Ecology

This work formulates a general framework of the WFE Nexus at the national level, which includes all pertinent interactions between water, food, and energy sources and demands. Figure 1 depicts the feedbacks involving resource availability and consumption. The causal loops of the developed model for national-scale assessment are shown in Fig. 2. The model depicted in Fig. 2 proposes reducing consumption to reduce the water crisis to the extent possible. By reducing water use and pollution the environmental water requirement can be reduced, thus alleviating the water crisis. This paper’s objective is sustainable management by reducing per capita water use (in the residential section) and per capita energy use (in the domestic, public, and commercial section). The WFE nexus is modeled as a dynamic system for demand management applied to the stocks of energy, surface water, and groundwater resources to calculate their input and output rates (flows) at the national level while providing for environmental flow requirements (Fig. 3). The national modeling approach is of the lumped type, meaning that inputs and outputs to the stocks of water and energy represent totals over an entire country (in the case study, Iran); therefore, the models does not consider intra-country regional variations. The units of water resources and energy resources are expressed in cubic meters and MWh, respectively.Figure 1Feedbacks between resources and uses in the WFE nexus taking into account environmental considerations.Full size imageFigure 2The causal loops of the model developed for simulating the WFE nexus.Full size imageFigure 3Flow diagram of the WFE Nexus system.Full size imageBalance of water resourcesThe study of water exchanges in a country is based on the law of conservation of matter. The following sections present calculations pertinent to the annual balance of surface and groundwater resources.Surface water resourcesThe national runoff generated in a country’s high-elevation areas (or high terrain) and low-elevation areas (plains) is quantified with the following equations:$${preheight}_{t}=HeightCotimes {Precipitation}_{t}$$
(1)

in which ({preheight}_{t}) = volume of precipitation that falls in high-elevation areas during period t, (HeightCo) = the percentage of total precipitation that falls in high-elevation areas, and ({Precipitation}_{t}) = volume of precipitation during period t.$${preplain}_{t}=PlainCotimes {Precipitation}_{t}$$
(2)

in which ({preplain}_{t}) = volume of precipitation that falls in the plains during period t, and (PlainCo) = the percentage of total precipitation that falls in plains (low elevation areas).$${SInflow}_{t}=HeighSInflowCotimes {preheight}_{t}+PlainSInflowCotimes {preplain}_{t}+{OutCSW}_{t}+{Dr}_{t}$$
(3)

in which ({SInflow}_{t}) = the total volume of surface flows during period t, (HeighSInflowCo) = the runoff coefficient in high-elevation areas, (PlainSInflowCo) = the runoff coefficient in the plains, ({OutCSW}_{t}) = the difference between the volume of surface inflow and outflow through a country’s border during period t; and ({Dr}_{t}) = the flow of groundwater resources to surface water resources (i.e., baseflow) during period t.It is possible to calculate the water use after calculating the annual surface water originating by precipitation. Some of the water use by the agricultural, industrial, and municipal sectors becomes return flows. Equations (4) through (9) show how to calculate the surface water use and the water return flows to the surface water sources.$${DomWD}_{t}={Population}_{t}times PerCapitaWatertimes 365$$
(4)

in which ({DomWD}_{t}) = the volume of water use in the municipal sector during period t, ({Population}_{t}) = the population of the country during period t, and (PerCapitaWater) = per capita drinking water use (cubic meters per person per day).$${IndDomWD}_{t}={DomWD}_{t}+{IndWD}_{t}$$
(5)

in which ({IndDomWD}_{t}) = the volume of water use in the municipal and industrial sectors during period t, and ({IndWD}_{t}) = the volume of water use in the industrial sector during period t.The water use by the agricultural sector accounts for the water footprint of agricultural products, which measures their water use per mass of produce, and adjusting the water use by including water losses and agricultural return flows. A separate sub-agent (AGR agent) is introduced to perform the calculations related to the agricultural sector to simplify the dynamic-system model (main model), and the required outputs (BWAgr, GWAgr) of the dynamic system model are called by the agent in the main model (see Figs. 3 and 4). The BWAgr is given by the expression within parentheses in Eq. (6).Figure 4Agricultural subsystem modeled in the AGR agent (shows how to calculate the blue and gray water footprints of agricultural products).Full size image$${AgrWD}_{t}=left(sum_{iin A}{BW}_{i}times {Product}_{i,t}right)times frac{1}{{E}_{Agr}}+OtherAgrWD$$
(6)

in which ({AgrWD}_{t}) = the volume of agricultural water use during period t, ({BW}_{i}) = blue water footprint of agricultural product i (cubic meters per ton), ({Product}_{i,t}) = the amount of production of agricultural product i during period t (tons), ({E}_{Agr}) = the overall irrigation efficiency, (OtherAgrWD) = the volume of water consumed by agricultural products not included in the set A of agricultural products (in cubic meters). The set A includes those agricultural products with the largest yields and shares of the national food basket.$${AgrReW}_{t}={AgrWD}_{t}times AgrReCo$$
(7)

in which ({AgrReW}_{t}) = the volume of water returned from agricultural water use during the period t, and (AgrReCo) = the coefficient of water returned from agricultural water use.$${IndDomReW}_{t}={IndDomWD}_{t}times IndDomReCo$$
(8)

in which ({IndDomReW}_{t}) = the volume of water returned from industrial and municipal water use during period t, and (IndDomReCo) = the coefficient of water returned from industrial and municipal water uses.$${ReSW}_{t}=IndDomReSWCotimes {IndDomReW}_{t}+AgrReSWCotimes {AgrReW}_{t}$$
(9)

in which ({ReSW}_{t}) = the volume of water returned from water uses to surface water resources during period t, (IndDomReSWCo) = the percentage of water returned from municipal and industrial water use to surface water resources, and (AgrReSWCo) = the percentage of water returned from agricultural water use to surface water resources.Water is applied to produce energy, and Eqs. (10) through (15) perform the related calculations. The ({WEIF}_{t}) variable in Eq. (14) is necessary to account for the volume of water saved as a result of the energy savings. A PR model is introduced to account for such water savings (see Fig. 3).$${Diff}_{t} ={OutputE}_{t}-{OutputE}_{t}^{P}$$
(10)

in which ({Diff}_{t})= the difference between the energy used in the main model during period t and the energy used in period t in the PR model, ({OutputE}_{t}) = the sum of energy uses during period t in the main model (the method of calculating ({OutputE}_{t}) is described in detail in “Energy uses”), and ({OutputE}_{t}^{P}) = the sum of energy uses during period t in the PR model. Equations (11) and (12) account for the case when energy use exceeds energy production under current conditions, in which case energy exports are reduced. This prevents additional energy production to meet excess demand, and, consequently, there would not be increases in water use.$${Diff}_{t} le 0,,,{if,,func}_{t}=0$$
(11)
$${Diff}_{t} >0,,,{ if,,func}_{t}={Diff}_{t}$$
(12)

in which ({ iffunc}_{t}) = the amount of energy saved during period t.Equation (13) calculates the water required to produce energy:$${{TotalWE}_{t}=Coal}_{t}times ENwateruseC+{Gas}_{t}times ENwateruseG+{OilPetroleumP}_{t }times ENwateruseO+{Nuclear}_{t}times ENwateruseN+{Elec}_{t}times ENwateruseE$$
(13)

