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    Intolerant baboons avoid observer proximity, creating biased inter-individual association patterns

    All research methods included in this study were performed in accordance with the relevant guidelines and regulations, under ZA/LP/81996 research permit, with ethical approval from the Animal Welfare Ethical Review Board (AWERB) at Durham University. The authors confirm the study was carried out in compliance with ARRIVE guidelines.All inter-individual association data was collected between June 2018 and June 2019 on a wild habituated group of Afro-montane chacma baboons in the western Soutpansberg Mountains, South Africa (central coordinates S29.44031°, E23.02217°) (for study site description see2). The study group was habituated circa 2005 and was the focus of intermittent research attention until 2014. The study area experienced long-term anthropogenic activities (local farming, forestry, and residences) prior to 2005, as such, consistent interactions with humans have been ongoing with this population for some time. From 2007 onwards numerous researchers were able to collect expansive datasets on the study group (e.g. Refs.17,18), indicating that habituation was at a typical level found elsewhere (also validated by AA and RH, who had researched chacma baboons elsewhere). From 2014 the group received full day (dawn until dusk) follows 3–4 days a week, with occasional gaps of up to 5 weeks in duration. These gaps did not appear to effect habituation levels, likely due to the presence of other researchers at the field site who always tried to act benignly when encountering the habituated group. The follow schedule was designed so that the study group retained as much of their natural interactions with predators as possible by ensuring the baboons spent significant time without observers who may influence the frequency and nature of predator–prey interactions19.The study site was located in a private nature reserve and the study group was not hunted during observation gaps or engaged in any conflict with humans, other than occasionally being scared (chasing, yelling, throwing stones etc.) from a small plantation by local workers, usually resulting in alarm barks and fleeing responses. However, the study group appeared adept at recognising the differences between researchers and these threats20. The majority of the study group’s home-range typically overlapped with the core area of the Lajuma Research Centre, and as a result, interactions with staff living in the area, unfamiliar researchers, and tourists were frequent. However, the baboons had not engaged in ‘raiding’ residences, threatening humans, or any other potentially negative symptom of habituation before the end of this study.Sampling methodology for proximity associations30-s focal sampling was used to collect proximity associations between all group members (excluding infants). All data was collected between June and December 2018 and January and June 2019; the majority of 2018s data was collected during the wet season, whilst most of 2019s data was collected during the dry season. To account for time of day, each day was split into four time-periods that were seasonally adjusted ensuring each period accounted for 25% of the current day length. A randomly ordered list of individuals was produced for each day, the first individual identified from the top 15 (approx. 20% of group size) individuals on the list was sampled immediately. Individuals could only be sampled once per time period per day, and a maximum of twice total per day. All individuals received at least 14 focal observations per time period (56 total) across the study period (see below for how we handled uneven sampling for some individuals). A video camera was used by AA (the only observer to collect this data) to record all focal observations (Panasonic HC-W580 Camcorder). At the end of the 30-s focal observation the identities of all neighbouring conspecifics within 5 m, 2.5 m, 1 m, and touching the focal animal were recorded (audibly by AA). We chose the end of the focal observation to record this data as this was most likely to reflect the conditions during the focal, i.e., the observer had been in proximity for at least 30 s.Neighbour information was extracted from video footage and entered manually by AA and AW. Data was split into separate years to reflect an observation gap of several weeks and to understand whether there was consistency in the hypothesized effects through time and to reflect underlying differences in environmental conditions during the two study periods; during the dry season fruits and seeds are scarce and day lengths are several hours shorter than in the wet season such that day journey lengths are often shorter in the dry season and animals are much more sedentary which could impact inter-individual spacings. In 2018 each individual was sampled between 28 and 30 times; 28 focals were randomly selected from each individual to make sampling even. For 2019 there were between 25 and 27 focals per individual; 25 samples of each individual were randomly selected. Observations were undertaken at a range of distances. For both years the median end observer distance was 4.5 m; data was thus split into close focal observations of less than or equal to 4.5 m (2018: n = 918, 2019: n = 809), and observations greater than 4.5 m (2018: n = 902 2019: n = 816). See supporting information Table S1 for summary statistics of the observation distances of each individual.We did not make any attempt to record our focal data evenly across the various habitats at our field site (see Supporting information text S1 for complete habitat descriptions) as