Ecology

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

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Systematic reviewA systematic review (PROSPERO Registered: CRD42020165355) of all cluster-randomised control trials currently published on ITNs [including conventional nets (CTNs), pyrethroid-only long-lasting nets (pyrethroid-nets), and pyrethroid-piperonyl butoxide synergist nets (pyrethroid-PBO ITNs)], IRS or a combination of both interventions was completed to validate an established transmission model for Plasmodium falciparum malaria parameterised using entomological assessment of the interventions. Three search platforms, Web of Knowledge, PubMed and Google Scholar were used and further studies were included from three recent Cochrane reviews that have focused on individual- or cluster- randomised control trials testing either ITNs, IRS or both26,27,28. Our search criteria focused on studies within Africa, and those reporting an epidemiological outcome such as parasite prevalence or clinical incidence in a defined age-cohort. A total of 138 studies were initially identified for further assessment (Supplementary Fig. S2).Those papers identified through the systematic review went through another round of screening to ensure they fell within the scope of the work and were compatible with existing modelling parameterisation. These criteria included (i) the intervention falls within an existing World Health Organization recommendation (so trials, or arms of trials, investigating pyrethroid-pyriproxyfen ITNs29 or insecticide-treated curtains30 were excluded), (ii) the entomological impact of the product had been previously statistically characterised as part of the modelling framework (trials investigating DDT31 or propoxur IRS32 were excluded), (iii) the study was within the Africa continent, (iv) the study randomised interventions in the intervention arm across the community (i.e., interventions were not targeted to individuals or risk groups within the community)33,34,35, and (v) the study was not reporting a cluster-randomised design36. A full description of why studies and arms were excluded is provided in Data S1.1.RCTs can assess the public health impact of interventions using different epidemiological endpoints. The two most common metrics used in malaria RCTs is infection prevalence (generally assessing parasitemia in a particular age group using microscopy or rapid diagnostic tests) or clinical incidence (typically assessed using active case detection in a cohort, which had previously been cleared of infection). These metrics are both equally valid though may give different results. For example, it may be harder to change malaria parasite prevalence with a partially effective intervention in a high-transmission setting (where people have a high chance of being reinfected) compared to a low-transmission setting (where reinfection is less common). Similarly, estimates of clinical incidence will vary depending on the study design and regularity of follow-up. For example, there are practical constraints on the number of times people within an active cohort can be tested. In areas of higher transmission incidence estimates will be greater the more regularly the cohort is tested as people infected multiple times between screening will be less common. This information on the regularity of screening is not always reported making it difficult to adjust models accordingly. It is also important to account for cluster-level effects when interpreting trial results, and this cluster-level data is also mostly unavailable37. The systematic review identified more studies that evaluated interventions in their ability to change malaria prevalence, with 13 out of 14 RCTs showing how the intervention changed parasite prevalence between the study arms compared with 8 RCTs, which reported changes in clinical incidence. Therefore, we focus on prevalence as our metric for epidemiology impact in this framework though note this should be repeated with clinical incidence estimates should more data become available. The final dataset had 73 cross-sectional surveys of prevalence in a defined age-cohort, 37 trial arms from 13 different RCTs.Characterising the entomological impact of ITNs and IRSExperimental hut trials (EHTs) measure the outcome of wild, free-flying, mosquito attempting to feed on volunteers resting indoors in the presence of an indoor intervention38. This includes (i) whether or not a mosquito is deterred away from a hut, which has the intervention (calculated by the number of mosquitoes found in the control hut relative to the intervention hut), (ii) whether the mosquito exits without feeding (repellence, measured as the percentage of alive unfed mosquitoes inside the intervention hut), (iii) the percentage entering the hut that successfully blood-feed, or (iv) the percentage of mosquitoes which die. Intervention efficacy is typically summarised for the intervention huts relative to a no-intervention (or untreated net) control huts, be it induced mortality (the increase in the percentage of mosquitoes dying over a 24-h period) or blood-feeding inhibition (the reduction in the percentage of