Phenological sensitivity to climate across taxa and trophic levels.

Differences in phenological responses to climate change among species can desynchronise ecological interactions and thereby threaten ecosystem function. To assess these threats, we must quantify the relative impact of climate change on species at different trophic levels. Here, we apply a Climate Sensitivity Profile approach to 10,003 terrestrial and aquatic phenological data sets, spatially matched to temperature and precipitation data, to quantify variation in climate sensitivity. The direction, magnitude and timing of climate sensitivity varied markedly among organisms within taxonomic and trophic groups. Despite this variability, we detected systematic variation in the direction and magnitude of phenological climate sensitivity. Secondary consumers showed consistently lower climate sensitivity than other groups. We used mid-century climate change projections to estimate that the timing of phenological events could change more for primary consumers than for species in other trophic levels (6.2 versus 2.5-2.9 days earlier on average), with substantial taxonomic variation (1.1-14.8 days earlier on average).

Spatial and habitat variation in aphid, butterfly, moth and bird phenologies over the last half century.

Global warming has advanced the timing of biological events, potentially leading to disruption across trophic levels. The potential importance of phenological change as a driver of population trends has been suggested. To fully understand the possible impacts, there is a need to quantify the scale of these changes spatially and according to habitat type. We studied the relationship between phenological trends, space and habitat type between 1965 and 2012 using an extensive UK dataset comprising 269 aphid, bird, butterfly and moth species. We modelled phenologies using generalized additive mixed models that included covariates for geographical (latitude, longitude, altitude), temporal (year, season) and habitat terms (woodland, scrub, grassland). Model selection showed that a baseline model with geographical and temporal components explained the variation in phenologies better than either a model in which space and time interacted or a habitat model without spatial terms. This baseline model showed strongly that phenologies shifted progressively earlier over time, that increasing altitude produced later phenologies and that a strong spatial component determined phenological timings, particularly latitude. The seasonal timing of a phenological event, in terms of whether it fell in the first or second half of the year, did not result in substantially different trends for butterflies. For moths, early season phenologies advanced more rapidly than those recorded later. Whilst temporal trends across all habitats resulted in earlier phenologies over time, agricultural habitats produced significantly later phenologies than most other habitats studied, probably because of nonclimatic drivers. A model with a significant habitat-time interaction was the best-fitting model for birds, moths and butterflies, emphasizing that the rates of phenological advance also differ among habitats for these groups. Our results suggest the presence of strong spatial gradients in mean seasonal timing and nonlinear trends towards earlier seasonal timing that varies in form and rate among habitat types.

Feather corticosterone content in predatory birds in relation to body condition and hepatic metal concentration.

This study investigated the feasibility of measuring corticosterone in feathers from cryo-archived raptor specimens, in order to provide a retrospective assessment of the activity of the stress axis in relation to contaminant burden. Feather samples were taken from sparrowhawk Accipiter nisus, kestrel Falco tinnunculus, buzzard Buteo buteo, barn owl Tyto alba, and tawny owl Strix aluco and the variation in feather CORT concentrations with respect to species, age, sex, feather position, and body condition was assessed. In sparrowhawks only, variation in feather CORT content was compared with hepatic metal concentrations. For individuals, CORT concentration (pgmm(-1)) in adjacent primary flight feathers (P5 and P6), and left and right wing primaries (P5), was statistically indistinguishable. The lowest concentrations of CORT were found in sparrowhawk feathers and CORT concentrations did not vary systematically with age or sex for any species. Significant relationships between feather CORT content and condition were observed in only tawny owl and kestrel. In sparrowhawks, feather CORT concentration was found to be positively related to the hepatic concentrations of five metals (Cd, Mn, Co, Cu, Mo) and the metalloid As. There was also a negative relationship between measures of condition and total hepatic metal concentration in males. The results suggest that some factors affecting CORT uptake by feathers remain to be resolved but feather CORT content from archived specimens has the potential to provide a simple effects biomarker for exposure to environmental contaminants.

Relationship between the concentrations of dissolved organic matter and polycyclic aromatic hydrocarbons in a typical U.K. upland stream.

