Ecology

EDITORIAL
26 August 2020

Researchers are starting to understand the behaviour of insects ravaging parts of Asia, Africa and the Middle East. This work must be furthered, funded and field-tested.

Desert locusts have been swarming in some areas since late 2019.Credit: Fredrik Lerneryd/Getty

On top of coronavirus, many countries are dealing with a second dangerous plague. Since the end of 2019, gigantic swarms of the desert locust Schistocerca gregaria have been devouring crops across East Africa, the Middle East and southwest Asia. It is the worst locust crisis some regions have seen for 70 years.
The upsurge — which has been linked to unusually heavy rains and a tropical cyclone on the Arabian Peninsula — has produced devastating swarms in Kenya, Ethiopia, Somalia, Yemen and India, with many more countries under threat. At least 20 million people are at risk of losing their food supplies and livelihoods, according to the Food and Agriculture Organization (FAO) of the United Nations. Swarms typically contain between 4 billion and 8 billion locusts, and can eat in a day the equivalent of what at least 3.5 million people would consume.
Governments and research organizations in the affected countries are fighting to control the insects, largely by spraying pesticides from planes. But it can seem like a losing battle. The swarms are being dealt with at the 11th hour: only after the juvenile insects, which are known as hoppers, gather to take flight.

But researchers are making progress. They are starting to understand how the insects communicate; some have used data from other outbreaks to design tools to predict when and where the next ones will happen. They are calling for more real-time data to inform agricultural policies.
All this is crucial work, but just first steps. Equally important is the need to test, improve and eventually act on these findings. The results must be turned into something practical that can be used in the fight against the desert locust.
Chemical attraction
One long-standing mystery is what causes the locusts to come together periodically in sky-blackening swarms. In this issue of Nature, Xiaojiao Guo and her colleagues report one answer: they identify a sweet-smelling pheromone produced by the migratory locust Locusta migratoria, a different species that also forms swarms. The researchers, at the Chinese Academy of Sciences and Hebei University, isolated 35 compounds emitted by this insect (X. Guo et al. Nature 584, 584–588; 2020). They tested a handful for their ability to attract other locusts, and found that the pheromone 4-vinylanisole (4VA) had the strongest results. The researchers also discovered that when just four or five locusts congregate, they start to produce 4VA, which then attracts others to create a swarm (see New & Views).
The researchers identified a gene, Or35, which produces a receptor that detects the pheromone. Using CRISPR–Cas9 gene editing, they showed that locusts with a mutated Or35 were unable to detect or respond to 4VA.
Locust forecast
In a different study, published last month, Emily Kimathi and her colleagues created the first draft of a machine-learning algorithm designed to predict desert-locust breeding sites (E. Kimathi et al. Sci. Rep. 10, 11937; 2020). The team at three institutions in Kenya, working with the FAO, combined more than 9,000 locust records from Mauritania, Morocco and Saudi Arabia with information on rainfall, temperature and soil and sand moisture. The algorithm performed well at predicting breeding sites in all three locations.

All these promising findings could, at least in theory, be used in complementary ways. The model could point to potential breeding sites, where an artificial pheromone might be released to attract locusts so that they can be trapped and destroyed before they breed in large numbers. But first, the findings must clearly be validated, extended and tested in the field. The machine-learning model needs to be refined. Researchers must establish whether 4VA has the same effect on the destructive desert locust as on the migratory locust and whether other signals are involved; much more work would be needed before an artificial pheromone could be created; and researchers must investigate practical issues such as how, where and when to distribute traps.
Major locust upsurges happen infrequently — the last event was 15 years ago — and so national and international funders have not prioritized such research. That is one reason countries have not been prepared for attacks: locust surveillance, including in-country research, has been weakened by years of under-funding. This cannot be allowed to continue. It isn’t known how quickly swarms will return after the present outbreak. But countries must be prepared when they do.

