Philippa Kaur

Study siteThis study was conducted at La Selva Biological Station, located in the lowland Atlantic forest of Costa Rica (10°26′ N, 83°59′ W). The mean annual temperature is 26 °C; mean annual precipitation is 4 m and all months have mean precipitation  > 100 mm39. La Selva has undulating topography, with elevation varying between 10 and 140 m above sea level. La Selva Biological Station includes multiple land uses; our analysis includes 103.5 hectares of forest, comprising 33.0 ha of old-growth forest and 70.5 ha of forests with past human disturbance (secondary forests, abandoned agroforestry, abandoned plantation, selectively-logged forests); here, we refer to all areas with past human disturbance as “secondary forests”. This study area does not include the full extent of old-growth or secondary forests at La Selva—we focused our drone data collection on this area because it contained the most severe apparent disturbance from the blowdown. Forests with past human disturbance have been naturally regenerating for a range of time (since 1955–1988); we excluded secondary forests with regeneration starting after 1988.Lidar dataWe use two airborne lidar datasets to quantify dynamics in canopy structure and ACD. Data were collected in 2009 and 2019 (Supplementary Table 2). Data from 2009 were collected by a fixed-wing aircraft over the entire reserve; data from 2019 were collected using the Brown Platform for Autonomous Remote Sensing40. We focused on an area 1.4 km2 in size that includes the region of most severe damage from the blowdown (Supplementary Fig. 1). Both lidar sensors were discrete-return systems. To minimize variation in lidar height estimates from variable laser beam divergence and detector characteristics, we only used data from first returns for all analyses. For the 2019 drone-based lidar with higher native point density and a wider scan angle range40, we limited our analysis to lidar returns with scan angle ± 15 degrees and randomly subsampled data to a homogenous resolution of 10 pts m−2. Previous research demonstrates that lidar data collected above densities of 1 pts m−2 have similar predictive power for determining many forest properties (including tree height, tree density, and basal area)41; both lidar datasets in this study are above this density threshold. All lidar data were projected using EPSG 32,616.For all lidar data, we calculated height above ground using a digital terrain model (DTM) created from lidar data collected in 2006 and validated using 4184 independent measurements within the old-growth forest (intercept =  − 0.406, slope = 0.999, r2 = 0.994, RMSE = 1.85 m; Supplementary Table 2)42. We verified that the horizontal geolocation accuracy with  More

Examining the serum metabolome profiles of bighorn sheep captured by the three primary techniques used to capture wild ungulates revealed significant changes in polar metabolite levels between the different animal groups, and trends that persisted throughout the analyses when directly comparing, in a pairwise fashion, specific capture techniques. Results from PLS-DA modeling and analysis of the top 15 metabolites that contribute most (VIP  > 1.2) to the separation of the three capture groups revealed that amino acid levels of tryptophan, valine, isoleucine, phenylalanine, and proline were highest in animals captured by dart, with intermediate levels in animals capture using dropnets, and lowest in animals captured using the helicopter method (Fig. 3A). One-way ANOVA analyses identified additional amino acids that displayed similar decreasing level trends from dart to dropnet to helicopter capture (dart  > drop net  > helicopter) methods, and included arginine, asparagine, aspartate, cysteine, glutamate, and glutamine, glycine, histidine, leucine, lysine, serine, and tyrosine (Fig. 4). These metabolite level changes suggest a shift in amino acid metabolism, and a potentially higher catabolism of these compounds as a function of increasingly more energetically intense and possibly more stressful capture methods such as helicopter capture.Of these amino acids, aspartate, glycine, and glutamate function as precursors for neurotransmitter synthesis, and may therefore be valuable indicators of the capture techniques’ impacts on animal health and changes to their physiological state. Glutamate is a fundamental component of nitrogen excretion in the urea cycle, and its lower serum levels in animals captured by helicopter support the idea of altered metabolite flow through the urea cycle. In addition to these patterns, decreasing levels of aspartate were observed in samples of dropnet and helicopter captured animals compared to the levels found in the dart-captured animals. The change regarding urea cycle alterations also manifested itself in differential serum urea levels, with fold changes (FC) between the groups decreasing significantly with capture techniques, with a mean FC difference of 1.4 for the dart-captured group, 0.26 for the dropnet-captured group, and − 0.3 for the helicopter-captured animals (Supplementary Table S2). As urea recycling is a prominent feature of ruminant metabolism and urea flux can rapidly change, the urea concentration changes observed between the three capture techniques support an impact on urea cycle intermediates29. While the trend of an overall decrease in urea cycle intermediates parallels a similar trend in amino acid concentrations, the extent to which amino acid metabolism is linked to changes in urea cycle activity is difficult to evaluate due to the nature of nitrogen recycling in the rumen of these ruminants.Other metabolites found in significantly higher concentrations in the serum samples of dart-captured animals compared to the two other techniques included: formate, glucose, 3-hydroxybutyrate, dimethylamine, carnitine (Fig. 3A). Propionate, which was observed to be higher in the dart and dropnet captured animals than that of helicopter captured animals (Fig. 4) is of interest, as it is the main precursor for glucose synthesis in the liver of ruminants30, and potentially reflect a higher dependence of ruminants on gluconeogenesis due to the almost complete conversion of available dietary carbohydrates to volatile fatty acids in the rumen31. As animal capture via nets increases physical activity as the animals struggle to free themselves from entanglement, generally resulting in longer times animals are under physical restraint, as well as the increased physical exertion and stress as they attempt to flee the pursuing helicopter, the observed decrease in serum propionate levels may reflect increased needs to generate glucose de novo via gluconeogenesis.This interpretation of the metabolite data is reinforced by the observation of significantly elevated levels of O-acetylcarnitine in the drop net and helicopter net gun animal capture groups compared to the darted animals (Fig. 4). As an important element of the carnitine/acyl-carnitine shuttle and import of fatty acids into the mitochondria for β-oxidation, acyl-carnitine is a major contributor to the flow of acyl groups into the TCA cycle, and a robust indicator of cardiac output and, by extension, TCA cycle activity levels in mammals32. Additional metabolites that displayed distinctly increasing trends based on capture method (dart  More