Concentrations of total and freely dissolved polycyclic aromatic hydrocarbons (PAHs) and dissolved organic carbon (DOC) were measured in water collected during four sampling events at five sites from the River Wyre. The sites are typical of streams draining upland organically rich soils in northwest U.K. Freely dissolved PAHs were separated from those associated with DOC using a flocculation method. The sum of concentrations of the total and freely dissolved PAHs analyzed ranged from 2.71 to 18.9 ng/L and 2.61 to 16.8 ng/L, respectively. PAH concentrations and PAH fluxes derived from concentrations and water flow rates generally increased downstream, the trend in the latter being more pronounced. The concentration of individual PAHs containing five or more aromatic rings was found to be strongly correlated to the DOC concentration (p < 0.0001), suggesting common terrestrial sources and hydrological pathways. In contrast, no significant relationships were observed between concentrations of PAHs with four or fewer rings and DOC. Concentrations of PAHs with more than four rings showed similar seasonal variation as DOC concentration (peaking in the late summer), while variation in two or three ring PAHs was out of phase with DOC (peaking in the winter). As the PAH-DOC relationship appeared partly dependent on the molecular weight of the PAHs, a linear regression function that included an interaction between this variable and DOC concentration was used to model PAH concentrations over a 2 year period to estimate annual fluxes. The relationship identified between PAH concentrations and DOC should help to enhance interpretation of PAH monitoring data that are currently sparse both spatially and temporally and, thus, enable more robust assessments of the potential risks of these environmental pollutants to sensitive aquatic organisms and human water supplies.

Warming effects on greenhouse gas fluxes in peatlands are modulated by vegetation composition.

Understanding the effects of warming on greenhouse gas feedbacks to climate change represents a major global challenge. Most research has focused on direct effects of warming, without considering how concurrent changes in plant communities may alter such effects. Here, we combined vegetation manipulations with warming to investigate their interactive effects on greenhouse gas emissions from peatland. We found that although warming consistently increased respiration, the effect on net ecosystem CO2 exchange depended on vegetation composition. The greatest increase in CO2 sink strength after warming was when shrubs were present, and the greatest decrease when graminoids were present. CH4 was more strongly controlled by vegetation composition than by warming, with largest emissions from graminoid communities. Our results show that plant community composition is a significant modulator of greenhouse gas emissions and their response to warming, and suggest that vegetation change could alter peatland carbon sink strength under future climate change.

Food web de-synchronization in England's largest lake: an assessment based on multiple phenological metrics.

Phenological changes have been observed globally for marine, freshwater and terrestrial species, and are an important element of the global biological 'fingerprint' of climate change. Differences in rates of change could desynchronize seasonal species interactions within a food web, threatening ecosystem functioning. Quantification of this risk is hampered by the rarity of long-term data for multiple interacting species from the same ecosystem and by the diversity of possible phenological metrics, which vary in their ecological relevance to food web interactions. We compare phenological change for phytoplankton (chlorophyll a), zooplankton (Daphnia) and fish (perch, Perca fluviatilis) in two basins of Windermere over 40 years and determine whether change has differed among trophic levels, while explicitly accounting for among-metric differences in rates of change. Though rates of change differed markedly among the nine metrics used, seasonal events shifted earlier for all metrics and trophic levels: zooplankton advanced most, and fish least, rapidly. Evidence of altered synchrony was found in both lake basins, when combining information from all phenological metrics. However, comparisons based on single metrics did not consistently detect this signal. A multimetric approach showed that across trophic levels, earlier phenological events have been associated with increasing water temperature. However, for phytoplankton and zooplankton, phenological change was also associated with changes in resource availability. Lower silicate, and higher phosphorus, concentrations were associated with earlier phytoplankton growth, and earlier phytoplankton growth was associated with earlier zooplankton growth. The developing trophic mismatch detected between the dominant fish species in Windermere and important zooplankton food resources may ultimately affect fish survival and portend significant impacts upon ecosystem functioning. We advocate that future studies on phenological synchrony combine data from multiple phenological metrics, to increase confidence in assessments of change and likely ecological consequences.

A new approach to characterising and predicting crop rotations using national-scale annual crop maps.