our previous research indicated there was little difference in general spatial cohesion/inter-individual proximity patterns across these habitats (see Supporting information text S2 and Table S2). As a result, we considered it unlikely that there were fundamental differences in inter-individual association patterns across habitats, or that observers struggled to reliably detect or identify neighbours in dense habitats. We do acknowledge, however, that there will always be an element of bias with such methods, as observations were avoided, aborted, or excluded if visual obstructions (e.g., cliffs, rocks, walls, buildings, very dense vegetation etc.) prohibited accurate assessments; the observations used in the current study are from occasions when these factors were not an issue.During this study the group contained between 85 and 92 individuals. Age-sex class was defined according to secondary sexual characteristics (e.g., testes descending/enlarging, sexual swelling, canine eruption) and changes in pelage throughout juvenile development (see Supporting information text S3 for full descriptions). All 65 non-infant individuals that were present during 2017 (when displacement tolerances were calculated) and still remaining in the group by the end of 2019 were used in this study (4 individuals from the prior FID study were no longer present). There were a high number of births between 2018 and 2019, but none were independent by the time either of our sampling periods begun in 2018 or 2019. There was no immigration of foreign individuals, but two individuals disappeared, both during the 2018 focal sampling period. As a result, we had a very consistent pool of individuals to sample from during this study. We removed all data associated with the two individuals who disappeared as their occurrences as neighbours would have been poorly sampled (due to missing more than half the study) relative to the rest of the group which would have led to statistical biases21.Flight initiation distance procedureIndividual displacement tolerance estimates were previously quantified in our previous research2 using a flight initiation distance (FID) procedure22 that was completed between October 2017 and April 2018, prior and independent to the commencement of proximity association focal sampling in June 2018. Individual baboons were approached by an observer, and the distance at which the animal displaced away from the observer measured (see Supporting information Table S2 for summary statistics). This procedure was repeated 24 times for each individual baboon, with approaches spread evenly across two observers differing in familiarity. At the beginning of each approach we also recorded several behavioural, social, and environmental factors that could have hypothetically influenced an individual’s FID2 including whether the animal was engaged (e.g., digging or grooming) or not engaged (e.g., resting, chewing food, being groomed), habitat type (open/closed: see Supporting text S1), whether the animal was on the ground or sat on a low branch or rock within 50 cm of the ground, the number of conspecifics within 5 m of the focal animal, and whether there had been any external events within the preceding 5 min (e.g., alarm calls, aggressions, encountering another group or predator). During the approach, we also recorded the visual orientation distance (the distance at which the focal animal directed its line of vision towards the head of the approaching observer) and whether one of the focal animal’s neighbours had displaced/fled before the focal animal. Although all but neighbour flee first and external events showed some importance for predicting looking (see Table S4), FID was found to be distinct amongst individuals and repeatable within each individual, evidence that displacement tolerance may be an individual level trait2. Full details of methods, statistical analysis, and results (including comparison to the original model) for this updated model are in Supporting information text S4, with model summary results for the previous and updated models in Tables S3 and S4.The notion of an observer approaching a habituated primate may be considered atypical or likely to result in habituation/sensitization effects or agonistic behaviours being directed towards the approaching observers. However, our previous study2 showed that almost all approaches resulted in the animal passively relocating (98.85%), a very benign response identical to the behaviours of subordinate baboons displacing away from dominant conspecifics. This suggests that in this group, observers may be considered equivalent to a high-level social threat2. Throughout observation periods on habituated animals, observers are likely to approach or displace animals either incidentally or accidentally multiple times throughout the day, especially during lengthy focal observations. As such, the approach methodology is unlikely to represent a stimulus outside of the norm for our study animals. This may explain why displacement responses were so passive and why there was no evidence of habituation or sensitization effects across the group or individually through a range of temporal periods2 or after life-threatening events20. As a result, our situation was possible without risk of causing stress or anxiety in the study subjects, eliciting agonistic behaviours towards observers, or interfering with their prior habituation levels.Statistical analysisInfluence of tolerance and observer distance on inter-individual association patternsQuantifying displacement toleranceTo quantify displacement tolerance towards observers we extracted the individual conditional modes from the updated FID model using the