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Sampling and dataThe data consist of gillnet catches of brown trout (N = 5733, caught during 2008–2009) from 21 lakes situated along an altitudinal gradient (30 m above sea level, m.a.s.l.-800 m.a.s.l.) in mid-Norway and Sweden (Fig. 5). The lakes were sampled within three main types of vegetation zonation in the catchment area that ranged from the southern boreal to the alpine zone. The lowland lakes were situated in the southern boreal zone dominated by coniferous woodland and forest, but there were also large areas of alder (Alnus sp.) as well as some broad-leaved deciduous woodland. Average annual and July air temperature are 4–6 and 12–16 °C, respectively46. Middle boreal catchment area is dominated by coniferous woodland, forest and mires. Average annual and July air temperature are, respectively, 2–4 and 8–12°C46. Vegetation around the high altitude lakes were dominated by bilberry (Vaccinium myrtillus), grass heaths and dwarf birch (Betula nana) scrub, with annual and July air temperature of − 2 to 0 and 6–12°C46. The clustering of lakes within vegetations zones can be seen in Fig. 5. The epilimnetic water temperature across a sample of the lakes in the altitudinal gradient in this study seems to be within the general trends in the air temperature47.Figure 5Study lake positions (filled dots) and names. Unfilled large circles connects the different lakes with the most representative weather stations (stars) in the area (in terms of altitude, vegetation zones and landscape). The dashed line constitute the national border between Norway and Sweden. The figure was produced using Adobe illustrator.Full size imageAll lakes were sampled using standardised gillnet series consisting of single mesh nets (25 × 1.5 m) with mesh sizes 12.5, 16, 19.5, 24, 29 and 35 mm47. Three nets were linked together making chains with alternating mesh sizes in order to represent all mesh sizes at different depths in each lake at each sampling. This gillnet series catches brown trout with a slight bias in favour of larger individuals48 that was assumed similar in all lakes. The nets were distributed along the shoreline, and the lakes were fished during summer, with different effort (i.e., number of gillnet series) depending on lake size. Weight per unit catch effort (CPUE) based on total weight of the brown trout catch per 100 m2 gillnet area per night was used as a proxy for biomass density. Since, differences in environmental conditions across lakes cause large variations in body size and hence per capita resource demands, biomass was considered a better measure of population density than number of individuals for among-lake comparisons. Length (total length, mm) and weight (g) at catch were measured for every individual in the full data set. Age, sex, maturation status and back-calculation of length-at-age was undertaken for a randomly selected representative subset (N = 889) of the data. Growth and spawning probability ogives49 were modeled based on this subset. Scale samples and otoliths were taken and used for age determination, of which scales were used primarily, and scales were used for back-calculation of growth50. Distance between the annuli was measured, and a direct proportional relationship between the length of the fish and the scale radius was assumed51. If the scales were difficult to read, which was the case for more slow-growing individuals from the low altitude lakes were the annuli were less distinct, otoliths were used for determining the age. As we did not have complete records of water temperature, area and time specific summer air temperature and precipitation measurements were obtained from an online database (www.eklima.no, Norwegian Metrological Institute). The database contained historical weather data from the closest representative (i.e., corresponding in distance, altitude and operational period) weather stations to the respective lakes (Fig. 5). This resulted in overlapping temperature and precipitation regimes for some of the lakes as there were in total five different weather stations that were most representative within the area containing the 21 lakes. Further, as there was some variation in how complete the different measurements were within years, we also had to calculate the sum of summer precipitation for a shorter period of the summer compared to the average mean air temperature. Both measurements still being good proxies for experienced summer conditions in the bulk of the growth season. The effect of temperature and precipitation was thus derived from the spatio-temporal variation in observations between these five weather stations, where the historic temporal variation corresponds to recorded climate components relevant to years for the back calculated age of the individual fish in the specific lakes (resulting in a total of 29 distinct measurements, see variation in Table 2) Epilimnetic water of lakes usually reflects warming trends in air temperature well, however hypolimnetic temperature variation might not be very correlated to the air temperature. Yet, changes in air temperature might indeed influence the thermal stratification of a lake and thus the environment and conditions for a fish52. There are good reasons to believe that most of the lakes in our study obtained some sort of thermal stratification during the summer season. Nonetheless, we chose not to model air to water temperature for the few measurements of water temperatures we had, and extrapolate this relationship to the full spatio-temporal resolution of the data. The rationale for this was threefold: (1) We were interested in exploring potential effects and relationships of easily available climate components, such as air temperature, simplifying the model concept; (2) we did not have access to detailed data on lake bathymetry so that hypothetical modeled air-to-water relationships would be rather uncertain; (3) we had no detailed information on how the brown trout was distributed in the water column during the summer period in study lakes. However, compared to similar lakes, there are reasons to believe that brown trout mainly feed and stay in the upper six meters of the water column, as well as epibenthic areas with high invertebrate abundances53,54, where both areas often are overlapping and highly influenced by the air temperature.Table 2 Description of candidate variables used in the model selection process determining the most supported model for individual growth of brown trout.Full size tableData analysis and model descriptionsOverall processWe used linear mixed model approaches to parameterize environmental effects on key life history traits for brown trout. Specifically: Length at age was parameterized as function of the environment (e.g., summer temperature, population density, winter NAO and summer precipitation). Spawning probability were modeled as functions