Philippa Kaur

Protected areas
We determined the structural connectivity of the global network of PAs by quantifying intact continuous pathways (areas largely devoid of high anthropogenic pressures) between PAs. Data on PA location and boundary were obtained from the May 2019 World Database of Protected Areas (WDPA)70. We only considered PAs that had a land area of at least 10 km2. As China removed most of its PAs from the public May 2019 WDPA version, we used the April 2018 WDPA for China only, which contained the full set of Chinese PAs at the time. It is important to note that our statistics may differ from those reported by countries and territories due to methodologies and dataset differences used to measure terrestrial area of a country or territory.
Measure of human pressure
We used the latest global terrestrial human footprint (HFP) maps—a cumulative index of eight variables measuring human pressure on the global environment—to calculate the average human pressure between PAs35. While there are other human pressure maps74,75,76, the HFP is a well-accepted dataset that provided a validation analysis using scored pressures from 3114 × 1 km2 random sample plots. The root mean squared error for the 3114 validation plots was 0.125 on the normalized 0–1 scale, indicating an average error of approximately 13%. The Kappa statistic was 0.737, also indicating high concurrence between the HFP and the validation dataset. The HFP 2013 map uses the following variables: (1) the extent of built human environments, (2) population density, (3) electric infrastructure, (4) crop lands, (5) pasture lands, (6) roads, (7) railways, and (8) navigable waterways.
Navigable waterways such as rivers and lakes are included within HFP as they can act as conduits for people to access nature35. In the latest HFP (2013), rivers and lakes are included based on size and visually identified shipping traffic and shore side settlements. Venter et al. treated the great lakes of North America, Lake Nicaragua, Lake Titicaca, Lake Onega, Lake Peipus, Lake Balkash, Lake Issyk Kul, Lake Victoria, Lake Tanganyika and Lake Malawi, as they did navigable marine coasts (i.e. only considered coasts as navigable for 80 km either direction of signs of a human settlement, which were mapped as a night lights signal with a Digital Number (DN)  > 6 within 4 km of the coast35). Rivers were included if their depth was >2 m and there were night-time lights (DN  > = 6) within 4 km of their banks, or if contiguous with a navigable coast or large inland lake, and then for a distance of 80 km or until stream depth is too shallow for boats. To map rivers and their depth, Venter et al. used the hydrosheds (hydrological data and maps based on shuttle elevation derivatives at multiple scales) dataset on stream discharge, and the following formulae: stream width = 8.1× (discharge[m3/s])0.58; and velocity = 4.0 × (discharge[m3/s])0.6/(width[m]); and cross-sectional area = discharge/velocity; and depth = 1:5× area/width35.
Each human pressure was scaled from 0–10, then weighted within that range according to estimates of their relative levels of human pressure following Sanderson et al.77. The resulting standardized pressures were then summed together to create the HFP maps for all non-Antarctic land areas35.
Within the main manuscript, we defined intact land as any 1 km2 pixel with a HFP value not higher than or equal to 4. Within this threshold, all areas with a HFP score higher than 4 are defined as nonintact. While previous analyses showed that a >4 score is a key threshold above which species extinction risk greatly increases39, we recognized that there is no one true threshold, which impacts all species equally. Some species may require no human pressure to successfully disperse, while others might successfully navigate through more intensively modified landscapes. Therefore, we conducted our analyses for two additional HFP thresholds. The first used a HFP score More

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Global P losses from soils and soil P balances
All continents result in negative P balances (e.g., net P losses from agricultural systems, Table 1; Fig. 2) except Asia, Oceania and Australia, with Asia having a slightly positive but near zero P balance. This is in spite of high to very high chemical fertilizer inputs (with a range of 1.7 to 13 kg ha−1 yr−1 between the different continents, with national values reaching up to 14 and 19 kg ha−1 yr−1 for the European Union 15 old Member States (EU15, member states joining before 2004 mainly in western and northern Europe) and China, respectively). Most negative P balances are indicated for Africa due to very low chemical fertilizer input of 1.7 kg ha−1 yr−1 paired with high losses due to soil erosion of 9.6 kg ha−1 yr−1. South America as well as Central and Eastern Europe (NEU11; which are the new member states joining the EU after 2004 with the exception of Cyprus and Malta) also exhibit high P losses but for different reasons. South America has a very high chemical fertilizer input but also high losses due to soil erosion paired with high P exports due to organic P management (calculated as the sum of manure and residue input minus plant uptake). In contrast, the eastern European Union New Member States (NEU11) have rather low erosional losses but also very low chemical fertilizer input. With the hypothetical assumption of no replenishment due to chemical fertilizer (e.g., due to economic or technical constraints), calculation of soil P balances results in negative balances globally, as well as for all the continents and regions considered (depletion between 4 and 20 kg P ha−1 yr−1; Figs. 3 and 4; Table 1). The latter demonstrates the vulnerability of today’s global land management system and its strong dependency on chemical P fertilizers from non-renewable mineable P deposits.
Table 1 Main phosphorus (P) statistics in kg P ha−1yr−1 for all continents and selected countries.
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Fig. 2: Global average phosphorus (P) losses due to soil erosion in kg ha−1 yr−1.