ranef function in brms. Conditional modes are often referred to as Best Linear Unbiased Predictors (BLUPs) and are the difference between the predicted mean population-level response for a given set of treatments (i.e., population-level effects) and the predicted responses for each individual, and therefore infer the extent to which each individual differs from the population mean. The conditional modes and their associated standard deviations can be found in supporting information Table S5.To validate that the conditional modes from the updated model were both representative of the individual’s flight responses and in line with the estimates produced from our previous study2 we performed additional tests. Firstly, we performed a Pearson’s correlation between the conditional modes from the updated model and the conditional modes from the previous article. Individual tolerance estimates were consistent (r(63) = 0.915, p  More

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    Physiological and morphological effects of a marine heatwave on the seagrass Cymodocea nodosa

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    Six decades of warming and drought in the world’s top wheat-producing countries offset the benefits of rising CO2 to yield

    Wheat production and yield vis-à-vis climate trendsWheat is currently grown in all six continents except Antarctica. The leading producers include China, the Russian Federation, Ukraine, Kazakhstan (RUK), India, USA, France, Canada, Pakistan, Germany, Argentina, Turkey, Australia, and United Kingdom (Fig. 1 and Supplementary Table 1). The total grain production of these twelve countries is estimated at 600 megatons (2019 data), which accounts for over 78% of the global wheat production. The top three producers are China with 133.6 megatons per year (Mt y−1), RUK with 114.1 Mt y−1, and India with 103.6 Mt y−1. RUK contains the largest harvested area of 45.8 million hectares, followed by India with 29.3 million hectares and China with 23.7 million hectares (Fig. 1A). Despite a relatively small harvested area of 10.1 million hectares (only 22% of RUK’s harvested area), the United Kingdom, France, and Germany account for the world’s highest yields per hectare, with 8.93 tons ha−1, 7.74 tons ha−1, and 7.40 tons ha−1, respectively (compared with the world’s average yield of only 3.2 tons ha−1), accounting for a total yearly production of 79.9 Mt y−1.Figure 1Global wheat area and trends in wheat yield and climate in top-twelve global wheat producers (1961–2019). (A) Worldwide wheat cropping area (%)29, total harvested area (106 hectares in 2019), and wheat production (megatons for 2019) of the top 12 global wheat producers (China, RUK—Russia, Ukraine, and Kazakhstan, India, USA—hard red winter (HRW) and hard red spring (HRS), France, Canada, Pakistan, Germany, Argentina, Turkey, Australia, and United Kingdom) (Map was generated in Python 3.8.5; http://www.python.org). (B) Changes in wheat yield (tons per hectare) and (C) climate—mean daily temperature (red dashed line; °C) and the seasonal water balance represented as potential evaporation minus precipitation (blue line; PET—P in millimeters of H2O). A positive trend in PET-P indicates an increase in water deficit. The seasonal atmospheric [CO2] in μmol CO2 per mol−1 air is also shown in the insert of C (black line). Temperature, PET-P, and [CO2] shown in C are averaged values over the wheat-growing period and the shared area of the wheat-growing areas of the top 12 global wheat producers. Decadal trends in temperature (red) and PET-P (blue) as well as the significance levels of these trends are presented in C.Full size imageWhile all these twelve major wheat producers saw an increase in yield during the last six decades (Fig. 1B), China displayed the most noteworthy increase with a nearly sevenfold higher yield in 2019 than in 1961 and a mean total increase of 5.19 tons ha−1 for the period of 1961–2019. Germany, the UK, and France reported comparable yield increases of 5.20 tons ha−1, 5.19 tons ha−1, and 4.81 tons ha−1, respectively, during this period, suggesting an approximately 1.6-fold improvement since 1961 (Fig. 1B). Australia, RUK, and Turkey reported the lowest gains with only 0.87 tons ha−1, 1.26 tons ha−1, and 1.71 tons ha−1, respectively, representing improvements of 67%, 150%, and 175% in yield per hectare since 1961.Yield increase occurred despite the steep rise in temperature (nearly 1.2 °C) in the twelve countries during the last six decades (Fig. 1C). Water deficit—calculated as the difference between potential evaporative demand and precipitation (PET—P; mm H2O y−1)—also increased by an average of (sim) 29 mm of H2O for the same period. Increases in yield since the early 1960s were likely due to breeding and agrotechnological advances, improved management, and a steep rise in atmospheric [CO2] of (sim) 98 μmol mol−1, from 315.9 μmol mol−1 in 1961 to 413.4 μmol mol−1 in 2019 (insert in Fig. 1C).Unraveling the impacts of climate and [CO2] on yieldBased on previous studies30,31, we used a log-linear model to quantify the impact of [CO2] and daily minimum (Tmin), maximum (Tmax), and mean (Tmean) temperatures, as well as seasonal water deficit (PET-P), and rainfall distribution on wheat yield. Climate variables were obtained from the TerraClimate data set32, while monthly records of [CO2] from the Mauna Loa station were used to model the effects of CO2 (see “Methods”). To quantify wheat yield as a function of climate variables and [CO2], we included all 12 countries in the regression analysis. Supplementary Table 2 presents summary statistics of all variables, while Supplementary Fig. 1 depicts trends in Tmean and PET-P per