Nature 584, 497 (2020)
doi: 10.1038/d41586-020-02453-8

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Overall simulation approach
We estimated net changes in soil organic, above-ground and below-ground living biomass carbon stocks due to mangrove-related LCC that occurred globally between 1996 and 2016, for the year 2016. We did not estimate carbon sequestration because while sequestration rates in mangrove forests are higher than in many other ecosystems, there is only limited capacity for this process to substantially impact global carbon fluxes due to the small area of mangrove forest7,32,39. In addition, global high-resolution maps of mangrove carbon sequestration rates are not available.
Uncertainty in estimates of the area of mangrove loss and gain, carbon stock density, date of deforestation or forestation, proportional carbon stock degradation due to deforestation, and proportional carbon stock accumulation due to forestation, was carried forward using a bootstrap simulation method, through which 1000 replications were used to generate median estimates and 95% confidence intervals. A summary of the sources of data, simulation parameters, and modelling of parameter variability is provided in Supplementary Table 1. For each bootstrap iteration, the area of mangrove gain and loss, carbon stock density, rt, and at values were simulated within each patch of mangrove gain or loss. The D, F, Drt, and Fat values for each patch were then calculated. The median value of the 1000 replicates was used as the estimate. The 2.5 and 97.5 percentiles of the 1000 simulation estimates of change in carbon stock were calculated, thus corresponding to bootstrap 95% confidence intervals for each of the estimated losses or gains in carbon stock within each patch. A comparable bootstrap method was applied to simulate carbon stocks in 1996 for each patch of mangrove present at that time.
In addition to the sources of uncertainty incorporated in the bootstrap simulation, we made several key methodological decisions that could be expected to impact the conclusions of the study. We conducted four sensitivity analyses to quantify the impacts of such decisions. The sensitivity analyses were conducted only for the region of Southeast Asia, because one sensitivity analysis required detailed and spatially explicit data on the replacement land uses following mangrove deforestation, which are only available with the necessary categorisation for Southeast Asia5. Methodological details and results of the sensitivity analyses are included in Supplementary Methods 1.
Mangrove areal extent
We used the Global Mangrove Watch (GMW) datasets of mangrove cover to quantify deforestation and forestation14,15,16,17. While other global mangrove map products exist for specific years42,43, GMW provides maps of extent from multiple years, allowing temporal comparison. The mangrove extent in 1996 and 2016 was mapped using the GMW data products, which are derived from ALOS PALSAR and Landsat satellite-borne sensor data14,15,16. Mangrove-related LCC was defined either as a conversion from mangrove to another form of land or water cover between the 1996 and 2016 datasets (deforestation), or vice versa (forestation). Any change in mangrove cover that was reversed before the end of the study period was therefore not captured. Areas of overlapping and nonoverlapping mangrove extent were compared between these dates to quantify mangrove present in 1996 that was not present in 2016, mangrove present in 2016 that was not previously present in 1996, and areas of no change in mangrove cover between 1996 and 2016. For each patch of mangrove cover in 1996, gain, and loss, the area was calculated under the Eckert VI equal-area projection. This projection was used to calculate the area of mangrove and mangrove change polygons only, and all other analyses were conducted using the World Geodetic System (WGS) 1984 projection17.
The GMW mapping of mangrove forest has an error rate15, leading to quantifiable uncertainty over the presence of mangroves at each location in 1996 and 2016. Accuracy statistics have not been published for each year in the GMW dataset17, so we assumed that all years had an identical accuracy to the best-documented year, which is 201015. Published error rates correspond to individual pixels in the original GMW dataset, so we modelled uncertainty at this spatial scale. For each pixel of recorded mangrove gain or loss, there is a probability that it is an erroneous, false positive example of gain or loss. For each pixel of mangrove or non-mangrove that is recorded in GMW as being the same in 2016 as in 1996, there is a similar probability of error—a false-negative case of gain or loss. While false positive gain and loss errors can be quantified fully based on the available information, we were not able to incorporate false negative errors in the simulation (Supplementary Methods 2). However, false-negative errors can be expected to impact estimates of forestation and deforestation area almost equally, while false-positive errors are biased toward a greater effect on estimates of deforestation (Supplementary Methods 2). For these reasons we incorporated only false positive classification uncertainty into the bootstrap simulation and generation of carbon stock change confidence intervals. Uncertainty in the areal extent of mangroves was not incorporated into the bootstrap simulation for the estimate of carbon stocks in 1996. As the uncertainty estimation for areal extent change does not include false negative classification errors, we do not report confidence intervals for area change statistics, and report only the median estimates from the bootstrap replicates.
For areas of mangrove loss and gain, we incorporated the probability of false positive recording of loss and gain into the simulation. For each pixel within each patch of mangrove gain, we simulated whether it was actually not mangrove in 1996 according to the misclassification error rate for non-mangrove (Supplementary Table 2), and whether it was truly mangrove in 2016 according to the misclassification error rate for mangrove (Supplementary Table 2). The simulated number of gain pixels in each patch was thus calculated as the number of pixels that were simulated to have been both not mangrove in 1996, and mangrove in 2016. Similarly, for each pixel recorded as mangrove deforestation, there is a probability that it was a false positive example of loss. For each pixel within each patch of mangrove loss, we simulated whether it was truly mangrove in 1996 according to the misclassification error rate for mangrove (Supplementary Table 2), and whether mangrove was truly absent in 2016 according to the misclassification error rate for non-mangrove (Supplementary Table 2). The simulated number of loss pixels in each mangrove patch was thus calculated as the number of pixels that were simulated to have been mangrove in 1996, and not mangrove in 2016.
Carbon stock density
Spatial patterns in mangrove carbon densities were quantified using previously published datasets of soil carbon to 1 m depth36, and above- and belowground tree biomass carbon37. Both datasets are derived from systematic reviews of the literature, so may be biased towards relatively high-quality mangrove forests, rather than those that have experienced some natural or anthropogenic disturbance37. The resulting maps of carbon stock density are therefore likely to represent an upper estimate for the potential carbon stock density at each location37. As the dates and resolutions of these mangrove carbon datasets differed from the GMW mangrove extent, per hectare carbon densities were extracted for each patch of mangrove extent in 1996, gain, and loss of mangrove. Where possible, we extracted mean carbon densities for the 0.05 degree grid cell (approximately 5 km) in which the centre of the mangrove patch coincided (Supplementary Table 3). Where data did not coincide at this resolution, 0.5° grid cells (approximately 50 km) were used (Supplementary Table 3). Any remaining data gaps were filled using the global mean carbon stock density (Supplementary Table 3).
Uncertainty in the estimate of carbon stock was modelled as a normally distributed random variable, with the mean value taken as the reported carbon stock density extracted from the published map layers36,37, and the standard deviation of the distribution taken as the reported root mean squared error (RMSE) between the model predictions and validation data36,37. The RMSE for soil carbon is reported in the study as 109 Mg per hectare36. The RMSE for aboveground biomass carbon was calculated as 104.1 Mg per hectare, based on a plot of observed versus predicted values digitised from the original study37 (Supplementary Fig. 2). The soil and biomass carbon stock densities for each patch of mangrove in 1996, patch of mangrove loss, and patch of mangrove gain were simulated, and values of less than zero were replaced with zero, to avoid negative carbon densities.
Loss and accumulation of mangrove carbon
After mangrove deforestation, carbon is lost gradually over a period of time, with biomass carbon typically depleting more rapidly than carbon stored in soils13. We modelled temporal losses of soil carbon stocks according to a previously-published meta-analysis of the proportion of the reference carbon stock (rt) lost over time13. For losses of biomass carbon stocks, we used a meta-analysis of temporal changes in the proportion of the reference tree diameter as a proxy for biomass carbon stock, because a meta-analysis of temporal changes in biomass carbon stock was not available13. The shape of these temporal rt relationships can be observed in Supplementary Fig. 3a, b. The approximate date of mangrove deforestation was quantified by cross-referencing several dates from the GMW dataset to establish the dates of presence and absence17. We cross-referenced the dates of 1996, 2007, 2010, and 2016 to identify the dates of mangrove presence and absence at each location.
Uncertainty in proportional losses of carbon due to mangrove deforestation was incorporated in two ways. First, there is uncertainty in the date of mangrove deforestation since the most recent observed date of presence. To model uncertainty in the date of deforestation we used a uniform distribution to select a date between the most recent date of observed mangrove presence and the oldest date of observed mangrove absence. Second, there is uncertainty in the relationship between the time since deforestation and proportion of mangrove carbon lost, quantified as the error present in the regression models (Supplementary Fig. 3a, b). We simulated the projected proportions of mangrove carbon remaining as a function of the length of time since deforestation (2016—date of deforestation), accounting for the error inherent in each linear model (Supplementary Methods 3).
As mangrove forests grow, they typically accumulate carbon in soil and tree biomass stocks, until reaching the value held by the reference community12,44. This process can be slow, taking from 20 to more than 50 years12,44. At a given point in time before the climax community is reached, the mangrove ecosystem contains a proportion of the value held in the climax community (at). The whole-ecosystem carbon accumulation curve for afforesting mangroves was estimated using data taken from a meta-analysis of blue carbon ecosystem restoration18, that we used to estimate the proportion of the reference ecosystem carbon accumulated following restoration (Supplementary Methods 4). The shape of the temporal relationship can be observed in Supplementary Fig. 3c. We used this general relationship describing restoration of all blue carbon ecosystems, because a mangrove forestation-specific meta-analysis is not currently available. To assess the impacts of this selection on the study findings, we also conducted a sensitivity analysis using data from two case studies of mangrove soil carbon and biomass accumulation in foresting mangroves (Supplementary Methods 2). The approximate date of mangrove forestation was quantified by cross-referencing several dates from the GMW dataset to establish the dates of presence and absence14,15,16. We cross-referenced the dates of 1996, 2007, 2010, and 2016 to identify the dates of mangrove presence and absence at each location.
Uncertainty in gains of carbon due to mangrove forestation was incorporated in two ways. First, there is uncertainty in the date of mangrove forestation since the most recent observed date of presence. To model uncertainty in the date of forestation we used a uniform distribution to select a date between the most recent date of observed mangrove absence and the oldest date of observed mangrove presence. Second, there is uncertainty in the relationship between the time since deforestation and proportion of mangrove carbon lost, quantified as the error present in the meta-analytic regression model (Supplementary Fig. 3c). We simulated the projected proportions of mangrove carbon remaining as a function of the length of time since deforestation (2016—date of deforestation), accounting for the error inherent in each linear model45 (Supplementary Methods 3).
We estimated D, F, rt, and at for each patch of mangrove gain and loss between 1996 and 2016. These data were then used to quantify four indicators of net change in mangrove carbon stocks, to evaluate the sensitivity of estimation to the inclusion or exclusion of afforestation and remnant carbon processes. The first indicator estimated the maximum carbon stock at risk of loss due to deforestation (D), following the approach used in the most recent global estimate of potential mangrove carbon emissions9. The second indicator estimated net loss of carbon assuming 100% carbon loss and gain rates (D − F). For the third indicator, we estimated the carbon stock loss due to deforestation but accounting for remnant carbon (Drt)8. Finally, the fourth indicator estimated net changes in mangrove carbon stock between 1996 and 2016 accounting for both forestation and proportional accumulation and loss rates of carbon following LCC (Drt − Fat). For mapping of spatial variability in net gains and losses of mangrove carbon stocks, we quantified the net change in mangrove carbon stock (Fat − Drt) by summarising all patches of mangrove gain and loss with their centroids located in cells across a global grid (Figs. 2 and 3). More