The chromatic scale represents the P losses estimates, while the gray color indicates the cropland areas that were excluded from the modeling due to data unavailability. Note that classes are not regularly scale ranked but are divided into six classes using the quantile classification method. Only plant available fractions were considered. For the more residual P fractions please refer to Table 1 or Figs. 3 and 4).

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Fig. 3: Global P soil pools and depletion due to erosion.

Arrows indicate fluxes (positive: net input to soils, negative: depletion of soils). *Organic P management = sum of manure and residue input minus plant uptake. Non-plant P = non-plant available P. Inorganic and organic P give plant available fractions. Total soil P: sum of P fractions lost from soil via erosion with relative errors. No/with chemical = P balance with and without chemical fertilizer.

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Fig. 4: Soil P pools and depletion due to erosion in Africa, Europe and North America.

AD = Atmospheric Deposition. CF = Chemical Fertilizer. OM = Organic P management = sum of manure and residue input minus plant uptake. Arrows indicate fluxes (positive: net input to soils, negative: depletion of soils). Non-plant P = non-plant available P. Inorganic and organic P give plant available fractions. Soil Plost: sum of P fractions lost from soil via erosion with relative errors. No/with chemical = P balance with and without chemical fertilizer.

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Our area related calculations result in an average P loss for arable soils, due to erosion by water, of approximately (5.9_{ – 0.79% }^{ + 1.17% }) kg ha−1 yr−1 globally (Fig. 3, Table 1). This is around 60% of the rates given by Smil16 who estimated 10 kg P ha−1 yr−1 from arable fields due to soil erosion by water. Our total P losses due to soil erosion by water from arable soils globally result in 6.3 Tg yr−1 with 1.5 Tg yr−1 for organic and 4.8 Tg yr−1 for inorganic P. With these values the results are at the lower end of the range discussed in the literature (between 1–19 Tg yr−1, Table 2).
Table 2 Global fluxes from soils/arable systems to waters as discussed in recent literature. For comparability, all values were normalized to 1 billion ha of arable land (with original values given in brackets).
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Liu34 in estimating net input to, and output from, cropland systems indicated a net loss of P from the world’s croplands of about 12.8 Tg  yr−1 (calculating input from atmosphere, weathering and chemical fertilizer versus output from organic P management, soil erosion and runoff), which would be, according to their calculations, the same order of magnitude as synthetic fertilizer input (13.8 Tg yr−1 for statistical year 2003/2004). Our calculations result in an approximate net soil loss due to erosion of 6.3 Tg yr−1 with a global average chemical fertilizer input of 9.2 Tg yr−1. The fluxes clearly show the critical dependence on chemical fertilizer globally, with a hypothetical net-average-area related depletion of 10.7 kg ha−1 yr−1 globally without the compensation due to chemical fertilizer, from which a loss of 5.2 kg ha−1 yr−1 stems from organic P management (sum of manure and residue input minus plant uptake) and 5.9 kg ha−1 yr−1 from soil erosion (Table 1). Continental and national erosional P losses are between 40 and 85% of total P losses from agricultural systems with the exception of Europe and Australia (16 and 19%, respectively). Globally, as well as for Africa, South America and Asia, P soil losses due to erosion are higher than losses due to organic P management.
A recent quantification of atmospheric P dust input, based on dust measurements in the Sierra Nevada, concluded that measured dust fluxes are greater than, or equal to, modern erosional outputs and a large fractional contribution relative to bedrock35. However, even though their measured and modeled maximum atmospheric P fluxes were in the same order of magnitude as the atmospheric flux data of Wang et al.36 used in this study (0.1 kg ha−1 yr−1 for North America), erosion rates from sediment trap measurements in their investigated forest ecosystems were considerably lower (maximum of 0.06 kg ha−1 yr−135) than might be expected in arable lands worldwide. As such, we can clearly not agree with the above conclusion.
Mitigation of the soils P status in the long term by decreasing the deficits of the current organic P management seems difficult and rather unlikely in many regions of the world (see discussion of continental balances below). As such, and considering the expected shortage of P supply from industrial fertilizers in the future, the evaluation of P fluxes clearly shows that soil erosion has to be limited to the feasible absolute minimum in the future.
Only a small fraction of the total soil P is plant available, because a large fraction is either bound in, adsorbed to, or made unavailable by occlusion in minerals (apatite and occluded P)37. Our modeling approach follows the approach of Yang et al.37 that builds on existing knowledge of soil P processes and data bases to provide spatially explicit estimates of different forms of naturally occurring soil P on the global scale. Yang et al.37 uses data acquired with the Hedley fractionation method38 which splits soil P into different fractions that are extracted sequentially with successively stronger reagents and which are merged into various inorganic and organic pools. There is great uncertainty to associate these fractions with functional plant uptake39,40 but some recent work aims at quantifying residence times of the different fractions17,41. We present the total P loss as well as the plant-available pool (Figs. 3 and 4, Table 1) as the most labile P which can participate to plant nutrition in a time scale up to months17 (i.e., the so-called labile inorganic P, inorganic P bound to secondary minerals, labile and stable organic P33,37). We contrast these more labile, short lived fractions with fractions considered very stable which would not be plant available at short time scales (i.e., inorganic P associated with minerals such as apatite or occluded P17, corresponding to 52% of total soil P in our estimates at the global scale).
Verification of soil P loss with a comparison to riverine P exports
Verification of our proposed P soil losses might be done indirectly via a comparison to riverine P loads. However, to verify our on-site P soil loss approach with off-site P river export data requires two prerequisites: (i) an estimation of the agricultural erosion and runoff contribution to total P loads in the rivers (as the latter will be the total sum of agricultural, urban and industrial runoff) and (ii) an assumption on sediment delivery rates (e.g., the percent of sediments reaching the rivers from the total eroded sediments) to estimate P loads to rivers from on-site P soil loss. Regarding the first prerequisite, we searched for published river P export data with a separation of the agricultural erosion and runoff contribution from total P loads in the rivers. Regarding the second prerequisite, there is a lack of models describing integrated sediment-delivery to rivers on a continental or global scale, but it has been discussed that sediment delivery ratios generally decrease as drainage area increases, ranging from roughly 30–100% in small catchments (≤0.1 km2) to 2–20% at large spatial scales (e.g., ≥1000 km2)42. For our comparison, we used a range of average sediment delivery rates between 11–30% as used in recent large scale studies from continental to global scale43,44. In doing so, we would like to point out, that even if 70–89% of the P lost from agricultural soils might be re-deposited within catchments, potential threats of P loss from soils are not reduced. Erosion (and thus P loss) occurs predominantly on agricultural soils while re-deposition will mostly occur in depositional hollows, wetlands, riparian zones or buffer strips. Thus, P is lost as a nutrient on food and feed production sites but re-deposited as a potential ecological threat to biodiversity and ecosystem health due to its eutrophication effect in less intensively or unmanaged ecosystems. Last but not least we would like to point out that RUSLE only considers soil displacement due to rill and inter-rill erosion neither considering tillage and gully erosion nor land sliding.