country.Since climate variables tend to be correlated over time (Supplementary Table 3), controlling for all of these variables in the model facilitates the estimation of their distinct effect on yield. We used country-specific trends to distinguish changes in wheat yield related to climate and [CO2] from those attributed to agrotechnological advancements, changes in country-specific policies, and other local-changing factors (e.g., economic and population growth; more information on how this was done can be found in “Methods”). We also included country-specific effects across all models to account for unobserved time-invariant heterogeneity at the country level, such as geographical properties, edaphic characteristics, and other local-specific features (see “Methods”).Table 1 reports the estimated regression coefficients of four models, (1) using only temperature variables (T), (2) temperature and water-related (i.e., seasonal rainfall distribution and water deficit as PET-P) variables (T + W), (3) including [CO2] (T + W + C), and (4) the interaction between [CO2] and climate variables (T + W + C + interactions).Table 1 Effects of climate variables and [CO2] on log wheat yields of the world’s major wheat producers.Full size tableAmong the temperature measures, only Tmean had a consistently significant effect on yield (p  More

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    Microbial functional changes mark irreversible course of Tibetan grassland degradation

    Literature studyLiterature considering the effect of pasture degradation on SOC, N, and clay content, as well as bulk density (BD), was assembled by searching (i) Web of Science V.5.22.1, (ii) ScienceDirect (Elsevier B.V.) (iii) Google Scholar, and (iv) the China Knowledge Resource Integrated Database (CNKI). Search terms were “degradation gradient”, “degradation stages”, “alpine meadow”, “Tibetan Plateau”, “soil”, “soil organic carbon”, and “soil organic matter” in different combinations. The criteria for including a study in the analysis were: (i) a clear and comprehensible classification of degradation stages was presented, (ii) data on SOC, N, and/or BD were reported, (iii) a non-degraded pasture site was included as a reference to enable an effect size analysis and the calculation of SOC and N losses, (iv) sampling depths and study location were clearly presented. (v) Studies were only considered that took samples in 10 cm depth intervals, to maintain comparability to the analyses from our own study site. The degradation stages in the literature studies were regrouped into the six successive stages (S0–S5) according to the respective degradation descriptions. In total, we compiled the results of 49 publications published between 2002 and 2020.When SOM content was presented, this was converted to SOC content using a conversion factor of 2.032. SOC and N stocks were calculated using the following equation:$${{{{{rm{Elemental; stock}}}}}}=100* {{{{{rm{content}}}}}}* {{{{{rm{BD}}}}}}* {{{{{rm{depth}}}}}}$$
    (1)
    where elemental stock is SOC or N stock [kg ha−1]; content is SOC or N content [g kg−1]; BD is soil bulk density [g cm−3] and depth is the soil sampling depth [cm].The effect sizes of individual variables (i.e., SOC and N stocks as well as BD) were quantified as follows:$${{{{{rm{ES}}}}}}=,frac{(D-R)}{R* 100 % }$$
    (2)
    where ES is the effect size in %, D is the value of the corresponding variable in the relevant degradation stage and R is the value of each variable in the non-degraded stage (reference site). When ES is positive, zero, or negative, this indicates an increase, no change, or decrease, respectively, of the parameter compared to the non-degraded stage.Experimental design of the field studyLarge areas in the study region are impacted by grassland degradation. In total, 45% of the surface area of the Kobresia pasture ecosystem on the TP is already degraded2. The experiment was designed to differentiate and quantify SOC losses by erosion vs. net decomposition and identify underlying shifts in microbial community composition and link these to changes in key microbial functions in the soil C cycle. We categorized the range of Kobresia root-mat degradation from non-degraded to bare soils into six successive degradation stages (S0–S5). Stage S0 represented non-degraded root mats, while stages S1–S4 represented increasing degrees of surface cracks, and bare soil patches without root mats defined stage S5 (Supplementary Fig. 1). All six degradation stages were selected within an area of about 4 ha to ensure equal environmental conditions and each stage was sampled in four field replicates. However, the studied degradation patterns are common for the entire Kobresia ecosystem (Supplementary Fig. 1).Site descriptionThe field study was conducted near Nagqu (Tibet, China) in the late summer 2013 and 2015. The study site of about 4 ha (NW: 31.274748°N, 92.108963°E; NE: 31.274995°N, 92.111482°E; SW: 31.273488°N, 92.108906°E; SE: 31.273421°N, 92.112025°E) was located on gentle slopes (2–5%) at 4,484 m a.s.l. in the core area of the Kobresia pygmaea ecosystem according to Miehe et al.8. The vegetation consists mainly of K. pygmaea, which covers up to 61% of the surface. Other grasses, sedges, or dwarf rosette plants (Carex ivanoviae, Carex spp., Festuca spp., Kobresia pusilla, Poa spp., Stipa purpurea, Trisetum spp.) rarely cover more than 40%. The growing season is strongly restricted by temperature and water availability. At most, it lasts from mid-May to mid-September, but varies strongly depending on the onset and duration of the summer monsoon. Mean annual precipitation is 431 mm, with roughly 80% falling as summer rains. The mean annual