Ecology

Study siteOur study site is in an intensive agricultural landscape in NW Spain known as “Tierra de Campos”, which occupies part of three out of nine provinces of Castilla-y-León region (Palencia, Valladolid, and Zamora). This area is considered the main “hot-spot” of tularemia in Spain and Southern Europe16 and is characterized by higher-than-average vole abundances during outbreaks17.Iberian hare abundance indexYearly occurrence of vole outbreaks in NW Spain between 1996 and 2020 (i.e., 1997, 1998, 2007, 2008, 2014, 2019) were identified based on reports in the news (historical reconstruction18) and more recently (from 2009 onward) using common vole abundance indices obtained from live-trapping monitoring (i.e.4,19).To study the Iberian hare population trends we used regional hunting statistics available from the regional government (Junta de Castilla-y-León, CAZDATA Project, https://medioambiente.jcyl.es/web/es/caza-pesca/cazdata-banco-datos-actividad.html [Cited 2022 Sep 23]), which included hunting records as well as the number of hunting licences from 1974 to 2020. We used the number of hunted hares divided by the number of hunting licences each year as an abundance index for hares in “Tierra de Campos” (compiling data from the provinces of Palencia, Zamora and Valladolid). CAZDATA Project is an initiative proposed by the Hunting Federation of Castilla y León, which has the support of the regional government and, more importantly, the commitment of almost 60% of the hunting societies in the community to implement a system for monitoring hunting activity. Since this information is gathered by hunters for the benefit of the hunting activity, we are confidence on its reliability to carry out the present study.
Francisella tularensis prevalence in Iberian haresWe compiled data on F. tularensis prevalence in Iberian hares from 2007 to 2016 using previously published information from a passive surveillance program carried out by the “Regional Network of Epidemiological Surveillance” (Red de Vigilancia Epidemiológica de la Dirección General de Salud Pública) of Castilla-y-León region20. This provided us with information on hare tularemia prevalence (amount of positives/number of screened individual) each year within the three provinces from “Tierra de Campos”.Statistical analysesTo study Iberian hare population trends, we calculated an index of yearly hare population instantaneous growth rate (PGR) using the hunting bag data (hare abundance index) from 1996 to 2020. Hare PGR was calculated as follows:$$PGR= lnleft(frac{{AI}_{t}}{{AI}_{t+1}}right)$$where ln stands for natural logarithm, AIt is Iberian hare abundance index on year t. and AIt+1 is the Iberian hare abundance index on year t + 1. PGRs were estimated yearly from 1996 to 2019. This dependent variable was fitted to a Generalized Linear Mixed Model using the glmmTMB function (GLMMTMB, package glmmTMB21) and a gaussian family distribution and identity link function. The categorical variable vole outbreak year (i.e., with two levels: years with (1) or without vole outbreak (0), hereafter “Vole”) and “Province” (i.e., with three levels: Palencia, Valladolid and Zamora), and their interaction were used as explanatory variables. “Year” of sampling was included as a random factor (i.e., 1996–2019). Significance of the fixed effects in the models was calculated with Type II tests using the function Anova in the car package22. We previously checked the model for overdispersion and distribution fitting using function simulateResiduals (package DHARMa23, simulations = 999). The variable PGR expresses the change between year t and t + 1. We included AI at t as a covariate in the model, in order to take into account density-dependence in hare PGR (the extent to which the abundance changes in between year t and t + 1 depends on the abundance during year t). For this to make biological sense, we rescaled the covariable AI so that it has mean equal to zero. Thus, the effect of the other predictor variables in the model (i.e., “Vole” and “Province”) was interpreted as the effect that these variables have on PGR when the abundance value is at 0. Thus, the effect of “Vole” and “Province” on PGR will be obtained by the mean value of abundance.We assessed the effect of vole outbreak years on the Iberian hare’s population PGR by running a multiple Pearson correlation (function ggscatter) between PGR and AI, considering both, PGR for all the years of the study period (i.e., 1996–2019) and only those years where vole outbreaks were detected (i.e., 1997, 1998, 2007, 2008, 2014, 2019).Finally, we tested for difference in the prevalence of F. tularensis on Iberian hare’s during years with or without vole outbreaks using a GLMMTMB21 with a binomial family distribution and a logit link function, where prevalence of F. tularensis in hares was the dependent variable, and “Vole” outbreak years and “Province” (i.e. Palencia, Valladolid and Zamora) were the responses variables. In this case, the variable “Vole” outbreak years included three levels (i.e. 0 = no vole outbreak, 1 = vole outbreak year, 2 = one year after vole outbreak), to assess if F. tularensis prevalence in hare also persist one year after a vole outbreak. “Year” of sampling was included as a random factor (i.e., 2007–2016). Due to the limited sample size, we did not include the interaction between “Vole” and “Province” to not overfit the model. We also previously checked the model for overdispersion and distribution fitting using function simulateResiduals (package DHARMa23, simulations = 999). All analysis were carried out using the R statistical computing environment24. More