A comparison of calculated potential P export to rivers from our on-site soil P losses is well within the range of published riverine P exports (Table 3). Beusen et al.45 used total suspended sediment measurements from the GEMS-GLORI database to extrapolate spatially distributed sediment rates for the world’s largest rivers with land use, topography, lithology and precipitation as factors in a multiple linear regression approach accounting for soil erosion as well as sediment trapping. The associated nutrient exports for all continents, as well as global assessment were made by calibrating nutrient export to sediment rates with the model Global News45 using established correlations between sediment and nutrient concentrations. In comparing their P export with suspended sediments in rivers45 to our assessments, we underestimated P export globally as well as for all continents, with the exception of Africa (Table 3), which is, however, (i) strongly related to assumed sediment delivery rates and (ii) our RUSLE application only considering rill and inter-rill erosional processes with an unknown contribution of gullies, landslides and tillage erosion.
Table 3 On-site P loss from gross soil erosion (this study), calculated potential riverine loads with sediment delivery ratios between 11–30%43,44 and comparison to global and regional riverine P export studies. All values in kg P ha−1yr−1.
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An analysis of 17 large scale European catchments (250–11,000 km2) quantifying the loss of P to surface waters from an off-site perspective (P flux measurement in waters) resulted in 0.05–1.5 kg ha−1 yr−1 of P loss due to soil erosion and runoff from agricultural lands into streams and rivers (excluding a Greek catchment with a very high P export of 6 kg ha−1 yr−1)46. Recalculating our on-site soil P losses with sediment delivery ratios between 11–30% results in rates for geographic Europe between 0.1–0.4 kg ha−1 yr−1 which are at the lower end of the range of Kronvang, et al.46 (Table 3). Potential P losses as riverine export due to agricultural runoff and erosion of 143 watersheds across the U.S.47 are in the same range as the calculated P loss assessment of our study, while P loads to Lake Erie assessed in a regional study48 seem slightly higher (Table 3). However, no partitioning between agricultural, urban or industrial fluxes was possible from the latter study. An assessment of the Yangtze River Basin (with 1.8 Million km2 near 20% of the whole Chinese territory) gives modeled soil P losses (on-site perspective) between 0–196 kg ha−1 yr−1 demonstrating the huge spatial heterogeneity of on-site soil erosion rates49. The model output was calibrated with total measured nutrient loads in rivers, while the partitioning differed between dissolved point and non-point as well as adsorbed non-point pollution which can be mostly attributed to soil erosion of agricultural fields. The average P loads due to soil erosion from agricultural fields in the Yangtze River Basin compares well with the range assessed in our study for China (2.7 versus a range of 1.4 to 3.7 kg ha−1 yr−1, respectively). The same holds true for assessment comparisons of Africa50 and South America51 (Table 3), even though it should be considered that especially for these latter studies scale differs considerably from our approach.
Regional P losses and balances
Parallel to the distribution pattern and dynamics of global soil erosion by water32, P losses from soils due to water erosion are most dramatic in countries and regions with intensive agriculture and/or extreme climates (e.g., droughts followed by significant rain events or high frequencies of heavy rain storms) due to high erosivity effects52. As such, our calculations result in extremely high P losses due to erosion ( >20 kg ha−1 yr−1) in regions such as eastern China, many regions in Indonesia, parts of east and south-eastern Africa (Ethiopia, Eritrea, Mozambique), Central America and parts of South America (South-Eastern Brazil; Southern Chile, Peru (Fig. 2)). A very high P loss (10 to 20 kg ha−1 yr−1) is estimated for parts of Southern Africa (South Africa, Madagascar, Tanzania) and South America (Bolivia) and a high loss (5–10 kg ha−1 yr−1) for most of India, as well as regions in Southern Africa (Angola, Zambia) and South America (Uruguay) (Fig. 2). Even though the underlying erosion model algorithm does not calculate the net catchment output but rather the on-site displacement of soil sediments which might then be re-located to other parts of the fields or even buried at depositional places, the considered on-site field management will clearly be confronted with substantial P losses due to soil erosion by water. Only considering agronomic P inputs and outputs without including P losses due to erosion by MacDonald et al.22 resulted in a very different global P pattern: most widespread large deficits were in South America (North-Eastern countries, e.g., Argentina and Paraguay), the northern United States and Eastern Europe while the largest surpluses covered most of East Asia, Western and Southern Europe, the coastal United States, South-Eastern Brazil and Uruguay.
With average soil depletion due to erosion of 9.6 kg ha−1 yr−1 in Africa, the overall P balance is already negative by 9.7 kg ha−1 yr−1 today (Table 1, Fig. 4). As the average P depletion in Africa due to negative fluxes in organic P management equals the input fluxes from the atmosphere plus chemical fertilizer, African farmers could decrease P losses to near zero with effective soil erosion mitigation. Even though the system’s P depletion due to organic P management is relatively low in Africa (−2.2 kg ha−1 yr−1) compared to a global average (−5.2 kg ha−1 yr−1), the high overall P losses are unlikely to be covered neither from a mitigated and more sustainable organic P management nor from increased chemical fertilizer input. P fluxes due to organic P management are calculated here as the sum of manure and residue input minus plant uptake (which results in biomass export in arable systems with the exception of residues left on the field). The overall sum of plant uptake is likely to increase with increased need for food and feed parallel to a predicted population and livestock growth in Africa in the future. Many soils in sub-Saharan Africa have already been characterized as deficient for levels of plant-available P for the last decades53. Manure and residue input is simultaneously in demand in Africa today (shortage of biomass in general, low animal production and even if there is manure available, there are no means to transport it to where it is needed), which results in the recommendations of an integrated farm management with combinations of organic and inorganic fertilizers54,55,56. With the inorganic P fertilizers becoming increasingly scarce, the depletion due to organic P management can be expected to increase in Africa in the future. Simultaneously, today’s prices for chemical fertilizer can already be 2–6 times more expensive for a farmer in Africa than in Europe due to higher transport and storage costs3, even though Africa itself has the highest geological P deposits in the world (according to today’s estimates 80% of the global geological P deposits are located in Morocco and the Western Sarah8). As such, and if the political situation does not change dramatically (e.g., that the P supplies are marketed within Africa instead of being exported to US, Europe and China), the only realistic means of reducing P depletion of African soils today, and in the future, is to drastically reduce soil erosion.
We recognize that the values calculated in Table 1 and Fig. 4 are gross estimates over large scales and that spatial context and scale, especially on the African continent is important. P deficiency is a country, district, farm and soil specific issue in Africa, for example parts of east Africa and the Sahel have substantial deficiencies57. In sub Saharan Africa ~40% of soils are considered to have low nutrient reserves ( More

