samples were only used to estimate densities for those species not regularly captured with other methods. Two hundred fifteen morphospecies were only found in black light samples. Black light traps, which can sample large areas, are an efficient means of generating diversity estimates for nocturnal flying insects. However, black light traps generate activity densities rather than absolute densities, over sample areas that vary within and between nights. To convert counts to density estimates for morphospecies collected only in black lights we paired them with an analogous species (based on taxonomy and body size). These density analogues were captured at the same site in both black lights and at least one other method with explicit absolute densities. We estimated the mean relative abundance ratios of these species in black lights across all sites in which they co-occurred. We then multiplied the density of the ecological analog by these abundance ratios to estimate densities of all species that were collected only in the black light traps.Larval biomass densityTo estimate the density of larval Formicidae, lepidoptera and coleoptera, we partitioned them according to the relative abundance of their adults present in the same treatments. Larvae for many hemimetabolous invertebrates can be difficult to identify without molecular methods. When larvae could not be identified to species, they were binned into categories of Formicidae, lepidoptera and coleoptera. We then collected body size information and density estimates for these stages. Larval densities were then partitioned according to the relative abundance of adults within each treatment. Adults were eligible to receive larvae if (1) they belonged to the same taxonomic rank as the larvae, (2) they did not already have any observed larval stages, (3) there were available resources for their larvae in the treatment.Vertebrate samplingSmall mammalsTo estimate the density of mammals less than 2.5 kg we used live traps uniformly distributed over a 90 m × 90 m grid38. We placed 100 traps 10 m apart, alternating between large (7.5 cm × 9 cm × 23 cm) and extra-large Sherman traps (10 cm × 11.5 cm × 38 cm)39. Traps were baited (a mix of oats, peanut butter, bird seed and molasses) and covered with natural materials for insulation. Traps were locked open and pre-baited for three days and then operated for three consecutive nights. Traps were closed during the day due to high daytime temperatures and lack of shade, particularly in the high severity burn areas. Traps were re-opened in the late afternoon. Captured mammals were identified to species (or individuals during recaptures), weighed, measured and fit with ear tags for future identification, then released. In 7,906 trap nights (adjusted for trap failures), we had 988 captures of 11 species of small mammals.Live-trapping and marking of small mammals allowed us to estimate their abundance using spatial capture-recapture (SCR) models40. These models use individual detections in combination with detection locations (here, location of a given live trap within the grid) to estimate density while accounting for imperfect detection and variation in detection due to variability in individual exposure to the trapping grid. When data are collected across several trapping grids, as in the present study, the joint modeling of all data allows estimation of grid-level covariate effects on density41. We used this framework to analyze data from all 27 plots jointly and allowed for density to vary according to burn category of a site (unburned, low-to-moderate severity or high severity), so that, for example, all unburned sites had the same density of a given species. If a species was never caught in a burn category, we fixed its density (and consequently, its biomass) to 0 for all sites in that burn category.Because trapping data for most species was too sparse to fit species-specific models, we grouped ecologically similar species and built models that shared parameters among grouped species. In this manner, we analyzed the joint data of mice and chipmunks: Brush mouse (Peromyscus boylii), Deer mouse (Peromyscus maniculatus), Pinyon mouse (Peromyscus truei) and Western harvest mouse (Reithrodontomys megalotis), Yellow-pine chipmunks (Tamias amoenus), Long-eared chipmunks (Tamias quadrimaculatus) and Shadow chipmunks (Tamias senex).In the mouse model, Brush mouse and Pinyon mouse densities were assumed to have the same response to burn category (but not the same densities), whereas Deer mice were allowed a different response to burn category. This choice was made based on capture frequencies of species in the different burn categories. Pinyon mice and Western harvest mice had a fixed density of 0 in low-to-moderate and high severity burn sites. All species shared the same detection parameters.Even combined, the chipmunk data captures were too sparse to estimate species specific densities. We therefore treated all chipmunks like a single species to estimate overall chipmunk density, then calculated species specific densities by multiplying overall density with the proportion of individuals of a given species in the data. For example, if species A made up 50% of all individuals in the data, we would calculate density for species A by multiplying overall chipmunk density by 0.5. This model entails the assumption that the effect of burn category on density was the same for all chipmunks, which seems reasonable based on capture frequencies.We built species-specific SCR models for California ground squirrels (Otospermophilus beecheyi) and Dusky-footed woodrats (Neotoma fuscipes); for the latter we fixed density to 0 in high severity sites. Northern flying squirrels were only caught 3 times at unburned sites, so we set its density to 0 in low-to-moderate and high severity sites, and used a published density estimate from a Sierra Nevada site42 for unburned sites. Because individuals of Trowbridge’s shrew (Sorex trowbridgii) were never recaptured and had high rates of trap mortality, we estimated their abundance using non-spatial removal models43 for unburned and moderately burned sites and set their abundance to 0 at high severity sites. We transformed abundance to density by dividing it by the 8100 m2 area (90 × 90 m) of the live trapping grid.We fit SCR models in R using the package “secr” ver. 3.1.344, and removal models using the package RMark ver. 2.2.445.BirdsTo estimate the diversity and density of birds, we conducted point count surveys46. Each site had two point-count stations, spaced at least 200 m apart, that were surveyed on the same day. Counts were conducted at each site on three different days per season. To mitigate migration effects all counts were conducted between mid-June and mid-July. Bird surveys did not take place when it was raining, extremely cold (20 kmh). Wind speeds were obtained with handheld anemometers. Point counts began 15 minutes after sunrise and were completed by 10:00 AM, corresponding to when passerine birds are most active. Each survey consisted of one ten-minute count, split into two five-minute periods. Each individual bird was recorded only once over the entire count. Trained observers identified all birds to species by sight or sound and estimated the number of individuals of each species within 100 m of the sampling point. This gave us a sample area of 15,708 m2 at each site. Birds that flew through or were detected outside of the survey area or survey time were documented but not included in richness or density estimates. During 162 point-surveys we observed 1,039 birds from 52 species (107 individuals could not be identified).The repeated point counts allowed us to estimate bird abundance and density using N-mixture models47. These models use repeated counts of individuals to estimate abundance within a sampling unit (in this case, the 100 m radius point count) while accounting for imperfect detection. Because abundance estimates refer to a set area, these can be converted to densities. Because data for some bird species were sparse, we fit data of all species jointly in a community model48. In community models, each species has its own set of parameters, but species-specific parameters are modeled as coming from a common underlying distribution that is shared by the community of species (essentially, a species-level random effect). This constitutes a form of information sharing, which improves parameter estimates for data-sparse species. In our community N-mixture model, we allowed for species-specific detection probabilities, as well as species-specific effects of burn category on abundance. We further included a fixed (across species) effect of the amount of wind during a given survey on detection, as wind can impair auditory detection of birds. We implemented the community N-mixture model in a Bayesian framework using the program JAGS ver. 4.2.049 accessed through R with the package jagsUI ver. 1.5.050. JAGS fits models using Markov chain Monte Carlo (MCMC) algorithms; we ran 100,000 MCMC iterations and discarded the first 50,000 as burn-in. We checked for chain convergence using the Gelman-Rubin statistic51; all parameters had a value 2.5 kg we used camera traps. At each site, we deployed two Reconyx HC600 Hyperfire cameras, which have a no-glow infrared flash, preventing disturbance to wildlife and detection by humans. To reduce false triggers, we set cameras in shaded locations and cleared vegetation in front. Cameras were deployed along landscape features that suggested animal movement: game trails, forest openings, abandoned dirt roads, etc. Cameras were set at least 50 m apart and strapped to trees 40 cm above the ground and 1–2 m away from the edge of a trail or opening. Cameras were operated for 24 hours a day, with 3 consecutive pictures per trigger and no time lapse between triggers. All 54 camera traps were installed by early-July and retrieved in mid-September. Cameras were checked after four to six weeks for SD card and battery replacement. All pictures were reviewed manually for species identification; identified pictures of animals were organized into camera and species-specific folders for post-processing in the R computing environment ver. 3.4.352 using the package camtrapR ver. 0.99.553. Even though some bird and small mammal species were occasionally photographed by camera traps, we excluded their photo-records from further analysis. After adjusting for malfunction, cameras operated for 4,238 trapping nights over which they assembled 12,243 independent records (detections that were at least one hour apart if of the same species at the same camera) comprising 10 species of mammals >2.5 kg.Because camera-trap images do not allow identification or counting of individuals for analysis with SCR or N-mixture models, we used the Royle-Nichols (RN) occupancy modeling framework54 to estimate abundance of medium/large mammal species. Occupancy models55 use repeated species detection/non-detection data from a collection of sampling locations to estimate species occurrence probability while accounting for imperfect detection. The RN model makes use of the fact that the probability of detecting a species at a site increases with the abundance of that species at that site, allowing estimation of site-level abundance from species level detection/non-detection data. We used the R package camtrapR53 to convert raw camera trap data for each species into a binary (detected = 1, not detected = 0) location-by-occasion format. Because of their proximity, we considered both cameras at a given plot to constitute a single sampling location. We defined an occasion as 10 consecutive days of sampling. To account for malfunctioning of some cameras, we calculated effort as the number of days per occasion that each pair of cameras was functional.Because data for some species were sparse, we jointly analyzed data for all species in a community RN model (see Birds for a description of community models); we allowed for species-specific detection probabilities as well as species-specific effects of burn category on species abundance and included a fixed effect (across species) of effort on detection. The RN model returns sampling location level estimates of abundance, but in contrast