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Random forest: feature importance and interactivityOur random forests produced highly accurate predictions of local stability when trained on model output from the full dataset (e.g., AUC = 0.998 across all 5 parameters, see Fig. 2A) and all tested subsets. Running random forests on the full results set with all five parameters as predictors indicated both demographic and trophic rates were important to understanding resultant model stability. Moreover, results reveal that whether in multi-stage (red line; Fig. 2A) or single stage herbivory (e.g., ({a}_{2}) = 0, ({a}_{F}) ≥ 0; blue line Fig. 2A), parameters’ contribution to predictive power is related to their interactivity with other parameters (blue line; Fig. 2A). Note, a similar analysis with ({a}_{2})  > 0 and ({a}_{F}) = 0 is not possible because this type of herbivory is always stable.This interactivity was apparent in our attempts to understand how our specific parameters affected the behavior of our model in Eq. (1) via studying their effects as features in driving random forest predictions. Initial investigations into individual feature effects revealed that the effect of any single feature (parameter) on trophic dynamics could change substantially based on the values of our other features (parameters). Specifically, the average marginal effects (e.g., PD plots; Fig. S3) on simulation dynamics belied a high degree of variability in feature effects throughout the simulation data (e.g., ICE plots; Fig. S3).Breaking down results into further subsets of set specific attack rates with varying demographic rates revealed that this variability in feature effects was largely based on the changes in feature importance and effect over different allocations of herbivory on ontogenetic stages. This breakdown affected the relationship between importance and interactivity (Fig. 2A) such that it was inconsistent but still visible in aggregate across our simulation parameters (Fig. 2B,C). Figure 2D–F depict how different allocations and intensity of herbivory across plant ontogeny change the influence of each demographic parameter in driving model stability.Given how the influence of plant demographic rates over model behavior changed across trophic allocation (Fig. 2D–F), we first focused in depth analysis on variable demographic rates across static allocations of herbivore attack rates. By limiting the number of varying features, we use multivariate analysis to develop a fuller understanding of dynamics in subsections of the data which functioned as a scaffolding for further investigation. Specifically, we took a hierarchical approach, first developing an understanding of single-stage herbivory as a basis to study single-stage dominant herbivory (Fig. 3), which then leads us to a better overall understanding of our system’s dynamics across all trophic rates.Figure 3Interactive feature effects on model behavior. Across different herbivory allocations, partial dependence (PD) plots (A,C,E) show interactive effects between maturation rates on categorical simulation stability. Threshold plots (B,D,F) extend this analysis to include gradations of seed production rates. (A,B) Herbivory allocation ({a}_{F}) = 1.0 and ({a}_{2}) = 0.0. (A) Partial dependence plot shows probability of stability across all values of ({r}_{F}). (B) Threshold plot shows the location of the threshold between stable and unstable dynamics in {({g}_{12}),({g}_{2F})} parameter space as a function of seed production levels (({r}_{F})). (C,D) Herbivory allocation ({a}_{F}) = 0.2 and ({a}_{2}) = 1.0. (C) Partial dependence plot shows probability of stability across all values of ({r}_{F}). (D) Threshold plot shows the location of the threshold between stable and unstable dynamics in {({g}_{12}), ({g}_{2F})} parameter space as a function of seed production levels (({r}_{F})). (E,F) Herbivory allocation ({a}_{F}) = 1.0 and ({a}_{2}) = 0.2. (E) Partial dependence plot shows probability of stability across all values of ({r}_{F}). (F) Threshold plot shows the location of the threshold between stable and unstable dynamics in {({g}_{12}), ({g}_{2F})} parameter space as a function of seed production levels (({r}_{F})).Full size imageSingle stage consumptionIn the case of the seedling-only herbivore (({S}_{2}); via ({a}_{2})  > 0 and ({a}_{F}) = 0), all simulations produced stable trophic dynamics. This occurs because density loss in the seedling stage means more juveniles never reach maturity and reproduce themselves19. This essentially reduces the effective reproduction rate, limits the reproductive plant density, and decreases resources available to the herbivore (similar to lowering