temperature is −1.2 °C, while the mean maximum temperature of the warmest month (July) is +9.0 °C2.A characteristic feature of Kobresia pastures is their very compact root mats, with an average thickness of 15 cm at the study site. These consist mainly of living and dead K. pygmaea roots and rhizomes, leaf bases, large amounts of plant residue, and mineral particles. Intact soil is a Stagnic Eutric Cambisol (Humic), developed on a loess layer overlying glacial sediments and containing 50% sand, 33% silt, and 17% clay in the topsoil (0–25 cm). The topsoil is free of carbonates and is of neutral pH (pH in H2O: 6.8)5. Total soil depth was on average 35 cm.The site is used as a winter pasture for yaks, sheep, and goats from January to April. Besides livestock, large numbers of plateau pikas (Ochotona) are found on the sites. These animals have a considerable impact on the plant cover through their burrowing activity, in particular the soil thrown out of their burrows, which can cover and destroy the Kobresia turf.Sampling designThe vertical and horizontal extent of the surface cracks was measured for each plot (Supplementary Table 2). Vegetation cover was measured and the aboveground biomass was collected in the cracks (Supplementary Table 2). In general, intact Kobresia turf (S0) provided high resistance to penetration as measured by a penetrologger (Eijkelkamp Soil and Water, Giesbeek, NL) in 1 cm increments and four replicates per plot.Soil sampling was conducted using soil pits (30 cm length × 30 cm width × 40 cm depth). Horizons were classified and then soil and roots were sampled for each horizon directly below the cracks. Bulk density and root biomass were determined in undisturbed soil samples, using soil cores (10 cm height and 10 cm diameter). Living roots were separated from dead roots and root debris by their bright color and soft texture using tweezers under magnification, and the roots were subsequently washed with distilled water to remove the remaining soil. Because over 95% of the roots occurred in the upper 25 cm5, we did not sample for root biomass below this depth.Additional soil samples were taken from each horizon for further analysis. Microbial community and functional characterization were performed on samples from the same pits but with a fixed depth classification (0–5 cm, 5–15 cm, 15–35 cm) to reduce the number of samples.Plant and soil analysesSoil and roots were separated by sieving (2 mm) and the roots subsequently washed with distilled water. Bulk density and root density were determined by dividing the dry soil mass (dried at 105 °C for 24 h) and the dry root biomass (60 °C) by the volume of the sampling core. To reflect the root biomass, root density was expressed per soil volume (mg cm−3). Soil and root samples were milled for subsequent analysis.Elemental concentrations and SOC characteristicsTotal SOC and total N contents and stable isotope signatures (δ13C and δ15N) were analyzed using an isotope ratio mass spectrometer (Delta plus, Conflo III, Thermo Electron Cooperation, Bremen, Germany) coupled to an elemental analyzer (NA 1500, Fisons Instruments, Milano, Italy). Measurements were conducted at the Centre for Stable Isotope Research and Analysis (KOSI) of the University of Göttingen. The δ13C and δ15N values were calculated by relating the isotope ratio of each sample (Rsample = 13C/12C or 15N/14N) to the international standards (Pee Dee Belemnite 13C/12C ratio for δ13C; the atmospheric 15N/14N composition for δ15N).Soil pH of air-dried soil was measured potentiometrically at a ratio (v/v) of 1.0:2.5 in distilled water.Lignin phenols were depolymerized using the CuO oxidation method25 and analyzed with a gas chromatography-mass spectrometry (GC–MS) system (GC 7820 A, MS 5977B, Agilent Technologies, Waldbronn, Germany). Vanillyl and syringyl units were calculated from the corresponding aldehydes, ketones, and carboxylic acids. Cinnamyl units were derived from the sum of p-coumaric acid and ferulic acid. The sum of the three structural units (VSC = V + S + C) was considered to reflect the lignin phenol content in a sample.DNA extraction and PCRSamples were directly frozen on site at −20 °C and transported to Germany for analysis of microbial community structure. Total DNA was extracted from the soil samples with the PowerSoil DNA isolation kit (MoBio Laboratories Inc., Carlsbad, CA, USA) according to the manufacturer’s instructions, and DNA concentration was determined using a NanoDrop 1000 spectrophotometer (Thermo Fisher Scientific, Wilmington, DE, USA). The extracted DNA was amplified with forward and reverse primer sets suitable for either t-RFLP (fluorescence marked, FAM) or Illumina MiSeq sequencing (Illumina Inc., San Diego, USA): V3 (5’-CCT ACG GGN GGC WGC AG-3’) and V4 (5’-GAC TAC HVG GGT ATC TAA TCC-3’) primers were used for bacterial 16 S rRNA genes whereas ITS1 (5’-CTT GGT CAT TTA GAG GAA GTA A-3’), ITS1-F_KYO1 (5’-CTH GGT CAT TTA GAG GAA STA A-3’), ITS2 (5’-GCT GCG TTC TTC ATC GAT GC-3’) and ITS4 (5’-TCC TCC GCT TAT TGA TAT GC-3’) were used for fungi33,34. Primers for Illumina MiSeq sequencing included adaptor sequences (forward: 5’-TCG TCG GCA GCG TCA GAT GTG TAT AAG AGA CAG-3’; reverse: 5’-GTC TCG TGG GCT CGG AGA TGT GTA TAA GAG ACA G-3’)33. PCR was performed with the Phusion High-Fidelity PCR kit (New England Biolabs Inc., Ipswich, MA, USA) creating a 50 µl master mix with 28.8 µl H2Omolec, 2.5 µl DMSO, 10 µl Phusion GC buffer, 1 µl of forward and reverse primer, 0.2 µl MgCl2, 1 µl dNTPs, 0.5 µl Phusion HF DNA Polymerase, and 5 µl template DNA. PCR temperatures started with initial denaturation at 98 °C for 1 min, followed by denaturation (98 °C, 45 s), annealing (48/60 °C, 45 s), and