Ecology

Collection of wolf hair samplesHair samples were collected by researchers from opportunistically found-dead wolves upon standard necropsy (all the Alpine and part of the Iberian samples) or in the field (all the Dinaric-Balkan and most of the Iberian samples), or from legally harvested wolves (only in the Scandinavian population). At the time of sample collection, wolves were legally harvested in Sweden, Slovenia, and Spain, and under total protection in Portugal and Italy. Hair samples were collected from four body regions, when possible: lumbar (n = 133), dorsal cervical (n = 66), tail (n = 33) and ventral thorax (n = 27) (Tables S1 and S2). The hair was cut as close as possible to the skin with scissors to avoid collecting hair follicles, but in some samples, hairs were pulled from the carcass. Samples were stored at room temperature in paper envelopes. Age, sex, date, and cause of death/capture, geographical location, body mass, and total length were obtained for most of the wolves.Age was estimated by the dental eruption and wear or cementum age analysis and classified as ‘juveniles’ ( 2 years)40, or ‘unknown’. Sex was assessed by inspection of genitalia. Causes of death were classified as ‘acute’, likely lasting minutes to hours (vehicle accident and legal or illegal shooting); ‘subacute’, likely lasting hours to days (drowning, poisoning, trapping and intraspecific aggression); ‘chronic’, likely lasting several weeks (infectious diseases—canine distemper, canine parvovirosis, leptospirosis; sarcoptic mange; or neoplastic diseases) or ‘unknown’. Total length was obtained by measuring with metric tape (1 mm precision) the distance from snout to the distal end of the last tail vertebrae. The body mass was measured with 100 g precision with scales.The detailed protocol for the handling of wolves live trapped in the scope of ecological and conservation studies (n = 7, all from the Iberian population) has been previously described5. Traps were monitored twice every day, in the early morning and late afternoon, hence the duration of restraint after capture was unknown for 8 wolves, potentially up to 12 h. Trap-alarms were deployed in the capture of 2 wolves, with 41 and 70 min intervals between activation of the alarm and administration of the drugs. Live trapping was conducted under permits issued by the nature conservation authorities of Portugal (Instituto de Conservação da Natureza e das Florestas: 338/2007/CAPT, 258/2008/CAPT, 286/2008/CAPT, 260/2009/CAPT, 332/2010/MANU, 333/2010/CAPT, 336/2010/MANU, 26/2012/MANU, and 72/2014/CAPT) and Spain (Dirección Xeral de Conservación da Naturaleza, Xunta de Galicia: E-0020/13-PNPE, 095/2013; Consejería de Medio Ambiente, Principado de Asturias: 31/08/2017-BOPA 05/09/17) and according to European Union directives on the protection of animals used for scientific purposes (Directive 2010/63/EU) and international wildlife standards41,42. The study was undertaken in compliance with the ARRIVE guidelines43.Cortisol extractionThe protocol for the extraction of cortisol from the hair was adapted from previously described procedures15,27. Forty mg of guard hairs were separated from the undercoat and placed in 15 ml falcon tubes. Hair follicles were cut whenever found in the sample. For each sample, the length of three intact hairs was recorded. The samples were washed twice with 40 µl of distilled water/mg hair and three times with the same amount of isopropanol. In each washing step, the samples and washing solution were vortexed, the supernatant discarded, and the hair dried using clean paper towels. After the final wash, samples were dried overnight at room temperature and 30 mg of hair cut into a 2 ml polypropylene screw cap plastic tube with five 4 mm steel beads added to each tube.The hair was ground to a fine powder in a FastPrep sample homogenizer (MP Biomedicals, USA) for four times 1 min at 6.0 m/s. 50 µl methanol/mg hair were added to each sample and sonicated for 30 min at 50 Hz at 50 °C. The samples were incubated for 18 h at 50 °C in an orbital shaker at 160 rpm, centrifuged for 15 min at 14,000g at 20 °C, and 1000 µl of supernatant was collected to a screw cap glass chromatography vial and dried at room temperature in a gentle stream of nitrogen gas. Due to restrictions on laboratory use during the SARS-Cov-2 pandemic, some batches of samples were instead evaporated overnight on a suction hood. This unexpected change in the methanol evaporation protocol was recorded and accounted for in the statistical analysis.Cortisol quantificationA commercial competitive ELISA kit (Cortisol free in Saliva ELISA, Demeditec, Germany) was used to quantify the concentration of cortisol, following the manufacturer’s instructions. The kit plate wells are provided coated with polyclonal rabbit antibody against cortisol, and cortisol-horseradish peroxidase was used as conjugate. According to the manufacturer, the cross-reactivity of the test