intrinsic reproduction in the classic Lotka–Volterra model). In fact, seedling herbivory only induced oscillations at higher handling times, a common effect of high handling time (results not shown).On the other hand, concentrating consumption on the fecund stage ((F)) can induce both stable and oscillating trajectories (Fig. S4). Consumption of (F) does not induce the same regulation of reproductive potential that stabilizes under seedling-only consumption, and so is vulnerable to boom/bust populations cycles. We chose the two most consistently important (Fig. 2B) and interactive (Fig. 2C and Fig. S5) parameters, ({g}_{12}) and ({g}_{2F}), in order to search for dominant effects on model behavior and their interactions. These parameters functioned as focal axes for our two-dimensional PD plots36. These PD plots depict the estimates of marginal effect of each parameter on random forest predictions, which in this case is categorical stability (Fig. 3A). We can see that stability estimates are increased by lowering either or both per-capita germination and/or maturation rates (({g}_{12}) and ({g}_{2F})). Demographically, reduced maturation rates shift the ratio of plant population density across its ontogeny, creating a larger juvenile population shielded from consumer pressure. Trophically, this restricts resources for the herbivore, thereby limiting losses in plant density due to herbivory (({theta }_{F})) relative to the overall plant density.This mechanism is so influential in determining trophic dynamics, its effect on stability is statistically detectable pre-simulation via equilibrium values. Losses in plant density due to herbivory are labeled under brackets in Eq. (1) as ({theta }_{F}) and ({theta }_{2}), which we can represent as ({theta }_{F}^{*}) and ({theta }_{2}^{*}) at equilibria. Relative to overall plant density we can define a ratio for plants of consumptive losses to total density (L:D ratio) such that:$$mathrm{L}:mathrm{D ratio}=({theta }_{F}^{*}+ {theta }_{2}^{*})/({S}_{1}^{*} +{S}_{2}^{*}+{F}^{*}).$$
(2)
When applied as a predictor variable on the same adult-herbivory subsection presented in Fig. 3A via a simple linear regression, we can see that L:D ratio alone explains ~ 45% of the variance of the maximum eigenvalue in simple linear models (F-statistic: 4578 on 1 and 5598 DF, p-value:  More

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When studying windfarm developments at the European region scale, the high densities of windfarm developments on blanket bog in Galicia and Greater Manchester (north England) are influenced by the total extent of the recognised blanket bog which is lower in Spain (31.2 km2 total extent) and in Greater Manchester (40.8 km2 total extent) in comparison to other regions (Fig. 1), although no relationship between the total extent of recognised blanket bog and the windfarm developments (wind turbines, tracks and total affected area) was found. Although the rest of the European regions across Spain showed lower densities of windfarm infrastructures (Fig. 2), the total extent of recognised blanket bogs across those regions was under 1 km2 (Fig. 1) meaning that the majority of recognised Spanish blanket bogs could be under threat due to their small size and the potential impact of windfarm infrastructures, if installed. In addition to this, previously unrecognised Spanish blanket bogs that have now been reported17 that could also be under pressure as the lack of formal recognition and protection leaves this habitat exposed to a range of anthropogenic activities, including windfarm developments. In fact, some examples of blanket bogs with extensive damage have been identified and reported in Galicia25, and more recently in Cantabrian blanket bogs17.Spanish unmapped areas of blanket bog at the edge-of-range of this habitat in the south of Europe are, therefore, particularly at risk from windfarm developments, and may disappear before their extent and importance can be defined40. Currently, new renewable energy regulations have been developed as a result of the climate emergency, and several windfarm developments have been proposed in ecologically sensitive areas, where blanket bogs have been reported (e.g. Sierra del Escudo, Spain) increasing the pressure on this habitat. Spanish blanket bogs also have specific characteristics, such as their small size as a consequence of the topographical limitations (e.g. slope) for their development26, meaning that they usually only cover the hill summits, where wind energy potential is at its greatest. Since blanket bogs are small and the windfarm development may cover all of the hill summit for their installation, many blanket bogs will be irrevocably damaged40.Most of the Galician blanket bogs were protected in 1999, under the Natura 2000 network and were declared as Special Area of Conservation (SAC) in 2014. However, between 1999 and 2012, Galician blanket bogs underwent severe and significant alterations in the peatland surface as a consequence of the large number of windfarm developments41 that were established during the period (Table A—Supplementary information), even when the site was incorporated into the Natura 