in which ({TotalWE}_{t}) = the volume of water required to produce the energy demand during period t,({Elec}_{t}) = the amount of electricity production during period t (MWh), and (ENwateruseE) = the water required per unit of energy generated by electricity (cubic meters per MWh), all other terms were previously defined.Equation (14) calculates the water savings:$${WEIF}_{t}=sum_{t=1}^{T}frac{{TotalWE}_{t}}{{OutputE}_{t}^{0}}times {if,,func}_{t}$$
(14)

in which ({WEIF}_{t})= the volume of water saved as a result of the energy saved during period t, T = the number of periods of simulation (T = 5 years).Part of the water used to produce energy from coal, oil, petroleum products, and nuclear fuel is accounted for in the industrial sector water use. For this reason, the volume of water to produce energy calculated with Eq. (15) is reduced by that part of water already accounted for in the industrial water use to avoid double accounting.$${WE}_{t}={Coal}_{t}times ENwateruseC+{Gas}_{t}times ENwateruseG+{OilPetroleumP}_{t }times ENwateruseO+{Nuclear}_{t}times ENwateruseN-INDEtimes {IndWD}_{t}-{WEIF}_{t}$$
(15)

in which ({WE}_{t}) = the volume of water required to produce different types of energy (except those included in the industrial sector) during period t, ({Coal}_{t}) = the energy produced with coal during period t (MWh), (ENwateruseC) = the water required per unit of energy produced with coal (cubic meters per MWh),({Gas}_{t}) = the amount of energy produced with natural gas during period t (MWh), (ENwateruseG) = the water required per unit of energy produced with natural gas (cubic meters per MWh), ({OilPetroleumP}_{t}) = the amount of energy produced with crude oil and other petroleum products during period t (MWh), (ENwateruseO) = the water required per unit of energy produced with crude oil and petroleum products (cubic meters per MWh),({Nuclear}_{t}) = the amount of nuclear energy produced during period t (MWh), (ENwateruseN) = the water required per unit of nuclear energy produced (cubic meters per MWh), and (INDE) = the percentage of industrial water use already accounted for in Eq. (5) (which pertains to water used in the coke coal, oil refineries, and nuclear fuel industries).Part of the discharge of springs enters the surface water sources, and this enters the calculation of the input to the surface water-resources stock in Eq. (16):$${InputSW}_{t}= SInflow+{ReSW}_{t}{+ Fountain}_{t}$$
(16)

in which ({InputSW}_{t}) = the volume of inflow water to surface water sources during period t, and ({Fountain}_{t}) = discharge of springs to surface water sources during period t, other terms previously defined.The output of the surface water resources includes water use and the infiltration of surface water into groundwater, the latter calculated with Eq. (17):$${SInflowInf}_{t}={SInflow}_{t}times SInflowInfCo$$
(17)

in which ({SInflowInf}_{t}) = the infiltration volume of surface water during period t, and (SInflowInfCo) = the infiltration coefficient of surface water.The output of the surface water resources stock is calculated using Eq. (18):$${OutputSW}_{t}={AgrSWDCo}_{t}times {AgrWD}_{t}+{IndSWDCo}_{t}times {IndWD}_{t}+{DomSWDCo}_{t}times {DomWD}_{t}+{mathrm{ WE}}_{t}+{SInflowInf}_{t}-{EvSwSea}_{t}$$
(18)

in which ({OutputSW}_{t}) = the output volume of surface water during period t, ({AgrSWDCo}_{t}) = the percentage of gross agricultural water use from surface water resources during period t, ({IndSWDCo}_{t}) = the percentage of industrial water use from surface water resources during period t, ({DomSWDCo}_{t})= the percentage of gross drinking water consumption from surface water sources during period t, and ({EvSwSea}_{t}) = the total volume of evaporation from surface water plus the discharge of surface water to the sea during period t.The balance of surface water resources is calculated based on Eq. (19):$$SWaterleft(tright)=underset{{t}_{0}}{overset{t}{int }}left[{InputSW}_{t}left(Sright)-{OutputSW}_{t}(S)right]dt+SWater(0)$$
(19)

in which (SWaterleft(tright)) = the stock of surface water resources at time t, (SWater(0)) denotes the stock of surface water at the initial time (t = 0).Groundwater resourcesGroundwater resources gain water from deep infiltration of precipitation in the plains and elevated areas from (1) inflows from outside of the study area, (2) infiltration from surface flows and return waters. Groundwater output factors also include the discharge of groundwater resources (wells, springs, and aqueducts), groundwater flow that moves outside the study area and evaporation. Infiltration of precipitation in the plains and in high terrain into groundwater resources is calculated with Eq. (20):$${Inf}_{t}=PrePInfCotimes {preplain}_{t}+PreHInfCotimes {preheight}_{t}$$
(20)

in which ({Inf}_{t}) = the volume of water entering groundwater sources through infiltration of precipitation during period t, (PrePInfCo) = the infiltration coefficient of precipitation in the plains, and (PreHInfCo) = the infiltration coefficient of rainfall in high terrain.Equation (21) calculates the volume of return water that accrues to groundwater resources:$${ReGW}_{t}=IndDomReGWCotimes {IndDomReW}_{t}+AgrReGWCotimes {AgrReW}_{t}$$
(21)

in which ({ReGW}_{t}) = the volume of water returned from water use that accrues to groundwater resources during period t, (IndDomReGWCo) = the percentage of water returned from municipal and industrial water use accruing to groundwater resources, and (AgrReGWCo) = the percentage of water returned from agricultural water use accruing to groundwater resources.The volume of groundwater input is calculated with Eq. (22):$${InputGW}_{t}={Inf}_{t}+{ReGW}_{t}+{SInflowInf}_{t}+{OutCGw }_{t}$$
(22)

in which ({InputGW}_{t}) = the volume of groundwater input during period t, and ({OutCGw }_{t}) = the difference between the volume of groundwater leaving and that entering the country during period t.The volume of groundwater output is calculated with Eq. (23):$${OutputGW}_{t}={AgrGWDCo}_{t}times {AgrWD}_{t}+IndGWDCotimes {IndWD}_{t}+DomGWDCotimes {DomWD}_{t}+{EvGwDr}_{t}$$
(23)

in which ({OutputGW}_{t}) = the volume of groundwater output during period t, ({AgrGWDCo}_{t}) = the percentage of gross agricultural water use from groundwater resources during period t, IndGWDCo = the percentage of industrial water use from groundwater resources during period t, DomGWDCo = the percentage of municipal water use from groundwater resources during period t, and ({EvGwDr }_{t}) = the total volume of evaporation from groundwater plus the drainage of groundwater resources to surface water resources at time t.Equation (24) calculates the annual balance of groundwater resources:$$GWaterleft(tright)=underset{{t}_{0}}{overset{t}{int }}left[{InputGW}_{t}left(Sright)-{OutputGW}_{t}left(Sright)right]dt+GWater(0)$$
(24)

in which GWater(t) = the groundwater resources stock at time t, (GWater(0)) denotes the stock of groundwater at the initial time (t = 0).Energy usesEnergy uses are calculated with Eqs. (25)–(27). The total national energy use includes the agricultural, industrial, transportation, and exports sectors’ energy demands. The energy uses by these sectors do not change during the implementation of the policy, and, consequently do not change the WFE Nexus in that period; therefore, they are not included in the calculations.$${WDTP}_{t}={DomWD}_{t}times {CEIntensity}_{t}$$
(25)