of individual length and age. We also allowed either the age effect or length effect on spawning probability to vary with temperature or summer precipitation. Individual fecundity (number of eggs produced) was predicted as a function of length and spawning probability. Annual survival estimates from age 1 and up was accessed using catch curve analysis, while first year survival was estimated based on a stock-recruitment function. The estimated parameters were utilized to feed an age structured matrix projection model23, enabling long-term population viability projections in an changing environment (see overview in Fig. 6). Although there are several choices of population models that might be utilized for inferring the population dynamics, such as IBMs55 and IPMs56, the age structured matrix model was deemed especially suited to model our systems as they are highly seasonal (with very reduced growth during winter) and thus producing a clear age structure in the data. Further description of the various modeling approaches are described below. All statistical analyses was done in R57.Figure 6A schematic overview of the processes involved in our model-setup. Red lines indicate drivers and connections acting on individual life history traits, blue lines indicates traits driving the population model and green lines indicates links to climate variables. In short, existing area and time specific climate data on summer precipitation (Prec) and mean summer air temperature (Temp), as well as time specific data on winter NAO-index (recorded NAO values during December, January, February and March, NAO.DJFM), were used to parameterize models for length at age 1 and length at age  > 1, as well as spawning probability at age. Length at age 1 was allowed to affect length at age  > 1, and in the simulations achieved length at age  > 1 was also influenced by the achieved length the previous year (L*). Length at age and spawning probability, both defined by climate variables, interacted in defining how many eggs a female was likely to produce (i.e. fecundity). Survival from eggs to small juvenile fish was based on a stock-recruitment relationship, where the stock was defined by the results from the population model (expected number of fish). Expected number of fish across all ages was also allowed to affect length at age  > 1. The model parameters was used to simulate long term population dynamics, where we also varied expected temperature change scenarios (steadily increasing mean temperatures and temperature variation, respectively, as well as a combination of the two latter scenarios). The populations long term rate of increase (λ) was inferred using the age structured population matrix model.Full size imageSize at ageData inspections prior to model development showed length at age to be surprisingly linear within the size and age distribution in our data (i.e. no obvious signs of asymptotic growth for fish in any of the sampled lakes). Length (L) was thus explored using a linear mixed effects model approach with the lme4-package58. Denoted, length for individual j in population i (Lij) could thus be expressed as:$${L}_{ij}={sumlimits_{k=1}^{p}}{chi }_{ijk}{beta }_{k}$$Here, β = (β1, …, βp)T is px1 vector (one column matrix) of unknown regression parameters, χiT = (χi1, …, χip) ∈ ℝp is the explanatory variables of interest (k + p  1, age was always included as a variable, and we also tested models including an effect of CPUE and first year growth on subsequent growth trajectories. Multiple candidate models where the different environmental effects were allowed to vary with age were constructed (Supplementary information S1). Population ID and individual ID were included as nested random effects in all candidate models exploring size at age  > 1, and population ID was included as a random effect for the models exploring size at age 1. The most supported models were selected based on AIC-values59. During the population simulation the variation in the predictions attributed to the random effect(s) was treated as random noise, and not explicitly included in the simulations.Spawning probabilityBrown trout is an iteroparous species, however under normal food conditions and harsh winters in Norway it might not spawn every year following maturity. Accordingly, we modelled likelihood of spawning at age, derived from the number of female individuals that was going to spawn the following autumn, rather than probability of maturation at age. Aging effects on spawning probability was included in the modelling as skipped-spawning individuals (i.e., mature females that skip spawning episodes, sensu Rideout and Tomkiewicz 60) were coded as non-spawners in the analysis. Probability of spawning (P) was calculated based on a maturation-ogives approach61, utilizing generalized linear mixed effects models in the lme4-package58. Binomial models as two-dimensional ogives, o(A, L) were considered in the model selection. Here, A and L represent age and length, respectively. In addition, we also explored how these ogives might change due to either a temperature effect, summer precipitation effect, or a measure of fish abundance (CPUE) including either as an additive effect in some candidate models (see Supplementary information S2). Population ID was always included as a random effect. In general, the probability of spawning could thus be described as:$${mathrm{Pr}left(spawningright)}_{ij}={beta }_{0i}+{beta }_{1i}{A}_{ij}+{beta }_{2i}{L}_{ij}+{beta }_{3i}{A}_{ij}{L}_{ij}+{beta }_{4i}{x}_{1i}+{a}_{i}+{varepsilon }_{ij}$$
where βs represent coefficients under estimation, Aij = age of individual j in population i, L = individual length, x1 represent a lake-specific environmental variable (if present in the candidate model, either summer temperature, CPUE or precipitation), ai is the estimated random lake-specific intercept and εij is the random residual variation assumed normally distributed on logit scale. The most supported model was selected based on AICc-values59.FecundityFemale fecundity (i.e., number of eggs per female) was predicted as a function of female length (mm) and two constants based upon published values for brown trout from Norway (F = e log(l)*2.21–6.15)62 multiplied by the probability of spawning (P) at size and age.SurvivalAnnual survival rates (s) for fish age ≥ 1 were based on estimations from catch-curve slopes utilizing the Chapman-Robson function in the FSA-package63. The survival was estimated based on descending catch curves, i.e., where numbers of caught individuals decreased as a function of age in the catch. Based on this slope we can derive an instantaneous mortality rate (Z), and from this the annual survival rate could be estimated from S = e-Z. Due to a restricted number of populations available for survival rates, the survival was estimated across all population. As it is unlikely that S would be constant across all age