2000 network (Table B—Supplementary information). Despite available scientific evidence that showed the potential environmental risks for these vulnerable ecosystems, windfarms were installed in what this work found to be the most extensive windfarm infrastructures across recognised European blanket bogs (Fig. 2).The incomplete current understanding of the extent of Spanish blanket bogs highlights the need to improve the completeness and representativeness of their current records across the Spanish Atlantic biogeographical region to include, within Natura 2000, a sufficient cover of their occupied area, in proportion to the representation of this natural habitat type in the Member state, for which it could therefore be concluded that the network is complete. Due to the increasing evidence highlighting how important the transitional areas are within the blanket bog complex42, other peatland types and wet heaths should be also considered when recognising and protecting blanket bogs. Mapping unrecorded blanket bogs must be a priority to fully understand the geographical and climatic range of this habitat, and obligatory protection under the Habitats Directive (92/43/EEC) is key to protecting the southern edge-of-range of this habitat.In addition to the lack of protection and updated inventories, the priority status included in the Habitats Directive, key to promoting their protection and restoration, is only for active blanket bogs, excluding other degraded blanket bogs with the potential to be active (carbon sinks), if they are restored. An approach similar to that of Scotland, where degraded blanket bogs are included33,39, could promote blanket bog restoration across Europe and improve the protection of this natural carbon storage.Many countries have also misinterpreted the active status of the blanket bog meaning that it is difficult to define whether the recognised blanket bog habitat is classed as a priority or not. Some countries, such as the Republic of Ireland, have classified as 7130 only active blanket bogs36, meaning that degraded blanket bogs lack appropriate classification and incorrectly applying the Habitat Directive designation as not all blanket bogs are included. The priority status is given when the habitat is particularly vulnerable or unique to the EU and necessitates additional measures for their protection and surveillance; however, whilst some blanket bogs may not act currently as carbon sinks, they still contain large amounts of carbon, and when restored they can recover their carbon sink function1, and then act to mitigate climate change.The issue of windfarm developments across the Republic of Ireland has been previously reported using a peat map43. However, despite researchers highlighting the importance of excluding vulnerable peatland ecosystems in future developments44, new areas of windfarms have been built affecting further recognised blanket bogs. At least 79 wind turbines have been installed in the Republic of Ireland since 2008 on recognised blanket bogs (Table A—Supplementary information) representing the 9.8% of the total onshore turbines installed in the country (Table 3), highlighting the importance of this conflict. The contribution of wind energy production to electricity supply was predicted to be up to 30% by 202044. In 2020, wind energy consumed in the Republic of Ireland represented 36%45. This represented an average annual increase of wind energy consumption of 16.9%45 between 2005 and 2020, which may explain part of the increase of 42% in wind turbines since 2008 (Table A—Supplementary information).Table 3 Total % of turbines on blanket bog (recognised/national inventories) in relation with the total turbines installed by country.Full size tableAcross Europe, several governments have developed climate action plans that over the next decade promote renewable energies to reduce carbon emissions. The government of the Republic of Ireland is aiming to generate up to 80% of electricity from renewable energy by 2030, providing support for onshore windfarm developments with an increase of up to 32% of the renewable energy production by 2030, but with a favourable preference for offshore wind energy production (up to 52% of the renewable energy production)46. This may help to reduce the conflict between blanket bogs and windfarm developments. Currently, windfarm annual energy production on blanket bogs accounts for 263.4 MW, 6.1% of the total production of wind energy in the Republic of Ireland47.The promotion of onshore wind energy production46 and the lack of protection of the full extent of blanket bogs are also threats that need to be considered in the Republic of Ireland. In 2008, a peat map was published showing the distribution of blanket bogs and raised bogs across the Republic of Ireland43. However, the inventory of current recognised blanket bogs under the Habitats Directive does not cover the full extent reported in this research43. While the total extent of recognised blanket bogs under the Habitats Directive 92/43/ECC reported a total of 3621 km2 of blanket bogs36, the real extent of blanket bogs across the country could be up to 2.5 times more (9202 