mosquitoes receiving a blood-meal).EHTs use specially built structures that follow a defined floor-plan and set of specifications. There are multiple designs of experimental hut as they were originally intended to replicate the predominant type of housing found in the local area. We recently conducted a systematic review to capture the average behaviours of mosquitoes across different hut designs19. The two most used huts in Africa are the West African design and East Africa hut39 (a third hut—the Ifakara hut—is not considered here39). The meta-analyses showed that the associations describing the probable outcome of a mosquito feeding attempt (deterrence, repellence, successful feeding, or death) varies according to hut design. It is unclear that hut design best predicts epidemiological impact.Meta-analyses of EHT data have shown how the entomological efficacy of pyrethroid-nets has diminished over time, probably due to the rise of pyrethroid-resistant mosquitoes16,19,40, though there may be some manufacturing changes41. EHTs are conducted throughout Africa but are limited to the sites where the huts are built and cannot directly inform estimates of ITN efficacy outside of these areas. The most widely used quantitative measure for approximating the phenotypic level of resistance in the local mosquito population is the discriminating-dose bioassay. There are two main types of discriminating assays, the WHO susceptibility bioassay and the CDC bottle bioassay42,43. Both these assays measure the proportion of local Anopheline mosquitoes that survive 24-h following exposure to a discriminatory dose of pyrethroid for 60 min. Results from these bioassays are highly variable44 though collating data from multiple tests has shown clear trends over time45. The relationship between the level of resistance in the local mosquito population (as measured in a discriminating-dose bioassay) and the mortality induced by ITNs in EHTs can be used to extrapolate the results from hut trials to other geographical regions16.Modelling rationaleThe two main metrics recorded in EHTs do not capture all entomological impacts of ITNs and IRS. Though useful, induced mortality does not consider the sub-lethal impact of interventions whilst blood-feeding inhibition fails to differentiate between preventing blood-meals and killing mosquitoes, which are likely to have very different epidemiological impacts. Killing mosquitoes reduces the force of infection for users and non-users (through a community effect) so the overall effectiveness of treated nets and IRS will vary according to how abundantly and regularly they are used by the local human population. In addition, the impact of ITNs and IRS is likely to vary between sites because of factors such as the disease endemicity itself driven by societal behaviours, seasonality of transmission and the use of other malaria control interventions, amongst others. This means that raw EHT data is unlikely to directly correlate with the results of RCTs.EHTs are widely used to parameterise malaria transmission dynamics mathematical models46,47,48. These models rigorously quantify the outcome of each mosquito feeding attempt and, by making a limited number of assumptions, can estimate an overall entomological efficacy by combining the impact of the level of personal protection elicited by the intervention to the user and the indirect community effect provided to both users and non-users. Transmission dynamics mathematical models are designed to mechanistically capture the underlying processes governing malaria transmission and so can account for known non-linear processes such as the acquisition of human immunity49,50,51. This enables these models to translate the entomological efficacy quantified in an EHT into predictions of epidemiological impact given the characteristics of the site. Unfortunately, to date, there are no published EHTs that have been conducted alongside RCT evaluation of ITNs or IRS products (and therefore evaluated against the same mosquito population). To overcome this issue we parameterise the models using a meta-analyses of 136 EHT results16,19 collated from across Africa, which quantifies how mosquito deterrence, repellence, successful feeding, or death varies with time since the intervention is deployed and according to the level of pyrethroid resistance in the local mosquito population (as measured by the discriminating-dose bioassay). This approach has been able to recreate the epidemiological impact observed in RCTs evaluating a small number of ITNs15 or IRS products9, but this is the first attempt at using this method to validate the modelling framework against all trials evaluating nets and IRS.There is considerable uncertainty in how the entomological efficacy of treated ITNs varies with the level of resistance in the local population. This is a key relationship determining how field discriminating-dose bioassay data should be interpreted yet it is highly uncertain, with a recent meta-analyses indicating that it is equally well explained by two different functional forms (the logistic or log-logistic functions)19. Similarly, it is unclear whether the epidemiological impact