Study region
Monterey Bay is the largest bay on the California coast, and has a total area of approximately 1,162 km2 (Fig. 1). Due to upwelling, the bay is highly productive and supports dense kelp forests, dominated by Macrocystis pyrifera, on rocky reefs that extend to approximately 20 m depth27. These forests have the capacity to regulate pH28,29,30 and sustain over 200 different species, from phytoplankton to marine mammals31.
Temperatures are the coolest in Monterey Bay during spring and early summer due to wind-driven upwelling32, driven by equatorward winds blowing over the California coast causing offshore Ekman transport33. During the same months, filaments of water originating from Point Año Nuevo to the north are trapped within the Bay, where they warm due to solar radiation, forming a lens of warm water close to shore34,35 (Fig. 1). Furthermore, a weak cyclonic eddy is observed within the bay due to the coastal geometry32,34. Inside the bay, sea breezes and tides drive diurnal and semi-diurnal currents that can lead to significant variability in environmental conditions17,36. During the upwelling period, salinity is approximately 34, temperature ranges from 9 to 13 °C at 17 m depth7, DO varies from as low as 100 μmol kg−1 (3.2 mg L−1 for T = 13 °C, S = 34 at 17 m) to as high as 300 μmol kg−1 (9.62 mg L−1 for T = 13 °C, S = 34 at 17 m), and pH varies from 7.7 to 8.13 (Supplementary Fig. 1).
The three primary barotropic tidal constituents in the region (M2, K1, and S2) are responsible for over 80% of the tidal amplitude observed18. In the southern region of the bay, tides are mainly responsible for the cross-shelf velocity36 and the interaction of these surface tidal currents with the steep topography create internal tides comprised of internal waves and bores occurring at tidal frequencies37,38. Internal tides are formed when currents move over steep slopes, dense waters are forced into shallower regions, and, as these waters sink back to depth, an internal wave is generated39. These internal waves have speeds8 on the order of 0.05–0.2 m s−1 and, as the coast steepens, can break forming internal bores that move upslope and bring cold, low DO, low pH waters into nearshore kelp forest ecosystems40. During upwelling, these processes drive variability in temperature, pH and DO over semi-diurnal and diurnal periods that can exceed the predicated changes in mean conditions predicted by global climate models for year 210041.
Model description
To better understand how tides and winds affect exposure of nearshore organisms to variability in temperature, DO, and pH under current and RCP climate scenarios, we used dynamic downscaling and developed a 2D coupled biogeochemical hydrodynamic model using ROMS42. The model domain was created based on the Monterey Bay continental shelf described in Walter et al.6 with a maximum offshore depth of approximately 80 m (Fig. 2). The biogeochemical model used is described in Fennel et al.43,44. We forced the model with representative wind (diurnal sea breeze accounting for regional winds), solar radiation, and tidal currents for the Monterey Bay region (Supplementary Figs. 2–6; Supplementary Table 1). Detail of the full model structure, including boundary and initial conditions, are included in the “Supplementary Information”.
Figure 2