classes we choose to make age specific survival rates, Sa, where the S1 (survival from age one to age two) was reduced, and S3-5 was slightly increased whereas all other Sa = S. The respective reduction and increase are described more in detail below. Survival rates for age 0–1, S0, was based on a stock-recruitment function (see further description under “Climate scenarios, calibration and population projections”).The projection matrixPopulation projections were derived utilizing an age-structured matrix population model23 in the popbio-package in R64. Changes in the age structure and abundance of brown trout was modelled from Nt+1 = K(E,N,t)Nt or rather:$${left[begin{array}{c}{N}_{1}\ {N}_{2}\ vdots \ vdots \ {N}_{{a}_{max}}end{array}right]}_{t+1}=left[begin{array}{ccccc}{f}_{1}left(L,P,{N}_{t}right){s}_{0}left({E}_{t}right)& {f}_{2}left(L,P,{N}_{t}right){s}_{0}left({E}_{t}right)& cdots & cdots & {f}_{{a}_{max}}left(L,P,{N}_{t}right){s}_{0}left({E}_{t}right)\ {s}_{a}& 0& cdots & cdots & 0\ 0& {s}_{a}& cdots & cdots & 0\ vdots & vdots & vdots & vdots & vdots \ 0& 0& 0& {s}_{a}& 0end{array}right]times {left[begin{array}{c}{N}_{1}\ {N}_{2}\ vdots \ vdots \ {N}_{{a}_{max}}end{array}right]}_{t}$$
where Nt is the abundance of brown trout across all age classes a = 1,…, amax at year t. Census time is chosen so that reproduction occurs at the beginning of each annual season. fa is the fecundity at age a (i.e., the number of offspring produced per individual of age a during a year). More specifically, f varies according to f(L,P,N), where variations in L (length) and P (probability to spawn) in turn is defined by climate variables and the number of individuals N. s is a constant and represent the survival probability of individuals from age a to age a + 1, and amax is the maximum age considered in the model. amax was set to 10 years in the simulations, as was also was the age of the oldest fish in the aged subset of the data (see frequency table in Supplementary information S2). Although varying between systems, the maximum age observed and simulated also corresponds to expected maximum age found in other systems in Norway65. S0 is a function of E, the numbers of eggs laid, where the relationship is determined by a stock-recruitment function.Consequently, K(E,N,t), the Leslie matrix, is a function of N and E. In each time step, the survival of individuals in age class amax is 0, whereas individuals at all other ages spawn and experience mortality as defined above. From the Leslie matrix K, we can infer the population’s long-term rate of increase, λ, from the dominant eigenvector of the matrix23.Climate scenarios, calibration and population projectionsTo explore the population effects of changes in summer air temperature or winter conditions we simulated different 100-years climate-change scenarios for a single lake, which included variations the climate variables in focus. The first scenario represented a status quo setting. Here, annual average summer air temperatures were randomly drawn from a normal distribution with mean and standard deviation from observed summer air temperatures from 1998–2009 in the study area. The second climate scenario randomly assigned temperatures as in scenario one, as well as allowing for more and more fluctuating annual summer temperatures as time progressed. This was done by adding a random variable t (~ N(0,0.03) times the number of the specific year (i.e., 1–100) in the 100-years climate change scenario. The third climate scenario, drew annual summer temperatures as in the first scenario, but included an increase in the average air summer temperature by 0.04 °C each year (i.e., 4 °C in total for the 100-year-scenario which is close to the expected mean increase in regional temperature following the regionally down-scaled RCP8.5 IPCC scenario66). The fourth climate scenario included an average summer temperature increase of 0.02 °C each year (close to the expected average temperature increase following the regionally down-scaled RCP4.5 IPCC scenario66), as well as allowing for more and more fluctuating annual summer temperatures as time progressed (as in scenario two). For all climate scenarios above, annual winter NAO-values was randomly drawn from a uniform distribution between − 1.5 and 1.5.We also simulated a second set of climate change scenarios, where summer temperatures were as described in the four scenarios above, however in all these scenarios we also included a trend of higher winter NAO values (meaning a general trend of warmer winters with more precipitation/snow in the study area, as predicted by the down scaled climate scenarios66). This was done by letting annual NAO-values be drawn from a random normal distribution with mean = 0.5, and standard deviation of 0.5.During the calibration process for the simulations, we altered the age specific survival estimates S1 and S3-5 so that average lambdas for the status quo climate scenario was relatively stable and close to 1 (i.e. no large changes in population size) based on 100 iterations of a 100 year-climate scenario. Specifically, S1 = S*0.6 and S3-5 = S*1.2, which is also assumed to be within the realistic range of survival rates for the specific age classes in the focal populations. S0 was derived from a stock recruitment function, and was thus allowed to vary as a function of density in the population. Specifically, from the total egg number (Et) at year t and the number of one-year olds at year t + 1 (N1,t+1) the stock-recruitment function could be estimated by fitting a Shepherd function67:$${N}_{1,t+1}=frac{a{E}_{t}}{{left(1+b{E}_{t}right)}^{c}}$$
where a = 0.04, b = 0.0000003 and c = 3.5. E is number of eggs deposited during t-1 spawning season, estimated as the total fecundity. The estimated N1,t+1 was used to estimate first-year survival (s0) from:$${s}_{0,t}=mathrm{ln}left({E}_{t-1}right)-mathrm{ln}left({N}_{1,t}right)$$All 100-years scenarios were simulated with 100 iterations to extract the variation in the expected population projections. CPUE in the simulations was included as a dynamic variable in the growth model, recalculated through the matrix projection model for each time step, i.e. year. Length at age, spawning probability and fecundity was predicted for each time step (i.e. pr year) as described above. The spawning probability did however not vary annually according to changes in the environment but was predicted according to the mean values of the environmental variables across all years the climate scenario. However, for climate scenarios with increasing mean temperature over time, the expected spawning probability was a function of the gradual mean temperature increase. Thus, by allowing the spawning probability reaction norm gradually to follow changes in the temperature, as predicted from the spawning model, we allowed the populations to gradually adapt the reaction norm to the respective changes. More