of ITNs or IRS is best captured by all experimental hut data combined (Supplementary Fig. S14C, D)19 or if the meta-analyses should be restricted to just West or East African hut design data alone. To rigorously differentiate between these options six different models are run for each trial arm (n = 37), varying both the relationship between discriminating-dose bioassay and EHT mosquito mortality (either the logistic or log-logistic function) and the data used in the EHT meta-analyses (all data, East or West African design huts). The ability of these models to recreate the observed results is statistically compared and the most accurate selected for the main analyses.Transmission dynamics modelThe malaria transmission model that we use here incorporates the transmission dynamics of Plasmodium falciparum between human hosts and Anopheles mosquito vectors. The differential equations and associated assumptions of the original transmission model52 have been comprehensively reported in the Supplementary Material from Griffin et al.53, Walker et al.54 and Winskill et al.55. The model has been extensively fitted to data on the relationship between vector density, entomological inoculation rate, parasite prevalence, uncomplicated malaria, severe disease and death49,52,53,56,57. Model equations and assumptions are provided in the Supplementary Methods and https://github.com/jamiegriffin/Malaria_simulation. Unless stated (Supplementary Data S1), default parameters are taken from these papers.Data requirements for model simulationThe transmission model can be parameterised to describe the specific ecology of each RCT location using data on the mosquito bionomics, seasonal transmission patterns, historic use of various interventions—principally insecticide-treated ITNs or the residual spraying of insecticides (IRS)—and baseline endemicity. These data are recorded within the research articles reporting the trials at the trial arm level (Supplementary Data S1.2 notes where data are available and which resources were used; Supplementary Data S1.3 lists the key data identified for model parameterisation) and Supplementary Fig. S1 provides a diagram of how they are combined to inform the model.Briefly, the Anopheles mosquito species composition at baseline is used to determine the proportion of mosquitoes with bespoke behaviours that could alter exposure risk to mosquito bites and thus transmission risk. Species-specific mosquito behaviours are parameterised from systematic reviews on anthropophagy, using the human blood index47,58,59, and the proportion of mosquito bites that are received indoors or in bed because this impacts the efficacy estimate for indoor interventions60.Other information that are specific to each trial also help interpret our success at predicting, or not, the observed results of an intervention tested in an RCT; the diagnostic used to measure prevalence or incidence is useful because different tests have different sensitivities61, which can be included in the model framework54. The baseline burden of infection is particularly important to enable the model to be calibrated to the endemicity of the study site by varying the number of mosquitoes per person (the human:mosquito ratio). This is determined by a cross-sectional estimate of parasite prevalence in a defined age-cohort at a particular time of year of the baseline survey.For any location, the current level of endemicity is determined by the historic interventions already operating at the site. Therefore, wherever possible, ITN use and the historic use of sprayed insecticides, as well as the estimated proportion of clinical cases that are drug-treated, are included as baseline parameters.In addition to the waning potency of insecticide active ingredient outlined above, the impact of nets can also wane because of changes in the proportion of people using them. This can be driven by the quality of the product, seasonal patterns in humidity or other social patterns of use62,63,64. Where data are available, this waning adherence to net use is captured by fitting an exponential decay function to the proportion of people using nets measured at cross-sectional surveys throughout the trials:$${{{{{{{mathrm{U}}}}}}{{{{{mathrm{sage}}}}}}}}_{i}={e}^{-{sigma }_{i}t}$$
(1)
where σ is a parameter determining how rapidly people stop using nets in an intervention arm i of the trial and t is time in years. Parameter estimates for pyrethroid-only and pyrethroid-PBO ITNs are provided for different levels of resistance for the 6 potential methods of associating bioassays and using data (Supplementary Data S1.4).The IRS product used is equally important as the entomological impact of different products vary, particularly for pyrethroid-based IRS in the presence of resistant mosquitoes9. Supplementary Data S1.5 show the parameter estimates for products included in the analysis.The seasonality of transmission has been defined previously for each RCT site (at the administration subunit 1 level) using normalised rainfall patterns obtained from the US Climate Prediction Center65. The daily time series are aggregated to 64 points per year for years 2002 to 2009. A