Snapshot of model crosshore velocity and temperature (contour lines) on 8 July 2013. Positive values are onshore, negative values are offshore. Contour lines (black) show isotherms with temperature labels.

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We estimated depth profiles for temperature, DO, and pH for the downscaled coupled hydrodynamic-biogeochemical model. We considered a homogeneous salinity (S = 34) for the entire domain since the variation in salinity is less than 0.4 over an entire year and less than 0.05 during the upwelling season in the Monterey Bay region27. Thus, initial and boundary stratification are assumed to be controlled by temperature alone. We represent initial (IC) and boundary conditions (BC) for temperature, DO, and pH using:

$$ {text{var}} left( {text{z}} right) = left{ {begin{array}{*{20}l} {Delta {text{var}} ;{text{D}}_{{{text{pyc}}}}^{alpha } } hfill & {{text{if}};{text{z}} ge {text{D}}_{{{text{pyc}}}} } hfill \ {Delta {text{var}} ;{text{z}}^{alpha } } hfill & {{text{otherwise}}} hfill \ end{array} } right. $$
(1)

where var is the identified variable (e.g. temperature), z is the depth, α is a fit coefficient for each variable determined from a least-squares fit to observational data, and Dpyc is the depth of the pycnocline. We used this method to estimate profiles of temperature, dissolved inorganic carbon (DIC), and DO for present and all downscaled future scenarios setting Dpyc to 17.5 m. The data used for the initial and boundary conditions (BC) of phytoplankton and chlorophyll profiles were taken from Schuckmann et al.45. The initial chlorophyll concentration was converted to zooplankton concentration (zoop = 0.34 × 10–3 mmol m−3) using Eq. 3 from Wiebe46, a method that has been used in other studies47,48. Detritus was initially set to zero in the entire domain. All biogeochemical variables were forced hourly at the southern boundary.
For each scenario, we estimated depth profiles for temperature, dissolved oxygen, and DIC using Eq. (1). In order to fit Eq. (1), we obtained our estimated values of each variable near the surface, at 80 m depth on the shelf during upwelling for Present and 200 m depth for Future, respectively. We used 200 m depth from the future data set as this is the most common depth of source waters for upwelling in the region49. Present surface and bottom values of temperature, DO, total alkalinity (TA), and DIC for present scenario were based on Koweek et al.27. The mean of the 3-month period of strong upwelling (May, June, and July) was used to obtain values of temperature (surface and depth), oxygen (surface), and DIC (surface) from Representative Concentration Pathway (RCP) for the year of 2100 from the 4th report of the IPCC50. Since only surface values for DO and DIC and no values for TA were available, we estimated values for these parameters at depth.
For DIC, we assumed that the ratio between surface and bottom values (80 m for Present and 200 m for Future) in present conditions will not change for future scenarios:

$$frac{{DIC}_{Surf}^{Present}}{{DIC}_{Bottom}^{Present}}=frac{2073}{2280}= 0.909$$
(2)