in which ({WDTP}_{t}) = the energy used in the extraction, transmission, distribution, and treatment of water in the water and wastewater system during period t, and ({CEIntensity}_{t}) = the energy intensity in the extraction, transmission, distribution, and treatment of water in water and wastewater systems during the period t (MWh per cubic meter).$${ResComPubED}_{t}=ResComPubPerCapitatimes {Population}_{t}$$
(26)

in which ({ResComPubED}_{t}) = the energy use by the domestic, commercial, and public sectors during period t, and (ResComPubPerCapita) = the per capita energy consumption by the domestic, commercial, and public sectors (MWh per person per year).$${OutputE}_{t}={ResComPubED}_{t}+{WDTP}_{t}$$
(27)
Environmental water needsThe gray water footprint is defined as the volume of freshwater that is required to assimilate the load of pollutants based on natural background concentrations and existing ambient water quality standards. The estimation of the gray water footprint associated with discharges from agricultural production is based on the load of nitrogen fertilizers, which are pervasive in agriculture. The gray water footprint in terms of nitrogen concentration has been estimated by Mekonnen and Hoekstra24,25, as written in Eq. (28):$${GW}_{t}^{Agr}=sum_{iin A}{GW}_{i}times {Product}_{i,t}$$
(28)

in which ({GW}_{t}^{Agr})= the volume of gray water in the agricultural sector during period t, and ({GW}_{i}) = the volume of gray water associated with the production of one ton of agricultural product i (cubic meters per ton)(.)There are no accurate estimates of the concentrations of pollutants per unit of industrial production, or of the concentration of pollutants in municipal wastewater. Therefore, the conservative dilution factor (DF), which is equal to 1 for untreated returned water from the municipal and industrial sectors, is applied in this work. Equation (29) is a simplified equation of the gray water footprint26. The fraction appearing on the right-hand side of Eq. (29) is equal to the DF.$${GW}_{t}^{IndDom}= frac{{C}_{eff}-{C}_{nat}}{{C}_{max}-{C}_{nat}}times {IndDomReW}_{t}times IndDomReUT$$
(29)

in which ({GW}_{t}^{IndDom}) = the gray water footprint of the municipal and industrial sectors during period t, ({C}_{eff}) = the nitrogen concentration in return water (mg/L), ({C}_{nat}) = the natural concentrations of contaminant in surface water (mg/L), ({C}_{max}) = the maximum allowable concentration contaminant in surface water (mg/L), and (IndDomReUT) = the percentage of untreated returned water from the municipal and industrial sectors.The total gray water footprint is obtained by summing the footprints associated with the municipal/industrial and agricultural sectors:$${TotalGW}_{mathrm{t}}={GW}_{t}^{IndDom}+{GW}_{t}^{Agr}$$
(30)

in which ({TotalGW}_{mathrm{t}}) = the volume of gray water from all sectors during period t.This work considers qualitative and quantitative environmental water needs. Equation (31) is used to calculate the total environmental water need. The Tennant method for calculating the riverine environmental flow requirement (or instream flow) stipulates that, based on the conditions of each basin, between 10 to 30% of the average long-term flow of rivers represents the environmental flow requirement27. The sum of these requirements across all the basins equals the environmental requirement of the entire region or country. Yet, by providing 10 to 30% of the average long-term flow of rivers the riverine ecosystem barely emerges from critical conditions, and is far from optimal ecologic functioning. The total environmental water need is equal to the sum of the environmental flow requirement plus the volume of water needed to dilute the contaminants entering the surface water sources:$${ENV}_{t}={TotalGW}_{t}+Tennant$$
(31)

in which ({ENV}_{t}) = the environmental flow requirement during period t, and Tennant = the environmental flow requirement calculated by the Tennant (1976) method.The policy evaluation indexThe available renewable water is calculated with Eq. (32):$${IN}_{t}={OutCGW }_{t}+ {SInflow }_{t}+{ Inf}_{t}-{EvGwDr}_{t}$$
(32)

in which ({IN}_{t})= the renewable water available before the application of environmental constraints during period t.The volume of manageable water is calculated with Eq. (33):$$REWleft(tright)=underset{{t}_{0}}{overset{t}{int }}left[INleft(tright)-ENVleft(tright)right]dt$$
(33)

in which REW (t) = the (cumulative) manageable and exploitable renewable water in the period t-t0.Equation (34) calculates the total water withdrawals by the agricultural, industrial, municipal, and energy production sectors:$${WDW}_{t}={OutputSW }_{t}+ {OutputGW}_{t}- {cheshmeh}_{t}$$
(34)

in which ({WDW}_{t}) = the sum of the withdrawals by the agricultural, industrial, municipal, and energy production sectors during period t.The cumulative water withdrawals are calculated with Eq. (35):$$withdleft(tright)=underset{{t}_{0}}{overset{t}{int }}WDWleft(tright)dt$$
(35)

in which (withdleft(tright)) = the sum of the withdrawals by the agricultural, industrial, municipal and energy production sectors in the horizon t-t0.Equation (36) calculates the water stress index:$${index}_{{t}_{f}}^{MRW}=frac{withd({t}_{f})}{REWleft({t}_{f}right)}times 100$$
(36)

in which ({index}_{{t}_{f}}^{MRW}) = the renewable water stress index at the end of the study period, and ({t}_{f}) = the period marking the end of the study horizon.Once the water and energy model is developed it must be calibrated with observational data prior to its use in predictions, as shown below. More