to bird and reptile surveys, which were explicitly linked to a specific sampled area, the area sampled by a camera trap is not easily defined. Mammals recorded in the relatively small detection zone of a camera use much larger areas. We calculated densities of large mammals by dividing the abundance estimates from the RN model by the average home ranges of the recorded species. We fit the community RN model in a Bayesian framework as described for birds, running 30,000 MCMC iterations and discarding the first 15,000 as burn-in. All chains converged, according to the Gelman-Rubin statistic.To obtain estimates for home range size and individual mass of large mammals, we searched the scientific literature for studies conducted in similar habitat (temperate coniferous forests) and for home ranges, during the summer and fall seasons. When no information from a similar habitat was available, we used studies from forested areas. When no information for target species was available, we used information from congeners. When multiple studies were available, we calculated the mean across studies, weighted by sample size if provided. Similarly, when studies provided information separately for males and females, we calculated a weighted mean. For some large mammal species, we only found information on annual home ranges, and to approximate a summer/fall home range, we multiplied these annual ranges by 0.67.BatsTo survey bat community composition and abundance, we conducted acoustic sampling at each site. We placed a Wildlife Acoustics Songmeter SM4BAT FS echolocation detector at each site for a minimum of three consecutive nights. The detectors were set to record from sunset to sunrise when bats are most active. We attached microphones to t-posts placed in open areas, elevating them 2.5 meters from the ground to minimize signal attenuation from nearby vegetation and canopy. Bat calls were analyzed using SonoBat software to assess likely species for each file. These identifications were vetted by an experienced bat researcher, Ted Weller (USDA Forest Service). One site (B3U1) was not sampled with bat detectors, whereas two sites had one and two additional nights sampled, respectively. During 81 recorder nights, acoustic recorders captured 17,484 calls, of which 7348 were identified to 17 species.Similar to camera traps, bat calls recorded by acoustic recorders do not allow counting or identifying individuals. Therefore, we built a community RN model to estimate abundance for bats. The model structure was identical to that described for medium/large mammals, except that it did not include any covariate on detection probability. Analogous to our medium/large mammal analysis, we used literature information on average home range size to convert bat abundance to bat density. We ran the bat model for 150,000 iterations and discarded the first 75,000 iterations as burn-in. All chains converged according to the Gelman-Rubin statistic.ReptilesTo estimate community composition and density of reptiles, we conducted timed searches within the 90 m × 90 m small mammal trapping grids at each site. This gave us a sample area of 8100 m2 at each site. Reptile surveys were conducted either prior to small mammal trapping or two weeks post to minimize impacts from sampling disturbance. Reptile surveys were conducted between 8:00 am and 10:00 am by teams of two to four, for a total of one person-hour. We performed reptile surveys three times at each site, with at least one week between surveys. During searches, all refugia (i.e., rocks, logs) within the plot were carefully overturned and then replaced. In addition to reptile species, we documented time of day and weather, as well as cover type and cover length if applicable. Our reptile surveys yielded 145 records of two snake species and four lizard species. Two additional snake species were observed incidentally during other sampling methods.Repeated counts of reptiles allowed us to use N-mixture models47 to estimate abundance and density. However, reptile data were extremely sparse and contained few species, precluding the use of a full community model as fit for birds. Instead, we structured reptile data into three groups: snakes (Western terrestrial garter snake (Thamnophis elegans) and Yellow-bellied racer (Coluber constrictor)), Alligator lizards (Elgaria coerulea and E. multicarinata) and Western fence lizards (Sceloporus occidentalis). We then combined data from all groups in a single N-mixture model, allowing for group-specific abundances. Due to sparse data, we only estimated an effect of burn category on abundance for fence lizards. Abundance for other species groups was assumed to be constant across burn categories in the model. To obtain species and burn category specific estimates of abundance, we combined model estimates of abundance with raw counts of individuals. For the two snakes, we calculated species level abundance by burn category by multiplying overall model-estimated snake abundance with the proportion of individuals made up by each species in each burn category. Because some Elgaria sp. observations could not be identified to species level, we did not attempt to calculate species specific abundances (but we note that the majority of observations was of E. coerula). We calculated species/group density by dividing abundance by the size of the sampling unit, in this case, a 90 × 90 m square. We fit the N-mixture model in R using the package unmarked ver. 0.12.256.Species identificationVertebrates were identified in the field or from photographs. Invertebrate specimens were collected and fixed in the field for identification in the lab where they were split into morphospecies using published keys57,58 and consultation with experts at the UC Davis Bohart Museum. Morphospecies were not always identified to lower taxonomic levels but were distinguished from similar species by coarse morphological characters. Prior to density estimation some morphospecies were aggregated based on taxonomy, body size, sample methodology, co-occurrence patterns and the difficulty in distinguishing members of the group. The rationales behind aggregations are documented in the raw invertebrate sampling data.Body size estimationFood webs are commonly organized around body size59,60,61,62,63 and we include length and mass estimates for all life stages in our data set. We assume mean body size does not change substantially across burn severities and use a single estimate for all life stages regardless of treatment. When possible, we measured 10 individuals for each morphospecies and life stage (for invertebrates and small mammals only, as individuals from other taxonomic groups were not handled). Body mass for small mammals was measured directly with a Pesola scale. Body length estimates for invertebrates were derived from the longest measurement. Volumetric estimates were derived by applying the measured length, height and width to the three-dimensional shape that most closely approximates that of the organism (e.g. ellipsoid, rectangular prism, cylinder, hemisphere, cone). We estimated biomass by multiplying these volumetric estimates by a tissue density of 1.1 g/mL30.Body sizes for organismal life stages observed but not captured (e.g. adult birds and large mammals) were estimated from the literature. Life stages for many organisms were not directly observed (e.g. lepidoptera eggs) but are almost certainly present. For these stages we again used the literature to estimate the body sizes. For example, we used a data set of egg sizes from 6,700 insect species to inform estimates for the species in our study64. The species list indicates the estimation method and literature source used to estimate the body size of each individual life stage is included in the species list.LinksEcological networks can be broken into two types: Undirected and directed. Undirected networks include bipartite networks (e.g. plant-pollinator, host-parasite) and social-networks. In undirected networks, interactions (links) and their participants (nodes) are observed at the same time. Links are not inferred in undirected ecological networks (unless false-negatives due to sampling error are taken into account) because they are directly observed (e.g. tick removed from a lizard). Undirected links can be weighted (interaction strength) by counting observations.Directed networks include food webs. Most food web links are inferred because it is not feasible to evaluate all possible consumer-resource interactions in a system through direct observation. The number of possible consumer-resource interactions in a food web is equivalent to the number of nodes squared. There were 3,084 nodes in the Eldorado National Forest, resulting in 9,511,056 potential consumer-resource interactions. In the scope of an ecological study, it is rarely feasible to directly observe most feeding links for most species15, much less an entire web, though molecular methods are bringing this closer to possibility for some guilds (e.g. large mammalian herbivores)65. While often detailed for birds and mammals, published accounts of direct observations of diets are often general (e.g. “Species A eats organic matter”) or entirely lacking for other groups. Restricting link assignments only to those that are directly observed will not create an accurate or unbiased food web.To assign resources to consumers we supplemented our own field observations with published diet records, expert opinion and rule-based filters. In the absence of direct observation, rule-based filters are an important tool for sorting through the huge number of potential feeding interactions. Filters varied with the species and ontogenetic stages to which they were applied but consisted of two main types: Encounter and compatibility. Encounter filters determine the potential for consumers to interact with resources. Encounter filters are based on habitat co-occurrence, forging strategy and diel activity patterns. Applied to links that have passed through encounter filters, compatibility filters determine the outcome of a potential encounter. Compatibility filters are based on consumer diet information, consumer diet breadth, resource palatability, and consumer-resource body size ratios. For inclusion in the food web, a link must pass through both filters.Link assignmentAll species were considered potential resources. For feasibility and reproducibility, species were broken into groups of encounter filters. First, we separated plants, fungi and detritus from other metazoans.Fig. 2Sampling sites map and King Fire perimeter. We sampled 27 sites total in the Eldorado National Forest, California, three years after the King Fire, nine in each burn category: Unburned, moderate severity, and high severity. Features not indicated in legend are typical of topological maps.Full size imageFig. 3Representative map of sampling design. We employed 19 different methods to estimate the richness and biomass density of organisms in the Eldorado National Forest, California, three years after the King Fire. Methods were conducted entirely within or centered within the 200 × 200 m site perimeter. Methods were paired in space and time when useful (e.g. black lights and acoustic bat surveys), and separated when necessary to avoid interference (e.g. small mammal trapping grid and vegetation transects). All methods at a site were conducted over 4–5 consecutive days.Full size imageThe first encounter filter was a loose group consisting of primary producers, non-living resources (e.g. detritus, carcasses), and saprophytes. In this first filter, primary producers were broken into parts (e.g. root, seed, leaves, etc). With few exceptions (i.e. moss, lichens, algae) primary producers in this filter group were evaluated as families or species. Saprophytes were evaluated as spores or adults. These groups served as the encounter filter for primary consumers and fungivores.Next, we partitioned all other metazoans into smaller encounter filters based on phylogeny, behavior, activity, habitat and palatability. A species’ life stage can be a member of multiple resource groups. These groups served as the encounter filter for most predatory or omnivorous organism stages. These encounter filters included:

Flying invertebrates

Nocturnal flying invertebrates

Diurnal flying invertebrates

Non-flying invertebrates

Ground-dwelling invertebrates

Ground-dwelling invertebrates excluding spider eggs

Soft-bodied ground dwelling invertebrates

Invertebrates on plants

Invertebrates on plants excluding spider eggs

Soft-bodied invertebrates on plants

Invertebrates on trees

Reptiles

Birds

Small mammals

Large mammals

Encounter filter exposure was tailored to consumer type. For consistency, consumers were broken into phylogenetic and ontogenetic guilds (e.g. lepidoptera larvae). For example, web-building spiders were exposed to flying insects, but not small-mammals encounter filters. Compatibility filters are specific to species and stage. For example, while both are exposed to the flying-insects encounter filter, adult and juvenile web-building spiders will have different compatibility filters because they have different consumer-resource body size ratios. Phylogenetic and ontogenetic consumer guilds are detailed below, but the decisions for each of the 178,655 links assignments in the food web can be found in and reproduced with the R code accompanying this manuscriptSpidersAs common, generalist insectivores, spiders are important consumers in the network66. To assign them resource links, spiders were broken into three ontogenetic stages: adult, egg, juvenile. Spiders were then separated into 17 consumer guilds by Family. The 87 spider species that could not be identified at the family level were assumed to be web-building spiders. To assign feeding interactions, we applied one or more invertebrate resource group filters (see above) to the members (species and stages) of each spider guild. Next, each link passed through a consumer-resource body size filter before being included in the network. For example, web-building spiders were assigned to capture flying insects and consume insects larger than 20% of their own body-length (as mesh size sets a lower limit of prey size) and less than 200% of their own body length67. Because of the generality of their filter-feeding hunting strategy, web-building spiders accounted for nearly half (32,663) of interactions with spiders as consumers. Additionally, when it was reported in the literature, adult members of a guild were assigned to consume nectar.Herbivorous beetlesTo assign consumer links to non-predatory beetles, their larval and adult stages were separated into Family-level feeding guilds. When information was available, links were evaluated at the species-stage level rather than the Family level. Resource links containing primary producers and non-living resources (detritus, carcasses, dung, etc) were assessed based on direct observation, published reports and expert opinions. When feeding on living plants, beetles were assigned to specific tissue types (e.g. flower, leaves, roots). For example, Cerambycidae_sp_2 was assigned to feed on the stems of Arctostaphylos patula as well as stems of plants from the family Rhamnaceae. This assigned 10,289 consumer links to herbivorous beetles.Larval lepidopteraCaterpillars were the most speciose herbivore group in the network. Larval lepidoptera vary greatly in host plant range and the quantity of information on their feeding habitats and were not lumped into guilds whenever possible. Microlepidoptera were not identified below the order level and were assumed to feed like Gelechiidae caterpillars (many species of which have been documented in Eldorado National Forest). We assigned links to caterpillars with resource filters of host plants and parts based on published diet records, palatability, expert opinion and observed co-occurrence between potential hosts and adults. This assigned 2,434 consumer links to caterpillars.Adult lepidopteraAdult lepidoptera feed primarily or exclusively on nectar from flowering plants. The host ranges for most adult lepidoptera are not well described. To assign nectar links, adult lepidoptera were grouped into families and passed through resource filters based on published records, co-occurrence, and expert opinion. Non-feeding adult moths were removed from link consideration. These filters assigned 5,854 nectar links to adult lepidoptera.Adult hymenoptera as herbivoresMost species of hymenoptera are parasitoids as larvae and free-living as adults (eusocial hymenoptera are a notable exception). Many adult parasitic wasps supplement their protein intake with nectar and pollen. The plant host ranges for solitary adult wasps are not well known. We applied nectar and pollen resource filters to adult hymenoptera based on published records, co-occurrence and expert opinion. These filters assigned 4,880 links to adult hymenoptera.Predatory beetlesPredatory and scavenging beetles are important consumers in terrestrial ecosystems. At the family level, the diets of predatory beetles are well-described relative to other arthropods. We grouped predatory and scavenging beetles into family guilds. We applied resource filters to predatory and scavenging beetles based on published records and expert opinion, habitat overlap, foraging strategy, palatability, and body size. These filters assigned 10,320 resource links to predatory beetles.FliesFlies in the Eldorado National Forest are a speciose group with diverse consumer strategies: herbivores, predators, scavengers, detritivores, parasitoids, and micropredators. Because of their trophic diversity each dipteran family was treated as its own guild, even then there were often large ontogenetic diet shifts across stages within a guild. We applied resource filters to dipterans based on published records and expert opinion of their diet. These filters were based on habitat overlap, palatability, and for predatory stages consumer-resource body size ratios. These filters assigned 9,541 resource links to flies.Hymenopteran parasitoidsParasitoid wasps are a diverse group that can have strong, even regulatory, effects on their host populations. We applied resource filters to hymenopteran parasitoid larvae in a three-step process. First, we used published records and expert opinion to establish their host range and host specificity. Next, we looked for the subset of potential hosts that co-occured with adult parasitoids across the most sites. Finally, we retained the subset of those host species whose adult body sizes were equivalent to or slightly larger than adult parasitoids. These filters assigned 4,695 host links to parasitoid wasps.Other hymenopteraThe remaining hymenoptera were composed of wood wasps and gall wasps, as well as bees, predatory wasps and ants. Host plant links were assigned to wood wasps and gall wasps in the same way as larval lepidoptera. Nectar links were assigned to bees in the same way as adult lepidoptera. Prey links were assigned to solitary and eusocial wasps in the same way as insectivorous gleaning birds. Ants were treated as generalist omnivores whose links were assigned links in a manner similar to all of the above.HemipteraHemiptera were the most abundant consumer group in the Eldorado National Forest. Hemiptera use their proboscis to feed on fluids, either as herbivores or predators. To accommodate this variation in consumer strategies resource filters were assigned to individual hemiptera species based on phylogeny. Host plants were assigned to herbivores based on field observations, published records and expert opinion. Plant fluids (with the exception of sap from soft woods) were not included in the node list, so herbivores were assigned feeding links based on the plant tissues that they pierced (i.e. stem or leaves). Predatory hemiptera were assigned insects on plants passed through consumer-resource body size filters. When more specific dietary information was available resource filters were further refined. These filters assigned 3,177 links to the hemipteraCollembolaFrom soil to canopy, springtails were widely distributed across Eldorado National Forest habitats and are important resources for arthropod secondary consumers. Collembola are herbivores, detritivores and fungivores. Little diet information is available for collembola, which were assigned resource filters based on habitat and phylogeny (order). Habitat was determined by collection method, and resource availability was determined by habitat. These, phylogenetic-habitat-resource filters assigned 140 links to the collembola.Bark licePsocoptera were common on plants in the Eldorado National Forest, feeding on algae, lichen, moss, fungi and detritus. They exhibit little variation in diet and were assigned resource filters as a group at the Order level. This small set of resource filters assigned 120 links to the psocoptera.ThripsThysanoptera were common and abundant consumers in the Eldorado National Forest, whose diets can range from herbivory to facultative predation and predation. To accommodate this variation in consumer strategy, resource filters were assigned to individual thysanoptera species based on phylogeny. Host plants for herbivores were based on field observations, supplemented with published records and expert opinion. Thysanoptera predators were assigned soft-bodied insects on plants passed through consumer-resource body size ratio filters. These resource filters assigned 924 links to thysanoptera.BatsBats are important consumers of arthropods. Arthropod encounter filters were determined by bat foraging strategies reported in the literature. Juvenile bats were treated separately. Aerial hawking bats capture nocturnal flying insects. Gleaning bats capture insects on plants. Pallid bats (Antrozous pallidus) which fly close to the ground, capture arthropods on the ground and understory plants. These arthropod filters then passed through consumer-resource body size ratio filters, which assigned 4,621 links to adult bats.BirdsBirds were the most speciose vertebrate group in the Eldorado National Forest. This diversity is reflected in their diets. Bird resource filters were based on published records and expert opinion. Herbivorous birds that were reported to feed on a type of plant tissue were assumed to feed on all tissues of that type, unless the literature indicated resource specialization. For example, if a bird was reported to feed on a fruit, it was assumed to feed on all fruit in the system. Resource links for predatory and omnivorous birds were passed through consumer-resource body size ratio filters. These filters assigned 56,584 links to birds as consumers.Small mammalsMammals 2.5 kg were comparatively uncommon but important consumers in the Eldorado National Forest. Large mammal resource filters were based on published records and expert opinion. Herbivorous large mammals that were reported to feed on a type of plant tissue were assumed to feed on all tissues of that type, unless the literature indicated resource specialization. Resource links for omnivorous and predatory large mammals were passed through consumer-resource body size ratio filters. These filters assigned 840 links to large mammals as consumers.Juvenile mammalsJuvenile mammals were considered unweaned. Nursing mammals were included in the species list because they are more vulnerable than adults and are potential resources to different consumers as a result. Unweaned mammals “feed” on their mothers, who are considered their only resource link. Nursing filters assigned 38 links to juvenile mammals.ReptilesWhile not diverse or common, reptiles on the mountain slopes of Eldorado National Forest are potentially important consumers for many groups. Reptile resource filters were assigned based on published records and expert opinion. All resource links were passed through consumer-resource body size ratio filters. These filters assigned 1,487 links to reptiles.MitesMites are ubiquitous and important consumers in many habitats but are often overlooked because of their small size and taxonomic difficulty. Mites were treated as consumer groups based on taxonomy. Resources for predatory mites were based on habitat and passed through consumer-resource body size ratios filters. Resource filters assigned 2,971 links to mites.Miscellaneous arthropodsA few arthropods not discussed above were not speciose enough to be dealt with separately here. These remaining miscellaneous arthropods were treated as groups at various taxonomic levels. Encounter and compatability filters for these groups were based on published records. Encounter filters for predatory (centipedes, odonates, pseudoscorpions, solfugids, mantids, opiliones, neuroptera, embioptera, pauropoda, eusocial wasps) and omnivorous (dermaptera) groups were based on habitat and passed through consumer-resource body size filters. Encounter filters were assigned by habitat for detritivores, herbivores and fungivores (archeognatha and isopods). These filters assigned 3,157 links to miscellaneous arthropods as consumers. More

Coupled model experiments for detecting vegetation-climate feedbackWe quantified changes of near-surface (2-m) air temperature (Ta) in response to the observed NH greening for all active growing seasons during 1982–2014 using IPSL-CM. We defined the three growing seasons (spring, summer, and autumn) across the entire NH domain as periods of March-April-May (MAM), June-July-August (JJA), and September-October-November (SON), respectively. For each season, a pair of transient numerical experiments was performed by modifying LAI: a dynamic vegetation experiment (SCE) forced by annually and seasonally varying LAI from satellite observations36, and three seasonal control experiments (({{{{{{rm{LAI}}}}}}}_{{{{{{rm{CTL}}}}}}}^{{{{{{rm{MAM}}}}}}}), ({{{{{{rm{LAI}}}}}}}_{{{{{{rm{CTL}}}}}}}^{{{{{{rm{JJA}}}}}}}), and ({{{{{{rm{LAI}}}}}}}_{{{{{{rm{CTL}}}}}}}^{{{{{{rm{SON}}}}}}}) for MAM, JJA, and SON, respectively) forced by annually varying LAI for all seasons, except in the season of interest when the LAI was fixed to the climatological conditions observed during 1982–2014 (Fig. S1). For all experiments, other boundary conditions, including sea surface temperature (SST), sea ice fraction (SIC), and atmospheric CO2 concentrations, were kept consistent (Methods). Therefore, differences between SCE and the control experiments characterized the effects of the observed LAI changes on Ta (hereafter denoted as ΔTa), both intra- and inter-seasonally. Multimember paired ensembles were generated for each coupled model experiment by performing 30 repeated runs but with different initial conditions (see Methods).The capacity of the IPSL-CM GCM for simulating the seasonal variations and spatial patterns of Ta was assessed by comparing the SCE simulation results with the observation-based Ta data (Methods). Throughout most of the growing season (May to October), the SCE simulation well reproduced the increasing trend and interannual variability of the NH land mean Ta observed during 1982–2014 (Fig. S2). Observational data showed that the strongest NH warming occurred in early spring (March and April) and late autumn (November). However, the SCE simulation failed to capture the exceptionally strong warming during the transitional seasons, leading to the underestimation of the annual mean warming trend (SCE: 0.237 ± 0.024 °C decade−1; observed: 0.362 ± 0.048 °C decade−1). This underestimation stemmed from a negative bias in the increase of downwelling shortwave radiation, possibly due to an absence of short-lived forcing and bias in the cloud systems37. Overall, the SCE reproduced the geographical patterns of seasonal warming reasonably well (Fig. S3), which strengthened our confidence in the model projections. Notably, it successfully captured the observed amplified warming over pan-arctic and semi-arid regions, as well as the few cases of regional cooling, such as that over northwestern North America during MAM (Fig. S3).Intra-seasonal temperature responses to NH LAI changesFor the period from 1982 to 2014, satellite-retrieved LAI showed statistically significant increasing trends (p  0.1), strong and significant JJA cooling (−0.044 ± 0.008 °C decade−1, p  0.1) (intra-seasonal feedbacks shown in Fig. 1b). The LAI-induced JJA Ta trend was equivalent to cooling of −0.15 ± 0.03 °C in JJA over the study period, offsetting the overall SCE-simulated near-surface air warming over this period by ~12.5%. This strong JJA cooling was further supported by a significant negative correlation (r = −0.64, p  0.1) or SON (r = 0.07, p  > 0.1) (Fig. S4a, c), during which the LAI-induced changes accounted for only 1.3% (MAM) and −3.2% (SON) of the concurrent greenhouse warming. We also verified the robustness of our results by performing equilibrium experiments with an independent model, the NCAR Community Atmosphere Model coupled with Community Land Model (CAM-CLM, Methods). Indeed, this model generated a similarly strong LAI-induced cooling in JJA (−0.18 °C, p  0.1) and SON (−0.05 °C, p  > 0.1) (Fig. S5).Fig. 1: Intra- and inter-seasonal temperature responses to leaf area index (LAI) changes.a Monthly trends (shadings) of Northern Hemisphere (NH) mean LAI during 1982–2014 used as input to the seasonal simulations. The dashed curve and transparent bars indicate trends of monthly LAI and seasonally aggregated LAI values, respectively. b Linear trends of Ta driven by LAI changes within the same season (intra-seasonal) and other growing seasons (inter-seasonal). Error bars in a, b indicate uncertainty ranges [1 – standard deviation (SD)]. c Monthly trends of LAI-induced air temperature changes (ΔTa), with red and blue shadings representing positive and negative trends, respectively. The bottom panel shows the overall ΔTa trends induced by LAI changes in all growing seasons, calculated as the sum of ΔTa trends from the three seasonal runs shown separately in the above panels. ***p  More