DataWe reviewed PPS reporting rates of AMR in healthy animals and animal food products in China between 2000 and 2019 (Supplementary Text S1). We focused on three common food animal species: chicken, pigs and cattle. Dairy cattle and meat cattle were pooled in this study, in consistency with the categorization adopted in the maps of livestock created by the Food and Agriculture Organization27. The review focused on four common foodborne bacteria: E. coli, non-typhoidal Salmonella, S. aureus and Campylobacter. We recorded resistance rates reported in PPSs, defined as the percentage of isolates tested resistant to an antimicrobial compound. In addition, we extracted the anatomical therapeutic chemical classification codes of the drugs tested, the year of publication, the guidelines used for susceptibility testing, the latitude and longitude of sampling sites, the number of samples collected and the host animals. We recorded sample types for each survey, including live animals, slaughtered animals, animal products and faecal samples. Each sample was taken from one animal or animal product. These sample types were pooled in the current analysis. In total, 10,747 rates of AMR were extracted from 446 surveys (Supplementary Fig. 8), including 318 surveys from China’s National Knowledge Infrastructure (CNKI), the leading Chinese-language academic search engine. All data extracted in the review are available at https://resistancebank.org.Two steps were taken to ensure comparability of the resistance rates extracted from the surveys. First, the panel of drug–bacteria combinations extracted from each survey was that recommended for susceptibility testing by the World Health Organization Advisory Group on Integrated Surveillance of Antimicrobial Resistance40. This resulted in the extraction of 6,295 resistance rates for 76 drug–bacteria combinations. Second, resistance rates were harmonized using a methodology4 accounting for potential variations in the clinical breakpoints used for antimicrobial susceptibility testing (Supplementary Text S1). There are two major families of methods used for susceptibility testing in this dataset: diffusion methods (for example, disc diffusion) and dilution methods (for example, broth dilution). Previous works have shown good agreement between the two approaches in measuring resistance in foodborne bacteria4,46. For each family of methods, variations of breakpoints may result from differences between laboratory guidelines systems (European Committee on Antimicrobial Susceptibility Testing vs Clinical and Laboratory Standards Institute), or from variations over time of clinical breakpoints within a laboratory guidelines system (Clinical and Laboratory Standards Institute or European Committee on Antimicrobial Susceptibility Testing). Here we accounted for both situations using distributions of minimum inhibitory concentrations and inhibition zones obtained from eucast.org (Supplementary Text S1).Trends in AMRWe defined a composite metric of AMR to summarize trends in resistance across multiple drugs and bacterial species. For each survey, we calculated the proportion of antimicrobial compounds with resistance higher than 50% (P50). For each animal–bacteria combination, we assessed the significance of the temporal trends of P50 between 2000 to 2019 using a logistic regression model, weighted by the log10-transformed number of samples in each survey.For each bacteria–drug (antimicrobial class) combination, we estimated prevalence of resistance by calculating a curve of the distribution of resistance rates across all surveys (Fig. 2). The analysis was conducted for surveys published between 2000 and 2009, and between 2010 and 2019, respectively. The distribution was estimated at 100 equally spaced intervals from resistance rates of 0% to 100%, using kernel density estimation. We used the centre of mass of the density distribution to estimate prevalence of resistance. The calculation was conducted for six animal–bacteria combinations. This included E. coli in chicken, pigs and cattle, Salmonella in chicken and pigs, and S. aureus in cattle. The remaining animal–bacteria combinations were excluded due to limited sample size, only represented in 32 out of 446 PPSs. The analysis was restricted to antimicrobial classes represented by at least 10 resistance rates. In addition, we estimated the association between resistance rates and the ease of obtaining antimicrobials from the market, using data from online stores (Supplementary Text S3).Geospatial modellingWe interpolated P50 values from the survey locations to create a map of P50 at a resolution of 10 × 10 km across China. The approach followed a two-step procedure47. In step 1, three ‘child models’ were trained using four-fold spatial cross-validation to quantify the relation between P50 and environmental and anthropogenic covariates (Supplementary Text S2 and Supplementary Table 1). In step 2, the predictions of the child models were stacked using universal kriging (Supplementary Text S2). This approach combined the ability of the child models to capture interactions and non-linear relationships between P50 and environmental and anthropogenic covariates, as well as the ability to account for spatial autocorrelation in the distribution of P50.The outputs of the two-step procedure were a map of P50 (Fig. 3) and a map of uncertainty on the P50 predictions (Supplementary Fig. 9 and Supplementary Text S2). The overall accuracy of the geospatial model was evaluated using the area under the AUC. The contribution of each covariate was evaluated by permuting sequentially all covariates, and calculating the reduction in AUC compared with a full model including all covariates (Supplementary Fig. 4). The administrative boundaries used in all maps were obtained from the Global Administrative Areas database (http://www.gadm.org).Identifying (optimal) locations for future surveys on AMRWe identified the locations of 50 hypothetical new surveys—the rounded average number of surveys conducted per year (54 surveys per year) between 2014 and 2019 in China. The location of each new survey was determined recursively such that it minimized the overall uncertainty levels on the geographical trends in AMR across the country. This process took into account the locations of existing surveys and the location of each additional hypothetical survey. The objective of this approach was to maximize gain in information about AMR given the resource invested in conducting surveys.The map of uncertainty consisted of the variance in the child model predictions Var(PBRT,PLASSO–GLM,PFFNN) (step 1) across 10 Monte Carlo simulations, where PBRT, PLASSO–GLM, and PFFNN were the predictions of P50 using boosted regression trees, logistic regression with LASSO regularization, and feed-forward neural network, and the kriging variance VarK (step 2):Vartotal = Var(PBRT,PLASSO–GLM,PFFNN) + VarKIn this study, the location of hypothetical surveys was solely based on VarK, instead of the sum of both terms. This approach was preferred because including both terms would have required to hypothesize P50 values associated with surveys to be conducted in the future, adding an additional source of uncertainty that cannot be quantified. In any case, the uncertainty attributable to VarK was 4.1 times the Var(PBRT,PLASSO–GLM,PFFNN) (Supplementary Text S2).The allocation of new surveys was based on a map of ‘necessity for additional surveillance’ (NS), defined as:NS = VarK × Wwhere VarK reflects the uncertainty of the spatial interpolation, and W is the log10-transformed population density of humans48, animals27 in total, and in chicken, pigs and cattle, separately, which reflected exposure (Supplementary Fig. 10). Animal population density was calculated here as the sum of population-corrected units of pigs, chicken and cattle, using methods described by Van Boeckel et al.7. We adjusted the values of W such that its density distribution equals that of VarK. Concretely, for each pixel i, we calculated the quantile of Wi on the map of W, and replaced the value by the corresponding value of VarK at the same quantile. VarK and W were both standardized to range [0,1], thus giving each term equal weight in the need for surveillance.Four approaches were used to distribute 50 surveys across China based on the map of NS. The reduction in uncertainty on AMR level associated with each of the four spatial configurations of the hypothetical surveys was evaluated by calculating the reduction in the mean values of NS across 7,857 possible pixels on the map of China.First, we used a ‘greedy’ approach where all possible locations for additional surveys were tested. Concretely, the first hypothetical survey was placed at each of the 7,857 possible pixel locations, and a revised map of NS(+1 survey) was calculated for each of the placements. The survey was eventually placed in the pixel that led to the largest reduction in NS(+1 survey). The map of NS was then revised to account for the reduction in uncertainty in the neighbourhood of the new survey. The process was repeated recursively for the next hypothetical surveys (2nd–50th). This approach, by definition, yields the optimal set of locations to reduce uncertainty, but it also bears a considerable computational burden, because every possible location is tested (Npixels = 7,857) by the geospatial model for each hypothetical survey.The second approach developed was a computational approximation to the greedy approach, hereafter referred to as the ‘overlap approach’. This approach exploits a key feature of the kriging procedure: the decrease of the kriging variance (VarK) with increasing proximity to existing survey locations. Each additional survey reduces the variance of the geospatial model at its own location, but also in its surrounding area (Supplementary Fig. 11). The ‘overlap approach’ selects an optimal set of locations that reflect a compromise between high local NS and distance to other surveys. It iteratively selects new locations based on the highest local NS penalized by the degree of overlap between the hypothetical new surveys and existing surveys (Supplementary Fig. 12). The first survey was placed at the location Xp,Yp with the highest local NS (Supplementary Fig. 12, part 1). Then the value of NS at each pixel location Xi,Yi was recalculated as ({mathrm{NS}}_{{(+1,{mathrm{survey}})}X_i,,Y_i}={mathrm{NS}}_{X_i,,Y_i}times(1-{mathrm{overlap}},{mathrm{area}}/{mathrm{neighborhood}},{mathrm{area}})) (Supplementary Fig. 12, Part 2), where the neighbourhood area was the circular area of decreased kriging variance around a new survey, and its radius was the distance until which NS decreased due to this new survey; the ‘overlap area’ is the shared area of the neighbourhoods of location Xp,Yp and of location Xi,Yi. The radius of the neighbourhood was determined using a sensitivity analysis, optimized by approximate Bayesian computation (sequential Monte Carlo)49 (Supplementary Text S5). The optimal neighbourhood radius was chosen such that it minimizes reduction in NS across all pixels. The procedure (Supplementary Fig. 12, parts 1 and 2) was repeated recursively for the hypothetical surveys (2nd–50th).The third approach tested consisted of distributing surveys equally between provinces to reflect a common approach to disease surveillance based on equal allocation of resources between administrative entities. Twenty-two provinces with the highest human population were assigned two surveys, and the remaining six provinces were assigned one survey per province. The exact location of each survey was randomly selected inside a province. Finally, all approaches were compared with the fourth approach (the random approach) as a ‘null model’, in which the 50 hypothetical surveys were located randomly across the country without any geographic weighting criteria. The reduction in NS associated with the third and fourth approaches, which was compared to the greedy approach and overlap approach, was the average over 50 simulations.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