Therefore, to find the bottom values for future scenarios we divided RCP surface values by this ratio:

$$frac{{DIC}_{Surf}^{RCP8.5} }{0.909}= frac{2167 }{0.909} = 2384;mathrm{mmol;C};{mathrm{m}}^{-3}$$
(3)

Surface and bottom TA values were kept the same as present conditions, following Feely et al.3. For DO, bottom values for RCP 2.6 and 8.5 were approximated from Figs. 5 and 6 of Bopp et al.51. We calculated the ratios between surface and bottom DO for Present, RCP 2.6, and RCP 8.5. The ratio of surface:bottom DO was not constant across scenarios, so we approximated the ratios for RCPs 4.5 and 6.0 using linear least squares fit (Table 1; example calculation in the “Supplementary Information”). We, then applied the values from Table 1 in order to calculate α assuming Dpyc = 17.5 m. We then used Eq. (1) to generate the initial and boundary conditions (Fig. 3).
Table 1 Values for present (empirical data) and future (global ocean models) surface and estimated bottom conditions used to fit Eq. 3 and used as boundary conditions for the downscaled model runs.
Full size table

Figure 3

Initial and Boundary Conditions profiles for present and future scenarios: (a) temperature, (b) O2, (c) DIC, (d) pH.

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We calculated pH and Ωar using the CO2SYS52 package in MATLAB using temperature, salinity, DIC, and Total Alkalinity (TA) from the simulations at the offshore location where the bottom depth was 15 m. We assumed concentrations of phosphate and silica based on Koweek et al.27. We used dissociation constants for H2CO3 and HCO3 from Dickson and Millero53 and hydrogen sulfate ion constant (HSO-4) from Dickson54. All surface oxygen values were shifted positively 60 mmol m−3 (1.87 mg L−1 for T = 13 °C and S = 34) in order to simulate the high primary production due to kelp forests29, which is not specifically accounted for in the model. Overall, temperature at the bottom remained constant across all scenarios, with exception of RCP6.0 where temperature increased 0.2 °C (Supplementary Table 2).
Integrated exposure
Field observations24,55 and laboratory experiments5,56 have shown that below sub-lethal thresholds marine organisms inhabiting nearshore marine habitats in upwelling systems exhibit signs of physiological stress when exposed to elevated temperatures, low oxygen levels, or low pH waters, which is especially detrimental especially for calcifying species. Exposure of organisms to stressful temperature, DO, and pH conditions (φth where φ refers to temperature, DO, or pH) was done by subtracting the threshold value for a given organism and life stage from the model or observational data at 15 m water depth, then setting all positive values to zero for pH and O2, and all negative values to zero for temperature. Next, we estimated integrated exposure (Eint) by integrating absolute exposure over a period of a week with a window interval of 1 h:

$$ emptyset^{prime} = emptyset – emptyset_{th} left{ {begin{array}{*{20}c} {emptyset^{prime} > 0 to emptyset^{prime} = 0;for;pH;and;O_{2} } \ {emptyset^{prime} < 0 to emptyset^{prime} = 0;for;temperature} \ end{array} } right. $$ $$ E_{int} = mathop int limits_{0}^{t} left| {emptyset^{prime}} right|dt $$ (4) Thresholds of temperature, dissolved oxygen and pH (16 °C57, 4.8525 mg L−1, and pH of 7.523 respectively) representing non-interactive negative impacts on juvenile red abalone growth were based on literature values5,23,57. Integrated exposure quantified the time and degree of stress an organism experiences, similar to the degree heating week with units of oC w, or day (oC d) measure used to estimate thermal stress on coral reefs58, and has been previously used to understand the exposure of juvenile abalone populations to similar stressors in an empirical field study57. Overall, red abalone threshold values chosen had a strong negative effect on the species5,23,57, therefore, we used Eint as a proxy for estimating the potential impact of future conditions on abalone growth and survival. Fertilization response We estimated fertilization success using results of Boch et al.25 where the fertilization response of red abalone (Haliotis rufescens) was quantified in response to multiple stressor climate conditions (high temperature, low DO, and low pH). Fertilization in abalone occurs over relatively short periods, therefore Eint would not provide an appropriate estimate in such cases. While the process of fertilization occurs over short periods, adult red abalone exhibit an extended spawning season, over which environmental conditions may vary greatly based on our modeling results. Thus, we used the equations from Boch et al.25 to examine how fertilization success over a one-month period might be affected by environmental variability, specifically the interactive effects of ph and temperature. Changes in DO did not show a strong effect on fertilization in their experiments (Fig. 4; see Supplementary Table 3 for parameter values): Figure 4 Proportional Fertilization (Prop. Fert.) as a function of pH for red abalone Eq. (5)—blue line; Eq. (6)—black line based on Boch et al.25 (Fig. 4a,c). For our study we used 15.5 °C as a transition between the two curves shown. Full size image For temperatures = 13 °C: $$ {text{Logit}};left( {% Fert.} right) = left{ {begin{array}{*{20}l} {{upbeta}_{0} + {upbeta}_{{{text{pH}}}} {text{pH}}} hfill & {{text{for}};{text{pH}} le {text{BP}}} hfill \ {left( {{upbeta}_{{{text{pH}}}} + left( {{upbeta}_{2} - {upbeta} } right)} right){text{pH } - text{ offset}}} hfill & {{text{for}};{text{pH}} > {text{BP}}} hfill \ end{array} } right. $$
(5)