to selected steroids is low (Table S3), the intra-assay variation is 3.8–5.8% and the inter-assay variation is 6.2–6.4%. Samples, standards, and controls were tested in duplicate.The 4-parameter standard curve was calculated from the log-transformed cortisol concentration of the standard solutions and their measured OD45044. Standard curves were estimated using the software GraphPad Prism 6.04 (GraphPad Software, La Jolla, California USA), and yielded an average R2adjusted = 0.991 (range 0.968–0.999). The cortisol concentration of the reconstituted samples was estimated from the standard curve and converted to cortisol concentration as picograms (pg) of cortisol/mg of guard hair.Intra and inter-assay coefficients of variation were estimated for six ELISA assays of 37–40 samples each. The low and high controls included in the kit were used to estimate the inter-assay coefficient of variation and the duplicate runs of each sample were used to estimate the intra-assay coefficient of variation. Linearity was assessed by two-fold dilutions (1:1, 1:2, 1:4 and 1:8) of 4 extracted samples, comparing the expected and observed concentrations. Recovery was assessed by spiking 6 ground hair samples with known concentrations of cortisol (50, 25, 12.5, 6.25 pg/mg, and no spiking), comparing the expected and observed concentrations.The intra-assay coefficient of variation of the ELISA assays ranged from 6.50 to 9.97% (average 7.66%). The inter-assay coefficient of variation was 11.54% for the low concentration controls and 9.08% for the high concentration controls (average 10.31%). Assay linearity was 91% for the 1:2 dilution, 103% for 1:4, and 117% for 1:8 (average 103%). The recovery of cortisol averaged 94%, being 73% for the 50 pg/mg spiked samples, 74% for 25 pg/mg, 95% for 12.5 pg/mg, and 113% for 6.25 pg/mg.Determinants of hair cortisol concentrationThe potential determinants of HCC investigated included wolf intrinsic variables: sex, age, body condition, body structural size, month of death/capture, and wolf population. The scaled mass index was selected as a measure of body condition45 and estimated from the log-transformed body weight (g) and total length (mm). Log-transformed total length was used as an indicator of body structural size46. Samples were assigned to the Iberian, Alpine, Dinaric-Balkan, or Scandinavian wolf populations16 from the geographical location of the death or live-trapping sites (Fig. 1).The relationship between HCC and additional variables related to the sampling procedure or to the work conducted in the laboratory (length of hair used for cortisol extraction, sample storage time, body region, cause of death/capture, and methanol evaporation protocol), herein referred to as methodological variables, was also investigated as potential confounding variables. Sample storage time was the period in months between death/capture and cortisol extraction. In those samples for which only the year of death was available, 30 June was assigned as the date of death, solely to estimate storage time. All continuous variables were standardized to their z-scores.Statistical analysisFirst, the effect of body region was investigated by a linear mixed model with HCC as the dependent variable, and the independent variables body region, as a categorical fixed effect, and individual wolf, as a random effect. The lumbar region was set as the reference class as it was the most represented in our sample (Table S1). Data from 27 wolves for which samples were available from all 4 body regions were used in this analysis. Four outliers in the dataset violated the assumption of normality in the residuals of the model comparing HCC across body regions (Fig. S1A) and were excluded from this model’s dataset (Fig. S1B).Second, the effect of intrinsic and methodological variables on HCC from the lumbar body region was investigated by another linear mixed model with sex, age, body condition, body structural size (standardized log-transformed total length), cause of death/capture, wolf population, hair length, sample storage time, and methanol evaporation protocol as fixed effect independent variables. The month of death/capture was included as a random effect. Reference classes for the categorical variables were set as female, adult, acute death, Iberian population, and methanol evaporation by nitrogen gas stream. Two outliers in the dataset violated the assumption of normality in the residuals of the model (Full model, Table S4) and were excluded from this analysis (Fig. S1C,D).The goal of this analysis was to assess the relationship between HCC and wolf intrinsic variables, controlling for the potential confounding effect of the methodological variables. Starting from the full model (Table S4), models including all possible combinations of variables were ranked by their AICc using the package “MuMIn”47 in R 3.6.148. The most supported model was selected for inference and models with ΔAICc  More