Contents of Cd and Ca in Panax notogensing rootsThe Ca content of P. notoginseng roots increased significantly with the increase of lime application rates under the same concentration of oxalic acid sprayed on leaves (Table 2). Compared with no lime application, the Ca content was the highest increased by 212% under 3750 kg hm−2 lime without spraying oxalic acid. The content of Ca slightly increased with the increase of oxalic acid spraying concentrations under the same rate of lime application.Table 2 Effects of foliar spraying of oxalic acid on contents of Cd and Ca in roots of Panax notoginseng under Cd stress.Full size tableThe contents of Cd in roots ranged from 0.22 to 0.70 mg kg−1. The content of 2250 kg hm−2 Cd decreased greatly with the increase of lime application rates under the same spraying concentration of oxalic acid. Compared with the control, the root Cd contents decreased by 68.57% under the application of 2250 kg hm−2 lime and 0.1 mol L−1 oxalic acid spraying. The Cd contents of P. notoginseng roots decreased significantly with the increase of oxalic acid spraying concentrations under application of non-lime and 750 kg hm−2 lime. The root Cd contents decreased at first and then increased with the increase of oxalic acid concentrations under the application of 2250 kg hm−2 lime and 3750 kg hm−2 lime. In addition, the Bivariate analysis showed that the Ca content of P. notoginseng roots was significantly affected by lime (F = 82.84**), and the Cd content of P. notoginseng roots was significantly affected by lime (F = 74.99**) and oxalic acid (F = 7.72*).MDA contents and relative antioxidase activitiesThe content of MDA decreased greatly with the increase of the rates of lime application and oxalic acid spraying concentrations. There was no significant difference in the content of MDA in the roots of P. notoginseng with non-lime and 3750 kg hm−2 lime application. Under 750 kg hm−2, 2250 kg hm−2 lime application, the MDA content with 0.2 mol L−1 oxalic acid spraying concentration treatment decreased by 58.38% and 40.21% comparing with non-oxalic acid spraying application, respectively. The content of MDA (7.57 nmol g−1) was the lowest under 750 kg hm−2 lime application and 0.2 mol L−1 oxalic acid spraying treatment (Fig. 1).Figure 1Effects of foliar spraying of oxalic acid on contents of malondialdehyde in roots of Panax notoginseng under Cd stress. Notes The figure legend showed the spray concentration of oxalic acid (mol L−1), different lowercase letters indicate significant differences between treatments at the same lime application rate (P  Rb1  > R1. The contents of the three saponins had no significant difference with increase of the concentrations of oxalic acid spraying and no application of lime (Table 4).Table 4 Effects of foliar oxalate application on the percentages of three saponins in roots of Panax notoginseng under Cd stress.Full size tableThe contents of R1 with 0.2 mol L−1 oxalic acid spraying was significantly lower than that without oxalic acid spraying and rates of 750 or 3750 kg hm−2 lime application. Under the concentration of 0 or 0.1 mol L−1 oxalic acid spraying, there was no significant difference in contents of R1 with increase of rates of lime application. Under the concentration of 0.2 mol L−1 oxalic acid spraying, the contents of R1 with 3750 kg hm−2 lime was significantly lower 43.84% than that without lime application (Table 4).The contents of Rg1 increased at first and then decreased with the increase of oxalic acid spraying concentrations and 750 kg hm−2 lime application. Under the application rates of 2250 or 3750 kg hm−2 lime, the contents of Rg1 decreased with the increase of oxalic acid spraying concentration. With the same concentration of oxalic acid spraying, the Rg1 content increased at first and then decreased with the increase of lime application rates. Compared with the control, except that the Rg1 content with three concentrations of oxalic acid spraying and 750 kg hm−2 lime was higher than that of the control, the contents of Rg1 in the roots of P. notoginseng under other treatments was lower than that of the control. The Rg1 content was the highest with 750 kg hm−2 lime and 0.1 mol L−1 oxalic acid spraying treatment, which was higher 11.54% than that of the control (Table 4).The contents of Rb1 increased first and then decreased with the increase of oxalic acid spraying concentration and 2250 kg hm−2 lime application. The content of Rb1 with 0.1 mol L−1 oxalic acid spraying reached the maximum value of 3.46%, which was higher 74.75% than that without oxalic acid spraying treatment. Under other lime application treatments, there was no significant difference among different oxalic acid spraying concentrations. With 0.1 and 0.2 mol L−1 oxalic acid spraying treatments, the contents of Rb1 decreased at first and then decreased with the increase of lime application rates (Table 4).Contents of flavonoidsWith the same concentration of oxalic acid spraying, the content of flavonoids increased at first and then decreased with the increase of the amounts of lime application. There was no significant difference in the content of flavonoids under different concentrations of oxalic acid spraying without the application of lime or 3750 kg hm−2 lime. Under 750 and 2250 kg hm−2 lime application, the content of flavonoids increased at first and then decreased with the increase of the concentration of oxalic acid spraying. Under the treatment of 750 kg hm−2 application and 0.1 mol L−1 oxalic acid spraying, the content of flavonoids was the highest, which was 4.38 mg g−1, which was higher 18.38% than that of the same rate of lime application and without spraying oxalic acid. The content of flavonoids with 0.1 mol L−1 oxalic acid spraying treatment increased by 21.74% compared with that without oxalic acid spraying treatment and 2250 kg hm−2 lime application (Fig. 5).Figure 5Effects of foliar spraying of oxalate on the contents of flavonoids in roots of Panax notoginseng under Cd stress.Full size imageBivariate analysis showed that the content of soluble sugar in P. notoginseng root was significantly relationship with the amount of lime application and the concentration of oxalic acid spraying. The content of soluble protein in root was significantly relationship with lime application rates, both of lime and oxalic acid. The contents of free amino acid and proline in roots were significantly relationship with lime application rates, oxalic acid spraying concentrations, both of lime and oxalic acid (Table 5).Table 5 Variance analysis of the effects of oxalic acid, calcium and cadmium on the contents of multiple medicinal ingredients in the roots of Panax notoginseng (F value).Full size tableThe content of R1 in the root of P. notoginseng was significantly relationship with oxalic acid spraying concentrations, lime application rates, both of lime and oxalic acid. The content of flavonoids was significantly relationship with oxalic acid spraying concentrations, lime application rates. More

Pachauri, R. K. et al. Climate change 2014: synthesis report. Contribution of Working Groups I, II and III to the fifth assessment report of the Intergovernmental Panel on Climate Change. (IPCC, 2014).Thackeray, S. J. et al. Phenological sensitivity to climate across taxa and trophic levels. Nature 535, 241–245 (2016).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Walther, G.-R. et al. Ecological responses to recent climate change. Nature 416, 389–395 (2002).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Root, T. L., MacMynowski, D. P., Mastrandrea, M. D. & Schneider, S. H. Human-modified temperatures induce species changes: Joint attribution. Proc. Natl. Acad. Sci. USA 102, 7465–7469 (2005).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Menzel, A. et al. European phenological response to climate change matches the warming pattern. Glob. Change Biol. 12, 1969–1976 (2006).ADS 
Article 

Google Scholar 
Rosenzweig, C. et al. Attributing physical and biological impacts to anthropogenic climate change. Nature 453, 353–357 (2008).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Myneni, R. B., Keeling, C. D., Tucker, C. J., Asrar, G. & Nemani, R. R. Increased plant growth in the northern high latitudes from 1981 to 1991. Nature 386, 698–702 (1997).ADS 
CAS 
Article 

Google Scholar 
Walkovszky, A. Changes in phenology of the locust tree (Robinia pseudoacacia L.) in Hungary. Int. J. Biometeorol. 41, 155–160 (1998).ADS 
Article 

Google Scholar 
Crick, H. Q. P. & Sparks, T. H. Climate change related to egg-laying trends [8]. Nature 399, 423–424 (1999).ADS 
CAS 
Article 

Google Scholar 
Both, C., Van Asch, M., Bijlsma, R. G., Van Den Burg, A. B. & Visser, M. E. Climate change and unequal phenological changes across four trophic levels: Constraints or adaptations?. J. Anim. Ecol. 78, 73–83 (2009).PubMed 
Article 

Google Scholar 
Brown, J. L., Li, S. H. & Bhagabati, N. Long-term trend toward earlier breeding in an American bird: A response to global warming?. Proc. Natl. Acad. Sci. USA. 96, 5565–5569 (1999).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Miller-Rushing, A. J., Lloyd-Evans, T. L., Primack, R. B. & Satzinger, P. Bird migration times, climate change, and changing population sizes. Glob. Chang. Biol. 14, 1959–1972 (2008).ADS 
Article 

Google Scholar 
Hughes, L. Biological consequences of global warming: Is the signal already apparent?. Trends Ecol. Evol. 15, 56–61 (2000).CAS 
PubMed 
Article 

Google Scholar 
Kudo, G., Nishikawa, Y., Kasagi, T. & Kosuge, S. Does seed production of spring ephemerals decrease when spring comes early?. Ecol. Res. 19, 255–259 (2004).Article 

Google Scholar 
Doi, H., Gordo, O. & Katano, I. Heterogeneous intra-annual climatic changes drive different phenological responses at two trophic levels. Clim. Res. 36, 181–190 (2008).Article 

Google Scholar 
Both, C., Bouwhuis, S., Lessells, C. M. & Visser, M. E. Climate change and population declines in a long-distance migratory bird. Nature 441, 81–83 (2006).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Post, E. & Forchhammer, M. C. Climate change reduces reproductive success of an Arctic herbivore through trophic mismatch. Philos. Trans. R. Soc. B Biol. Sci. 363, 2369–2375 (2008).Article 

Google Scholar 
Thackeray, S. J. et al. Trophic level asynchrony in rates of phenological change for marine, freshwater and terrestrial environments. Glob. Chang. Biol. 16, 3304–3313 (2010).ADS 
Article 

Google Scholar 
Visser, M. E. & Both, C. Shifts in phenology due to global climate change: The need for a yardstick. Proc. R. Soc. B Biol. Sci. 272, 2561–2569 (2005).Article 

Google Scholar 
Hegland, S. J., Nielsen, A., Lázaro, A., Bjerknes, A. L. & Totland, Ø. How does climate warming affect plant-pollinator interactions?. Ecol. Lett. 12, 184–195 (2009).PubMed 
Article 

Google Scholar 
Brown, C. J. et al. Quantitative approaches in climate change ecology. Glob. Chang. Biol. 17, 3697–3713 (2011).ADS 
PubMed Central 
Article 