extension (72 °C, 30 s). These steps were repeated 25 times, finalized again with a final extension (72 °C, 5 min), and cooling to 10 °C. Agarose gel electrophoresis was used to assess the success of the PCR and the amount of amplified DNA (0.8% gel:1.0 g Rotigarose, 5 µl Roti-Safe Gelstain, Carl Roth GmbH & Co. KG, Karlsruhe, Germany; and 100 ml 1× TAE-buffer). PCR product was purified after initial PCR and restriction digestion (t-RFLP) with either NucleoMag 96 PCR (16 S rRNA gene amplicons, Macherey-Nagel GmbH & Co. KG, Düren, Germany) or a modified clean-up protocol after Moreau (t-RFLP)35: 3× the volume of the reaction solution as 100% ethanol and ¼x vol. 125 mM EDTA was added and mixed by inversion or vortex. After incubation at room temperature for 15 min, the product was centrifuged at 25,000 × g for 30 min at 4 °C. Afterwards the supernatant was removed, and the inverted 96-well plate was centrifuged shortly for 2 min. Seventy microliters ethanol (70%) were added and centrifuged at 25,000 × g for 30 min at 4 °C. Again, the supernatant was removed, and the pallet was dried at room temperature for 30 min. Finally, the ethanol-free pallet was resuspended in H2Omolec.T-RFLP fingerprintingThe purified fluorescence-labeled PCR products were digested with three different restriction enzymes (MspI and BstUI, HaeIII) according to the manufacturer’s guidelines (New England Biolabs Inc., Ipswich, MA, USA) with a 20 µl master mix: 16.75 µl H2Omolec, 2 µl CutSmart buffer, 0.25 or 0.5 µl restriction enzyme, and 1 µl PCR product for 15 min at 37 °C (MspI) and 60 °C (BstUI, HaeIII), respectively. The digested PCR product was purified a second time35, dissolved in Super-DI Formamide (MCLAB, San Francisco, CA, USA) and, along with Red DNA size standard (MCLAB, San Francisco, USA), analyzed in an ABI Prism 3130 Genetic Analyzer (Applied Biosystems, Carlsbad, CA, USA). Terminal restriction fragments shorter than 50 bp and longer than 800 bp were removed from the t-RFLP fingerprints.16 S rRNA gene and internal transcribed spacer (ITS) sequencing and sequence processingThe 16 S rRNA gene and ITS paired-end raw reads for the bacterial and fungal community analyses were deposited in the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) and can be found under the BioProject accession number PRJNA626504. This BioProject contains 70 samples and 139 SRA experiments (SRR11570615–SRR11570753) which were processed using CASAVA software (Illumina, San Diego, CA, USA) for demultiplexing of MiSeq raw sequences (2 × 300 bp, MiSeq Reagent Kit v3).Paired-end sequences were quality-filtered with fastp (version 0.19.4)36 using default settings with the addition of an increased per base phred score of 20, base-pair corrections by overlap (-c), as well as 5′- and 3′-end read trimming with a sliding window of 4, a mean quality of 20 and minimum sequence size of 50 bp. Paired-end sequences were merged using PEAR v0.9.1137 with default parameters. Subsequently, unclipped reverse and forward primer sequences were removed with cutadapt v1.1838 with default settings. Sequences were then processed using VSEARCH (v2.9.1)39. This included sorting and size-filtering (—sortbylength,—minseqlength) of the paired reads to ≥300 bp for bacteria and ≥140 bp for ITS1, dereplication (—derep_fulllength). Dereplicated sequences were denoised with UNOISE340 using default settings (—cluster_unoise—minsize 8) and chimeras were removed (—uchime3_denovo). An additional reference-based chimera removal was performed (—uchime_ref) against the SILVA41 SSU NR database (v132) and UNITE42 database (v7.2) resulting in the final set of amplicon sequence variants (ASVs)43. Quality-filtered and merged reads were mapped to ASVs (—usearch_global–id 0.97). Classification of ASVs was performed with BLAST 2.7.1+ against the SILVA SSU NR (v132) and UNITE (v7.2) database with an identity of at least 90%. The ITS sequences contained unidentified fungal ASVs after UNITE classification, these sequences were checked (blastn)44 against the “nt” database (Nov 2018) to remove non-fungal ASVs and only as fungi classified reads were kept. Sample comparisons were performed at the same surveying effort, utilizing the lowest number of sequences by random selection (total 15,800 bacteria, 20,500 fungi). Species richness, alpha and beta diversity estimates, and rarefaction curves were determined using the QIIME 1.9.145 script alpha_rarefaction.py.The final ASV tables were used to compute heatmaps showing the effect of degradation on the community using R (Version 3.6.1, R Foundation for Statistical Computing, Vienna, Austria) and R packages “gplots”, “vegan”, “permute” and “RColorBrewer”. Fungal community functions were obtained from the FunGuild database46. Plant mycorrhizal association types were compiled from the literature38,39,40,41,47,48,49,50. If no direct species match was available, the mycorrhizal association was assumed to remain constant within the same genus.Enzyme activityEnzyme activity was measured to characterize the functional activity of the soil microorganisms. The following extracellular enzymes, involved in C, N, and P transformations, were considered: two hydrolases (β-glucosidase and xylanase), phenoloxidase, urease, and alkaline phosphatase. Enzyme activities were measured directly at the sampling site according to protocols after Schinner et al.51. Beta-glucosidase was incubated with saligenin for 3 h at 37 °C, xylanase with glucose for 24 h at 50 °C, phenoloxidase with L-3,4-dihydroxy phenylalanine (DOPA) for 1 h at 25 °C, urease with urea for 2 h at 37 °C and alkaline phosphatase on P-nitrophenyl phosphate for 1 h at 37 °C. Reaction products were measured photometrically at recommended