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COMMENT
26 July 2021

Biodiversity needs every tool in the box: use OECMs

To conserve global biodiversity, countries must forge equitable alliances that support sustainability in traditional pastoral lands, fisheries-management areas, Indigenous territories and more.

Georgina G. Gurney

0
,

Emily S. Darling

1
,

Gabby N. Ahmadia

2
,

Vera N. Agostini

3
,

Natalie C. Ban

4
,

Jessica Blythe

5
,

Joachim Claudet

6
,

Graham Epstein

7
,

Estradivari

8
,

Amber Himes-Cornell

9
,

Harry D. Jonas

10
,

Derek Armitage

11
,

Stuart J. Campbell

12
,

Courtney Cox

13
,

Whitney. R. Friedman

14
,

David Gill

15
,

Peni Lestari

16
,

Sangeeta Mangubhai

17
,

Elizabeth McLeod

18
,

Nyawira A. Muthiga

19
,

Josheena Naggea

20
,

Ravaka Ranaivoson

21
,

Amelia Wenger

22
,

Irfan Yulianto

23
&

Stacy D. Jupiter

24

Georgina G. Gurney

Georgina G. Gurney is a senior research fellow in environmental social science at the Australian Research Council Centre of Excellence for Coral Reef Studies, James Cook University, Townsville, Australia.

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Emily S. Darling

Emily S. Darling is director, Coral Reef Conservation, Wildlife Conservation Society.

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Gabby N. Ahmadia

Gabby N. Ahmadia is director, Marine Conservation Science, Ocean Conservation, World Wildlife Fund.