Regionally-downscaled climate change projections
We used a management strategy evaluation (MSE) applied to ensemble projections of a climate-enhanced multispecies stock assessment within the integrated modeling framework of the Alaska Climate Change Integrated Modeling project (ACLIM)19. For this, six high resolution downscaled projections of oceanographic and lower trophic level conditions in the Bering Sea (using the Regional Ocean Modeling System49,50) were coupled to the BESTNPZ nutrient-phytoplankton-zooplankton model51; we refer to this model complex throughout this paper as the Bering10K ROMSNPZ, or just ROMSNPZ, model. Boundary conditions were driven by three global general circulation models (GFDL-ESM2M52, CESM153, and MIROC-ESM54) projected (2006–2099) under the high-baseline emission scenario Representative Concentration Pathway 8.5 (RCP 8.5) and midrange global carbon mitigation (RCP 4.5; note, that for CESM under RCP 4.5, projections from 2080–2100 were unavailable so conditions from 2080–2099 were held constant at 2080 conditions for that scenario only) future scenarios from the Coupled Model Intercomparison Project phase 5 (CMIP5)29,55. Hermann et al.30,56 also report on downscaled hindcasts of oceanographic and lower trophic conditions in the EBS from 1970–2012 (see refs. 30,56,57 for detailed descriptions of model evaluation and performance). For each downscaled model simulation, we replicated the National Marine Fisheries Service Alaska Fisheries Science Center annual summer bottom-trawl survey in time and space in the ROMSNPZ model (using historical mean survey date at each latitude and longitude of each gridded survey station) to derive estimates of sea surface and bottom temperatures (Fig. 1). We additionally used a polygon mask of the survey area to estimate the average zooplankton abundance in the system during spring, summer, winter, and fall months. These indices were derived for each climate projection scenario, as well as a persistence scenario where conditions were held constant at the average of those for 2006–2017 from a hindcast simulation. All index projections were bias corrected to the 2006–2017 hindcast period using the delta method assuming unequal variance in the GCM projections and hindcast58 such that:

$$T_{mathop {{{mathrm{fut}}}}limits^prime ,y} = bar T_{{mathrm{hind}},overrightarrow {scriptstyle{mathrm{ref}}} } + frac{{sigma _{{mathrm{hind}},overrightarrow {scriptstyle{mathrm{ref}}} }}}{{sigma _{{mathrm{fut}}, overrightarrow{scriptstyle{mathrm{ ref}}} }}}left( {T_{{mathrm{fut}},y} – bar T_{{mathrm{fut}},overrightarrow {scriptstyle{mathrm{ref}}} }} right)$$
(1)

where (T_{mathop {{{mathrm{fut}}}}limits^prime ,y}) is the bias-corrected projected timeseries, (T_{{mathrm{fut}},y}) is the raw projected timeseries, (bar T_{{mathrm{hind}},overrightarrow {scriptstyle{mathrm{ref}}} }) is the mean of the hindcast during the reference years (overrightarrow {{mathrm{ref}}}) (2006–2017), (bar T_{{mathrm{fut}},overrightarrow {scriptstyle{mathrm{ref}}} }) is the mean of the raw projected timeseries during the reference years (overrightarrow {{mathrm{ref}}}), (sigma _{{mathrm{hind}},overrightarrow {scriptstyle{mathrm{ref}}} }) is the standard deviation of the hindcast during the reference years (overrightarrow {{mathrm{ref}}}), (sigma _{{mathrm{fut}},overrightarrow {scriptstyle{mathrm{ref}}} }) is the standard deviation of the raw projection timeseries during the reference years (overrightarrow {{mathrm{ref}}}).
Climate-enhanced multispecies stock assessment model
Bias-corrected indices were then used as covariates in the climate-enhanced multispecies stock assessment model for the Bering Sea (hereafter CEATTLE)59 to evaluate the performance of alternative management approaches on future fish biomass and catch. CEATTLE is a climate-enhanced multispecies statistical age-structured assessment model with parameters for growth that are functions of temperature (i.e., temperature-specific average weight-at-age) and predation that are functions of temperature (via a bioenergetics-based predation sub-model)59,60,61. Since 2016, the model has been used operationally in the Bering sea as a supplement to the annual BSAI pollock stock assessment61. Various configurations of CEATTLE are possible; for this study we chose one where temperature-specific predator and prey interactions influenced natural mortality, temperature influenced weight-at-age, and the spawner-recruit relationship was a function of physical and biological future conditions as well as random variability (i.e., a climate-informed multispecies model). We fit the model using penalized maximum likelihood to survey biomass, diet, and fishery harvest data for three groundfish species pollock, Pacific cod, and arrowtooth flounder from the EBS in the EBS over the period 1979–2017. We also used the Bering10K ROMSNPZ model to produce detailed hindcasts of temperature for the period 1970–2017. We used hindcast-extracted timeseries from the ROMSNPZ model and CEATTLE model estimates of recruitment ((R_{i,y,l})) and spawning biomass ((B_{i,y – 1})) in hindcast year y for each species i to fit a climate-enhanced logistic recruitment per spawner model36, such that:

$$ln left( {hat R_{i,y}} right) = alpha _i – beta _{0,i}B_{i,y – 1} + ln left( {B_{i,y – 1}} right) + {{{bf{B}}}}_i{{{bf{X}}}} + varepsilon _{i,y}$$
(2)