Fourier function is fitted to these data to capture seasonality by reconstructing annual rainfall patterns54,66. We deliberately do not match rainfall data from the respective RCTs, which would likely improve the model estimates because we are ultimately testing whether this framework has predictive power across future years or alternative ecologies, where we will not know how rainfall will exactly impact mosquito densities and hence malaria transmission.Statistical analysisThe mean simulated malaria prevalence (matching the age-cohort of the trial) is recorded for all RCT surveys timepoints. This equates to a total of 73 cross-sectional surveys post-implementation. The process was repeated using the 6 different entomological parameter sets (the relationship between bioassay and hut trial mortality and the hut design used to summarise treated net entomological impact). An illustration of the different models and their fit to data is demonstrated in Supplementary Fig. S17 for a recent study trialling pyrethroid-only nets, pyrethroid-PBO ITNs alone or in combination with a long-lasting IRS product in Tanzania5. The difference between the observed and predicted prevalence at each timepoint is shown for all RCTs in Supplementary Fig. S18. A simple linear regression is conducted comparing observed and predicted results are summarised in Supplementary Table 3. Let Xi denote the malaria prevalence predicted by the model at timepoint i while Yi is the observed prevalence. The regression,$${Y}_{i}=m{X}_{i}$$
(2)
for i = 1,…,c + n, where m is the gradient between the observed and predicted result (consistent across studies), c is the number of post-intervention datapoints in the control arms and n is the number of post-intervention datapoints in the intervention arms (c + n = 73 for analyses of all RCTs). Better fitting models have a higher adjusted R2 (adjusted R2 values of one indicate the model is perfectly predicting the trial result) whilst the gradient of the regression m indicates any bias (with value of one reporting the model can predict prevalence equally well across the endemicity range). Results are presented for all ITNs and IRS RCTs and separately for RCTs of different types of (pyrethroid-only ITNs, pyrethroid-PBO ITNs and IRS, Supplementary Table 3). The log-logistic model (results 4–6 in Supplementary Table 3) describing the relationship between bioassay and hut trial mortality consistently fits the data better, with models fit using either all hut trial data or East African design huts having a similar accuracy (adjusted R2 = 0.95). This parameter combination also had the least bias, with the best fit regression line being closer to one.The average efficacy of the different ITNs and IRS combinations was calculated by comparing malaria prevalence for the different trial arms to the respective control arms at matched timepoints following the introduction of interventions. Let ({E}_{{jk}}^{l}) be the relative reduction in the malaria prevalence between the control (k = 0) to intervention (k = 1) arms at matched timepoint j in the same trial for either the predicted (l = Xjk) or observed (l = Yjk) malaria prevalence,$${E}_{j}^{X}=({{X}_{j0}-{X}}_{j1})/{X}_{j0},{{{{{rm{ and }}}}}},{E}_{j}^{Y}=({Y}_{j0}-{Y}_{j1})/{Y}_{j0}$$
(3)
for j = 1,…,n. The goodness of fit for the efficacy estimates is calculated in a similar manner to the prevalence estimates by substituting in ({E}_{j}^{X}) and ({E}_{j}^{Y}) into Xi and Yi in E2, respectively. Models are on average able to estimate the efficacy of the interventions at different timepoints (Supplementary Table 3). Estimates for some timepoints diverge substantially (for example, the study testing conventional nets in the Gambia relative to untreated nets67 measured negative effect in one setting; the treated net arm having more infected children whereas the model predicted a 12.5% reduction due to the CTN (with parameters derived from all EHT data and the log-logistic function, 4 in Supplementary Table 3), Supplementary Data S1.8), but in most studies the trial average (averaged across all timepoints) is remarkably consistent. Accuracy is lower than estimates of absolute prevalence, in part because the difference between the percentage of people slide positive in low-endemicity settings may be relatively modest in absolute terms but might represent a substantial difference as a percentage. It is also important to note that when the models do systematically miss some timepoints, this is consistent across the control and treated arms. For example, in the Protopopoff et al. study in Tanzania5 (Figs. S14 and S17) efficacy is over-estimated in all arms 18 months after the start of the trial, but the relative difference between the arms (in terms of ordering, and the efficacy estimate) is relatively consistent. This indicates that unmeasured factors, such as differences in the timing and duration of the rainy season, may have occurred across all trial arms. As previously, the log-logistic functional form describing the relationship between bioassay and hut trial mortality consistently fits the data better (Supplementary Table 3, options 4 to 6). The models fit describing