Cropping decisions affect the nature, timing and intensity of agricultural management strategies. Specific crop rotations are associated with different environmental impacts, which can be beneficial or detrimental. The ability to map, characterise and accurately predict rotations enables targeting of mitigation measures where most needed and forecasting of potential environmental risks. Using six years of the national UKCEH Land Cover® plus: Crops maps (2015-2020), we extracted crop sequences for every agricultural field parcel in Great Britain (GB). Our aims were to first characterise spatial patterns in rotation properties over a national scale based on their length, type and structural diversity values, second, to test an approach to predicting the next crop in a rotation, using transition probability matrices, and third, to test these predictions at a range of spatial scales. Strict cyclical rotations only occupy 16 % of all agricultural land, whereas long-term grassland and complex-rotational agriculture each occupy over 40 %. Our rotation classifications display a variety of distinctive spatial patterns among rotation lengths, types and diversity values. Rotations are mostly 5 years in length, short mixed crops are the most abundant rotation type, and high structural diversity is concentrated in east Scotland. Predictions were most accurate when using the most local spatial approach (spatial scaling), 5-year rotations, and including long-term grassland. The prediction framework we built demonstrates that our crop predictions have an accuracy of 36-89 %, equivalent to classification accuracy of national crop and land cover mapping using earth observation, and we suggest this could be improved with additional contextual data. Our results emphasise that rotation complexity is multi-faceted, yet it can be mapped in different ways and forms the basis for further exploration in and beyond agronomy, ecology, and other disciplines.

Long-term rise in riverine dissolved organic carbon concentration is predicted by electrolyte solubility theory.

The riverine dissolved organic carbon (DOC) flux is of similar magnitude to the terrestrial sink for atmospheric CO2, but the factors controlling it remain poorly determined and are largely absent from Earth system models (ESMs). Here, we show, for a range of European headwater catchments, that electrolyte solubility theory explains how declining precipitation ionic strength (IS) has increased the dissolution of thermally moderated pools of soluble soil organic matter (OM), while hydrological conditions govern the proportion of this OM entering the aquatic system. Solubility will continue to rise exponentially with declining IS until pollutant ion deposition fully flattens out under clean air policies. Future DOC export will increasingly depend on rates of warming and any directional changes to the intensity and seasonality of precipitation and marine ion deposition. Our findings provide a firm foundation for incorporating the processes dominating change in this component of the global carbon cycle in ESMs.

Spatial patterns reveal negative density dependence and habitat associations in tropical trees.

Understanding how plant species coexist in tropical rainforests is one of the biggest challenges in community ecology. One prominent hypothesis suggests that rare species are at an advantage because trees have lower survival in areas of high conspecific density due to increased attack by natural enemies, a process known as negative density dependence (NDD). A consensus is emerging that NDD is important for plant-species coexistence in tropical forests. Most evidence comes from short-term studies, but testing the prediction that NDD decreases the spatial aggregation of tree populations provides a long-term perspective. While spatial distributions have provided only weak evidence for NDD so far, the opposing effects of environmental heterogeneity might have confounded previous analyses. Here we use a novel statistical technique to control for environmental heterogeneity while testing whether spatial aggregation decreases with tree size in four tropical forests. We provide evidence for NDD in 22% of the 139 tree species analyzed and show that environmental heterogeneity can obscure the spatial signal of NDD. Environmental heterogeneity contributed to aggregation in 84% of species. We conclude that both biotic interactions and environmental heterogeneity play crucial roles in shaping tree dynamics in tropical forests.

Comment on Pescott & Jitlal 2020: Failure to account for measurement error undermines their conclusion of a weak impact of nitrogen deposition on plant species richness.

Estimation of the impacts of atmospheric nitrogen (N) deposition on ecosystems and biodiversity is a research imperative. Analyses of large-scale spatial gradients, where an observed response is correlated with measured or modelled deposition, have been an important source of evidence. A number of problems beset this approach. For example, if responses are spatially aggregated then treating each location as statistically independent can lead to biased confidence intervals and a greater probably of false positive results. Using methods that account for residual spatial autocorrelation, Pescott & Jitlal (2020) re-analysed two large-scale spatial gradient datasets from Britain where modelled N deposition at 5 × 5 km resolution had been previously correlated with species richness in small quadrats. They found that N deposition effects were weaker than previously demonstrated leading them to conclude that "previous estimates of Ndep impacts on richness from space-for-time substitution studies are likely to have been over-estimated". We use a simulation study to show that their conclusion is unreliable despite them recognising that an influential fraction of the residual spatially structured variation could itself be attributable to N deposition. This arises because the covariate used was modelled N deposition at 5 × 5 km resolution leaving open the possibility that measured or modelled N deposition at finer resolutions could explain more variance in the response. Explicitly treating this as spatially auto-correlated error ignores this possibility and leads directly to their unreliable conclusion. We further demonstrate the plausibility of this scenario by showing that significant variation in N deposition at the 1 km square resolution is indeed averaged at 5 × 5 km resolution. Further analyses are required to explore whether estimation of the size of the N deposition effect on plant species richness and other measures of biodiversity is indeed dependent on the accuracy and hence measurement error of the N deposition covariate. Until then the conclusions of Pescott & Jitlal (2020) should be considered premature.