wavelengths (578, 690, 475, 660, and 400 nm, respectively).SOC stocks and SOC lossThe SOC stocks (in kg C m−2) for the upper 30 cm were determined by multiplying the SOC content (g C kg−1) by the BD (g cm−3) and the thickness of the soil horizons (m). SOC losses (%) were calculated for each degradation stage and horizon and were related to the mean C stock of the reference stage (S0). The erosion-induced SOC loss of the upper horizon was estimated by considering the topsoil removal (extent of vertical soil cracks) of all degraded soil profiles (S1–S5) and the SOC content and BD of the reference (S0). To calculate the mineralization-derived SOC loss, we accounted for the effects of SOC and root mineralization on both SOC content and BD. Thus, we used the SOC content and BD from each degradation stage (S1–S5) and multiplied it by the mean thickness of each horizon (down to 30 cm) from the reference site (S0). The disentanglement of erosion-derived SOC loss from mineralization-derived SOC loss was based on explicit assumptions that (i) erosion-derived SOC losses are mainly associated with losses from the topsoil, and (ii) the decreasing SOC contents in the erosion-unaffected horizons were mainly driven by mineralization and decreasing root C input.Statistical analysesStatistical analyses were performed using PASW Statistics (IBM SPSS Statistics) and R software (Version 3.6.1). Soil and plant characteristics are presented as means and standard errors (means ± SE). The significance of treatment effects (S0–S5) and depth was tested by one-way ANOVA at p  More

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    Validation of quantitative fatty acid signature analysis for estimating the diet composition of free-ranging killer whales

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    Population-specific association of Clock gene polymorphism with annual cycle timing in stonechats

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    Cohort dominance rank and “robbing and bartering” among subadult male long-tailed macaques at Uluwatu, Bali

    Study siteWe conducted this research at the Uluwatu temple site in Bali, Indonesia. Uluwatu is located on the Island’s southern coast, in the Badung Regency. The temple at Uluwatu is a Pura Luhur, which is a significant temple for Balinese Hindus across the island and is therefore visited regularly for significant regional, community, family, and household rituals by Balinese people from different regions throughout the year18. During the period of data collection hundreds of tourists also visit the Uluwatu temple each day. The temple sits on top of a promontory cliff edge, with walking paths in front of it that continue in loops to the North and South. These looping pathways surround scrub forests, which the macaques frequently inhabit but the humans rarely enter.In 2017–2018 there were five macaque groups at Uluwatu, which ranged throughout the temple complex area, and beyond. All groups are provisioned daily with a mixed diet of corn, cucumbers, and bananas by temple staff members. The two groups included in this research are the Celagi and Riting groups. We selected these groups because they previously exhibited significant differences in robbing frequencies whereby Riting was observed exhibiting robbing and bartering more frequently than Celagi1. Furthermore, both groups include the same highly trafficked tourist areas in their overlapping home ranges relative to the other groups at Uluwatu, theoretically minimizing between group differences in the contexts of human interaction1,19.Data collectionJVP collected data from May, 2017 to March, 2018 totaling 197 focal observation hours on all 13 subadult males in Celagi and Riting that were identified in May–June 2017. Subadult male long-tailed macaques exhibit characteristic patterns of incomplete canine eruption, sex organ development, and body size growth, which achieves a maximum of 80% of total adult size18. Mean sampling effort per individual was 15.2 hours (h), with a range of 1.75 h, totaling 102.75 h for Riting and 94.75 h for Celagi. The data collection protocol consisted of focal-animal sampling and instantaneous scan sampling20 on all six subadult males in the Celagi group, and all seven subadult males in the Riting group. Focal follows were 15 minutes in length. Sampling effort per individual is presented in Table 1. A random number generator determined the order of focal follows each morning. In the event a target focal animal could not be located within 10 minutes of locating the group, the next in line was located and observed. Data presented here come from focal animal sampling records of state and event behaviors. Relevant event behaviors consist of agonistic gestures used for calculating dominance relationships, including the target, or interaction partner, of all communicative event behaviors and the time of its occurrence. All changes in the focal animal’s state behavior were noted, recording the time of the change to the minute.Table 1 Focal Subadult male long-tailed macaques in Celagi and Riting at Uluwatu, Bali, Indonesia.Full size tableDuring focal samples we recorded robbing and bartering as a sequence of mixed event and state behaviors. We scored both the robbery and exchange phases as event behaviors, and the interim phase of item possession as a state behavior. We record a robbery as successful if the focal animal took an object from a human and established control of the object with their hands or teeth, and as unsuccessful if the focal animal touched the object but was not able to establish control of it. For each successful robbery we recorded the object taken. Unsuccessful robberies end the