the entomological efficacy of any net using all EHT data predicts efficacy data better with East African design hut data providing similar accuracy (adjusted R2 = 0.64 vs. 0.62, respectively). Following this we select the log-logistic functional form to describe the relationship between mortality in the discriminating-dose bioassay and EHT and characterise the entomological efficacy of treated ITNs using data from both East and West African design huts for the main analyses (Fig. 2B, C).The ability of the best-performing model (Supplementary Table 3, column 4: log-logistic function and all EHT data) to capture the relative drop in prevalence over time compared to the baseline (pre-intervention) estimate is shown in Supplementary Fig. S19. This value is denoted as ({dot{E}}_{t}^{l}) and is calculated as,$${dot{E}}_{t}^{X}=({X}_{0}-{X}_{t}),{{{{{rm{and}}}}}},{dot{E}}_{t}^{Y}=({X}_{0}-{Y}_{t})$$
(4)
where ({X}_{0}) is the malaria prevalence at baseline (prior to intervention deployment with the exception of Chaccour et al.68) observed from the RCT and the model is calibrated to this endemicity. Xt is then the subsequent cross-sectional survey observed for each study, and RCTs have different numbers of surveys ranging from 1 to 4 in the published literature. The corresponding model estimate is represented by Yt. Estimates are calculated for all post-intervention timepoints in both control and intervention arms and are shown in Fig. S19A. The difference between ({dot{E}}_{t}^{X}) and ({dot{E}}_{t}^{Y}) can be used to explore how closely the model is able to predict this absolute difference observed in the trials (a value of 0 indicates exact match, high predictive ability). The model overestimates the performance of IRS only, deployed in 1995 using the pyrethroid IRS ICON CS 10% (Syngenta), but otherwise there is no difference in the models’ ability to estimate different ITN interventions or combination net and IRS interventions, be it the absence of an intervention, conventional dipped-nets, pyrethroid-only nets, pyrethroid-PBO ITNs with or without IRS (Fig. S19B). All code is available69.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

Sampling of coastal communitiesHere, we integrated data from five different projects that had surveyed coastal communities across five countries47,48,49,50. Between 2009 and 2015, we conducted socioeconomic surveys in 72 sites from Indonesia (n = 25), Madagascar (n = 6), Papua New Guinea (n = 10), the Philippines (n = 25), and Tanzania (Zanzibar) (n = 6). Site selection was for broadly similar purposes- to evaluate the effects of various coastal resource management initiatives (collaborative management, integrated conservation and development projects, recreational fishing projects) on people’s livelihoods in rural and peri-urban villages. Within each project, sites were purposively selected to be representative of the broad range of socioeconomic conditions (e.g., population size, levels of development, integration to markets) experienced within the region. We did not survey strictly urban locations (i.e., major cities). Because our sampling was not strictly random, care should be taken when attempting to make inferences beyond our specific study sites.We surveyed between 13 and 150 households per site, depending on the population of the communities and the available time to conduct interviews per site. All projects employed a comparable sampling design: households were either systematically (e.g., every third house), randomly sampled, or in the case of three villages, every household was surveyed (a census) (see Supplementary Data file). Respondents were generally the household head, but could have been other household members if the household head was not available during the study period (i.e. was away). In the Philippines, sampling protocol meant that each village had an even number of male and female respondents. Respondents gave verbal consent to be interviewed.The following standard methodology was employed to assess material style of life, a metric of material assets-based wealth48,51. Interviewers recorded the presence or absence of 16 material items in the household (e.g., electricity, type of walls, type of ceiling, type of floor). We used a Principal Component Analysis on these items and kept the first axis (which explained 34.2% of the variance) as a material wealth score. Thus, each community received a mean material style of life score, based on the degree to which surveyed households had these material items, which we then scaled from 0 to 1. We also conducted an exploratory analysis of how material style of life has changed in two sites in Papua New Guinea (Muluk and Ahus villages) over fifteen and sixteen-year time span across four and five-time periods (2001, 2009, 2012, 2016, and 2002, 2009, 2012, 2016, 2018), respectively, that have been surveyed since 2001/200252. These surveys were semi-panel data (i.e. the community was surveyed repeatedly, but we did not track individuals over each sampling interval) and sometimes occurred in different seasons. For illustrative purposes, we plotted how these villages changed over time along the first two