Crops and their landrace study areasFood crops whose genetic resources are researched and conserved by CGIAR international agricultural research centres or by the CePaCT of the SPC were included in this study. Crop landrace distributions were modelled and conservation analyses conducted within recognized primary and, for some crops, secondary regions of diversity, where these crops were domesticated and/or have been cultivated for very long periods, and where they are, thus, expected to feature high genetic diversity and adaptation to local environmental and cultural factors (Supplementary Tables 1 and 2)9,13. These regions were identified through literature review (Supplementary Information) and confirmed by crop experts.Occurrence dataOur crop landrace group distribution modelling and conservation gap analysis rely on occurrence data, including coordinates of locations where landraces were previously collected for ex situ conservation and reference sightings. For ex situ conservation records, occurrences marked as landraces were retrieved from two major online databases: the Genesys Plant Genetic Resources portal33 and the World Information and Early Warning System on Plant Genetic Resources for Food and Agriculture (WIEWS) of the Food and Agriculture Organization of the United Nations34. Occurrences were also obtained directly from individual international genebank information systems: AfricaRice, the International Transit Centre and Musa Germplasm Information System of Bioversity International35, CePaCT, International Center for Tropical Agriculture (CIAT), International Maize and Wheat Improvement Center (CIMMYT), International Potato Center (CIP), International Center for Agricultural Research in the Dry Areas (ICARDA), International Crops Research Institute for the Semi-arid Tropics (ICRISAT), International Institute of Tropical Agriculture (IITA) and International Rice Research Institute (IRRI), as well as from the United States Department of Agriculture (USDA) Genetic Resources Information Network (GRIN)–Global36 and the Comisión Nacional para el Conocimiento y Uso de la Biodiversidad (CONABIO)37. Occurrences were compiled from the Global Biodiversity Information Facility (GBIF), with ‘living specimen’ records classified as ex situ conservation records and the remaining serving as reference sightings for use in distribution modelling. Reference occurrences were also drawn from published literature (Supplementary Information). Duplicated observations within or between data sources were eliminated, with a preference to utilize the most original data. Coordinates were corrected or removed when latitude and longitude were equal to zero or inverted, located in water bodies or in the wrong country or had poor resolution ( 10 (ref. 60). The predictors and whether they were selected for the modelling of each landrace group are presented in Supplementary Table 4.We generated a random sample of pseudo-absences as background points in areas that (1) were within the same ecological land units61 as the occurrence points, (2) were deemed potentially suitable according to a support vector machine classifier that uses all occurrences and predictor variables and (3) were farther than 5 km from any occurrence62. The number of pseudo-absences generated per crop group was ten times its number of unique occurrences.MaxEnt models were fitted through five-fold (K = 5) cross-validation with 80% training and 20% testing. For each fold, we calculated the area under the receiving operating characteristic curve (AUC), sensitivity, specificity and Cohen’s kappa as measures of model performance. To create a single prediction that represents the probability of occurrence for the landrace group, we computed the median across K models. Geographic areas in the form of pixels with probability values above the maximum sum of sensitivity and specificity were treated as the final area of predicted presence13.Ex situ conservation status and gapsThree separate but complementary metrics were developed to compare the geographic and environmental diversity in current ex situ conservation collections to the total geographic and environmental variation across the crop landrace group distribution model and, thus, to identify and quantify ex situ conservation gaps13.A connectivity gap score (SCON) was calculated for each 2.5-arc-minute pixel within the distribution model by drawing a triangle63,64 around each pixel using the three closest genebank accession occurrence locations as vertices and then deriving normalized values for the pixel based on distance to the triangle centroid and vertices13. The SCON of a pixel is high—closer to 1 on a scale of 0–1—when its corresponding triangle is large, when the pixel is close to the centroid of the triangle or when the distance to the vertices is large. A high SCON represents a greater probability of the pixel location being a gap in existing ex situ collections.An accessibility gap score (SACC) was calculated for each 2.5-arc-minute pixel in the distribution model by computing travel time from each pixel to its nearest genebank accession occurrence location based both on distance and the speed of travel, defined by a friction surface13,45. Travel time scores were normalized by dividing pixel values by the longest travel time within the distribution model, with the final score ranging from 0 to 1. A high SACC value for a pixel reflects long travel times from existing genebank collection occurrences and, thus, represents a higher probability of the pixel location being a gap in existing ex situ collections.An environmental gap score (SENV) was calculated for each 2.5-arc-minute pixel in the distribution model by conducting a hierarchical clustering analysis using Ward’s method with all the predictor variables from the distribution modelling. The Mahalanobis distance between each pixel and the environmentally closest genebank accession occurrence location was then computed13. Environmental distance scores were normalized between 0 and 1. A high SENV value for a pixel reflects a large distance to areas with similar environments where landraces have previously been collected for genebank conservation and, thus, represents a higher probability of the pixel location being a gap in existing ex situ collections.Spatial ex situ conservation gaps were determined from the conservation gap scores using a cross-validation procedure to derive a threshold for each score. We created synthetic gaps by removing existing genebank occurrences in five randomly chosen circular areas with a 100 km radius within the distribution model. We then tested whether these artificial gaps could be predicted by our gap analysis, identifying the threshold value of each score that would maximize the prediction of these synthetic gaps. Performance for each of the five gap areas was assessed using AUC, sensitivity and specificity. The average cross-area threshold value was calculated for each score to discern pixels with a high likelihood of finding ex situ conservation gaps and that, thus, were higher priority for further field sampling. These were pixels with combined gap scores above the threshold, assigned a value of 1, as opposed to the relatively well-conserved areas below the threshold, which were assigned a value of 0.The three binary conservation gap scores were then mapped in combination, resulting in pixels across the distribution model with gap values ranging from 0 to 3. Pixels with a value of 0 display no connectivity, accessibility or environmental gaps and are considered well represented