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Vera N. Agostini

Vera N. Agostini is deputy director, Fisheries and Aquaculture Division, Food and Agriculture Organization of the United Nations.

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Natalie C. Ban

Natalie C. Ban is associate professor in environmental studies at the University of Victoria, Canada.

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Jessica Blythe

Jessica Blythe is assistant professor in environmental sustainability at Brock University, St Catharines, Canada.

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Joachim Claudet

Joachim Claudet is a senior researcher at the National Center for Scientific Research, CRIOBE, France.

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Graham Epstein

Graham Epstein is a postdoctoral research associate at the School of Politics, Security and International Affairs at the University of Central Florida, Orlando, USA.

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Estradivari

Estradivari is a researcher at the Leibniz Center for Tropical Marine Research (ZMT), Germany, and a conservation research manager, World Wildlife Fund Indonesia.

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Amber Himes-Cornell

Amber Himes-Cornell is a fisheries officer, Fisheries and Aquaculture Division, Food and Agricultural Organization of the United Nations.

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Harry D. Jonas

Harry D. Jonas is an international lawyer at Future Law, Kota Kinabalu, Malaysia, and co-chair of the IUCN WCPA Specialist Group on Other Effective Area-based Conservation Measures.

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Derek Armitage

Derek Armitage is a professor in the School of Environment, Resources and Sustainability, University of Waterloo, Canada.

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Stuart J. Campbell

Stuart J. Campbell is senior director, RARE Indonesia.

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Courtney Cox

Courtney Cox is senior director, Rare, Washington DC, USA.

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Whitney. R. Friedman

Whitney R. Friedman is a postdoctoral fellow at the University of California, Santa Barbara, USA.

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David Gill

David Gill is an assistant professor of marine science and conservation at Duke University, Durham, North Carolina, USA.

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Peni Lestari

Peni Lestari is a socioeconomic marine specialist, Wildlife Conservation Society.

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Sangeeta Mangubhai

Sangeeta Mangubhai is director, Fiji Program, Wildlife Conservation Society.

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Elizabeth McLeod

Elizabeth McLeod is global reef lead, The Nature Conservancy, Arlington, Virginia, USA.

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Nyawira A. Muthiga

Nyawira A. Muthiga is director, Marine Conservation Program, Kenya Program, Wildlife Conservation Society.

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Josheena Naggea

Josheena Naggea is a PhD candidate at Stanford University, California, USA, studying conservation in her home country of Mauritius.

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Ravaka Ranaivoson

Ravaka Ranaivoson is marine director, Madagascar Program, Wildlife Conservation Society.

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Amelia Wenger

Amelia Wenger is a research fellow in the School of Earth and environmental sciences at the University of Queensland, Brisbane, Australia, and a conservation scientist at the Wildlife Conservation Society.

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Irfan Yulianto

Irfan Yulianto is a researcher and lecturer at Institut Pertanian Bogor University, Indonesia, and a Senior Manager, Wildlife Conservation Society.

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Stacy D. Jupiter

Stacy D. Jupiter is Melanesia regional director at the Wildlife Conservation Society.

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Customary fishing-rights holders from Totoya Island, Fiji, marking a sacred reef area as a no-fishing zone.Credit: Keith Ellenbogen

Global support is growing for the 30 × 30 movement — a goal to conserve 30% of the planet by 2030. In May, the G7 group of wealthy nations endorsed the commitment to this target that had been made by more than 50 countries in January. It is likely to be the headline goal when parties to the Convention on Biological Diversity (CBD) meet to discuss the latest global conservation agreement in May 2022 in Kunming, China.So where do the sacred forests of Estonia or shipwrecks in North America’s Great Lakes come in? What do these share with managed fishing grounds in Fiji and bighorn-sheep hunting areas in Mexico? All have the potential to be recognized using a conservation policy tool called other effective area-based conservation measures, or OECMs. Together with protected areas — from Malaysia’s Taman Negara National Park to the Cerbère-Banyuls Marine Reserve in southern France — OECMs could help to achieve the 30% target.Devised in 2010 and defined in 2018, the OECM tool is little known outside specialist circles. Less than 1% of the world’s land and freshwater environments and less than 0.1% of marine areas are currently covered under this designation. Meanwhile, biodiversity is in free fall and protected areas alone can’t stem the loss. Both designations are among the international policy instruments being negotiated ahead of the CBD conference.We call on the CBD parties and the conservation community of policymakers, scientists, practitioners and donors to study and use OECMs more, alongside protected areas. This policy tool can advance equitable and effective conservation if CBD parties stay true to the convention’s intent to sustain biodiversity rather than ‘achieve’ area-based targets. But more groundwork must be laid to realize its potential.Improvements are needed in research, policy and practice. Local managers and CBD parties need better ways to assess whether potential OECMs contribute to sustaining biodiversity, so that areas are properly designated. The conservation community needs to develop processes to ensure that OECM recognition strengthens, rather than displaces, existing local governance. And researchers need to articulate the value of OECMs to encourage policymakers to use them.Bigger toolkit Protected areas have expanded rapidly in the past 10 years, and now cover 15.7% of the world’s land and fresh water, and 7.7% of the marine realm. Defined by the CBD as areas designated or regulated and managed for biodiversity conservation, they are an essential conservation approach. But some have failed to be equitable or effective: aligning biodiversity goals with local values, needs and governance can be difficult in some contexts1,2. This conflict can lead to inequities, non-compliance and poor biodiversity outcomes.
Indigenous rights vital to survival
OECMs can have an important and complementary role3. The tool recognizes managed areas that sustain biodiversity, irrespective of their objective. OECM recognition can support Indigenous and local communities in managing their lands and seas — be it for hunting, fishing or other cultural practices — while conserving nature. It opens up new conservation opportunities in landscapes where there is relatively light human usage, such as pastoralism with a low density of livestock. These regions make up nearly 56% of the world’s lands, and contain more Key Biodiversity Areas — sites of global important to biodiversity — than do remaining large wild areas4. So, management approaches that accommodate the ways people use landscapes and seascapes are crucial.Some managed areas do not safeguard biodiversity5. But there is a wealth of evidence suggesting that many do. For example, a study of the Peruvian Amazon found that Indigenous peoples’ territories were, on average, more effective than state-governed protected areas at preventing deforestation6. A review of 61 areas managed under territorial-use rights in fisheries in Chile found positive effects on biodiversity; some had levels of fish biomass and biodiversity that were comparable to those in a protected area that restricts all fishing7. And abandonment of agricultural management systems involving low-intensity farming methods in Europe — such as traditional haymaking in Romania — has been linked repeatedly to biodiversity loss8.Perhaps many of these could be recognized as OECMs (see ‘Conservation potential’). Doing so depends on the consent of the relevant governing bodies, and whether the managed area meets the CBD’s definition and criteria for OECMs, including demonstrated or expected biodiversity outcomes.