principal components.SensitivityWe asked each respondent to list all livelihood activities that bring in food or income to the household and rank them in order of importance. Occupations were grouped into the following categories: farming, cash crop, fishing, mariculture, gleaning, fish trading, salaried employment, informal, tourism, and other. We considered fishing, mariculture, gleaning, fish trading together as the ‘fisheries’ sector, farming and cash crop as the ‘agriculture’ sector and all other categories into an ‘off-sector’.We then developed three distinct metrics of sensitivity based on the level of dependence on agriculture, fisheries, and both sectors together. Each metric incorporates the proportion of households engaged in a given sector (e.g., fisheries), whether these households also engage in occupations outside of this sector (agriculture and salaried/formal employment; referred to as ‘linkages’ between sectors), and the directionality of these linkages (e.g., whether respondents ranked fisheries as more important than other agriculture and salaried/formal employment) (Eqs. 1–3)$${{{{{{rm{S}}}}}}}_{{{{{{rm{A}}}}}}}=,frac{{{{{{rm{A}}}}}}}{{{{{{rm{A}}}}}}+{{{{{rm{NA}}}}}}},times ,frac{{{{{{rm{N}}}}}}}{{{{{{rm{A}}}}}}+{{{{{rm{NA}}}}}}},times ,frac{left(frac{{{{{{{rm{r}}}}}}}_{{{{{{rm{a}}}}}}}}{2}right),+,1}{{{{{{{rm{r}}}}}}}_{{{{{{rm{a}}}}}}}+,{{{{{{rm{r}}}}}}}_{{{{{{rm{na}}}}}}}+1}$$
(1)
$${{{{{{rm{S}}}}}}}_{{{{{{rm{F}}}}}}}=,frac{{{{{{rm{F}}}}}}}{{{{{{rm{F}}}}}}+{{{{{rm{NF}}}}}}},times ,frac{{{{{{rm{N}}}}}}}{{{{{{rm{F}}}}}}+{{{{{rm{NF}}}}}}},times ,frac{left(frac{{{{{{{rm{r}}}}}}}_{{{{{{rm{f}}}}}}}}{2}right),+,1}{{{{{{{rm{r}}}}}}}_{{{{{{rm{f}}}}}}}+,{{{{{{rm{r}}}}}}}_{{{{{{rm{nf}}}}}}}+1}$$
(2)
$${{{{{{rm{S}}}}}}}_{{{{{{rm{AF}}}}}}}=,frac{{{{{{rm{AF}}}}}}}{{{{{{rm{AF}}}}}}+{{{{{rm{NAF}}}}}}},times ,frac{{{{{{rm{N}}}}}}}{{{{{{rm{AF}}}}}}+{{{{{rm{NAF}}}}}}},times ,frac{left(frac{{{{{{{rm{r}}}}}}}_{{{{{{rm{af}}}}}}}}{2}right),+,1}{{{{{{{rm{r}}}}}}}_{{{{{{rm{af}}}}}}}+,{{{{{{rm{r}}}}}}}_{{{{{{rm{naf}}}}}}}+1}$$
(3)
where ({{{{{{rm{S}}}}}}}_{{{{{{rm{A}}}}}}}), ({{{{{{rm{S}}}}}}}_{{{{{{rm{F}}}}}}}) and ({{{{{{rm{S}}}}}}}_{{{{{{rm{AF}}}}}}}) are a community’s sensitivity in the context of agriculture, fisheries and both sectors, respectively. A, F and AF are the number of households relying on agriculture-related occupations within that community, fishery-related and agriculture- and fisheries-related occupations within the community, respectively. NA, NF and NAF are the number of households relying on non-agriculture-related, non-fisheries-related, and non-agriculture-or-fisheries-related occupations within the community, respectively. N is the number of households within the community. ({{{{{{rm{r}}}}}}}_{{{{{{rm{a}}}}}}}), ({{{{{{rm{r}}}}}}}_{{{{{{rm{f}}}}}}}) and ({{{{{{rm{r}}}}}}}_{{{{{{rm{af}}}}}}}) are the number of times agriculture-related, fisheries-related and agriculture-and-fisheries-related occupations were ranked higher than their counterpart, respectively. ({{{{{{rm{r}}}}}}}_{{{{{{rm{na}}}}}}}), ({{{{{{rm{r}}}}}}}_{{{{{{rm{nf}}}}}}}) and ({{{{{{rm{r}}}}}}}_{{{{{{rm{naf}}}}}}}) are the number of times non-agriculture, non-fisheries, and non-agriculture-and-fisheries-related occupations were ranked higher than their counterparts. As with the material style of life, we also conducted an exploratory analysis of how joint agriculture-fisheries sensitivity has changed over time in a subset of sites (Muluk and Ahus villages in Papua New Guinea) that have been sampled since 2001/200252. Although our survey methodology has the potential for bias (e.g. people might provide different rankings based on the season, or there might be gendered differences in how people rank the importance of different occupations53), our time-series analysis suggest that seasonal and potential respondent variation do not dramatically alter our community-scale sensitivity metric.ExposureTo evaluate the exposure of communities to the impact of future climates on their agriculture and fisheries sectors, we used projections of production potential from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) Fast Track phase 3 experiment dataset of global simulations. Production potential of agriculture and fisheries for each of the 72 community sites and 4746 randomly selected sites from our study countries with coastal populations >25 people/km2 were projected to the mid-century (2046–2056) under two emission scenarios (SSP1-2.6, and SSP5-8.5) and compared with values from a reference historical period (1983–2013).For fisheries exposure (EF), we considered relative change in simulated total consumer biomass (all modelled vertebrates and invertebrates with a trophic level >1). For each site, the twenty nearest ocean grid cells were determined using the Haversine formula (Supplementary Fig. 5). We selected twenty grid cells after a sensitivity analysis to determine changes in model agreement based on different numbers of cells used (1, 3, 5, 10, 20, 50, 100; Supplementary Figs. 6–7), which we balanced off with the degree to which larger numbers of cells would reduce the inter-site variability (Supplementary Fig. 8). We