Long-term effects of atmospheric deposition on British plant species richness.

The effects of atmospheric pollution on plant species richness (nsp) are of widespread concern. We carried out a modelling exercise to estimate how nsp in British semi-natural ecosystems responded to atmospheric deposition of nitrogen (Ndep) and sulphur (Sdep) between 1800 and 2010. We derived a simple four-parameter equation relating nsp to measured soil pH, and to net primary productivity (NPP), calculated with the N14CP ecosystem model. Parameters were estimated from a large data set (n = 1156) of species richness in four vegetation classes, unimproved grassland, dwarf shrub heath, peatland, and broadleaved woodland, obtained in 2007. The equation performed reasonably well in comparisons with independent observations of nsp. We used the equation, in combination with modelled estimates of NPP (from N14CP) and soil pH (from the CHUM-AM hydrochemical model), to calculate changes in average nsp over time at seven sites across Britain, assuming that variations in nsp were due only to variations in atmospheric deposition. At two of the sites, two vegetation classes were present, making a total of nine site/vegetation combinations. In four cases, nsp was affected about equally by pH and NPP, while in another four the effect of pH was dominant. The ninth site, a chalk grassland, was affected only by NPP, since soil pH was assumed constant. Our analysis suggests that the combination of increased NPP, due to fertilization by Ndep, and decreased soil pH, primarily due to Sdep, caused an average species loss of 39% (range 23-100%) between 1800 and the late 20th Century. The modelling suggests that in recent years nsp has begun to increase, almost entirely due to reductions in Sdep and consequent increases in soil pH, but there are also indications of recent slight recovery from the eutrophying effects of Ndep.

Tackling the Challenges of 21st-Century Open Science and Beyond: A Data Science Lab Approach.

In recent years, there has been a drive toward more open, cross-disciplinary science taking center stage. This has presented a number of challenges, including providing research platforms for collaborating scientists to explore big data, develop methods, and disseminate their results to stakeholders and decision makers. We present our vision of a "data science lab" as a collaborative space where scientists (from different disciplines), stakeholders, and policy makers can create data-driven solutions to environmental science's grand challenges. We set out a clear and defined research roadmap to serve as a focal point for an international research community progressing toward a more data-driven and transparent approach to environmental data science, centered on data science labs. This includes ongoing case studies of good practice, with the infrastructural and methodological developments required to enable data science labs to support significant increase in our cross- and trans-disciplinary science capabilities.

Data Integration for Large-Scale Models of Species Distributions.

With the expansion in the quantity and types of biodiversity data being collected, there is a need to find ways to combine these different sources to provide cohesive summaries of species' potential and realized distributions in space and time. Recently, model-based data integration has emerged as a means to achieve this by combining datasets in ways that retain the strengths of each. We describe a flexible approach to data integration using point process models, which provide a convenient way to translate across ecological currencies. We highlight recent examples of large-scale ecological models based on data integration and outline the conceptual and technical challenges and opportunities that arise.

Predicting disease risk areas through co-production of spatial models: The example of Kyasanur Forest Disease in India's forest landscapes.