where ({{{bf{B}}}}_i{{{bf{X}}_l}}) is the summed product of each covariate parameter (beta _{ij}) and the corresponding environmental covariate (X_{j,y}) for each bias-corrected environmental index (j = ( {1,2…n_j} )). We selected indices representative of ecological conditions important for groundfish recruitment in the Bering sea39; spring and fall large zooplankton abundances, survey replicated bottom temperature, and extent of the residual cold pool of extremely dense and cold sea water that persists across the EBS shelf following spring sea ice retreat. We assumed normally distributed (in log space) residual errors for each species ((varepsilon _{i,y} sim Nleft( {0,sigma _i^2} right))). The CEATTLE model was then projected forward where ROMSNPZ indices from individual projections drove growth, predation, and recruitment in each future simulation year36,62.
Evaluation of harvest management approaches
Previous authors have defined EM (i.e., the incorporation of ecosystem information into marine resource management) as a continuum between two paradigms of management and focus18. On one end is within-sector single-species management that considers ecosystem information (EAFM) and on the other is cross-sectoral whole of ecosystem management (i.e., EBM). EBFM is intermediate between these and is defined by quantitative incorporation of ecosystem interactions into assessment models and target setting (EBFM). Most fisheries management in the Bering Sea can be characterized as EBFM or EAFM, with increasing trends toward cross-sectoral coordination at the scale of EBM. Here we focus on one aspect on this scale of potential management options, operational EBFM and EAFM as captured through the CEATTLE multispecies stock assessment model and harvest policies decisions made annually under the constraint of the 2 MT cap (modeled via the ATTACH model).
MSE is a process of “assessing the consequences of a range of management strategies or options and presenting the results in a way which lays bare the tradeoffs in performance across a range of management objectives”63. MSE has been frequently used to evaluate alternative management strategies based on single-species estimation methods64. It is increasingly used to evaluate ecosystem management performance, although these evaluations are far less commonplace due to the complexity of modeling and assessing the performance of ecosystem level metrics64. Importantly, MSE “does not seek to proscribe an optimal strategy or decision”63, rather it aims to describe the uncertainty and tradeoffs inherent in alternative strategies and scenarios. In this case, through a series of workshops, we worked with managers and stakeholders to identify priority scenarios and outputs19. From this, risk, sensitivity, and uncertainty under contrasting climate scenarios were requested outputs of the analysis, as was the performance of current climate-naive EBFM policies.
A key component of MSE is identifying and quantifying uncertainty (i.e., process, observation, estimation, model, and implementation error) and representing it using an operating model. In the case of this MSE, the focus was on process error uncertainty due to variation in recruitment about the fitted stock-recruitment relationship, one major source of model error in the form of alternative climate scenarios, and implementation error. The MSE does not account for estimation error (uncertainty in the parameters of the operating model) nor observation error. This is because the estimates of recruitment and spawning biomass from CEATTLE for the BSAI are very precise (see Fig. 10 in ref. 60) and the estimation and operating models are therefore very similar. Thus, CEATTLE is the operating model for this MSE and implicitly the estimation method. In this approach we assume that while allowing for observation error would have increased overall error, the effect would have been minor compared to the investigated uncertainties. Future analyses using a full MSE (i.e., separate operating and estimation models) could evaluate the effect of observation error, but perhaps more importantly, the potential for model error, whereby the population dynamics model (on which the estimation method is based) differs from that of the operating model such that the estimates on which management decisions are made are biased relative to the true values in the operating model.
Given this we summarized the relative change in catch and biomass for the three species in the model under the following fishing scenarios (Fig. 1): (a) projections without harvest ((F_{i,y} = 0)) in each year y of scenario l for each species i, (b) projections under target harvest rate (Supplementary Fig. 7 left) and with a sloping harvest control rule (HCR) (Supplementary Fig. 7 right), (c) as in 2 but with the constraint of a 2 MT cap applied dynamically to the three focal species only.
Under the North Pacific Fishery Management Council (NPFMC) constraint of the 2 MT cap on cumulative total annual catch, realized harvest (i.e., catch) and specification of individual species harvest limits known as Total Allowable Catch (TAC; metric tons) are a function of the acceptable biological catch (ABC) for the given species, as well as ABC of other valuable species in the aggregate complex19,34 (https://github.com/amandafaig/catchfunction). TAC must be set at or below ABC for each species, therefore TAC of individual species are traded-off with one another to avoid exceeding the 2 MT cap. From 1981 to 1983, the TAC of pollock was reduced significantly below the ABC and in 1984 the 2 MT cap became part of the BSAI fishery management plan21,34,65. Pacific cod regulations have changed markedly over recent decades and it was only in the 1990s that in many years the catch and TAC approached its ABC. Thus, we used the socioeconomic ATTACH model (the R package ATTACHv1.6.0 is available with permission at https://github.com/amandafaig/catchfunction34) to model realized catch in each simulation year as a function of CEATTLE assessment estimates of ABC (tons) for pollock, Pacific cod, and arrowtooth flounder under future projections (2018–2100). This entailed three steps for each future simulation year (y):
1.
project the population forward from (y – 1) to (y) using estimated parameters from the multispecies mode of the CEATTLE model fit to data from 1979 to 2017 and recruitment based on biomass in simulation year (y) and future environmental covariates from the ROMSNPZ model downscaled projections (see “Methods” above) to determine ({mathrm{ABC}}_{i,y,l}) for each species (i) under each scenario (l) given the sloping harvest control rule for pollock, Pacific cod, and arrowtooth flounder in each simulation year (y);

2.
use ({mathrm{ABC}}_{i,y,l}) of each species from step 1 as inputs to the ATTACH model in order to determine the North Pacific Marine Fishery Council Total Allowable Catch (TACi,y,l) for the given simulation l year y;

3.
use TACi,y,l from step 2 to estimate catch (tons) in the simulation year (Fig. 1); remove catch from the population and advance the simulation forward 1 year.