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Physico-chemical conditionsSampling was performed at 6 stations representing the physical and chemical characteristics of the investigated area (Supplementary Table S1). Thermohaline properties were the result of horizontal advection of above-average salinities driven by a North Ionian cyclonic gyre controlled by the Adriatic Ionian Bimodal Oscillating System46. September and the whole summer of 2016 was characterized by extremely high temperatures, and precipitation in the climatologic expected range. A cyclone with a cold front followed by a strong Bora wind passed over the Adriatic a week before the cruise, in the period between the 16th and 20th of September 2016. Heat and mass exchange in the air-sea boundary layer were responsible for the characteristic vertical thermohaline profiles measured in late summer. Over the investigated area, the mixed layer depth located between 20 and 25 m was horizontally homogenous. The coldest water mass (temperature 12.94 °C, salinity 38.68) was located at the bottom of Jabuka Pit.Abundance of bacteria, autotrophic picoplankton and AAPBacterial abundances ranged between 0.05 and 0.46 × 106 cell mL−1 in all three areas, with a slightly higher average value in Jabuka Pit (0.31 × 106 cell mL−1). The bacterial abundances were the highest in the upper layers down to the 50 m deep layer and showed a decreasing trend towards the bottom (Supplementary Table S2). The portion of HNA bacteria ranged from 37.8 to 73.12% (on average 51.27%), with the prevalence of HNA over the LNA group below the epipelagic layer.Marine Synechococcus dominated the autotrophic picoplankton community with abundances ranging from 0.08 to 23.86 × 103 cell mL−1. The presence of Prochlorococcus cells was also detected in all samples in a range from a few cells to 1.33 × 103 cell mL−1. Picoeukaryotes also showed a similar range from a few cells to 0.83 × 103 cell mL−1. The highest abundances of picophytoplankton were measured in the upper 50 m, with the exception of the Palagruža Sill (PS) area, where an increase in abundance was observed at 100 m depth. Bacterial production ranged from 0.2 × 104 to 0.36 × 104 cell mL−1 h−1, with increased values in the shallow layers and a mostly uniform vertical distribution in the water column (Supplementary Table S2).AAP bacteria abundance ranged from 0.9 × 103 to 22.3 × 103 cell mL−1, thus constituting 0.42% to 6.83% of the bacteria. Their highest average contribution was observed in the South Adriatic Pit (4.11%), while on the vertical scale, their highest contribution was observed in the upper 20 m of the seawater (see Supplementary Table S2).Relationship between the picoplankton community and environmental parametersBased on biological characteristics (total prokaryotes, Synechococcus, Prochlorococcus, picoeukaryotes, heterotrophic nanoflagellates, aerobic anoxygenic phototrophs, high and low nucleic acid bacteria, bacterial production), we distinguished five picoplanktonic clusters (PIC-BMUs) and then searched for explanations of the observed patterns (Fig. 2A,B). The mean values of biological and physico-chemical parameters for each cluster are shown in Table 1.Figure 2(A) Bar plot representation of biotic (black) and abiotic (grey) parameters for neural gas best-matching units (picoplankton-PIC-BMUs) with relative frequency appearance for each neuron. TP-total prokaryotes, SYN-Synechococcus, PROCHL-Prochlorococcus, PE-picoeukaryotes, HNF-heterotrophic nanoflagellates, AAP-aerobic anoxygenic phototrophs, AAP%-portion of AAP, HNA% percentage of high nucleic acid content bacteria, LNA%-percentage of low nucleic acid content bacteria-LNA%, BP-bacterial production. (B) Water column distribution of Neural gas best-matching units (BMU, labels with numbers, and stained with a different colour for clearance, coloured non-labelled squares shows clarity) for measuring stations (SAP1-3, PS1-2 and JP1). The software MATLAB. version 7.10.0 (R2018). Natick, Massachusetts: The MathWorks Inc. (2018) (https://www.mathworks.com/) was used to generate the figure.Full size imageTable 1 Characteristics of biological (abundances of total prokaryotes-TP, Synechococcus-SYN, Prochlorococcus-PROCHL, picoeukaryotes-PE, heterotrophic nanoflagellates-HNF, aerobic anoxygenic phototrophs(AAP); contributions (%) of AAP, High nucleic acid content bacteria-HNA and Low nucleic acid content bacteria-LNA%; and bacterial production-BP) and environmental factors in the sampling terms assigned to the neural gas clusters.Full size tablePIC-BMU1 described a very rare pattern, found in only two samples from 10 m depth in Palaguža Sill and Jabuka Pit. They were characterised by the highest abundances of total prokaryotes with a dominance of HNA and elevated AAP abundance. These samples were unique in terms of hydrological parameters, as they represented an N-limited environment (TIN  More

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