Google Scholar 
Parmesan, C., Duarte, C., Poloczanska, E., Richardson, A. J. & Singer, M. C. Overstretching attribution. Nat. Clim. Chang. 1, 2–4 (2011).ADS 
Article 

Google Scholar 
Damos, P. & Savopoulou-Soultani, M. Temperature-driven models for insect development and vital thermal requirements. Psyche (London) 2012, (2012).Osawa, T. et al. Climate-mediated population dynamics enhance distribution range expansion in a rice pest insect. Basic Appl. Ecol. 30, 41–51 (2018).Article 

Google Scholar 
Kiritani, K. Predicting impacts of global warming on population dynamics and distribution of arthropods in Japan. Popul. Ecol. 48, 5–12 (2006).Article 

Google Scholar 
Ozawa, A., Uchiyama, T. & Kasai, A. Estimating the day on which the numbers of adult tea spiny whiteflies (Aleurocanthus camelliae Kanmiya and Kasai) peaked and the number of generations produced in a year based on the effective cumulative temperature in tea fields. Annu. Rep. Kansai Plant Prot. Soc. 58, 57–64 (2016) (in Japanese).Article 

Google Scholar 
Ozawa, A., Saito, T. & Ikeda, F. Effect of host plant and temperature on reproduction of American Serpentine Leafminer, Liriomyza trifolii (Burgess). Japan. J. Appl. Entomol. Zool. 43, 41–48 (1999) (in Japanese).Article 

Google Scholar 
Baier, P., Pennerstorfer, J. & Schopf, A. PHENIPS—A comprehensive phenology model of Ips typographus (L.) (Col., Scolytinae) as a tool for hazard rating of bark beetle infestation. For. Ecol. Manage. 249, 171–186 (2007).Article 

Google Scholar 
Karuppaiah, V. & Sujayanad, G. K. Impact of climate change on population dynamics of insect pests. (2012).Lipper, L. et al. Climate-smart agriculture for food security. Nat. Clim. Chang. 4, 1068–1072 (2014).ADS 
Article 

Google Scholar 
Campbell, B. M. et al. Reducing risks to food security from climate change. Glob. Food Sec. 11, 34–43 (2016).Article 

Google Scholar 
Kiritani, K. The low development threshold temperature and the thermal constant in insects and mites in Japan (2nd edition). Bull. Nationai Instirute Agro-Environmental Sci. 31, 1–74 (2012) (in Japanese).
Google Scholar 
Nagasawa, A., Takahashi, A. & Higuchi, H. Host plant use for oviposition by Trigonotylus caelestialium (Hemiptera: Miridae) and Stenotus rubrovittatus (Hemiptera: Miridae). Appl. Entomol. Zool. 47, 331–339 (2012).Article 

Google Scholar 
Higuchi, H. Ecology and management of rice bugs causing pecky rice. Japan. J. Appl. Entomol. Zool. 54, 171–188 (2010) (in Japanese).Article 

Google Scholar 
Ohtomo, R. Occurrence and control of Stenotus rubrovittatus (Hemiptera: Miridae) in Touhoku area in Japan. Japan. J. Appl. Entomol. Zool. 57, 137–149 (2013) (in Japanese).Article 

Google Scholar 
Kiritani, K. The impact of global warming and land-use change on the pest status of rice and fruit bugs (Heteroptera) in Japan. Glob. Chang. Biol. 13, 1586–1595 (2007).ADS 
Article 

Google Scholar 
Nagasawa, A. & Higuchi, H. Suitability of poaceous plants for nymphal growth of the pecky rice bugs Trigonotylus caelestialium and Stenotus rubrovittatus (Hemiptera: Miridae) in Niigata, Japan. Appl. Entomol. Zool. 47, 421–427 (2012).Article 

Google Scholar 
Gordo, O. & Sanz, J. J. Temporal trends in phenology of the honey bee Apis mellifera (L.) and the small white Pieris rapae (L.) in the Iberian Peninsula (1952–2004). Ecol. Entomol. 31, 261–268 (2006).Article 

Google Scholar 
Sparks, T. H. & Yates, T. J. The effect of spring temperature on the appearance dates of British butterflies 1883–1993. Ecography (Cop.) 20, 368–374 (1997).Article 

Google Scholar 
Stefanescu, C., Penuelas, J. & Filella, I. Effects of climatic change on the phenology of butterflies in the northwest Mediterranean Basin. Glob. Chang. Biol. 9, 1494–1506 (2003).ADS 
Article 

Google Scholar 
Dell, D., Sparks, T. H. & Dennis, R. L. H. Climate change and the effect of increasing spring temperatures on emergence dates of the butterfly Apatura iris (Lepidoptera: Nymphalidae). Eur. J. Entomol. 102, 161–167 (2005).Article 

Google Scholar 
Tabuchi, K. et al. Rice bugs in the Tohoku Region: Their occurrence and damage from 2003 to 2013. Bull. Natl. Agric. Res. Cent. Tohoku Reg. 117, 63–115 (2015) (in Japanese).
Google Scholar 
Jolly, W. M. & Running, S. W. Effects of precipitation and soil water potential on drought deciduous phenology in the Kalahari. Glob. Chang. Biol. 10, 303–308 (2004).ADS 
Article 

Google Scholar 
Kriticos, D. J. et al. CliMond: Global high-resolution historical and future scenario climate surfaces for bioclimatic modelling. Methods Ecol. Evol. 3, 53–64 (2012).Article 

Google Scholar 
Takeda, A. & Shimizu, K. Characteristics of the damaged grain caused by the sorghum plant bug, Stenotus rubrovittatus (Hemiptera: Miridae) at different infection periods. Annu. Rep. Kanto-Tosan Plant Prot. Soc. 2009, 85–87 (2009) (in Japanese).
Google Scholar 
Ishimoto, M. Seasonal Prevalence of Occurrence of the Rice Leaf Bug, Trigonotylus caelestialium (Kirkaldy) (Heteroptera: Miridae) on Paddy Rice Plants. Jpn. J. Appl. Entomol. Zool. 48, 79–85 (2004) (in Japanese).
Katase, M., Shimizu, K., Siina, S., Hagiwara, K. & Iwai, H. Seasonal occurrence of rice bugs in the northern part of Chiba Prefecture. Annu. Rep. Kanto-Tosan Plant Prot. Soc. 2007, 99–104 (2007) (in Japanese).
Google Scholar 
Shintani, Y. Effect of seasonal variation in host–plant quality on the rice leaf bug, Trigonotylus caelestialium. Entomol. Exp. Appl. 133, 128–135 (2009).Article 

Google Scholar 
Takada, M. B., Yoshioka, A., Takagi, S., Iwabuchi, S. & Washitani, I. Multiple spatial scale factors affecting mirid bug abundance and damage level in organic rice paddies. Biol. Control 60, 169–174 (2012).Article 

Google Scholar 
Yoshioka, A., Takada, M. B. & Washitani, I. Landscape effects of a non-native grass facilitate source populations of a native generalist bug, Stenotus rubrovittatus, in a heterogeneous agricultural landscape. J. Insect Sci. 14, 1–14 (2014).Article 

Google Scholar 
Watanabe, T. & Higuchi, H. Recent occurrence and problem of rice bugs. Plant Prot. 60, 201–203 (2006) (in Japanese).
Google Scholar 
Seino, H. An estimation of distribution of meteorological elements using GIS and AMeDAS data. J. Agric. Meteorol. 48, 379–383 (1993) (in Japanese).Article 

Google Scholar 
Ishigooka, Y., Tsuneo, K., Nishimori, M., Hasegawa, T. & Ohno, H. Spatial characterization of recent hot summers in Japan with agro-climatic indices related to rice production. J. Agric. Meteorol. 67, 209–224 (2011).Article 

Google Scholar 
Yamasaki, K. et al. Intraspecific variations in life history traits of two pecky rice bug species from Japan: Mapping emergence dates and number of annual generations. Ecol. Evol. (2021).Sakagami, Y. & Korenaga, R. Triangle method—A simple method for the estimation of total effective temperature. Japan. J. Appl. Entomol. Zool. 25, 52–54 (1981) (in Japanese).Article 

Google Scholar 
Shigehisa, S. Seasonal changes in egg diapause induction and effects of photoperiod and temperature on egg diapause in the sorghum plant bug, Stenotus rubrovittatus (Matsumura) (Heteroptera: Miridae). Japan. J. Appl. Entomol. Zool. 52, 229–232 (2008) (in Japanese).Article 

Google Scholar 
Ishimoto, M. Oviposition of sorghum plant bug, Stenotus rubrovittatus (Matsumura) (Heteroptera: Miridae) on rice plants. Japan. J. Appl. Entomol. Zool. 55, 193–197 (2011) (in Japanese).Article 

Google Scholar 
Ogata, M. et al. Fecundity and longevity in adults of the sorghum plant bug, Stenotus rubrovittatus (Matsumura) (Heteroptera: Miridae) under laboratory conditions. Japan. J. Entomol. 13, 129–132 (2010) (in Japanese).
Google Scholar 
Niiyama, T. & Iitomi, A. Optimal control timing of the rice leaf bug, Trigonotylus caelestialium (Heteroptera: Miridae). Annu. Rep. Soc. Plant Prot. North Japan. (in Japanese) (2003).Takeda, A., Shimizu, K., Shiina, S., Hagiwara, K. & Katase, M. Seasonal prevalence of Stenotus rubrovittatus (Hemiptera: Miridae) in a gramineous weed field and a rice field. Annu. Rep. Kanto-Tosan Plant Prot. Soc. 2008, 97–102 (2008) (in Japanese).
Google Scholar 
Niiyama, T. Studies on the ecology of Trigonotylus caelestialium and establishment of pesticide control techniques. Annu. Rep. Akita Prefect. Agric. Inst. 49, 147–180 (2009) (in Japanese).
Google Scholar 
Sato, M. Latest infestation period of Miridae causing pecky rice damage in Aomori Prefecture. Annu. Rep. Soc. Plant Prot. North Japan 2014, 129–134 (2014) (in Japanese).
Google Scholar 
Johnson, J. B. & Omland, K. S. Model selection in ecology and evolution. Trends Ecol. Evol. 19, 101–108 (2004).PubMed 
Article 

Google Scholar 
Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest package: tests in linear mixed effects models. J. Stat. Softw. 82, 1–26 (2017).Article 