Zoonotic diseases affect resource-poor tropical communities disproportionately, and are linked to human use and modification of ecosystems. Disentangling the socio-ecological mechanisms by which ecosystem change precipitates impacts of pathogens is critical for predicting disease risk and designing effective intervention strategies. Despite the global "One Health" initiative, predictive models for tropical zoonotic diseases often focus on narrow ranges of risk factors and are rarely scaled to intervention programs and ecosystem use. This study uses a participatory, co-production approach to address this disconnect between science, policy and implementation, by developing more informative disease models for a fatal tick-borne viral haemorrhagic disease, Kyasanur Forest Disease (KFD), that is spreading across degraded forest ecosystems in India. We integrated knowledge across disciplines to identify key risk factors and needs with actors and beneficiaries across the relevant policy sectors, to understand disease patterns and develop decision support tools. Human case locations (2014-2018) and spatial machine learning quantified the relative role of risk factors, including forest cover and loss, host densities and public health access, in driving landscape-scale disease patterns in a long-affected district (Shivamogga, Karnataka State). Models combining forest metrics, livestock densities and elevation accurately predicted spatial patterns in human KFD cases (2014-2018). Consistent with suggestions that KFD is an "ecotonal" disease, landscapes at higher risk for human KFD contained diverse forest-plantation mosaics with high coverage of moist evergreen forest and plantation, high indigenous cattle density, and low coverage of dry deciduous forest. Models predicted new hotspots of outbreaks in 2019, indicating their value for spatial targeting of intervention. Co-production was vital for: gathering outbreak data that reflected locations of exposure in the landscape; better understanding contextual socio-ecological risk factors; and tailoring the spatial grain and outputs to the scale of forest use, and public health interventions. We argue this inter-disciplinary approach to risk prediction is applicable across zoonotic diseases in tropical settings.

Integration of ground survey and remote sensing derived data: Producing robust indicators of habitat extent and condition.

The availability of suitable habitat is a key predictor of the changing status of biodiversity. Quantifying habitat availability over large spatial scales is, however, challenging. Although remote sensing techniques have high spatial coverage, there is uncertainty associated with these estimates due to errors in classification. Alternatively, the extent of habitats can be estimated from ground-based field survey. Financial and logistical constraints mean that on-the-ground surveys have much lower coverage, but they can produce much higher quality estimates of habitat extent in the areas that are surveyed. Here, we demonstrate a new combined model which uses both types of data to produce unified national estimates of the extent of four key habitats across Great Britain based on Countryside Survey and Land Cover Map. This approach considers that the true proportion of habitat per km2 (Zi ) is unobserved, but both ground survey and remote sensing can be used to estimate Zi . The model allows the relationship between remote sensing data and Zi to be spatially biased while ground survey is assumed to be unbiased. Taking a statistical model-based approach to integrating field survey and remote sensing data allows for information on bias and precision to be captured and propagated such that estimates produced and parameters estimated are robust and interpretable. A simulation study shows that the combined model should perform best when error in the ground survey data is low. We use repeat surveys to parameterize the variance of ground survey data and demonstrate that error in this data source is small. The model produced revised national estimates of broadleaved woodland, arable land, bog, and fen, marsh and swamp extent across Britain in 2007.

Model-based hypervolumes for complex ecological data.

Developing a holistic understanding of the ecosystem impacts of global change requires methods that can quantify the interactions among multiple response variables. One approach is to generate high dimensional spaces, or hypervolumes, to answer ecological questions in a multivariate context. A range of statistical methods has been applied to construct hypervolumes but have not yet been applied in the context of ecological data sets with spatial or temporal structure, for example, where the data are nested or demonstrate temporal autocorrelation. We outline an approach to account for data structure in quantifying hypervolumes based on the multivariate normal distribution by including random effects. Using simulated data, we show that failing to account for structure in data can lead to biased estimates of hypervolume properties in certain contexts. We then illustrate the utility of these "model-based hypervolumes" in providing new insights into a case study of afforestation effects on ecosystem properties where the data has a nested structure. We demonstrate that the model-based generalization allows hypervolumes to be applied to a wide range of ecological data sets and questions.

Using Qualitative and Quantitative Methods to Choose a Habitat Quality Metric for Air Pollution Policy Evaluation.