ex situ. Pixels with a value of 1 indicate a conservation gap in connectivity, accessibility or the environment; we consider these ‘low-confidence’ gaps. Pixels with a value of 2 indicate gaps in two metrics or ‘medium-confidence’ gaps, and values of 3 indicate gaps across all metrics or ‘high-confidence’ gaps. High-confidence gap areas are displayed on crop-conservation-gap maps (Fig. 2b and Supplementary Information) and conservation hotspot maps across crops (Fig. 4 and Extended Data Figs. 5–8).The representation of crop landrace groups in current ex situ conservation collections was calculated based on the final 1–3 value conservation-gap maps. The complement of the proportion of the modelled distribution considered as a potential conservation gap by any single gap score represents the minimum estimate of current representation; the complement of the proportion considered by all three scores as a gap, which is to say high-confidence gap areas, represents the maximum estimate (Supplementary Tables 1 and 2).While distribution modelling and conservation gap analyses were conducted at the crop landrace group level and results are presented in full in the Supplementary Information, for ease of comparison of results across crops, and to avoid bias towards crops with many landrace groups, we also calculated summary results at the crop level. Crops that had been assessed with geographic differentiations, including maize in Africa and Latin America and yams in the New World and the Old World, were also combined. For spatial results, the pixels in crop landrace group models were summed—that is, constituent landrace group models were combined. The minimum and maximum current conservation representation estimations at the crop level were then calculated based on combined spatial models.GBIF occurrence downloadsThe following occurrence downloads from the Global Biodiversity Information Facility (GBIF; https://www.gbif.org/, 2017−2021) were used: 10.15468/dl.rrntfr, 10.15468/dl.2f2v4h, 10.15468/dl.2ywlb7, 10.15468/dl.lnfelh, 10.15468/dl.ryrmfj, 10.15468/dl.8adf61, 10.15468/dl.nff5ys, 10.15468/dl.erxs6e, 10.15468/dl.vbfgho, 10.15468/dl.mjjk3x, 10.15468/dl.uppz1n, 10.15468/dl.938bgm, 10.15468/dl.hr87hm, 10.15468/dl.k1va80, 10.15468/dl.coqpu2, 10.15468/dl.lkoo9u, 10.15468/dl.e998mp, 10.15468/dl.vfbmm7, 10.15468/dl.tnp478, 10.15468/dl.6zxsea, 10.15468/dl.0lray8, 10.15468/dl.5sjgsw, 10.15468/dl.wkju6h, 10.15468/dl.7xzfvc, 10.15468/dl.autlf5, 10.15468/dl.fe2amw, 10.15468/dl.2zblvz, 10.15468/dl.ddplkj, 10.15468/dl.jbzejg, 10.15468/dl.ej5bha, 10.15468/dl.905pxd, 10.15468/dl.pim1vs, 10.15468/dl.vdridc, 10.15468/dl.b43gyv, 10.15468/dl.nnw3z7, 10.15468/dl.bnt9jc, 10.15468/dl.f5x2cg, 10.15468/dl.ub7zbg, 10.15468/dl.sggf2v, 10.15468/dl.ath5ve, 10.15468/dl.23k3ug, 10.15468/dl.cym376, 10.15468/dl.53bwzk, 10.15468/dl.fsad7h and 10.15468/dl.fm6p7z.Reporting SummaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. 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Our model focused on the trophic interactions among CoTS and two groups of coral within a feedback loop with natural and anthropogenic forcing. Our model draws on accepted features of the published dynamics described by Morello et al.37, Condie et al.28 and Condie et al.17, but is a substantial advance in terms of adding spatial structure and coupling with climate variables. Here we have resolved a fine spatiotemporal model structure, developed a novel recruitment formulation for CoTS, integrated tactical management control dynamics and incorporated the impact of broad-scale drivers upon the population dynamics of corals and CoTS at the local scale. Our model is formally fitted to a subset of the CoTS control program data described by Westcott et al.12. We operationalised our model as a tactical and strategic tool to inform how CoTS management strategies interact with alternative disturbance and ecological realisations at the sub-reef scale, the scale at which management operates.DataWe fitted our model to a subset of four reefs from the dataset described by Westcott et al.12, which were consistently and intensively managed (for a map with reef locations see Fig. 2). We restricted our focus to a subset to avoid parametrisation of reef and management site dynamics. Thus, ~39% of site visits were concentrated over the 13 management sites we considered, with a mean of 20.73 ± 5.5 (mean ± standard deviation) visits across the time series relative to a mean visitation rate of 12.23 ± 4.7 (mean ± standard deviation) for the rest of the sites. Each reef in the subset contained two or more management sites where each site was visited at least 18 times. The subset was used because it contained sufficient data for estimating the 11 model parameters for each management site. Across included sites were a range of CoTS densities, coral abundances and disturbance histories12,72,73. Given the intensity with which these sites were managed, they therefore provided us with a valuable opportunity to formally fit the interactions between management intervention, coral abundance and CoTS dynamics in the presence of regional sequential bleaching events.Model spatial structure and ecological componentsSpatially, we considered a circular 300 km region of the Great Barrier Reef centred between Cairns and Cape Tribulation, and resolved at a daily timescale and a sub-reef spatial scale, matching the scale at which observed data were resolved12,19. Reefs were randomly generated as points to capture possible spatial correlation in disturbance impacts between nearby reefs, as well as to allow variability in reef locations. Coral, CoTS and disturbance dynamics within the management sites of each reef were resolved relative to a 1 ha focal region. That is, each management site was captured as a 1 ha area representative of the whole site. In the Pacific, Acanthaster spp. disproportionally target faster-growing corals, predominantly Acropora, Pocillopora and Montipora22. Coral taxa characterised by slow growth rates and massive morphologies, such as Porites, are generally consumed less than expected based on their abundance22 and are thus non-preferred prey. The two modelled coral groups were the fast-growing favoured prey items of CoTS, and the slower-growing non-preferred prey. Processes resolved in the model included reproduction, density dependence, the effect of bleaching and cyclonic disturbances on corals and the impact of manual control (culling) upon CoTS and coral dynamics.CoTS population structureWe used an age-structured approach to model CoTS population dynamics. We defined our age classes to encapsulate plausible size-at-age variation due to plastic growth. This was achieved through linking catch size classes of the management control program19 to age classes through size-age relationships developed from observations spanning multiple environmental realisations, manipulated scenarios and methodologies55,70,74,75. Delayed growth in juvenile CoTS due to deferral of their switch to coral prey or composition of their pre-coral diet, may induce variability in the size-at-age of juveniles52,53. However, the population-level consequences of prolonged juvenile phases are not easily observed nor understood. For example, juveniles are subject to high mortality rates in situ, delayed growth may reduce lifetime fitness and there have been no observations of juveniles during spawning periods that would indicate protracted juvenile phases55,56,57. Consequently, suggests size-at-age is—due to an early life history mortality bottleneck or otherwise—predominantly