Google Scholar  More

There are two phases involved in supporting the technical quality of the dataset: (i) the process used by Taronga when collecting data for each shark-bite incident, and (ii) the consistency modifications that we made during manuscript development.Phase oneFor each shark-bite incident, Taronga attempts to contact the most-relevant person involved in the event as possible — e.g., victim, victim’s family, witnesses. Those contacted are asked to complete a questionnaire with information relating to the shark bite. If applicable, questionnaires can also be completed by a fisheries officer in the relevant State and sent to Taronga. Taronga works closely with experts in each State’s fisheries department to validate information sourced in media reports. Each shark-bite case is unique, so the validation process varies depending on details specific to each incident. Forensic shark scientists within each State department are contacted after each shark bite to confirm details related to the incident. For example, validating species responsible for the bite often requires forensic analysis through expert examination of bite marks or artefacts21,22,23,24,25. When available, video footage is analysed for validation of information, such as confirmation of shark species and length.The database is cross-checked annually for the previous year with the International Shark-Attack File in Florida, USA, as well as with fisheries officers to ensure consistency. The database is cross-checked with the acknowledgement that there are discrepancies between versions due to differences in inclusion criteria. For example, the International Shark-Attack File includes bites where the victim was bitten aboard a boat, whereas the database we present here does not include bites aboard a boat.We acknowledge the limitations associated with this database, such as differences in reporting over time. For example, incidents might be reported more in recent decades due to technological advances making reporting more accessible or media publicising these events more widely18. There might also be reporting biases, for example, victims could be more likely to report a bite by a large, potentially dangerous shark (e.g., white, tiger, or bull shark) rather than a smaller, less-dangerous shark species (e.g., wobbegong shark). We also completed a quality assessment of the original database fields and redesigned the data acquisition and entry process (see Phase two) to allow exploration of shark-bite trends and patterns in Australia.Phase twoWe identified errors and inconsistencies in database fields. To avoid additional errors and inconsistencies, and to obtain a quality-controlled database, we redesigned the process for gaining and entering information into the database. This included creating a data descriptor (Supplementary File 2) used as a protocol to inform which questions to ask in the questionnaire. The data descriptor also directs the format of database entries by specifying information required in each field and by indicating the format of each entry (i.e., numeric, descriptive, or categorical). We manually inspected all previously entered data and adapted them to match the data descriptor. We checked each entry using the filter function in Microsoft Excel to identify any spelling and grammatical errors in the fields and ensure that all categories were grammatically identical. We standardised all metrics during this process (e.g., the data descriptor now stipulates that all length measurements should be recorded in metres).We validated and standardised the geographical locations of shark bites by converting all coordinates into decimal degrees using Microsoft Excel. We subsequently plotted all coordinates using the ggmap library27 in R (Version 4.0.2) (R Core Team 2020) (Fig. 2). We corrected any unusual coordinates (e.g., outside of Australian waters) and crosschecked them with site descriptions and states to ensure validity.Fig. 2Geographical locations of 1,196 shark bites in Australia. Each shark-bite incident is indicated by a red dot. (a) all shark-bite incidents; (b) bites most likely inflicted by bull sharks, (c) tiger sharks, and (d) white sharks. Two bite incidents that occurred at the Australian external territory, Cocos (Keeling) Islands, are not included on these maps. Background layers show elevation and major, perennial watercourses.Full size imageDuring the development of the data-descriptor protocol, we converted some previous descriptive columns into categorical columns. These columns included (but are not limited to) victim activity, attractant, injury location, injury severity, and weather condition. Converting these columns into categories facilitates analysis to investigate shark-bite patterns. For example, we converted victim activity into a categorical field to restrict answers to the following: snorkelling, motorised boating, unmotorised boating, boarding, swimming, standing, diving, fishing, or other, rather than allowing answers in any format. We used this information to create a time series to show the activity of shark-bite victims in Australia over time (Figs. 3 and 4). Shark bites have increased for boarders (including surfboarding, bodyboarding, kiteboarding, sailboarding, wakeboarding, and stand-up paddle boarding) over time, particularly since 1960 (Figs. 3b, 4). This is likely due to the increase in popularity of board sports, particularly surfing, since the 1960s28. This trend is likely not reflected in Fig. 3a because shark bites are unlikely to be classed as ‘provoked’ during board riding.Fig. 3Number of shark bites (black, dashed line) and proportion of activity done by the victim at the time of shark-bite incidents in Australia from 1900 to 2022. Panels represent shark bites in Australia that are; (a) provoked or (b) unprovoked. Boarding includes surfboarding, bodyboarding, kiteboarding, sailboarding, wakeboarding, and stand-up paddle boarding. Swimming includes snorkelling, spearfishing, freediving, body surfing, clinging to an object, falling into water, floating, or wading. Diving includes scuba-diving, hookah diving, or hard-hat diving. Fishing includes cleaning fish. No data for years 1908 and 1970.Full size imageFig. 4Number of shark bites (black, dashed line) and proportion of activity done by the victim at the time of shark-bite incidents in Australia from 1900 to 2022. Panels represent shark bites in Australia that are; (a) fatal or (b) non-fatal. Boarding includes surfboarding, bodyboarding, kiteboarding, sailboarding, wakeboarding, and stand-up paddle boarding. Swimming includes snorkelling, spearfishing, freediving, body surfing, clinging to an object, falling into water, floating, or wading. Diving includes scuba-diving, hookah diving, or hard-hat diving. Fishing includes cleaning fish. No data for years 1908 and 1970.Full size imageThe column representing a shark-bite victim’s recovery status also requires a categorical response, restricting answers to: fatal, injured, or uninjured (Fig. 5). Since 1900, the proportion of shark-bite-related fatalities has decreased (Fig. 5). This trend is also true for the three species most attributed to shark-bite-related fatalities, white (Carcharodon carcharias), tiger (Galeocerdo cuvier), and bull (Carcharhinus leucas) sharks. The decrease in shark-bite-related deaths is likely due to advancements in medical responses to shark-bite victims over time14 and better understanding among surfers about using tourniquets to stem bleeding following increased certification as first responders in workplace occupational health and safety requirements. Bites resulting in an uninjured victim includes interactions where the shark might have bitten the victim’s equipment (i.e., surfboard, bodyboard, kayak) rather than biting the person.Fig. 5Proportion of victim-recovery status (fatal = grey; red = injured; blue = uninjured) resulting from all unprovoked shark bites in Australia between 1900–2022. Blank years represent years without any reported occurrences. (a) all shark-bite incidents, (b) bites most likely inflicted by bull sharks, (c) tiger sharks, or (d) white sharks. No data for years 1908 and 1970.Full size imagePreviously, entries in the injury location column were descriptive. We converted the column to a categorical field restricting answers to the following: arm, hand, lower arm, upper arm, shoulder, neck, head, torso, leg, foot, calf, thigh, pelvic region, or other. During the analyses process, we further categorised these injury locations into four body areas (head, arm, torso, and leg) to assess how injury location affects recovery status (fatal or injured) (Fig. 6). Fatality most often occurred following shark bites to the torso (Fig. 6). This is likely due to the injuries to organs and major arteries resulting in blood loss, which is a leading cause of shark-bite fatalities29. This is the first time that the location of a shark bite on the body has been assessed relative to recovery status.Fig. 6Proportion of Australian shark bites resulting in either fatality or injury categorised by injury location on the victim’s body (left panel; 250 bites resulting in fatal injury, 723 bites resulting in non-fatal injury) and by species (right panel; bites by 201 tiger, 170 bull, 258 white, and 303 bites other sharks).Full size imageUnderstanding how the location of a shark-bite wound relates to victim recovery has value in informing the development of shark-bite mitigations. For example, the development of shark-bite-resistant wetsuits could potentially result in higher survival rates of the user if the fabric is concentrated around the torso region30,31. Redesigning data acquisition and entry process to allow for categorical columns permits these types of analyses.Some detail of a shark-bite incident might be lost by converting previously descriptive columns into categorical columns. We addressed this by complementing categorical columns with accompanying fields to retain details of the incident. For example, injury location and injury severity columns are both categorical and allow for user-friendly data analysis, whereas the injury description column is descriptive and provides added detail about the victim’s injuries if applicable. Individually, all three columns address certain aspects of the victim’s injuries, and together, all three columns comprehensively summarise injuries to the shark-bite victim.Our analysis of the Australian Shark-Incident Database suggests that tiger sharks are proportionally responsible for the most fatalities of all shark species in Australia (38% of all tiger shark bites result in fatality), followed by bull sharks (32% of all bull shark bites result in fatality), and white sharks (25% of all white shark bites result in fatality) (Fig. 6). We emphasise that these figures represent the overall percentage of bites resulting in fatality since 1791 and do not account for possible changes over time. These figures are also proportional to the number of bites by each respective species. In Australia, white sharks are responsible for the largest number of bites on humans (361 total) compared to tiger (229 total) and bull sharks (197 total). At the time of publication, white and tiger sharks were each responsible for 91 and 86 total fatalities on humans in Australia, respectively.There were 540 incidents in which time of day was recorded. We used these data to assess whether particular shark-bite incidents are more likely to occur at specific times of day (Fig. 7). To standardise reported 24-hour times, we took the location (latitude, longitude) information and reported time and date of each incident using the getSunlightTimes function in the suncalc library in R32 to calculate whether the incident occurred in one of four light-availability categories: dawn, day, dusk, or night. Shark bites occur mostly during the day, which likely reflects time of day when there are more ocean users present. However, there is a slightly higher proportion of bites at dusk for bull sharks compared to tiger and white sharks (Fig. 7). Identifying these trends can assist authorities in developing data-driven educational messaging as a shark-bite mitigation measure. This is important considering that enhanced education is the preferred mitigation measure scored by ocean users in New South Wales33.Fig. 7Period of day (corrected for local time) distribution of provoked and unprovoked shark bites in Australia by shark species from 1791 to 2022 (n = 540 bites from all species, 70 from bull, 59 from tiger, and 217 from white sharks).Full size imageThese examples demonstrate that the Australian Shark-Incident Database will be useful for scientists to analyse environmental, social, and biological related shark-bite patterns in Australia. Use of the newly developed data descriptor to standardise future applications and account for quality assurance and control will aid in keeping the database consistent for ease of analysis and interpretation. Ultimately, the publishing of this database will improve our understanding of shark-bite incidents in Australia and will equip us with the knowledge to aim to avoid or predict these events in the future. More