For Temperatures = 18 °C:

$$mathrm{Logit};(mathrm{%}Fert.)= {upbeta }_{0} + ({upbeta}_{mathrm{p}H} + {upbeta }_{mathrm{A}})mathrm{p}H + {upbeta }_{mathrm{B}}$$
(6)

where ({upbeta }_{0}) and ({upbeta }_{mathrm{pH}}) are intercepts, (upbeta ) and ({upbeta }_{2}) are slope segments, ({upbeta }_{mathrm{A}}) is slope of the pH-temperature interaction (pH × Temperature Group), ({upbeta }_{mathrm{B}}) is accounts for high temperature effects, and BP is the curve breaking point. Since only curves for 13 °C and 18 °C were available, we used 15.5 °C as a transition where Eq. (5) was applied for temperature less than 15.5 °C and Eq. (6) was applied for temperature greater than 15.5 °C. Since more complex interpolation schemes yielded similar results, we used this straightforward method for clarity.
Model evaluation
We first assessed whether the oceanographic model was able to reproduce current (observed) oceanographic conditions in Monterey Bay. The model was expected to reproduce the main dominant semi-diurnal and diurnal periods of oscillations observed in the region as well as primary production regulation of DO and pH (excluding kelp) in the biogeochemical model. We had two requisites to consider the model performance satisfactory. First, we required that the model was capable of reproducing local minima for DO. Second, the model needed to be capable of reproducing the mean and extremes for temperature and pH. In addition, we anticipated observing lower values of DO in surface waters since we did not account for the higher primary productivity observed in kelp forests20, and therefore, DO oversaturation. To evaluate the biogeochemical model, we compared chlorophyll concentration in the model to satellite-derived chlorophyll estimates for the region. Monthly mean depth averaged chlorophyll by area (mg Chl m−1) was calculated for the model and for the months of May, June, and July from Sea-Viewing Wide Field-of-View Sensor (SeaWiFS)59 for the period of 2010–2017 to the closest region with data available next to our model simulation (cross section region in Fig. 1). Chlorophyll concentrations in the model were 2.12 mg Chl m−3 compared to 4.02 mg Chl m−3 estimated from SeaWifs. Thus, modeled values were within the range observed in the satellite data over this period.
In order to validate temporal variability in the model results, we estimated power spectra using the Thomson Multi-taper method (MTM)60. This method was chosen due to its robustness for stationary data with low variance. Power spectra allowed us to quantify the variability by frequency and confirm that the model was reproducing variability at dominant periods (M2, K1) observed in the region, as initial and boundary conditions in the model were based on regional observations. We applied the analysis over a 3-week window of upwelling for temperature, DO, and pH and compared with Booth et al.7 data (Fig. 5). Spectral analysis was used to ascertain the dominate temporal constituents of temperature, DO, and pH for all scenarios (present and RCP) between 12- and 24-h periods. The spectra were then integrated numerically to calculate the variability of temperature, DO, and pH, and the 95% confidence interval at each frequency band. Bootstrap analysis was applied to the integrated exposure calculation for each variable. In order to assure convergence in our estimates, 1,000 iterations with replacement were done using a sample size of 50% of the data. In the end, the confidence interval (CI) was calculated based on 2.5% percentile and 97.5% percentile of the distribution of the estimated means.
Figure 5

Time series for in situ5 and model data and power spectra of temperature (a,b), DO (c,d), and pH (e,f). Time series for the model was shifted to match tidal phase of the in situ data. cpd cycles per day.