Determine the annual ABC
We used end-of-century projections (2095–2099) to derive a maximum sustainable yield (MSY) proxy for future harvest recommendations (ABCi,y,l) for each scenario l. To replicate current management, we used a climate-specific harvest control rule that uses climate-naive unfished and target spawning biomass reference points ((B_{0,i}) and (B_{{mathrm{target},i}}), respectively) and corresponding harvest rates ((F_{i,y} = 0) and (F_{i,y} = F_{{mathrm{target}}}) and (B_{i,y,l}) in each simulation l year (y) for each species (i)

$${mathrm{ABC}}_{i,y,l} = mathop {sum }limits_a^{A_i} left( {frac{{S_{i,a}F_{{mathrm{ABC}},i,y,l}}}{{Z_{i,a,y,l}}}left( {1 – e^{ – Z_{i,a,y,l}}} right)N_{i,a,y,l}W_{i,a,y,l}} right)$$
(3)

where (W_{i,a,y,l}), (N_{i,a,y}), and (Z_{i,a,y,l}) is the climate-simulation specific annual weight, number, and mortality (i.e., influenced through temperature effects on recruitment, predation, and growth) at age (a) for (A_i) ages in the model, and (S_{i,a}) is the average fishery age selectivity from the estimation period 1979–201759,60. (F_{{mathrm{ABC}},i,y,l}) and was determined in each simulation timestep using an iterative approach66 whereby we: (i) first determined average (B_{0,i}) values in years 2095–2099 by projecting the model forward without harvest (i.e., (F_{i,y} = 0)) for each species under the persistence scenario. We then (ii) iteratively solved for the harvest rate that results in an average spawning biomass ((B_{i,y})) during 2095–2099, that is, 40% of (B_{0,i}) (i.e., (F_{{mathrm{target}},i})) for pollock and Pacific cod simultaneously, with arrowtooth flounder (F_{i,y}) set to the historical average (as historical F for arrowtooth flounder ≪(F_{40% })); once (F_{{mathrm{target}},i}) for pollock and Pacific cod were found, we then iteratively solved for (F_{{mathrm{target}},i}) for arrowtooth flounder (Supplementary Fig. 7 left panel)59,60. Last, (iii) to derive a climate-informed ({mathrm{ABC}}_{i,y,l}) in each simulation year, the North Pacific Marine Fisheries Council (hereafter, “Council”) Tier 3 sloping harvest control rule with an ecosystem cutoff at (B_{20% }) was applied to adjust (F_{{mathrm{ABC}},i,y,l}) lower than (F_{{mathrm{target}},i}) if the simulation specific (climate-informed) (B_{i,y,l}) was lower than 40% of the climate-naive (B_{0,i}) at the start of a given year or set to 0 if (B_{i,y,l} , More

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Affiliations

Department of Biology, University of Miami, Coral Gables, FL, USA
K. J. Feeley, C. Bravo-Avila, B. Fadrique & T. M. Perez

Fairchild Tropical Botanic Garden, Coral Gables, FL, USA
K. J. Feeley, C. Bravo-Avila & T. M. Perez

Universidad Nacional de Colombia Sede Medellín, Medellín, Colombia
D. Zuleta

Forest Global Earth Observatory, Smithsonian Tropical Research Institute, Washington DC, DC, USA
D. Zuleta