EquityOECMs can help to ensure that international conservation targets are legitimate to the many and diverse actors required to turn the tide on biodiversity loss.Too often, the costs of conservation are felt locally while many of the benefits are shared globally — from carbon sequestration to preserving genetic resources. For instance, rainforest conservation, including a protected area, in the Ankeniheny-Zahamena Corridor in Madagascar meant that local farmers of vanilla, cloves and rice bore opportunity costs representing 27–84% of their average annual household income. The protection scheme is intended to cut 10 million tonnes of carbon dioxide emissions over 10 years9.Such inequities can occur when protected areas do not prioritize local values and needs. Although protected areas can have multiple objectives, the widely followed guidance from the International Union for Conservation of Nature (IUCN) advises that nature conservation should retain priority over all other objectives. This can alienate people who manage areas for other reasons. Even in the instance of Indigenous Protected Areas in Australia, which have resulted in an array of social and biodiversity benefits, the IUCN definition can undermine Indigenous Australians’ conceptualization of humans as part of nature, which underpins their governance systems2. This stands in contrast to the Western world view of humans as distinct from nature — a concept that is embedded in the IUCN definition and conservation more generally2,3.
A spatial overview of the global importance of Indigenous lands for conservation
However, OECMs need not have conservation as an objective. This means that they can be used to recognize the contributions of a myriad of actors who manage areas that sustain nature, regardless of why they do so. Indigenous peoples, for instance, manage 37% of the world’s natural lands10 for many reasons, such as maintaining rights, harvesting and cultural identity2,10,11. Recognition of Indigenous territories as OECMs could help to overcome current challenges of insecure rights, insufficient funding and exclusion of these communities from decision-making12. For example, Indonesia has initiated revisions to its conservation laws to accommodate coastal OECMs, which could provide opportunities for Indigenous and local communities to gain legal recognition of their rights to use and manage fisheries.OECMs can thus ensure a more equitable approach to conservation decision-making. They enable the participation of those who govern areas that sustain biodiversity but who are currently not involved in decision-making. For example, fisheries-management organizations have rebuilt some fish stocks, contributing to biodiversity and wider ecosystem health, yet the fisheries and conservation sectors are often divided13. OECMs can foster cooperation between sectors, and encourage the participation of fisheries-management organizations in conservation decision-making.EffectivenessCollectively, alongside protected areas, OECMs can increase the effectiveness of the global conservation system in four key ways.First, they support management that is tailored to its context14, and aligned with local values, governance and traditional knowledge systems. This fosters the local leadership, support and compliance that are key to biodiversity benefits14. For example, in Mo’orea, French Polynesia, protected areas that restricted all fishing did not meet fishers’ needs, leading to non-compliance and relatively little change in the density and biomass of coral-reef fish15. Conversely, a management area in Labrador, Canada, implemented at the behest of crab fishers, maintained the fishery and increased the biomass of fish species such as Atlantic cod (Gadus morhua) and other, non-target species16. This area seems likely to meet the OECM criteria.

Estonia’s sacred groves are protected for their spiritual significance.Credit: Toomas Tuul/FOCUS/Universal Images Group via Getty