sequence, whereas successful robberies are typically followed by various forms of manipulating the object.The robbing and bartering sequence ends with one of several event behavior exchange outcomes: (1) “Successful exchanges” consist of the focal animal receiving a food reward from a human and releasing the stolen object; (2) “forced exchanges” are when a human takes the object back without a bartering event; (3) “dropped objects” describe when the macaque loses control of the object while carrying it or otherwise locomoting, and is akin to an “accidental drop”; (4) “no exchange” includes instances of the macaque releasing the object for no reward after manipulating it; and (5) “expired observation” consists of instances in which the final result of the robbing and bartering event was unobserved in the sample period (i.e., the sample period ended while the macaque still had possession of the object). A 6th exchange outcome is “rejected exchange,” which occurs when the focal animal does not drop the stolen object after being offered, or in some cases even accepting, a food reward. The “rejected exchange” outcome is unique in that it does not end the robbing and bartering sequence because a human may have one or more exchange attempts rejected before eventually facilitating a successful exchange, or before one of the other outcomes (2–5) occurs. For each successful exchange we recorded the food item the macaques received. Food items are grouped into four categories: fruits, peanuts, eggs, and human snacks. Snacks include packaged and processed food items such as candy or chips.Data analysisWe grouped the broad range of stolen items into classes of general types. “Eyewear” combines eyeglasses and sunglasses, while “footwear” combines sandals and shoes. “Ornaments” includes objects attached to and/or hanging from backpacks, such as keychains, while “accessories” includes decorative objects attached to an individual’s body or clothing like bracelets and hair ties. “Electronics” covers cellular phones and tablets. “Hats” encompasses removable forms of headwear, most typically represented by baseball-style hats or sun hats. “Plastics” is an item class consisting of lighters and bottles, which may be filled with water, soda, or juice. The “unidentified” category is used for stolen items which could not be clearly observed during or after the robbing and bartering sequence.“Robbery attempts” refers to the combined total number of successful and unsuccessful robberies. “Robbery efficiency” is a novel metric referring to the number of successful robberies divided by the total number of robbery attempts. The “Exchange Outcome Index” is calculated by dividing the number of successful exchanges by the total number of robbery attempts. We make this calculation using robbery attempts instead of successful robberies to account for total robbery effort because failed robberies still factor into an individual’s total energy expenditure toward receiving a bartered food reward and their total exposure to the risks (e.g., physical retaliation) of stealing from humans relative to achieving the desired end result of a food reward.Social rank was measured with David’s Score, calculated using dyadic agonistic interactions. We coded “winners” of contests as those who exhibited the agonistic behavior, while “losers” were the recipients of those agonistic behaviors21,22. We excluded intergroup agonistic interactions in our calculations of David’s Score.To account for potential variation in the overall patterns of interaction with humans between groups we calculated a Human Interaction Rate, which is the sum of human-directed interactions from focal animals in each group divided by the total number of observation hours on focal animals in that group.Statistical analysisWe ran statistical tests in SYSTAT software with a significance level set at 0.05. We used chi-square goodness-of-fit tests to assess the significance of differences in successful robberies between individuals for each group. To avoid having cells with values of zero, two focal subjects, Minion and Spot from Celagi, are excluded from this test because neither were observed making a successful robbery during the observation period. We also used chi-square goodness-of-fit tests to assess exchange outcome occurrences within each group, as well as a Fisher’s exact to test for significant differences in robbery outcomes between groups due to low expected counts in 40% of the cells. “Rejected exchange” events were not included in the analysis of robbery outcomes because they do not end the sequence and are therefore not mutually exclusive with the other robbery outcomes.We further tested for the effect of dominance position on robbery outcomes. Due to our small sample size and the preliminary nature of this investigation, we used Spearman correlations to assess the relationship between subadult male dominance position via David’s Score and (1) robbing efficiency and (2) the Exchange Outcome Index.Compliance with ethical standardsThis research complied with the standards and protocols for observational fieldwork with nonhuman primates and was approved by the University of Notre Dame Compliance IACUC board (protocol ID: 16-02-2932), where JVP and AF were affiliated at the time of this research. This study did not involve human subjects. This research further received a research permit from RISTEK in Indonesia (permit number: 2C21EB0881-R), and complied with local laws and customary practices in Bali. More

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    Age-based spatial distribution of workers is resilient to worker loss in a subterranean termite

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