Atmospheric nitrogen (N) deposition has had detrimental effects on species composition in a range of sensitive habitats, although N deposition can also increase agricultural productivity and carbon storage, and favours a few species considered of importance for conservation. Conservation targets are multiple, and increasingly incorporate services derived from nature as well as concepts of intrinsic value. Priorities vary. How then should changes in a set of species caused by drivers such as N deposition be assessed? We used a novel combination of qualitative semi-structured interviews and quantitative ranking to elucidate the views of conservation professionals specialising in grasslands, heathlands and mires. Although conservation management goals are varied, terrestrial habitat quality is mainly assessed by these specialists on the basis of plant species, since these are readily observed. The presence and abundance of plant species that are scarce, or have important functional roles, emerged as important criteria for judging overall habitat quality. However, species defined as 'positive indicator-species' (not particularly scarce, but distinctive for the habitat) were considered particularly important. Scarce species are by definition not always found, and the presence of functionally important species is not a sufficient indicator of site quality. Habitat quality as assessed by the key informants was rank-correlated with the number of positive indicator-species present at a site for seven of the nine habitat classes assessed. Other metrics such as species-richness or a metric of scarcity were inconsistently or not correlated with the specialists' assessments. We recommend that metrics of habitat quality used to assess N pollution impacts are based on the occurrence of, or habitat-suitability for, distinctive species. Metrics of this type are likely to be widely applicable for assessing habitat change in response to different drivers. The novel combined qualitative and quantitative approach taken to elucidate the priorities of conservation professionals could be usefully applied in other contexts.

The stress response of three-spined sticklebacks is modified in proportion to effluent exposure downstream of wastewater treatment works.

This study was conducted to investigate whether exposure to wastewater treatment works (WWTW) effluent affects the adaptive stress axis of fish resident within the receiving water. Three-spined sticklebacks (Gasterosteus aculeatus) were sampled from sites downstream of ten WWTWs in north-west England, selected to represent a range of human population equivalents between 1000 and 125,000. Following capture, indices of stress (whole-body cortisol and glucose concentrations) were measured both prior to, and following, the imposition of a standardised stressor to establish both baseline and stress-induced concentrations of cortisol and glucose. There was considerable between-site variation in size, and to a lesser extent condition, of the fish. Pre- and post-stress cortisol and glucose concentrations also varied significantly between-sites. A large proportion of the variation in both the somatic data and the stress response was explained by variation in the proportion of effluent contributing to total river flow at the study sites. Mass (r(2)=0.35, P<0.001) and length (r(2)=0.37, P<0.001) of the fish, and cortisol (r(2)=0.26, P<0.001) and glucose (r(2)=0.12, P<0.01) concentrations in unstressed sticklebacks, were positively related to the concentration of effluent across the sample sites. However, in stressed fish, cortisol (r(2)=0.32, P<0.001) and glucose (r(2)=0.14, P<0.001) concentrations exhibited a negative trend in relation to the effluent concentrations across sites. Individual variation in fish size did not account for the variation in either cortisol or glucose levels. These data provide the first indication that modulation of the stress axis in fish by anthropogenic factors might be widespread and of greater significance than hitherto assumed.

Field surveys reveal the presence of anti-androgens in an effluent-receiving river using stickleback-specific biomarkers.

This study was designed to assess whether the removal of endocrine disrupting chemicals (EDCs) and other substances from a Waste Water Treatment Works (WWTW) effluent (receiving water: R. Ray, Swindon, UK) by granular activated carbon (GAC) affected biomarkers of exposure to EDCs [vitellogenin (VTG) and spiggin] in male and female three-spined sticklebacks in the receiving water. A nearby river (R. Ock), with a negligible effluent loading, was used as a control. On each river fish were sampled from four sites on five occasions both before and after remediation of the WWTW effluent. The results show for the first time in a UK field study a clear seasonality of blood VTG concentrations in wild male fish, following closely the VTG profile in female fish from both rivers. VTG levels in male fish from the R. Ray were significantly reduced after the GAC installation. However, VTG levels in males from the control sites also varied significantly across the same period, reducing the significance of this finding. A laboratory exposure to oestradiol (using site-specific lower and upper levels of oestrogenic activity) failed to elevate VTG concentrations in male sticklebacks suggesting that concentrations in the effluent, even prior to remediation, may not have exceeded a critical sensitivity threshold. Most importantly, a significant increase in female kidney spiggin content (a highly specific biomarker of xeno-androgen exposure) occurred in fish in the R. Ray after the GAC installation to levels comparable with those in fish from the control river. The significance of this finding is strengthened by the fact that during the pre-remediation period in the R. Ray, female spiggin levels increased with increasing distance from the WWTW. Our results provide the first in vivo evidence of the presence of anti-androgens in a UK WWTW effluent. To our knowledge this is the first UK-based comprehensive field study on the effects of a WWTW upgrade on biomarkers of EDC exposure using a sentinel fish species and our findings confirm the value of the stickleback as a model species for studying EDCs both in the laboratory and in the wild.