Second, OECMs, together with protected areas, can help to ensure a well-connected conservation network in which all elements of biodiversity are represented and in which ecological processes, such as species movements, are sustained. For instance, Kenya’s wildlife conservancies provide geographical bridges between protected areas for the movement of wildlife such as zebras, and have potential to be recognized as OECMs.Third, OECMs can increase the diversity of tools in the global conservation system. This bolsters the system’s resilience to social and biophysical shifts, including climate change14. Redundancy in governance arrangements can help to mitigate risks associated with the current reliance on government-led protected areas, which are vulnerable to shifts in national priorities. For example, in 2017, the Bears Ears National Monument, a protected area in Utah, was downsized by 85% to make way for oil and gas exploration under a former US presidential administration.Fourth, OECMs help to bring conservation outcomes into focus. A key criterion for official designation is demonstrated or expected biodiversity outcomes, such as the restoration of a crucial habitat. This is not the case for protected areas, where a focus on coverage has, in some cases, led to expansion with scant biodiversity gains4.Five steps Key concerns remain about the misuse of OECM recognition. CBD parties might use it to meet commitments without actually tackling biodiversity loss. For example, in 2017, Canada increased the marine area it planned to report almost sixfold, by reclassifying 51 fishery closures as OECMs17. This decision was criticized on the grounds of insufficient evidence that these areas sustain biodiversity. Another concern is that, despite the focus on equity, any attempts to influence local governance could be perceived as a ‘land grab’ or ‘sea grab’ by external actors such as national governments, foreigners or international organizations. For example, the establishment of some privately owned protected areas in southern Chile has been suggested to have involved coercion and intimidation of smallholder farmers.
Area-based conservation in the twenty-first century
The conservation community needs to take the following five steps to overcome these key challenges to using the OECM policy tool.Show that they work. The 2019 IUCN Guidelines for Recognizing and Reporting OECMs provide clear criteria for identifying managed areas that are suitable for a full assessment against the CBD’s definition. However, research is needed on how to meet the crucial criteria of demonstrated or expected in situ conservation of biodiversity. This is challenging and resource-intensive, especially because of the variety of actors involved. Ideas based in Western science might not align with the knowledge systems of all involved.Guidelines should build on existing approaches for evaluation, such as the IUCN Green List for Protected and Conserved Areas and the Indicators of Resilience in Socio-ecological Production Landscapes (SEPLs). They should include recent advances focused on outcomes18 and should be tailored to different types of managed area. To ensure that these are salient, credible and legitimate to those governing OECMs, they should be co-produced by groups such as rights holders, civil-society organizations, government and industry, as well as by academics from various disciplines. This transdisciplinary approach is growing rapidly, with examples ranging from management at the national level (such as New Zealand’s Sustainable Seas National Science Challenge) to the monitoring of coral reefs as social-ecological systems19.

Pastoral lands in Africa are often governed to maintain sustainable grazing.Credit: Steve Pastor

Strengthen existing local governance. Many rights holders have raised concerns that formal recognition of their managed areas for conservation might infringe their rights. For example, few communities in Fiji have had their fisheries-management areas recognized under national conservation laws, because that currently requires the communities to waive their customary rights20.Engaging with global conservation processes might also erode self-determination through the imposition of external world views2,3,12. Although OECMs open the door to recognizing diverse relations between humans and nature, it is crucial that the need for demonstrated or expected biodiversity outcomes does not diminish other priorities and values.OECM recognition must strengthen existing local governance, rather than displace or substantially alter it. This will require guidelines to be informed by principles of procedural equity and tailored to different types of managed area. Their development should draw on existing approaches such as the Australian Indigenous-led Healthy Country Planning and Our Knowledge, Our Way guidelines, which have underpinned engagement with the national carbon sequestration scheme11.Secure funding. Funding for recognizing and reporting OECMs should be made available to ensure costs are not a barrier or burden for under-resourced groups. A prominent role for OECMs in the next CBD agreement will help — this policy guides conservation investments from nations and donors.
Sixty years of tracking conservation progress using the World Database on Protected Areas
Importantly, the diversity of managed areas that OECMs encompass can provide funding opportunities beyond conventional conservation funders, whose resources for protected-area funding are already overstretched. Conservation practitioners should engage private sectors that manage areas that could be recognized as OECMs, and access funding earmarked for other priorities such as health and development. For example, the Watershed Interventions for Systems Health project in Fiji, which aims to reduce waterborne diseases using nature-based solutions, is supported by both conservation and public-health funding.Conservation donors and practitioners should co-design new funding strategies for OECMs with those governing these areas. This will help to ensure that local priorities are supported. For example, Coast Funds, a unique conservation trust fund, was developed by First Nations people in collaboration with conservation practitioners and the forestry industry to support stewardship of the Great Bear Rainforest and Haida Gwaii regions of British Columbia, Canada.Agree on metrics. The record of progress towards the CBD’s area-based target, the World Database on Protected Areas, assumes that all reported protected areas have biodiversity conservation as a main objective. But some CBD parties report areas that have other primary objectives, such as sustainable harvesting20. This leads to inaccurate accounting at the global level, and to misunderstanding of management actually occurring on the ground. Canada, among others, is developing legislation that demarcates protected areas and OECMs. But it is not clear whether all CBD parties will do the same.Policymakers need to agree on targets that are based on outcomes — not just coverage — for both OECMs and protected areas. These might include, for example, changes in the populations of multiple species relative to a reference point. In constructing these targets, the conservation community should be guided by the development and health sectors, which have long used outcome targets. For example, the United Nations Sustainable Development Goal 1.2 aims to reduce at least by half the proportion of people living in multidimensional, regionally-defined poverty by 2030. A common currency of outcomes could alleviate concerns that there is an uneven burden of proof for the OECM and protected-area tools. It could also prevent the misuse of either to meet targets based on area without actually sustaining biodiversity.Include OECMs in other environmental agreements. Addressing the interrelated global challenges of biodiversity loss, climate change and sustainability requires the coordination of policy across sectors. Right now, OECMs appear only in CBD policy. But they could contribute to the mandates of other intergovernmental initiatives. Policymakers should include OECMs alongside protected areas in international agreements such as the Sustainable Development Goals, new global climate agreements being negotiated under the UN convention on climate, and the emerging UN treaty on marine biodiversity in areas beyond national jurisdiction.New targets negotiated at the upcoming CBD meeting will set the global conservation agenda over the next decade. If the steps we outline here are implemented, OECMs could be central to the transformations needed for a sustainable future for the planet.

Nature 595, 646-649 (2021)
doi: https://doi.org/10.1038/d41586-021-02041-4

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Computational sustainability research has been supported by an Expedition in Computing from the US National Science Foundation (NSF; CCF-1522054). eBird has been supported by the Leon Levy Foundation, the Wolf Creek Foundation, and NSF (DBI-1939187). Materials science research has also been supported by the AFOSR Multidisciplinary University Research Initiative (MURI) Program FA9550-18-1-0136, US DOE Award No.DE-SC0020383, and an award from the Toyota Research Institute. More