Philippa Kaur

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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

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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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

Scanning electron microscopy and energy spectrum analysisThe SEM images show the morphological structures of DA and Al-DA before and after adsorption (Fig. 1). DA and Al-DA have disk-like microstructures29 with sur-faces containing both large and small pores, that is, DA and Al-DA have unique multi-level pore structures. The main component of DA and Al-DA is silica, which has a large specific surface area, good thermal stability, and is a natural green material for use as a water treatment agent with a porous structure31. The micrographs show that before adsorption, the DA surface is smooth with a distinct pore structure, whereas modification with aluminium hydroxide makes DA coarse and loose because of the formation of amorphous aluminium hydroxide colloids32. After adsorption, the surface pore structure is covered over for DA and completely covered over for Al-DA, which indicates that F− reacts with Al3+ to form nanoscale precipitates22. The results of the EDS analysis (Fig. 2) show that the content of elemental Al increased from 3.96 to 12.74% after DA was modified with aluminium hydroxide, indicating that Al adhered effectively to the modified DA surface. After adsorption, the content of elemental Al decreased from 3.96 to 1.36% for DA and from 12.74 to 2.03% for Al-DA, which fully confirmed that fluorine preferentially combined with Al to form aluminium precipitates during adsorption, thereby decreasing the Al content.Figure 1SEM images of DA and Al-DA before and after adsorption. (A) Before DA adsorption. (B) After DA adsorption. (C) Before Al-DA adsorption. (D) After Al-DA adsorption.Full size imageFigure 2EDS graphs of DA and Al-DA before and after adsorption. (A) Before DA adsorption. (B) After DA adsorption. (C) Before Al-DA adsorption. (D) After Al-DA adsorption.Full size imageXRD analysisThe surface mineral composition and crystallinity of the materials before and after adsorption were analyzed by XRD (Fig. 3). In the DA and Al-DA patterns, the wide diffraction peaks at approximately 22.0°, 26.0°, and 50.0° mainly correspond to amorphous SiO2, and the diffraction peak at approximately 35° mainly corresponds to amorphous Al2O3, indicating that the material is polycrystalline29. It has been re-ported that amorphous materials may be good adsorbents because of a large specific surface area and numerous active sites33. Many Al(OH)3 peaks and NaCl peaks appear in the XRD pattern of Al-DA, indicating the successful modification of DA by aluminium hydroxide. After adsorption, Na3AlF6 peaks appear in the DA pattern, and Na3AlF6 and AlF3 peaks appear in the Al-DA pattern, whereas the characteristic peaks of NaCl are absent in the Al-DA pattern, which indicates the participation of NaCl in the adsorption process. It has been demonstrated that in the presence of excess sodium fluoride in the reaction solution, the generated aluminium fluoride combines with sodium fluoride to form a NaAlF4 intermediate, which is subsequently converted to cryolite complexes by further adsorption of sodium fluoride34. This result confirms the XRD mapping results.Figure 3XRD patterns of DA and Al-DA before and after adsorption.Full size imageInfrared analysisFigure 4 shows the FTIR spectra of DA and Al-DA before and after adsorption: peaks at 3418, 1635, 1096, 791, and 538 cm−1 appear in the spectrum of DA spectrum before adsorption, and peaks at 3630, 3449, 1637, 1094, 913, 793, and 538 cm−1, appear in the Al-DA spectrum before adsorption. The strong and broad band centered at 3418 cm−1 is due to the stretching vibration of the adsorbed water hydroxyl group (O–H) and the surface hydroxyl group, the vibrational peak at approximately 1635 cm−1 is probably from bound water or the surface hydroxyl group; the peaks at 1096 cm−1 and 538 cm−1 correspond to siloxane groups (Si–O–Si–) and an Al–O absorption band, respectively; and the strong oscillations at 791 cm−1 may be attributed to inorganic Al salts35,36,37. The original absorption peak in the DA spectrum is shifted in the spectrum of DA modified with aluminium hydroxide, confirming the successful modification of DA. The shift of the band at 3418 cm−1 in the DA spectrum to a higher frequency at 3623 cm−1 in the DA spectrum after fluoride absorption is caused by fluoride bonding and has been previously reported38. Another noticeable change in the spectra of DA and Al-DA before and after adsorption is the increase or decrease in the intensity of bending vibrations of specific peaks because the highly electronegative fluoride may have an inductive effect on the respective groups that leads to a blueshift, and the formation of hydrogen bonds leads to a redshift and broadening of the spectral band. The shifts and changes of these peaks indicate the interaction of fluoride with the respective groups29. The new peak at approximately 1170 cm−1 in the spectra of DA and Al-DA with adsorbed fluoride may be due to the formation of Al-F bonds6. The IR spectra show that the formation of a new bonding electronic structure by surface complexation with F− is one of the main mechanisms for the adsorption of F−.Figure 4FTIR spectra of DA and Al-DA before and after adsorption.Full size imageZeta potential analysisThe zeta potential of the material surface plays a very key role in the adsorption process, which reflects the surface charge properties of the material under different pH conditions, and also reflects the surface properties of the material. To obtain the zero charge point of the material, we studied the potential change of the material under different pH values. The results are shown in Fig. 5. In the range of pH 3–11, the zeta potential of the two adsorbents decreased linearly with the increase in pH, and the pHzpc of DA and Al-DA were 9.84 and 10.61, respectively. When pH  More

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