For this initial compilation, we focused on gathering traits from field guides and species accounts rather than the primary research literature because each represents the culmination of a comprehensive effort to describe a regional flora/fauna by local experts25. Authors of these guides have already done the hard work of scouring the literature, corresponding with fellow naturalists, and compiling occurrence records to support range, phenology, and habitat associations26. We began by performing a comprehensive review of all the holdings in the Florida Museum of Natural History’s McGuire Center for Lepidoptera and Biodiversity library, at the University of Florida. This, and subsequent searches in online databases, allowed us to compile a list of references that currently has more than 800 relevant resources.We initially identified the categories of trait information available in each resource and its format to target volumes for trait extraction and processing. Given the unequal availability of resources among regions, we had the explicit goal of identifying a corpus that would maximize the number of extractable trait data from as many butterfly species as evenly across the globe as possible. This led to our choice of 117 volumes within several global regions (Fig. 2, Supplementary Material S1) and a focus on measurements (wingspan/forewing length), phenology (months of adult flight and total duration of flight in months) and voltinism (the number of adult flight periods per year), habitat affinities, and host plants as traits (Table 1, Supplementary Material S2).Table 1 The total number of species represented by each trait in LepTraits 1.0.Full size tableTo process these resources, we developed a protocol to scan each volume, extract verbatim natural language descriptions, provide quality control for extraction, and then resolve given taxonomic names to a standardized list27. This provided a database of trait information in which each “cell” included all text from a single resource relevant to one trait category of a single taxon. In order to “atomize” the raw text into standardized metrics or a controlled list of descriptive terms, we developed a methodology appropriate to each trait. This resulted in a more fine-grained dataset in which each “cell” included a single, standardized trait value. Since the values of these taxon-specific traits frequently differed among resources, we then calculated “consensus” traits for each species, for example, the average forewing length (Table 1). A graphical representation of this process with an example trait is illustrated in Fig. 1.Fig. 1A graphical illustration of the processing workflow used to compile, scan, digitize, extract, atomize, and compile species trait records from literature resources. (1) Literature resources were examined for potential trait data and compiled into a single library; (2) each literature resource was scanned into.pdf format so that text could be readily copy and pasted from species accounts; (3) each.pdf file was uploaded to an online database with associated metadata for each literature resource; (4) trait extractors utilized an online interface to extract verbatim, raw text from designated resources; (5) verbatim, raw text extracts were either automatically (via regular-expressions and keyword searches) or manually atomized to a controlled vocabulary; (6) species consensus traits were calculated by aggregating resource-level records by name-normalized taxonomy. Rulesets were used for consensus trait building and are detailed in the supplementary material. Both resource-level and species consensus traits are presented in the dataset.Full size imageResource compilation and ingestionText sources from the master list were digitized by multiple participating institutions. They scanned each page of the book and converted the images to editable text with Abbyy FineReader optical character recognition (OCR) software (abbyy.com). These PDFs with copy-and-pastable text were then uploaded to a secure, online database that included citation information about each resource. The geographic breadth covered by each resource was designated using the World Geographic Scheme (WGS)28; this information was used to assess geographic evenness of our trait compilation efforts. Resource metadata, including the WGS scheme, were kept with each resource in an online database where individuals could access scanned copies of the resource for trait extraction.Verbatim data extractionIndividual workers were assigned to a resource and instructed to copy verbatim trait information from the original source. They then pasted that text into the relevant data field in a standardized, electronic form on an online portal designed to facilitate extraction and processing. Most field guides and other book-length resources are organized within a taxonomic hierarchy to describe traits of a family with a contiguous block of text, for example, family, then genus, species, and finally subspecies within species. We call these text blocks describing a single taxon “accounts” (e.g., family account, species account), and we recorded data at the taxonomic resolution provided in the original source. These taxonomic ranks included family, subfamily, tribe, genus, species, and subspecies. When information for a taxon was encountered outside its own account, the “extractor” (project personnel trained to manually extract verbatim text) assigned to glean data from the book entered this text into a separate entry for the taxon. Trait information from figure captions and tables were also extracted from the resource. Graphical representations of phenology and voltinism were common, and these visual data were converted to text descriptions. Each resource was extracted in stages, and each stage was subjected to a quality assurance and control process (see Technical Validation). This process corrected mistakes and attempted to find unextracted data overlooked by the extractor. These problems were corrected before the extractor could proceed with further trait extraction from the resource and were also used for training purposes.AtomizationVerbatim text extracts were subjected to an “atomization” process in which raw text was standardized into disaggregated, readily computable data. This conversion into the final trait data format (numerical, categorical, etc.) was two-pronged and involved both manual editing and semi-automated atomization of verbatim text. Regular expressions were used for most semi-automated atomization, including extraction of wing measurements, which were converted into centimeters. Keyword searches were also performed in the semi-automated pipeline for phenology, voltinism, and oviposition traits. For example, “univoltine” or “uni*” was searched for across the voltinism raw text, along with other search terms. All semi-automated atomization outputs were subject to quality assurance and control detailed further in Technical Validation. Manual atomization tasks were performed by multiple team members for traits which presented higher complexity. For example, habitat affinities and host plant associations were atomized manually along with a quality control protocol based on predefined rule sets that are described further in the Supplementary Material S3.Normalization and consensus traitsTo provide consensus traits at the species (and sometimes genus) level, we standardized nomenclature through a process we called “name-normalization,” which harmonizes taxonomy across all of our resources29. This name-normalization procedure relied on a comprehensive catalog of valid names and synonyms27. Following taxonomic harmonization, we compiled consensus traits based on rule sets specified in the metadata of each trait. For example, species-level consensus of primary and secondary host plant families required that at least one-third of the records for a given taxon list a particular family of plants (when multiple records were available).Categorical traits such as voltinism list all known voltinism patterns for a species regardless of geographic context. To this end, it is important that users of these data are aware that not all traits may be applicable to their study region. For example, some species may be univoltine at higher latitudes or elevations, but bivoltine elsewhere. We therefore present both the resource-level records as well as the species consensus traits for use in analysis.For this initial synopsis of butterfly species traits, we extracted records from 117 literature/web-based resources, resulting in 75,103 individual trait extraction records across 12,448 unique species, out of the ca. 19,200 species described to date27. Figure 2 indicates the geographic regions covered by our 117 resources, mapped at the resolution level-two regions in the World Geographic Scheme28. A full list of resources can be found in the Supplemental Material S1 as a bibliography. Similarly, the geographic distribution of trait records is indicated in Fig. 3. Resource and consensus species trait records varied in number and in the scope of taxonomic coverage. Table 1 indicates the number of unique records and species level records for each trait. Table 2 indicates the number of species-level records by family. Measurement traits, including wingspan and forewing length, were the most comprehensive traits extracted from our resource set. This represents one of the largest trait datasets and the most comprehensive dataset for butterflies to date.Fig. 2Geographic breadth of our butterfly trait resources. Using a global map of level-two regions (World Geographic Scheme, Brummitt 2001), we have indicated the total number of resources available within each geographic area). Grey areas indicate that no resources were extracted from that region.Full size imageFig. 3Geographic breadth of our butterfly trait records. Using a global map of level-two regions(World Geographic Scheme, Brummitt 2001), we have indicated the total number of trait records from each geographic region). Grey areas indicate that trait records were not extracted from that region.Full size imageTable 2 The number of species represented within each family in LepTraits 1.0.Full size table More