km2)43, highlighting the lack of protection and the potential further increase of the windfarms and peatlands conflict in the Republic of Ireland as it happens in Spain and Scotland.The lack of recognition of blanket bog habitat in combination with the promotion of wind energy production across the island of Ireland could affect further areas of blanket bog, increasing the degradation of blanket bogs. An urgent review of inventories needs to be promoted in both countries, the Republic of Ireland and Northern Ireland, to fully assess the impact of the extensive areas of windfarms across the whole island.In Scotland, the pressure of windfarm developments on blanket bogs is also evident, where the Scottish Planning Policy considers classes 1 and 2 as areas of significant protection; although, windfarm developments may be possible under some circumstances48 as is permitted under the Habitats Directive across the EU29. However, to assess the impacts of windfarms on peatlands in a consistent way and evaluate the environmental impact of potential new developments on carbon-rich soils, a carbon calculator has been developed by the Scottish Government49. The carbon calculator allows users to estimate the carbon savings of windfarms installed on peatlands, although they highlight the importance of long-term management in relation to the final net carbon calculation49. Nonetheless, installing windfarms on non-degraded peatlands has been reported as unlikely to reduce carbon emissions even when the management has been considered carefully and it should be avoided 30. Therefore, peatlands under classes 1 and 2 considered by the Scottish government as a priority should be excluded from any windfarm developments (currently representing over 16% of onshore turbines, Table 3); especially considering the current policy of increasing onshore windfarms in Scotland50. Long-term research is needed to fully assess the impacts before new windfarm developments are installed.The difference between the recognised blanket bogs included in the EU Habitats Directive and the Scottish national inventory highlights the importance of updating and defining the complete extent of blanket bogs to facilitate their protection and restoration.In this novel research, the extent of windfarm developments across all recognised European blanket bogs under the Habitats Directive have been assessed. Large extents of blanket bogs have already been damaged, concentrated in the edge-of-range of this habitat and directly affecting hundreds of hectares of blanket bog across the rest of Europe. The full potential long-term damage to the habitat functionality is still unclear, but scientific evidence supports the negative impacts of windfarm developments on this critical habitat. European blanket bogs need further scientific evidence to demonstrate the real benefit of incentivising the reduction of carbon emissions by installing onshore windfarm infrastructures on peatlands which are causing the degradation of the most important long-term natural carbon sink and storage ecosystems. A strategic restoration plan and appropriate relevant legislation would be beneficial to promote the safeguarding of blanket bogs in the UK after Brexit. An urgent revision and compliance of the legislation regarding the protection of blanket bogs needs to be implemented, especially under the current trend of promotion and increasing legislation on renewable energy to reduce carbon emissions. An improvement of the national inventories across the EU and UK protected area networks is critical to implement the recognition, protection, and restoration of this habitat, in order to guarantee its favourable conservation status and its function as a long-term carbon sink to mitigate climate change. More

Decline in organism size is seen as a major biological response to climate change, and can be particularly pronounced in aquatic ectotherms such as fish, with subsequent implications for fishery yield and food security. However, as well as being modulated by climate factors, the fish population size structure can also be impacted by biotic (competition, predation) and other human factors (harvesting). For migrating species such as salmon, while smaller size may represent reduced size at maturity, it may also indicate faster maturation. More

Shuang Gao from Bjerkens Center for Climate Research in Norway, and colleagues from Germany and the United States explored future changes in marine primary production and carbon uptake under climate scenarios using the Norwegian Earth-system model, with four river transport configurations incorporating established future economic development and nutrient-use efficiency pathways. The researchers find that riverine nutrient inputs lessen nutrient limitation under warmer conditions. In the future, the effect of increased riverine carbon may be larger than the effect of nutrient inputs on the projections of ocean carbon uptake. In the historical period, increased nutrient inputs are considered the most prominent driver of carbon uptake. The results of this study are subject to model limitations, and high-resolution models should be used to assess the future impact. More