concordant with growth curves of the literature55,70,74,75 and the size classes we have used here. Age classes comprised annual 0, 1, 2 and 3+ groups, with 3+ being an absorbing class – once there, they stay there. Age-0 ( ; 32.5)). This induced a slope change in the relationship between maximum wind velocity and its radius at a wind velocity of 32.5 m.s−1 (≥ category 3 intensities). However, whilst maximum wind velocity was modelled to determine ({d}_{{{{{{rm{m}}}}}}}), the overall size of the cyclone was uncorrelated with its intensity. The overall size was uniformly sampled from 130 to 460 km diameter which allowed for the potential of complete focal area coverage and for a range of intensity-size relationships to be captured. Given a cyclone footprint of radius ({d}_{0}) (km), wind velocity, (V) (m.s−1), at a distance, (d) (km), was interpolated104 through:$$Vleft(dright)=left{begin{array}{c}{V}_{0}+left({V}_{{{{{{rm{m}}}}}}}-{V}_{0}right){left(frac{sqrt{{d}_{0}}-sqrt{d}}{sqrt{{d}_{0}}-sqrt{{d}_{{{{{{rm{m}}}}}}}}}right)}^{alpha },,dge {d}_{{{{{{rm{m}}}}}}}\ {V}_{{{{{{rm{m}}}}}}}, , d ; < ; {d}_{{{{{{rm{m}}}}}}}end{array}right.$$ (32) The distance from the cyclone centre to the reef perimeter, (D) (km), is calculated through:$$D=sqrt{{left({x}_{{{{{{rm{rf}}}}}}}-{r}_{1}-{x}_{{{{{{rm{cyc}}}}}}}right)}^{2}+{left({x}_{{{rm{rf}}}}-{r}_{1}-{y}_{{{{{{rm{cyc}}}}}}}right)}^{2}}$$ (33) Thus, given a reef strike occurs ((sqrt{{d}_{0}}-sqrt{d}ge 0) required from non-integer (alpha)), the wind velocity experienced at said reef due to the tropical cyclone was calculated as (Vleft(Dright)). Wind velocity was subsequently categorised and damage to reef zone corals calculated as per Supplementary Table 4.We resolved stochasticity in cyclone dynamics in projection scenarios. In projected scenarios cyclone arrivals, locations and intensities were probabilistically sampled and their inflicted damage upon coral communities sampled from damage ranges. Cyclone locations, their footprints, intensity ranges and corresponding damage ranges were sampled from uniform distributions. Cyclone arrivals were sampled from a Poisson distribution and considered in scenarios from 2018 to 2029. Projections were averaged over 80 simulations to capture mean dynamics and bound trajectory uncertainty due to said stochasticity.Our cyclone model was calibrated to parameters sourced from the literature (Supplementary Tables 4-5). This was necessary since our data time series did not encompass a cyclone event and/or impacts upon a reef and cyclone-induced mortality is typically a key coral mortality source30. Consequently, we were unable to validate the impacts of cyclones through formal estimation in our model. However, our endeavours to source parameters from empirical and modelling studies in conjunction with our formulation allowed us to plausibly capture the cumulative outcomes of a cyclone event at discrete locations. Our cyclone model offers a limited complexity approach that is empirically grounded to simply resolve cyclone impacts in local-scale models without the need to be coupled to a regional-scale model.Cyclones, induced thermal stress and tactical managementThe occurrence of cyclone events was modelled to directly interact with both management interventions and thermal stress events. Cyclones were assumed to realistically preclude co-occurring co-located management interventions. This was such that a management site control visit was abandoned if a cyclone preceded or was forecast within five days of a control voyage. The later interaction of cyclones with thermal stress events operated through an induced thermal cooling of sea surface temperatures (SST) at impacted locations.In the case of the overlapping cyclone and thermally induced bleaching events, we first accounted for cyclone impacts. This was because, in addition to physical damage to corals, cyclones have the potential for regional-scale cooling of SST which can reduce coral bleaching43,107. To capture this interaction, we resolved the duration108,109 and amplitude107 of tropical cyclone-induced cooling. We captured this interaction through Degree Heating Weeks (DHW) which is a useful metric for the accumulated thermal stress experienced by corals94.The duration of tropical cyclone-induced cooling was modelled through a temporal-SST response curve consistent with the work of Lloyd and Vecchi108 and Vincent et al.109. Cooling rapidly occurs once a tropical cyclone arrives at a location and decays in an asymptotic manner over a period of ~40–60 days108,109. Temperatures however do not return to pre-cyclone levels and plateau at ~1/4 of the cooling signal amplitude below pre-cyclone levels108,109. We expressed this cooling response curve as it related to bleaching-induced coral mortality through DHWs.We based the average expected DHW cooling signal on the work of Carrigan and Puotinen107. This was achieved through scaling the difference in amplitude of overlapping thermal stress-tropical cyclone events and thermal stress only events—a cooling signal amplitude of ({{{{{{rm{DHW}}}}}}}_{{{{{{rm{Amp}}}}}}} sim 1.5) DHW. Consistent with the model of Carrigan and Puotinen107, we then resolved cooling within the radius of gale-force winds (category 1, 17 m.s−1) to model tropical cyclone-induced cooling. Depending on the size of the tropical cyclone, this meant that an individual cyclone would not necessarily cool all reefs within the model region. However, the culmination of multiple cyclones may have limited bleaching exposure for corals across the region107.We did not treat the cooling consequences of multiple cyclones additively nor the complex interplay of oceanic feedbacks upon cyclone intensity and cooling. Such processes were beyond the scope of our study and model. If multiple cyclones occurred within our model, then the cooling signal timeline was re-initialised at impacted reefs for the last tropical cyclone at said location. Non-impacted reefs maintained the timeline for the decay of the cooling signal originating from their previous tropical cyclone interaction.Once a tropical cyclone impacted a reef, the duration of the induced cooling signal was modelled. Price et al.110 found that cooling decays exponentially which is reflective of the recovery of SST following tropical cyclones as demonstrated by Lloyd and Vecchi108 and Vincent et al.109. We operationalised the exponential functional form in conjunction with the decay timelines of Lloyd and Vecchi108 and Vincent et al.109 and the DHW amplitude of Carrigan and Puotinen107. We modelled the level of cooling ({{{{{{rm{DHW}}}}}}}_{{{{{{rm{cool}}}}}}}) after ({d}_{{{{{{rm{postTC}}}}}}}) days post-cyclone event by:$${{{{{{rm{DHW}}}}}}}_{{{{{{rm{cool}}}}}}}left({d}_{{{{{{rm{postTC}}}}}}}right)=frac{1}{4}{{{{{{rm{DHW}}}}}}}_{{{{{{rm{Amp}}}}}}}+frac{frac{3}{4}{{{{{{rm{DHW}}}}}}}_{{{{{{rm{Amp}}}}}}}}{{e}^{{d}_{{{{{{rm{postTC}}}}}}}/10}}$$ (34) This ensured that once a reef experienced a tropical cyclone event, the cooling signal initialised at ({{{{{{rm{DHW}}}}}}}_{{{{{{rm{Amp}}}}}}}) and decayed to (sim frac{1}{4}{{{{{{rm{DHW}}}}}}}_{{{{{{rm{Amp}}}}}}}) after 40–60 days108,109. The rate of decay was given by the e-folding time (days required for the cooling signal to be reduced by a factor of (e)) which we took to be 10. This is consistent with the results of Price et al.110, Lloyd and Vecchi108 and Vincent et al.109 who found e-folding times ranging from 5 through to 20 days. Thermally induced bleaching mortality of corals was computed after cyclone physical damage and cooling had been