Model descriptionThe system dynamics model divided an urban dog population into the following subpopulations: (i) free-roaming dogs (both owned and unowned free-roaming, i.e. unrestricted dogs found on streets), (ii) shelter dogs (unowned restricted dogs living in shelters), and (iii) owned dogs (owned home-dwelling restricted dogs) (Fig. 1). The subpopulations change in size by individuals flowing between the different subpopulations or from flows extrinsically modelled (i.e. flows from subpopulations not included in the systems model; the acquisition of dogs from breeders and friends to the owned dog population, and the immigration/emigration of dogs from other neighbourhoods).Ordinary differential equations were used to describe the dog population dynamics. The models were written in R version 3.6.128, and numerically solved using the Runge–Kutta fourth order integration scheme with a 0.01 step sizes using the package “deSolve”29,30. For the baseline model, Eqs. (1–3) were used to describe the rates of change of dog subpopulations in the absence of management.Baseline free-roaming dog population (S):$$frac{dS}{dt}={r}_{s}times Stimes left(1-frac{S}{{K}_{s}}right)+alpha times O-delta times S$$
(1)
In the baseline model, the free-roaming dog population (Eq. 1) increases through the free-roaming dog intrinsic growth rate (rs), and the abandonment and roaming of dogs from the owned dog population (α) and decreases through adoption to the owned dog population (δ). The intrinsic growth rate is the sum of the effects of births, deaths, immigration, and emigration, which are not modelled separately. In this model, the growth rate of the free-roaming dog population is reduced depending on the population size in relation to the carrying capacity, through the logistic equation (rreal = rmax(1 − S/Ks))31. In the baseline simulation, the free-roaming dog population rises over time, until it stabilises at an equilibrium size.Baseline shelter dog population (H):$$frac{dH}{dt}=gamma times O-beta times H- {mu }_{h}times H$$
(2)
The shelter dog population (Eq. 2) increases through relinquishment of owned dogs (γ) and decreases through the adoption of shelter dogs to the owned dog population (β), and through the shelter dog death rate (µh). There is no carrying capacity for the shelter dog population as we assumed that more housing would be created as the population increases. This allowed calculation of the resources required to house shelter dogs.Baseline owned dog population (O),$$frac{dO}{dt}={r}_{o} times Otimes (1-frac{O}{{K}_{o}})+beta times H+delta times S-alpha times O-gamma times O$$
(3)
The owned dog population (Eq. 3) increases through the owned dog growth rate (ro), adoption of shelter dogs (β), and adoption of free-roaming dogs (δ); and decreases through abandonment/roaming (α) and relinquishment (γ) of owned dogs to the shelter dog population. The growth rate of the owned dog population (ro) combines the birth, death, and acquisition rates from sources other than the street or shelters (e.g. breeders, friends) and was modelled as density dependent by the limit to growth logistic formula (1 − O/Ko).Parameter estimatesDetailed descriptions of parameter estimates are provided in the supplementary information. The simulated environment was based on the city of Lviv, Ukraine. This city has an area of 182 km2 and a human population size of 717,803. Parameters were estimated from literature, where possible, and converted to monthly rates (Table 1). Initial sizes of the dog populations were estimated for the baseline simulation, based on our previous research in Lviv32. The carrying capacity depends on the availability of resources (i.e. food, shelter, water, and human attitudes and behaviour33) and is challenging to estimate. We assumed the initial free-roaming and owned dog populations were at carrying capacity. Initial population sizes for simulations including interventions were determined by the equilibrium population sizes from the baseline simulation (i.e. the stable population size, the points at which the populations were no longer increasing/decreasing).Table 1 Parameter description, parameter value, and minimum and maximum values used in the sensitivity analysis for the systems model.Full size tableEstimating the rate at which owned dogs are abandoned is difficult, as abandonment rates are often reported per dog-owning lifetime32,34 and owners are likely to under-report abandonment of dogs. Similarly, it is challenging to estimate the rate that owned dogs move from restricted to unrestricted (i.e. free-roaming). For simplicity, we modelled a combined abandonment/roaming rate (α) of 0.003 per month, estimated based on our previous research in Lviv and from literature34,35,36. We derive the owned dog relinquishment rate (γ) from New et al.37. We estimated shelter (β) and free-roaming adoption rates (δ) from shelter data in Lviv. We set the maximum intrinsic growth rate for the free-roaming dogs (rs) at 0.03 per month, similar to that reported in literature17,19,38. We assumed that demand for dogs was met quickly through a supply of dogs from births, breeders and friends and set a higher growth rate for the owned dog population (ro) at 0.07 per month.We assumed shelters operated with a “no-kill” policy (i.e. dogs were not killed in shelters as part of population management) and included a shelter dog death rate (µh) of 0.008 per month to incorporate deaths due to euthanasia for behavioural problems or health problems, or natural mortality. We modelled neutered free-roaming dog death rate (µn) explicitly for the CNR intervention at a minimum death rate of 0.02 per month38,39,40,41.InterventionsSix intervention scenarios were modelled (Table 2): sheltering; culling; CNR; responsible ownership; combined CNR and responsible ownership; and combined CNR and sheltering, representing interventions feasibly applied and often reported27. Table 2 outlines the equations describing each intervention. To simulate a sheltering intervention, a proportion of the free-roaming dog population was removed and added to the shelter dog population at sheltering rate (σ). In culling interventions, a proportion of the free-roaming dog population was removed through culling (χ).Table 2 Description of intervention parameters and coverages for simulations applied at continuous and annual periodicities.Full size tableFree-roaming dog population with sheltering intervention:$$frac{dS}{dt}={r}_{s}times Stimes left(1-frac{S}{{K}_{s}}right)+alpha times O-delta times S-sigma times S$$
(4)
Shelter dog population with sheltering intervention:$$frac{dH}{dt}=gamma times O-beta times H- {mu }_{h}times H+sigma times S$$
(5)
Free-roaming dog population with a culling intervention:$$frac{dS}{dt}={r}_{s}times Stimes left(1-frac{S}{{K}_{s}}right)+alpha times O-delta times S-chi times S$$
(6)
To simulate a CNR intervention, an additional subpopulation was added to the system (Eq. 7): (iv) the neutered free-roaming dog population (N; neutered, free-roaming). In this simulation, a proportion of the intact (I) free-roaming dog population was removed and added to the neutered free-roaming dog population. A neutering rate (φ) was added to the differential equations describing the intact free-roaming and the neutered free-roaming dog populations. Neutering was assumed to be lifelong (e.g. gonadectomy); a neutered free-roaming dog could not re-enter the intact free-roaming dog subpopulation. Neutered free-roaming dogs were removed from the population through the density dependent neutered dog death rate (µn); death rate increased when the population was closer to the carrying capacity. The death rate was a non-linear function of population size and carrying capacity modelled using a table lookup function (Fig. S1). Neutered free-roaming dogs were also removed through adoption to the owned dog population, and we assumed that adoption rates did not vary between neutered and intact free-roaming dogs.Neutered free-roaming dog population:$$frac{dN}{dt}=varphi times I-{mu }_{n}times N-delta times N$$
(7)
Intact free-roaming dog population with neutering intervention.$$frac{dI}{dt}={r}_{s}times Itimes left(1-frac{(I+N)}{{K}_{s}}right)+alpha times O-delta times I-varphi times I$$
(8)
To simulate a responsible ownership intervention, the baseline model was applied with decreased rate of abandonment/roaming (α) and increased rate of shelter adoption (β). To simulate combined CNR and responsible ownership, a proportion of the intact free-roaming dog population was removed through the neutering rate (φ), abandonments/roaming decreased (α) and shelter adoptions increased (β). In combined CNR and sheltering interventions, a proportion of the intact free-roaming dog population (I) was removed through neutering (φ) and added to the neutered free-roaming dog population (N), and a proportion was removed through sheltering (σ) and added to the shelter dog population (H).Intact free-roaming dog population with combined CNR and sheltering interventions:$$frac{dI}{dt}={r}_{s}times Stimes left(1-frac{(I+N)}{{K}_{s}}right)+alpha times O-delta times I-varphi times I- sigma times I$$
(9)
Intervention length, periodicity, and coverageAll simulations were run for 70 years to allow populations to reach equilibrium. It is important to note that this is a theoretical model; running the simulations for 70 years allows us to compare the interventions, but does not accurately predict the size of the dog subpopulations over this long time period. Interventions were applied for two lengths of time: (i) the full 70-year duration of the simulation; and (ii) a five-year period followed by no further intervention, to simulate a single period of investment in population management. In each of these simulations, we modelled the interventions as (i) continuous (i.e. a constant rate of e.g. neutering) and (ii) annual (i.e. intervention applied once per year). Interventions were run at low, medium, and high coverages (Table 2). As the processes are not equivalent, we apply different percentages for the intervention coverage (culling/neutering/sheltering) and the percent increase/decrease in parameter rates for the responsible ownership intervention. Intervention coverage refers to the proportion of dogs that are culled/neutered/sheltered per year (i.e. 20%, 40% and 70% annually) and, for responsible ownership interventions, the decrease in abandonment/roaming rate and increase in the adoption rate of shelter dogs (30%, 60% and 90% increase/decrease from baseline values). To model a low (20%), medium (40%) and high (70%) proportion of free-roaming dogs caught, but where half of the dogs were sheltered and half were neutered-and-returned, combined CNR and sheltering interventions were simulated at half-coverage (e.g. intervention rate of 0.7 was simulated by 0.35 neutered and 0.35 sheltered). For continuous interventions, sheltering (σ), culling (χ), and CNR (φ) were applied continuously during the length of the intervention. For annual interventions, σ, χ, and φ were applied to the ordinary differential equations using a forcing function applied at 12-month intervals. In simulations that included responsible ownership interventions, the decrease in owned dog abandonment/roaming (α) and the increase in shelter adoption (β) was assumed instantaneous and continuous (i.e. rates did not change throughout the intervention).Model outputsThe primary outcome of interest was the impact of interventions on free-roaming dog population size. For interventions applied for the duration of the simulation, we calculated: (i) equilibrium population size for each population; (ii) percent decrease in free-roaming dog population; (iii) costs of intervention in terms of staff-time; and (iv) an overall welfare score. For interventions applied for a five-year period, we also calculated: (v) minimum free-roaming dog population size and percent reduction from initial population size; and (vi) the length of time between the end of the intervention and time-point at which the free-roaming dog population reached above 20,000 dogs (the assumed initial free-roaming dog population size of Lviv, based on our previous research32, see Supplementary Information for detail).The costs of population management interventions vary by country (e.g. staff salaries vary between countries) and by the method of application (e.g. method of culling, or resources provided in a shelter). To enable a comparison of the resources required for each intervention, the staff time (staff working-months) required to achieve the intervention coverage was calculated. While this does not incorporate the full costs of an intervention, as equipment (e.g. surgical equipment), advertising campaigns, travel costs for the animal care team, and facilities (e.g. clinic or shelter costs) are not included, it can be used as a proxy for intervention cost. Using data provided from VIER PFOTEN International, we estimated the average number of staff required to catch and neuter the free-roaming dog population and to house the shelter dog population in each intervention, using this data as a proxy for catching and sheltering/culling. The number of dogs that can be cared for per shelter staff varies by shelter. To account for this, we estimated two staff-to-dog ratios (low and high). Table 3 describes the staff requirements for the different interventions.Table 3 Staff required for interventions and the number of dogs processed per staff per day.Full size tableUsing the projected population sizes, the staff time required for each staff type (e.g. number of veterinarian-months of work required) was calculated for each intervention. Relative salaries for the different staff types were estimated (Table 3). The relative salaries were used to calculate the cost of the interventions by:[Staff time required × relative salary ] × €20,000.Where €20,000 was the estimated annual salary of a European veterinarian, allowing relative staff-time costs to be compared between the different interventions. Average annual costs were reported.To provide overall welfare scores for each of the interventions, we apply the estimated welfare scores on a one to five scale, for each of the dog subpopulations, as determined by Hogasen et al. (2013)22. This scale is based on the Five Freedoms (freedom from hunger and thirst; freedom from discomfort; freedom from pain, injury, or disease; freedom to express normal behaviour; freedom from fear and distress42,43) and was calculated using expert opinions from 60 veterinarians in Italy22. The scores were weighted by the participants’ self-reported knowledge of different dog subpopulations, which resulted in the following scores: 2.8 for shelter dogs (WH); 3.5 for owned dogs (WO); 3.1 for neutered free-roaming dogs (WN); and 2.3 for intact free-roaming dogs (WI)22.Using these estimated welfare scores, we calculated an average welfare score for the total dog population based on the model’s projected population sizes for each subpopulation (Eq. 10). For interventions running for the duration of the simulation, the welfare score was calculated at the time point (t) when the population reached an equilibrium size. For interventions running for five years, the welfare score was calculated at the end of the five-year intervention. The percentage change in welfare scores from the baseline simulation were reported.$$Welfare score= frac{{H}_{t}times {W}_{H}+{O}_{t}times {W}_{O}+{N}_{t}times {W}_{N}+{I}_{t}times {W}_{I}}{{H}_{t}+{O}_{t}+{N}_{t}+{I}_{t}}$$
(10)
Model validation and sensitivity analysisA global sensitivity analysis was conducted on all parameters described in the baseline simulation and all interventions applied continuously, at high coverage, for the full duration of the simulation. A Latin square design algorithm was used in package “FME”44 to sample the parameters within their range of values (Table 1). For the global sensitivity analysis on interventions, all parameter values were varied, apart from the parameters involved in the intervention (e.g. culling, neutering, abandonment/roaming rates). The effects of altering individual parameters (local sensitivity analysis) on the population equilibrium was also examined for the baseline simulation using the Latin square design algorithm to sample each parameter, individually, within their range of values. Sensitivity analyses were run for 100 simulations over 50 years solved with 0.01 step sizes. More

Sala, E. et al. Protecting the global ocean for biodiversity, food and climate. Nature 592, 397–402 (2021).ADS 
CAS 
Article 

Google Scholar 
Gillispie, C. C., Gratton-Guinness, I. & Fox, R. Pierre-Simon Laplace, 1749-1827: A Life in Exact Science (Princeton Univ. Press, 1999).Dinmore, T. A., Duplisea, D. E., Rackham, B. D., Maxwell, D. L. & Jennings, S. Impact of a large-scale area closure on patterns of fishing disturbance and the consequences for benthic communities. ICES J. Mar. Sci. 60, 371–380 (2003).Article 

Google Scholar 
Hiddink, J. G., Hutton, T., Jennings, S. & Kaiser, M. J. Predicting the effects of area closures and fishing effort restrictions on the production, biomass, and species richness of benthic invertebrate communities. ICES J. Mar. Sci. 63, 822–830 (2006).Article 

Google Scholar 
Greenstreet, S. P. R., Fraser, H. M. & Piet, G. J. Using MPAs to address regional-scale ecological objectives in the North Sea: modelling the effects of fishing effort displacement. ICES J. Mar. Sci. 66, 90–100 (2009).Article 

Google Scholar 
Suuronen, P. et al. A path to a sustainable trawl fishery in Southeast Asia. Rev. Fish. Sci. Aquac. 28, 499–517 (2020).Article 

Google Scholar 
Amoroso, R. O. et al. Bottom trawl fishing footprints on the world’s continental shelves. Proc. Natl Acad. Sci. USA 115, E10275–E10282 (2018).CAS 
Article 

Google Scholar 
Atwood, T. B., Witt, A., Mayorga, J., Hammill, E. & Sala, E. Global patterns in marine sediment carbon stocks. Front. Mar. Sci. 7, 165 (2020).Article 

Google Scholar 
Smeaton, C., Hunt, C. A., Turrell, W. R. & Austin, W. E. N. Marine sedimentary carbon stocks of the United Kingdom’s exclusive economic zone. Front. Earth Sci. 9, 593324 (2021).Article 

Google Scholar 
Legge, O. et al. Carbon on the northwest European shelf: contemporary budget and future influences. Front. Mar. Sci. 7, 143 (2020).Article 

Google Scholar 
Melnychuk, M. C. et al. Identifying management actions that promote sustainable fisheries. Nat. Sustain. 4, 440–449 (2021).Article 

Google Scholar  More