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Our model results reflect current variability in temperature, DO, and pH in southern Monterey Bay (Fig. 5). In addition, mean temperature and pH values were not significantly different from in situ data. However, mean DO in our model was lower than present day averages, likely due to the lack of oxygen-producing kelp in our model61. Thus, the model accurately simulates diurnal, semi-diurnal, and higher order tidal components62. For Monterey Bay, diurnal (cycles per day (cpd) = 1) and semi-diurnal (cpd = 1.93) are the main temporal components of variability in temperature, oxygen, and pH and are well represented by our model with overlapping 95% confidence intervals (Fig. 5b,d,f). Higher frequency variability (cpd = 3) is also within the 95% confidence interval when comparing model and observations. However, frequencies occurring between peaks are not well resolved (Fig. 5b,d,f). We expect this is because we used a 2D model, and therefore, the model was unable to resolve all the physical processes occurring in the Monterey Bay. The oscillations between peaks in the observed data were likely due other coastal ocean processes such 3D circulation and ocean surface waves36 (as well as noise in the instruments). However, the variability at these periods did not have an appreciable effect on exposure calculations. Importantly, the model preserved the observed diurnal and semi-diurnal variability that is not present in global and regional scale climate models for future RCP scenarios (Fig. 5a,c,e).
Variability at 15 m
The model was able to simulate the main drivers of variability in temperature, DO, and pH (predominantly internal waves) for the region in study. Internal waves in the domain were seen as vertical changes in the u-component of the velocity (Fig. 2). Cross-shelf velocities ranged from − 0.05 to 0.1 m s−1, and were within the range found in other studies8,18,27. Before the arrival of the internal wave crest, isotherms were tilted downwards indicating previous downwelling. Flow in opposite directions between crests was observed during retreating of internal waves in the domain, as it has been observed in studies on internal waves with in situ data8,9.
High variability in temperature, DO, and pH was observed in all model runs (Supplementary Figs. 7–9). Mean temperatures were 10.63 °C (SD = 0.39), 13.81 °C (SD = 0.46), 15.46 °C (SD = 0.50), 15.45 °C (SD = 0.49), 16.96 °C (SD = 0.95) for present, RCP 2.6, RCP 4.5, RCP 6.0, and RCP 8.5, respectively (Supplementary Fig. 7). Overall, mean temperature increased as expected and exhibited similar variability (standard deviation [SD]) across all RCPs except for RCP 8.5 scenario. This was likely due to increased temperature stratification where surface waters warm faster than deeper waters resulting in higher temperature variability.
Mean dissolved oxygen values were 5.15 mg L−1 (SD = 0.74) for present, 4.80 mg L−1 (SD = 0.88) for RCP 2.6, 5.32 mg L−1 (SD = 0.77) for RCP 4.5, 5.02 mg L−1 (SD = 0.81) for RCP 6.0, and 4.93 mg L−1 (SD = 1.21) for RCP 8.5 (Supplementary Fig. 8). The SD for RCP 8.5 was almost twice that of other scenarios. This was likely due to a stronger gradient in oxygen (surface remains saturated while the values at depth are lower). RCP 2.6 had the lowest mean DO but not different than the rest and RCP 4.5 was 0.52 mg L−1 higher, though not significantly different than the present scenario. The consequence for the lowest mean DO in RCP 2.6 was related to the strength of density stratification61, and therefore, low oxygen at 15 m. Another scenario (not shown) was used where RCP 4.5 oxygen profile was applied using the density stratification from RCP 2.6 and the same low oxygen values found previously were also observed in the alternate run, supporting this mechanism.
pH variability was also high across all model runs (Supplementary Fig. 9). The mean value for pH decreased from 7.73 (SD = 0.07) for present to 7.44 (SD = 0.12) for the RCP 8.5 scenario. RCP 8.5 again had the highest variability among all scenarios. Otherwise, the pH range was ~ 0.075 for all other scenarios. Lower pH for the most extreme scenarios (RCP 6.0 and 8.5) has also been observed in large scale models12. More

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Sampling rational and design
The EPA in conjunction with local authorities and other public bodies in Ireland has undertaken a substantial characterization of the physical water environment and the impact of human activities on waters17. Therefore, the monitored river water bodies in Ireland and the national river monitoring program are designed to obtain sufficiently representative information across river typologies and on significant pressures to assign a WFD status to each water body across our entire river network (Fig. 1).
Fig. 1

(a) Map of hydrometric areas (HA) in the dataset and (b) locations of all river biomonitoring stations 2007–2018 in Ireland. Both maps created using EPA data. See Table 5 for more details.