accounted for.Formal model fittingWe formally fitted our coral-CoTS model simultaneously to coral cover data, catch-per-unit-effort data and catch numbers obtained from the management control program with dive effort (minutes) treated as an input (visits summarised in Supplementary Table 7)12. Simultaneously fitting CoTS and coral dynamics at concurrent locations was useful here as it allowed for coral cover trajectories to help inform local CoTS abundance (sensu CoTS feeding vs. coral trajectories63,79 and local site fidelity24). Our model also used Long Term Monitoring Program (LTMP) data (based on manta tows and provided by the Australian Institute of Marine Science) which provides an independent index of relative abundance of CoTS. This was such that our model here was developed and parametrised based on an earlier version37,111 which did not use CPUE information but was fitted to the LTMP data on CoTS relative abundance, as well as the corresponding coral cover, to estimate a number of CoTS-coral interaction parameters used in the present model (Supplementary Table 3).Fitting and estimation of our model were achieved through Maximum Likelihood Estimation (MLE). Our objective function was the outcome of combining the negative log-likelihood contributions arising from fitting the model to multiple sets of location-specific data, across a range of environmental and ecological realisations, in conjunction with penalty terms. Specifically, we fitted coral cover (data series ({x}^{{{{{{rm{Coral}}}}}}})) and CoTS CPUEs (data series ({x}^{{{{{{rm{CoTS}}}}}}})) at each management site which contained ({n}_{{{{{{rm{Coral}}}}}}}) and ({n}_{{{{{{rm{CoTS}}}}}}}) data points respectively. This involved fitting parameters that were specific to management sites (e.g. thermal stress - DHW), reefs (e.g. recruitment variability) as well as those that were common amongst reefs (e.g. CoTS consumption rates). A parametrisation that optimised one contribution was unlikely to optimise all contributions and hence we obtained a parametrisation across all reefs and sub-regions. For a modelled catch of (N) (sum of catches across age classes), a catchability coefficient (a constant of proportionality) of ({q}_{{{{{{rm{LL}}}}}}}^{{{{{{rm{prop}}}}}}}), and data standard deviation of ({sigma }_{{{{{{rm{LL}}}}}}}) our likelihood contribution arising from a management site CPUEs was given by:$$-{{log }}{{{{{rm{L}}}}}}left({q}_{{{{{{rm{LL}}}}}}}^{{{{{{rm{prop}}}}}}}N,{{sigma }_{{{{{{rm{LL}}}}}}}}^{2}{{{{{rm{|}}}}}}{x}_{i}^{{{{{{rm{CoTS}}}}}}}right) = {n}_{{{{{{rm{CoTS}}}}}}},{{{{{rm{ln}}}}}}left({sigma }_{{{{{{rm{LL}}}}}}}right)+{sum }_{i=1}^{{n}_{{{{{{rm{CoTS}}}}}}}}frac{{left({{{{{rm{ln}}}}}}left({x}_{i}^{{{{{{rm{CoTS}}}}}}}right)-{{{{{rm{ln}}}}}}left({q}_{{{{{{rm{LL}}}}}}}^{{{{{{rm{prop}}}}}}}{N}_{i}right)right)}^{2}}{2{{sigma }_{{{{{{rm{LL}}}}}}}}^{2}}$$ (35) From which the data series variance and catchability coefficient were computed for the maximum likelihood estimate. The derived variance and the catchability were respectively computed as per:$${sigma }_{{{{{{rm{LL}}}}}}}=sqrt{frac{1}{{n}_{{{{{{rm{CoTS}}}}}}}}{sum }_{i=1}^{{n}_{{{{{{rm{CoTS}}}}}}}}{left({{{{{rm{ln}}}}}}left({x}_{i}^{{{{{{rm{CoTS}}}}}}}right)-{{{{{rm{ln}}}}}}left({q}_{{{{{{rm{LL}}}}}}}^{{{{{{rm{prop}}}}}}}right)right)}^{2}}$$ (36) and$${q}_{{{{{{rm{LL}}}}}}}^{{{{{{rm{prop}}}}}}}=frac{1}{{n}_{{{{{{rm{CoTS}}}}}}}}{sum }_{i=1}^{{n}_{{{{{{rm{CoTS}}}}}}}}left({{{{{rm{ln}}}}}}left({x}_{i}^{{{{{{rm{CoTS}}}}}}}right)-{{{{{rm{ln}}}}}}left({N}_{i}right)right)$$ (37) Similarly, the likelihood contribution arising from fitting to a management site coral cover with standard deviation ({sigma }_{{Coral}}) was described by:$$-{{log }}{{{{{rm{L}}}}}}left(frac{{C}_{y,d}^{{{{{{rm{f}}}}}}}+{C}_{y,d}^{{{{{{rm{s}}}}}}}}{{K}^{{{{{{rm{coral}}}}}}}},{{sigma }_{{{{{{rm{Coral}}}}}}}}^{2}{{{{{rm{|}}}}}}{x}_{i}^{{{{{{rm{Coral}}}}}}}right) = {n}_{{{{{{rm{Coral}}}}}}},{{{{{rm{ln}}}}}}left({sigma }_{{{{{{rm{Coral}}}}}}}right)+{sum }_{i=1}^{{n}_{{{{{{rm{Coral}}}}}}}}frac{{left({ln}left({x}_{i}^{{{{{{rm{Coral}}}}}}}right)-left(frac{{C}_{y,d}^{{{{{{rm{f}}}}}},i}+{C}_{y,d}^{{{{{{rm{s}}}}}},i}}{{K}^{{{{{{rm{coral}}}}}}}}right)right)}^{2}}{2{{sigma }_{{{{{{rm{Coral}}}}}}}}^{2}}$$ (38) Where the standard deviation was given by:$${sigma }_{{{{{{rm{Coral}}}}}}}=sqrt{frac{1}{{n}_{{{{{{rm{Coral}}}}}}}}{sum }_{i=1}^{{n}_{{{{{{rm{Coral}}}}}}}}{left({{{{{rm{ln}}}}}}left({x}_{i}^{{{{{{rm{Coral}}}}}}}right)-{{{{{rm{ln}}}}}}left(frac{{C}_{y,d}^{{{{{{rm{f}}}}}},i}+{C}_{y,d}^{{{{{{rm{s}}}}}},i}}{{K}^{{{{{{rm{coral}}}}}}}}right)right)}^{2}}$$ (39) We computed the negative log-likelihood objective function by summing the contributions from all management sites across considered reefs.Fitting was conducted through the modelling language Automatic Differentiation Model Builder (ADMB) which implements a Quasi-Newton optimisation algorithm for estimation of parameters and provides Hessian based estimation of standard errors112. Penalty terms were added to our likelihood function to integrate a prior understanding of system dynamics and to reduce model variability. Penalty terms encompassed recruitment variability and the magnitude of catches observed in the data.Recruitment was expressed through recruitment deviations, ({r}_{y}), given a standard deviation of ({sigma }_{{{{{{rm{R}}}}}}}) about underlying modelled recruitment (sum of self-recruitment and immigration sources described previously). The recruitment variability negative log-likelihood penalty contribution was given by:$$-{{log }}{{{{{rm{L}}}}}}left(0,{sigma }_{{{{{{rm{R}}}}}}}^{2}{{{{{rm{|}}}}}}{r}^{{{{{{rm{rec}}}}}}}right)={sum }_{y=1}^{{{{{{rm{#Years}}}}}}}{sum }_{{{{{{rm{reef}}}}}}=1}^{{{{{{rm{#Reefs}}}}}}}{r}_{y,{{{{{rm{reef}}}}}}}^{{rec}}/2{sigma }_{{{{{{rm{R}}}}}}}^{2}$$ (40) An additional penalty term for model deviations from the magnitude of observed catches was encompassed. This was such that a constant of proportionality relating modelled catches to observed catches tended to one. For an allowed standard deviation of ({sigma }_{{{{{{rm{CM}}}}}}}), the likelihood function was penalised for deviations from unity proportionality, ({r}^{{{{{{rm{CM}}}}}}}), through:$$-{{log }}{{{{{rm{L}}}}}}left(0,{sigma }_{{{{{{rm{CM}}}}}}}^{2}{{{{{rm{|}}}}}}{r}^{{{{{{rm{CM}}}}}}}right)={sum }_{{{{{{rm{zone}}}}}}=1}^{{{{{{rm{#Zones}}}}}}}{r}_{{{{{{rm{zone}}}}}}}^{{{{{{rm{CM}}}}}}}/2{sigma }_{{{{{{rm{CM}}}}}}}^{2}$$ (41) Model simulations were conducted in ADMB with output analysis and visualisation conducted in MATLAB.Sensitivity to CoTS controlTo test whether our projected scenarios were consistent with the period over which data were collected, we conducted a model-based before and after comparison to the impact of control. Specifically, we used the fitted trajectory for sites, including both the coral data and CoTS control data (voyages and time spent), and compared this to the model-suggested coral trajectories if CoTS control had not taken place. These were modelled over the fitted period (2013–2018) and, unlike the projected scenarios (2019–2029), were variable in terms of the timing of control (amount of time between visits was variable), the amount of time spent at sites (not a consistent number of dive minutes per visit), CoTS dynamics (recruitment was fitted and hence different annually and between reefs), and in the level of thermal stress they experienced (different sites experienced different effective levels and some sites experience back-to-back events).Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