Authors
K. J. Feeley

C. Bravo-Avila

B. Fadrique

T. M. Perez

D. Zuleta

Corresponding author
Correspondence to K. J. Feeley. More

NPFWs are generally considered to have negative effects on the fig tree–fig pollinator mutualism according to theoretical studies2,4,6. Many empirical and experimental studies have justified this conclusion. Some studies show that NPFWs have a negative effect and found a negative correlation between the offspring number of NPFWs and the offspring number of pollinator and seeds9,13,14. For instance, Patel19 found no correlation between the offspring number of pollinator and NPFWs in their survey , and Peng et al.20 recorded a positive correlation between the offspring number of NPFWs and pollinator in figs of F. hispida . Our capacity to detect the real cost of development of different types of NPFWs and their exact effect on the mutualism is limited by many interaction factors13 and non-experimental empirical studies can underestimate or fail to detect the impact of NPFWs on this obligate mutualism when overall consideration was taken with different studies of pollinator-NPFW interactions in consideration18.
For early-ovipositing gallers, a remarkable feature of those species is that their galls usually fill the space of B-phase syconium cavities and can greatly hinder pollinator movement and oviposition, thus result in their specific consequences on the mutualism24. For example, Conchou et al.13 studied Ficicola spp. which is a genus of early-ovipositing large-sized gallers hosted by F. guianensis and found a substantial negative impact of this genus on the production of both pollinators and seeds. Here, we present another case that studies the effect of an early gall inducer.
Our study has explored the effect of early-ovipositing gallers on the growth of figs (fig size of matured figs) for the first time. In our study, it turns out that early-ovipositing gallers can greatly depress the growth of the figs. The mechanical injury caused by the insertion of the ovipositor of S. testacea to the tiny figs may be responsible for the depression of the growth of the Fig25. Another possible reason is the gall-inducing process26. Jansen-González26 et al. studied the gall-inducing process and larval feeding strategy of pollinating and non-pollinating fig wasp species associated with F. citrifolia and found that the way non-pollinating galler induces gall is quite different from pollinators and they exploit plant resources more aggressively26. With the involvement of the pollinators, the growth of figs improved greatly. The existence of both seeds and larvae of the pollinator, both of which is essential to the fitness of fig trees, may contribute to the improvement of the growth of the figs.
The variation trend of development ratio is consistent with fig volume, which can be explained by the saction mechanism applying to uncooperative cheaters27,28,29,30,31. Jander et al.27,28,29 found that the Ficus tree can sanction the pollen-free pollinators through decreasing their offspring development ratio and increasing the abortion rate of the unpollinated figs. Almost all the non-pollinating figs wasp species lay their eggs in the female flowers but not spread pollen to the fig, which is analogous to the cheaters of pollinators. It makes sense that the same sanction mechanism works on the non-pollinating fig wasps. When only S. testacea oviposit in the fig, most of the figs aborted, and the fig tree invests little nutrition for the remaining figs that survived. With the increase of oviposition and pollination by pollinators (2Cf + St treatments versus St treatment), and the decrease of mechanical injury by NPFWs (2Cf treatments versus St treatment), fig trees invest more nutrition to those figs and the development ratio of the galls increases. Jander et al.28 argued that sanctions can be modular or individual. In a modular sanction, all the offspring produced in the fruit is punished due to involvement of non-cooperative individuals. Our experiment can be a good test of this hypothesis as we can investigate accurately the development of the pollinator and the cheater (S. testacea) respectively. By comparing the theoretical value and the actual number of galls for 2Cf + St treatment, we can explore the source that leads to the decrease of the overall development ratio in the 2Cf + St treatment compared with the 2Cf treatments, thus determine if the sanction works at fig level (modular) or not. We found there is no significant difference between theoretical value and the actual number for 2Cf + St treatment, which suggests that the development ratio of the two species is independent of each other, so the lower development ratio of 2Cf + St treatment compared with 2Cf treatment was caused mainly by the low development ratio of S. testacea. For 2Cf + St treatment, the development ratio of pollinator didn’t decrease, and being pollinated didn’t change the development ratio of S. testacea, which means pollination didn’t increase the nutrition provided to galls of. S. testacea. Our results show that the sanction is not at a fig level, which is inconsistent with the sanction to the cheaters in pollinators (pollen-free pollinators)28,30. Perhaps the sanction mechanism is related to the identification of the cheaters. When the host can identify the cheaters, the sanction can work at individual level where they may only sanction the offspring of cheaters, such as S. testacea in this study. If the host can’t identify the cheaters, the sanction may work at fig level (modularly) through sanctioning all the wasps reproduced in the fig, such as the cheaters in the pollinators.
The production of seeds is influenced greatly by the wasp S. testacea. A former study shows that galls produced by early-ovipositing gallers often fill B-phase syconium cavities and hinder pollinator movement and oviposition24, which can explain the decline of the seed production. In our study, the oviposition behavior of pollinators was much less affected than the pollen spreading behavior due to the block of galls produced by S. testacea.
For the treatments with the pollinators, figs with S. testacea (2Cf + St treatment) have more galls than figs without S. testacea (2Cf treatment). This indicts that the existence of galls produced by S. testacea has little effect on the egg-laying behavior of the pollinator. Since pollinators lay their eggs in the female flowers one by one and the number of eggs to be laid is much less than the number of pollen to be spread, it’s easy to understand that oviposition is less affected than pollen spreading.
Our study shows that S. testacea has an obvious negative effect on the fig tree-fig pollinator mutualism. Oviposition by S. testacea leads to a drastic decrease in seed production, thus may harm the maintenance of stability of fig tree-fig pollinator mutualism if the fig is excessively parasitized by S. testacea. We think the mechanical injury from oviposition, the galling process, and the blocking of cavity by the galls of S. testacea may be responsible for the negative consequences. For figs in which only S. testacea oviposit, the total galls are much less than figs in which pollinators were introduced. There are two possible reasons. One possible reason is that the low density of the population of S. testacea results in the low oviposition rate. Another possible reason is that the fig tree may sanction the unpollinated figs, ie, the figs in which only S. testacea oviposit. Wang et al.30,31 found that the sanction mechanism also works on F. racemosa. Further more, they found that the sanction strength became stronger with an increase in foundresses30 (the wasps that enter the figs to oviposit). If too many S. testacea oviposit in figs, the figs are aborted more easily due to sanction by the tree. Only figs that contain a small number of galls survive.
Although many studies9,11,13,14,15,16,17,18 have shown that NPFWs can have a negative effect on the fig tree-fig pollinator mutulism due to the reduction in pollinator offspring or the seed production, some study has found that the NPFWs also can play a positive role in maintaining the stability of this obligate mutualism32. For example, parasites may stabilize and maintain the fig and fig wasp system through their effects on within- and between-tree reproductive phenology32. More specifically, oviposition by NPFWs can result in the asynchrony of the development of the figs, and increase the probabilities of pollinators finding oviposition sites, which is good for the maintenance of this mutualism32. More