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

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

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Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.This is a summary of: Qiao, L. et al. Soil quality both increases crop production and improves resilience to climate change. Nat. Clim. Change https://doi.org/10.1038/s41558-022-01376-8 (2022). More

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

cpDNA haplotype databaseDNA sequencing of the choloroplast (cp) markers produced sequences of the following lengths: 573 bp (atpB-rbcL); 487 bp (petG-trnP); 500 bp (trnL1-trnL2); and 593 bp (psbM-trnD). Alignment of the 352 individuals from the 44 populations yielded a total 28 variable sites: 11 in the atpB-rbcL spacer, seven in both the petG-trnP and psbM-trnD spacers, and three in the trnL1-trnL2 spacer (Supplementary Table S1). Based on these 28 variable sites (21 base substitutions and 7 deletions) across the combined intergenic regions, a total of 22 unique haplotypes were found (Fig. 1a).Figure 1(a) Chloroplast haplotype distribution in the Shorea leprosula populations. The pie chart colours indicate haplotype distributions; and sector areas are proportional to sample size (Map was generated by ArcGIS-ArcMap version 10.8). (b) STRUCTURE analysis identified two clusters (K = 2) corresponding to Region A and B.Full size imageSSR allele frequency databaseThe reproducibility of SSR genotyping was confirmed by achieving consistent genotypes from five independent PCR amplifications on a single individual for each of the ten SSR loci. Individual bar plots from STRUCTURE analysis are presented in Fig. 1b. At the highest Delta K likelihood scores, the best representation of the data was K = 2 suggesting that the 44 populations in Peninsular Malaysia can be divided into two main genetic clusters: Region A and Region B. The first cluster, ‘Region A’ consists of 12 populations, namely SBadak, BPerangin, BEnggang, GJerai, RTelui, GInas, GBongsu, Belum, Piah, BHijau, Korbu and Bubu. The second cluster, ‘Region B’ consists of 32 populations, namely Behrang, Ampang, HGombak, HLangat, SLalang, PPanjang, Berembun, Angsi, Kenaboi, Triang, Pasoh, BSenggeh, GLedang, Krau, TNegara, Terenggun, SBetis, USat, CTongkat, HTerengganu, Jengai, AGading, Tekam, Beserah, Jengka, Lentang, Lesong, ERompin, GArong, Labis, AHitam and Panti. Similarly, the UPGMA dendrogram analysis also divided the 44 populations into two genetic clusters (Fig. 2) corresponding to Region A and B of the STRUCTURE result.Figure 2Dendrogram showing the relationship between 44 populations of Shorea leprosula in Peninsular Malaysia based on the UPGMA cluster analysis of SSR markers.Full size imageSSR allele frequency databases were established according to Region A and B, and characterized to evaluate the relative usefulness of each SSR marker in forensic investigation. The distribution of allele frequencies for each locus is listed in Table S2 (Region A database) and Table S3 (Region B database). Forensic parameters are shown in Table 1, with a total of 143 alleles and 174 alleles detected in the Region A and B databases, respectively. The observed (Ho) and expected (He) heterozygosity ranged from 0.3570 to 0.8346 and 0.4375 to 0.8795, respectively for populations in the Region A database; and ranged from 0.3298 to 0.8356 and 0.3469 to 0.8793, respectively for populations in the Region B database. The power of discrimination (PD) for the SSR loci ranged from 0.601 to 0.972 and 0.554 to 0.975, in Region A and B databases, respectively. The most discriminating locus was Sle605 in both the Region A (PD = 0.972) and Region B (PD = 0.975) databases. Minimum allele frequency was adjusted for alleles falling below the thresholds of 0.0066 (Region A) and 0.0024 (Region B).Table 1 Genetic diversity and forensic variables (A: total number of alleles; Ho: observed heterozygosity; He: expected heterozygosity; PIC: polymorphic information content; HWE: Hardy–Weinberg equilibrium; MP: matching probability; PD: power of discrimination) for each the 10 SSR loci of Shorea leprosula in the Region A and B databases.Full size tableDeviations from HWE were detected in four of the SSR loci for Region A (SleT11, SleT15, SleT17 and Sle465) and six SSR loci in Region B (SleT01, SleT11, SleT15, SleT17, SleT29 and SleT31). We evaluated these loci in each population independently to rule out the possible presence of null alleles. There were four populations in Region A (GJerai, RTelui, GBongsu and Piah) where a single one locus deviated from HWE; whereas there were eight populations in Region B (Behrang, HGombak, SLalang, Angsi, Klau, USat, Jengka and Panti) with a single locus and a single population (GLedang) with two loci that deviated from HWE (Table S4). Observed deviation from HWE was substantially lower in each population (either absence or not more than two loci) and thus it might be due to Wahlund effect caused by population substructuring in both Region A and B. Linkage disequilibrium (LD) testing was used to evaluate the independence of frequencies for all the SSR genotypes. A total of 13.3% and 28.9% of the 45 pairwise loci were found significant evidence of LD for Region A and B, respectively. Some of the loci might be linked as a result of population substructuring and inbreeding (inbreeding coefficient = 0.0822 [Peninsular Malaysia]). These results are in line with observations in real populations, where the assumption of completely random mating and zero migration required for HWE and LD are unlikely to be met, either in humans, animals or plants 21,22,23.Mean self-assignment, the proportion of individuals correctly assigned back to their population, was 45.9% and ranged from 14.3% (Kenaboi) to 81.3% (CTongkat) between population (Table 2). At the regional level, correct assignment rate of individuals to their region of origin was higher, 87.4% for Region A and 90.0% for Region B, (average of 88.7%).Table 2 Self-assignment test outcomes for Shorea leprosula individuals at the population and regional levels.Full size tableConservativeness of the databaseThe coancestry coefficient (θ) for Peninsular Malaysia (0.0579) was higher than those of Region A (0.0454) and Region B (0.0500) (Table 3). A total of 4.54% and 5.00% of the genetic variability was distributed among populations within Region A and Region B, respectively. In terms of inbreeding coefficient (f), the value for the Region A database (f = 0.0892) was highest, followed by Peninsular Malaysia (f = 0.0822) and Region B (f = 0.0666). All the θ and f values were significantly greater than zero, demonstrated by the 95% confidence intervals not overlapping with zero. Both of the θ and f values were used to calculate the conservativeness of each database by testing the cognate database (Porigin) against the regional database (Pcombined). The databases were non-conservative at the calculated θ value. In order for both the Region databases (A and B) to be conservative, the value of θ was adjusted from 0.0454 to 0.1900 for Region A and from 0.0500 to 0.1500 for Region B. For the Region A database, the most common SSR profile frequency is 2.69 × 10–7 or 1 in 3.72 million and the rarest profile frequency is 1.84 × 10–14 or 1 in 54.3 trillion. For the Region B database, the most common SSR profile frequency is 1.06 × 10–7 or 1 in 9.43 million and the rarest profile frequency is 4.03 × 10–16 or 1 in 2.48 quadrillion.Table 3 Coancestry (θ) and inbreeding (f) coefficients for Shorea leprosula at each hierarchical level.Full size table More