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Seasonal conditions during crop growthThe groundnut crop produces optimum yield in the regions receiving rainfall between 200 to 1000 mm8. The total rainfall during groundnut growing season was 257.10 mm and 403.10 mm at Baljigapade in 2018 and 2019, respectively, wherein Pavagada (2018) total rainfall was 53.90 mm (Fig. 1). In 2018, both Pavagada and Baljigapade received very low and negligible rainfall during the reproduction and harvest stage of groundnut. Optimum temperature for groundnut production ranges between 20 to 30 °C and growth and pod formation limited below 16 °C and above 32 °C9. The monthly mean atmospheric temperature was ranged from 23 to 27 °C and 21 to 26 °C at Baljigapade in 2018 and 2019, respectively, wherein Pavagada ranged from 25 to 28 °C. At all three locations, the monthly mean atmospheric temperature was slightly high during the early vegetative growth of groundnut and it was progressively decreased as the crop reaches its maturity stage (Fig. 1). All three locations recorded higher and lower mean monthly sunshine hours (hours day−1) during peg initiation to pod filling stage (September and October) and early vegetative growth of groundnut (July and August), respectively.Growth parameters of groundnutAnalysis of variance revealed that treatment, location, and their interaction had a significant effect on plant height and number of branches at harvest (P  More

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I’m from a fishing family. My grandfather was a fisherman when he was a young man, working out of Fano, the Italian town where I grew up and still live. I’m used to the smell of fish.I’m pictured during an overnight shift on the fishing boat RIMAS. I work from 5 p.m. until 9 a.m. with fishermen from nearby Cesenatico on the north Adriatic Sea. It’s a small boat: there’s only six or so of us on board. At night, the fish are most active and we can avoid other vessels.The nets scrape the sea bed for the catch but sometimes they also catch turtles who often die in the nets or on board. That’s where I come in. The net I’m holding is designed to allow turtles to escape: it has a hole at the top they can swim out of. We call it TED — short for ‘turtle excluder device’. The TED is made from a high-strength plastic, and is based on decades of work and research aimed at reducing the bycatch of turtles from trawling. Turtles and some larger fish can leave through the escape hatch, but the current holds most of the catch in the net.I ensure that the net is working, and that the fishermen we’re collaborating with can still catch enough for their livelihoods while protecting turtles. The work is part of research by the Cetacea Foundation, based in Riccione, Italy, in collaboration with the University of Pisa, where I’m a field researcher. It is financed by the LIFE programme, the European Union’s funding instrument for the environment and climate action.I love this work. It means I’m not stuck in an office all day and instead can enjoy the ocean and work closely with people who live by the sea. I get to be a researcher who works outside, rather than being hunched over a microscope.When my grandfather was fishing in the 1970s, there were more fish and more turtles around. At the foundation, we save 50–60 turtles a year, most of them harmed because of fishing. If we can protect turtles by rolling out this device to fishermen all across the Adriatic, I’d see this work as a success.

Nature 604, 210 (2022)
doi: https://doi.org/10.1038/d41586-022-00930-w

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