also report 25th and 75th percentiles for the change in marine animal biomass across the model ensemble. Projections of the change in total consumer biomass for the 72 sites were extracted from simulations conducted by the Fisheries and marine ecosystem Model Intercomparison Project (FishMIP3,54). FishMIP simulations were conducted under historical, SSP1-2.6 (low emissions) and SSP5-8.5 (high emissions) scenarios forced by two Earth System Models from the most recent generation of the Coupled Model Intercomparison project (CMIP6);55 GFDL-ESM456 and IPSL-CM6A-LR57. The historical scenario spanned 1950–2014, and the SSP scenarios spanned 2015–2100. Nine FishMIP models provided simulations: APECOSM58,59, BOATS60,61, DBEM2,62, DBPM63, EcoOcean64,65, EcoTroph66,67, FEISTY68, Macroecological69, and ZooMSS11. Simulations using only IPSL-CM6A-LR were available for APECOSM and DBPM, while the remaining 7 FishMIP models used both Earth System Model forcings. This resulted in 16 potential model runs for our examination of model agreement, albeit with some of these runs being the same model forced with two different ESMs. Thus, the range of model agreement could range from 8 (half model runs indicating one direction of change, and half indicating the other) to 16 (all models agree in direction of change). Model outputs were saved with a standardised 1° spatial grid, at either a monthly or annual temporal resolution.For agriculture exposure (EA), we used crop model projections from the Global Gridded Crop model Intercomparison Project (GGCMI) Phase 314, which also represents the agriculture sector in ISIMIP. We used a window of 11×11 cells centred on the site and removed non-land cells (Supplementary Fig. 5). The crop models use climate inputs from 5 CMIP6 ESMs (GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, and UKESM1-0-LL), downscaled and bias-adjusted by ISIMIP and use the same simulation time periods. We considered relative yield change in three rain-fed and locally relevant crops: rice, maize, and cassava, using outputs from 4 global crop models (EPIC-IIASA, LPJmL, pDSSAT, and PEPIC), run at 0.5° resolution. These 4 models with 5 forcings generate 20 potential model runs for our examination of model agreement. Yield simulations for cassava were only available from the LPJmL crop model. All crop model simulations assumed no adaptation in growing season and fertilizer input remained at current levels. Details on model inputs, climate data, and simulation protocol are provided in ref. 14. At each site, and for each crop, we calculated the average change (%) between projected vs. historical yield within 11×11 cell window. We then averaged changes in rice, maize and cassava to obtain a single metric of agriculture exposure (EA).We also obtained a composite metric of exposure (EAF) by calculating each community’s average change in both agriculture and fisheries:$${{{{{{rm{E}}}}}}}_{{{{{{rm{AF}}}}}}}=,frac{{{{{{{rm{E}}}}}}}_{{{{{{rm{A}}}}}}}+,{{{{{{rm{E}}}}}}}_{{{{{{rm{F}}}}}}}}{2}$$
(4)
Potential ImpactWe calculated relative potential impact as the Euclidian distance from the origin (0) of sensitivity and exposure.Sensitivity testTo determine whether our sites displayed a particular exposure bias, we compared the distributions of our sites and 4746 sites that were randomly selected from 47,460 grid cells within 1 km of the coast of the 5 countries we studied which had population densities >25 people/km2, based on the SEDAC gridded populating density of the world dataset (https://sedac.ciesin.columbia.edu/data/set/gpw-v4-population-density-rev11/data-download).We used Cohen’s D to determine the size of the difference between our sites and the randomly selected sites.Validating ensemble modelsWe attempted a two-stage validation of the ensemble model projections. First, we reviewed the literature on downscaling of ensemble models to examine whether downscaling validation had been done for the ecoregions containing our study sites.While no fisheries ensemble model downscaling had been done specific to our study regions, most of the models of the ensemble have been independently evaluated against separate datasets aggregated at scales down to Large Marine Ecosystems (LMEs) or Exclusive Economic Zones (EEZs) (see11). For example, the DBEM was created with the objective of understanding the effects of climate change on exploited marine fish and invertebrate species2,70. This model roughly predicts species’ habitat suitability; and simulates spatial population dynamics of fish stocks to output biomass and maximum catch potential (MCP), a proxy of maximum sustainable yield2,62,71. Compared with spatially-explicit catch data from the Sea Around Us Project (SAUP; www.seaaroundus.org)70 there were strong similarities in the responses to warming extremes for several EEZs in our current paper (Indonesia and Philippines) and weaker for the EEZs of Madagascar, Papua New Guinea, and Tanzania. At the LME level, DBEM MCP simulations explained about 79% of the variation in the SAUP catch data across LMEs72. The four LMEs analyzed in this paper (Agulhas Current; Bay of Bengal; Indonesian Sea; and Sulu-Celebes Sea) fall within the 