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Study siteSocial hermit crabs (Coenobita compressus) were studied in Osa Peninsula, Costa Rica, at a long-term field site (Osa Conservation’s Piro Biological Station), where the population has been under study since 200817. Experiments were carried out from January to March 2019 at the beach-forest interface (Fig. 1A), an area where ‘fission–fusion’ social groupings30 continuously form and dissolve31 and where free-roaming individuals regularly travel17. All studies were undertaken during daylight hours (06:30–11:30 h) during periods of peak social activity.Figure 1Study site and experimental areas. (A) Satellite view of study site: a section of Piro beach, Osa Peninsula, Costa Rica. Dashed red squares indicate areas where experiments were carried out and schematic versions are shown below in (B) and (C) (Satellite image: created using Google Earth Version 9, https://earth.google.com/). (B) Overhead view of the section of the beach where free-roam experiments were carried out. Arrows denoting left and right correspond to stimulus directions during free-roam experiments. (C) Overhead view of the beach-forest interface where the handled experiments were carried out. Arrows denoting left, right, forest, and ocean correspond to stimulus directions during handled experiments. The solid red box represents the platform on which the artificial beach was created. For (B) and (C), environment is color coded: blue = ocean, yellow = beach sand, dark green = rainforest, light green = open grassy area with sparse trees. Compass in the bottom left of each panel shows cardinal directions.Full size imageWe conducted two separate sets of experiments, both involving a similar stimulus design (below). First, to determine whether free-roaming individuals were biased in their movement decisions by a collective, we performed a set of free-roam experiments (see “Experiment 1: Free-roam”). The free-roam experiments were conducted directly on the beach (Fig. 1B; 8° 23′ 39.5″ N, 83° 20′ 10.2″ W). Second, to determine whether an increase in danger influenced the relative independence versus social bias in individual movement, we performed a set of handled experiments (see “Experiment 2: Handled”). The handled experiments were conducted on a platform (Fig. 1C; 8° 23′ 33.2″ N, 83° 19′ 50.6″ W), which was immediately adjacent to the beach and situated within the range of the crabs’ normal daily movements. All reported compass bearings are relative to magnetic North (0°) unless otherwise specified.Stimulus designAs conspecific ‘stand-ins’, we used N = 60 Nerita scabricosta shells (C. compressus’ preferred shell species23), spanning a natural range of sizes (9–32 mm) within this population (Table S1; Fig. S1). To create a group of these stand-ins that we could manoeuvre as a collective, each shell was affixed using epoxy to one of four strands of clear fishing line, which were each 4 m long. These lines were spaced approximately 30 cm apart on a long wooden dowel (Figs. 2A,B, 3A,B). An equal number of shells (N = 15 shells per line) were distributed randomly along the 2 m of each fishing line furthest from the dowel. To allow the experimenter to manoeuvre the stimuli, without disturbing live crabs’ behaviour, another fishing line (4 m in length) was attached to the top of the dowel. With this line, the entire apparatus could be pulled by the experimenter from a distance, thereby simulating synchronised movement of the entire collective. To control for any influence the apparatus might have on focal individuals (other than that produced by the movement of the shell ‘stand-ins’), the entire apparatus—dowels and fishing lines—was replicated, just without any attached shells, for use as a control (Figs. 2C, 3C).Figure 2Free-roam experiments: stimuli and experimental design. (A) Photograph of a free-roam experiment in progress, with a drone hovering above and one of the authors (CD) pulling the simulated collective (Photo: Jakob Krieger). Schematics of stimuli are shown in B and C, with N = 3 free-roaming crabs also pictured. (B) Experimental stimuli: consisting of N = 60 shells arranged in four lines of fifteen shells each, attached to clear fishing line and fixed to a wooden dowel. (C) Control stimuli: four empty lines of clear fishing line, fixed to a wooden dowel. An experimenter moved the stimuli from a distance, by pulling another clear fishing line along an open strip of sandy beach in the presence of free roaming crabs. Each experiment was video recorded from above by an overhead drone.Full size imageFigure 3Handled experiments: stimuli and experimental design. (A) Photograph of the artificial beach created on a platform adjacent to the natural beach (Photo: Mark Laidre). Photo shows experimental stimulus and an opaque plastic cup in the center, under which a focal crab was placed prior to the start of each experiment. Schematics of stimuli are shown in (B) and (C). (B) Experimental stimuli: consisting of 60 shells arranged in four lines of fifteen, attached to clear fishing line and fixed to a wooden dowel. (C) Control stimuli: four empty lines of clear fishing line, fixed to a wooden dowel. The cup was removed by one experimenter from a distance via an attached clear fishing line on a pulley system; the stimulus was then maneuvered by a second experimenter, also from a distance, via another clear fishing line.Full size imageExperiment 1: Free-roamTo test whether the movement of the collective influenced free-roaming individuals’ travel direction, the stimuli were pulled across the beach at a uniform speed (1 m per min), within the natural range of the walking speed of social hermit crabs17,22,23. Each trial lasted 1 min. A total of N = 80 free-roam trials were conducted, N = 40 experimental (with the full collective, represented by all the shells) and N = 40 controls (with only the raw materials, but no shell collective). For each of the N = 80 trials, the movement of a single free-roaming focal individual was recorded.It is not uncommon to see multiple crabs moving parallel to (or perpendicular to) the shore, since many individuals will often be collectively attracted to eviction sites, injured conspecifics, or food items, with all the attracted individuals travelling in a roughly parallel formation16,17. For each trial in the free-roam experiments, the stimuli were pulled parallel to the shore (Fig. 1B), either to the right (116.1°) or to the left (296.1°). We did not pull the stimuli perpendicular to the shore, given the substantial slope from the forest down to the ocean, which would have confounded any such comparisons. Condition (experimental or control) and stimulus direction (right or left) were selected randomly, with balanced sample sizes (N = 20 for each). To ensure there was a free-roaming focal individual, whose movement we could measure in response to the stimulus, a trial was only carried out when at least one live crab was walking within approximately 30 cm of the stationary stimulus. Then pulling was initiated.To avoid disturbing live individuals by moving through or near the vicinity, we gathered overhead video footage of all experiments using a drone (Phantom advanced model GL300C). Drone video recorded all interactions between the focal individual and the simulated collective while the drone hovered at a height of approximately 2 m above the beach. At this height, there was no disturbance to natural behaviour or movement of the crabs, and the drone remained positioned overhead for at least 1 min prior to the start of a trial. Minor adjustments to position were then made between trials due to drone drift (i.e., slight movement of the drone due to wind).To randomly select focal individuals for video coding, we first split an image of the starting frame of each video file into a 4 × 4 matrix, with N = 16 equally-sized sections, and then used a random number generator to choose one section (repeating this step if no crabs were present in the selected section). Second, we numbered all individuals in the selected section and again used a random number generator to select the individual.To calculate bearings relative to magnetic North for the direction each focal crab moved, we first measured the angle of divergence (°) between the stimulus trajectory and the focal crabs’ trajectory. Focal crab trajectory—a proxy for the overall direction of the crab’s movement—was measured by drawing a straight line from the start-to-end position of that individual (see Fig. S2 and Vid. S1 for further explanation). Stimulus trajectory was measured in the same manner, using the shell closest to the focal at the beginning of the trial. Using Google Maps and the IGIS Map bearing angle calculator, we calculated the bearing of our stimuli (right and left) relative to true North (right: 114°, left: 294°). To determine bearings for our stimuli relative to magnetic North, we then used the Enhanced Magnetic Model (EMM) magnetic field calculator, provided by NOAA, to calculate the relevant declination (− 2.1°) for our coordinates on the dates the experiments were carried out, subtracting this value from true North. Thus, for the free-roam experiments, the bearing of a stimulus moving to the right, relative to magnetic North, was 116.1°, and the bearing of a stimulus moving to the left, relative to magnetic North, was 296.1°. Lastly, bearings for focal crabs’ directions, relative to magnetic North, could then be calculated using the new bearings of the stimuli and the angle of divergence between stimulus and crab trajectories.To gauge the level of interaction that focal individuals had with the collective, we recorded whether or not individuals initiated contact with shells in the experimental condition. An individual was classed as having initiated contact if it climbed onto a shell or touched a shell with its claws (Vid. S2). Additionally, we noted whether individuals were bumped by passing shells. An individual was classed as having been bumped if a moving shell hit it while the individual was withdrawn, stationary, or facing away from the moving shell (Vid. S3).To assess whether drone drift during experiments was a problem, we examined a random sample (N = 20) of the videos, both control (N = 10) and experimental (N = 10). We took N = 40 images from these 20 videos (i.e., two images from each video: one at the start of the 1-min trial and one at the end of the 1-min trial) and used a system wherein we marked the same two distinguishable fixed points on the landscape in each pair of images. We then overlaid the images in each pair, allowing us to see any longitudinal or latitudinal movement as well as any potential rotation of the drone. Nineteen of the N = 20 pairs of images showed virtually identical overlap of the markers, with just one image showing a minor gap between 1 of the 2 landmarks, suggesting slight rotation of the drone. We were therefore confident that drone drift was not an issue in our analyses.All videos were coded by CD. To measure inter-observer reliability for the angle of divergence (°) between stimulus trajectory and focal crabs’ trajectory (see Fig. S2), a random sample of videos (N = 41 total, N = 22 of experimental and N = 19 of control) were also coded by a second observer (MP) who was naïve to the competing hypotheses. There was strong inter-observer reliability in the measurements (F1,39 = 142.8, p  More

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