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The data collected covers the range of ecological conditions found in Irish rivers to assist the assignment of an ecological status as required by the WFD. Ecological status is an assessment of the quality of the structure and functioning of surface water ecosystems and it highlights the influence of pressures (e.g. pollution and habitat degradation) on several identifiable quality elements. As part of the WFD, ecological status is determined for each of the surface water body categories (i.e. rivers, lakes, transitional waters and coastal waters) using intercalibrated (see Technical Validation for further details) biological quality elements (BQEs) and supported by physico-chemical and hydromorphological quality elements. Ecological status for surface water bodies is primarily driven by the BQEs, namely fish, aquatic flora, macroinvertebrates and phytoplankton. The overall ecological status classification for any water body is determined, according to the ‘one out, all out’ principle, by the element with the worst status out of all the biological and supporting quality elements. In Ireland, macroinvertebrates are the main BQE determining the ecological status in rivers3.
The WFD requires BQE scores to be expressed as an Ecological Quality Ratio (EQR) to standardize and provide a common scale of ecological quality across participatory Member States using differing national methods18. The EQR is determined by expressing the observed result over the expected result which calculates a ratio score (Table 1). The ‘expected’ or ‘reference’ condition (EQR close to 1) is the natural, undisturbed environment, i.e. the benchmark. The assessment of the scale of anthropogenic pollution in any water body is based on the extent of deviation from expected reference conditions and follows the definitions as outlined in the WFD (Table 1). For example, ‘High status’ is defined as the biological, chemical and morphological conditions associated with no or very low human pressure, and therefore, little or no deviation from reference, ‘Good status’ means ‘slight’ deviation, ‘Moderate status’ means ‘moderate’ deviation, and so on. EQRs provide a common scale to ensure comparability across different pressures, allowing water managers to easily recognise and characterize impact facilitating the development of mitigation measures to restore or preserve ecological status17. To assess the network of rivers in Ireland, monitoring stations cover all 37 hydrometric areas (HA) providing a full national coverage (Fig. 1).
Table 1 The Q-value, ecological quality ratio (EQR)*, and corresponding WFD status and pollution gradient resulting from anthropogenic pressures.
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Field sampling and data collection
River macroinvertebrates are collected from June to September each year, when flows are likely to be relatively low. Occasionally, for operational or weather-related reasons, surveys may occur outside of this period. Two approached are used. The first, and principal methodology used (96.7% of surveys in dataset), is by kick-sampling with a standard pond net (230 × 225 mm frame with 1 mm mesh). In this approach a semi-quantitative two-minute macroinvertebrate kick-sample is collected from the riverbed preferably from the faster flowing riffle habitats19. A further one-minute hand search is carried out to locate macroinvertebrates that remain attached to the underside of the cobbles19. Depending upon the proportion of various habitats (e.g. glides, margins, pools), time may also be spent sampling these habitats with operators moving location approximately every 4 to 5-seconds over a 50 m stretch. Similar studies in Ireland and elsewhere have found that this sampling approach is sufficient to achieve a suitable representation of taxa for bioassessment of lotic habitats20,21. Occasionally, when the substratum (e.g. bedrock) or flow conditions make kick-sampling difficult, or the abundance of macroinvertebrates collected is extremely low (e.g. toxic pollution, see Kelly-Quinn et al.7), it may have been necessary to spend a longer amount of time sampling the river to accumulate a sufficient diversity and abundance of macroinvertebrates. In fast flowing steep rivers, it may have been necessary to kick deeper into the riverbed to disturb the organisms and include more of the marginal areas to ensure taxa are recorded19. This sampling approach requires avoidance of obvious localized disturbance (e.g. cattle access points) which may adversely influence the sample taken.
If the river depth is too deep to wade, a separate approach is taken. In this scenario, a bankside extension net sampling approach for deep (non-wadable) rivers is used to collect macroinvertebrates. It must be noted that this methodology is used less frequently than the kick-sampling approach. If employed, the depth and number of extension poles attached to a modified hand net will vary on a site by site basis. The net (frame and mesh dimensions as above) is then pulled upstream along the riverbed, generally at a perpendicular angle to the bank to cover as much surface area as possible with operators moving location after every pull over a 20 to 50 m stretch. The net may also need to be emptied between pulls to ensure that macroinvertebrates already collected are not lost inadvertently during the next pull. The extension net is also used to sweep along the water surface and marginal vegetation. This approach is conducted for a minimum of five minutes or until a representative sample is obtained (see Technical Validation for more details).
Once a live sample is collected it is assessed on the riverbank and the EPA Q-value classification is assigned (see Toner et al.1 for more details). This involves recording the taxa present at a suitable and attainable (under field conditions) taxonomic resolution (Table 2) and their categorical relative abundance (Table 3), determined using approximate counts. Once all taxa and their relative abundance have been recorded, the sample is returned to the river. Potential users should note that actual numbers of taxa have not been recorded and are therefore unavailable within the dataset. Similarly, taxonomic resolution may vary from what is outlined in Table 2. Indeterminate specimens may be brought back to the laboratory for identification under a microscope. Taxa are also occasionally returned to the laboratory and identified by microscope as a quality control measure. A brief description of the Q-value ecological quality rating (EQR) is outlined in Table 1. The typology of each river station is described in Table 4, after Kelly-Quinn et al.22,23.
Table 2 The level of macroinvertebrate identification carried out in the field during WFD biomonitoring assessments.
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Table 3 Abundance categories for macroinvertebrates recorded in the field during WFD biomonitoring assessments.
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Table 4 Typologies of Irish rivers.
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Each hydrometric area (Table 5 and Fig. 1) is generally surveyed on a three-year cycle; however, full surveys of certain hydrometric areas may be spilt across two concurrent years (e.g. HA 25), and on occasion a subset of stations were surveyed/resurveyed outside of the main survey year to closely track any progress in status changes following the implementation of a program of measures (Table 6). Certain stations were sampled on a more frequent basis such as seriously polluted sites (i.e. Red dot sites – Fanning et al.24), WFD high status objective sites, priority areas for action identified in Ireland’s national river basin plan17 and occasional sites of interest to local authorities and the EPA Office of Environmental Enforcement.
Table 5 Hydrometric area (HA) codes and HA names on the island of Ireland.
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Table 6 The number of river biomonitoring stations assessed by year and hydrometric area (HA), held by the EPA* 2007 to 2018.
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Within each hydrometric area, water bodies may have one or more sampling stations along their continuum. The number of stations may also vary between survey years, although, unless health and safety, or other unforeseen circumstances arise, the EPA attempt to sample the same stations in each survey cycle. Similarly, the numbers of water bodies and stations sampled within each hydrometric area will reflect the geographical area and length of river network. More