95% confidence interval of the linear regression relationship62. Another example, BOATS, is a dynamic biomass size-spectrum model parameterised to reproduce historical peak catch at the LME scale and observed catch to biomass ratios estimated from the RAM legacy stock assessment database (in 8 LMEs with sufficient data). It explained about 59% of the variability of SAUP peak catch observation at the LME level with the Agulhas Current, Bay of Bengal, and Indonesian Sea catches reproduced within +/-50% of observations61. The EcoOcean model validation found that all four LMEs included in this study fit very close to the 1:1 line for overserved and predicted catches in 200064,65. DBPM, FEISTY, and APECOSM have also been independently validated by comparing observed and predicted catches. While the models of this ensemble have used different climate forcings when evaluated independently, when taken together the ensemble multi-model mean reproduces global historical trends in relative biomass, that are consistent with the long term trends and year-on-year variation in relative biomass change (R2 of 0.96) and maximum yield estimated from stock assessment models (R2 of 0.44) with and without fishing respectively11.Crop yield estimates simulated by GGCMI crop models have been evaluated against FAOSTAT national yield statistics14,73,74. These studies show that the models, and especially the multi-model mean, capture large parts of the observed inter-annual yield variability across most main producer countries, even though some important management factors that affect observed yield variability (e.g., changes in planting dates, harvest dates, cultivar choices, etc.) are not considered in the models. While GCM-based crop model results are difficult to validate against observations, Jägermeyr et al14. show that the CMIP6-based crop model ensemble reproduces the variability of observed yield anomalies much better than CMIP5-based GGCMI simulations. In an earlier crop model ensemble of GGCMI, Müller et al.74 show that most crop models and the ensemble mean are capable of reproducing the weather-induced yield variability in countries with intensely managed agriculture. In countries where management introduces strong variability to observed data, which cannot be considered by models for lack of management data time series, the weather-induced signal is often low75, but crop models can reproduce large shares of the weather-induced variability, building trust in their capacity to project climate change impacts74.We then attempted to validate the models in our study regions. For the crop models, we examined production-weighted agricultural projections weighted by current yields/production area (Supplementary Fig. 1). We used an observational yield map (SPAM2005) and multiplied it with fractional yield time series simulated by the models to calculate changes in crop production over time, which integrates results in line with observational spatial patterns. The weighted estimates were not significantly different to the unweighted ones (t = 0.17, df = 5, p = 0.87). For the fisheries models, our study regions were data-poor and lacked adequate stock assessment data to extend the observed global agreement of the sensitivity of fish biomass to climate during our reference period (1983-2013). Instead, we provide the degree of model run agreement about the direction of change in the ensemble models to ensure transparency about the uncertainty in this downscaled application.AnalysesTo account for the fact that communities were from five different countries we used linear mixed-effects models (with country as a random effect) for all analyses. All averages reported (i.e. exposure, sensitivity, and model agreement) are estimates from these models. In both our comparison of fisheries and agriculture exposure and test of differences between production-weighted and unweighted agriculture exposure we wanted to maintain the paired nature of the data while also accounting for country. To accomplish this we used the differences between the exposure metrics as the response variable (e.g. fisheries exposure minus agriculture exposure), testing whether these differences are different from zero. We also used linear mixed-effects models to quantify relationships between the material style of life and potential impacts under different mitigation scenarios (SSP1-2.6 and 8.5), estimating standard errors from 1000 bootstrap replications. To further explore whether these relationships between the material style of life and potential impacts were driven by exposure or sensitivity, we conducted an additional analysis to quantify relationships between the material style of life and: 1) joint fisheries and agricultural sensitivity; 2) joint fisheries and agricultural exposure under different mitigation scenarios. We present both the conditional R2 (i.e., variance explained by both fixed and random effects) and the marginal R2 (i.e., variance explained by only the fixed effects) to help readers compare among the material style of life relationships.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

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