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Study siteThis study took place at a temperate reef called the “Chicken Rock” in waters off the coast of North Carolina, USA, between Cape Hatteras and Cape Lookout (Raleigh Bay; Fig. 1). The seafloor of the Chicken Rock is composed of low-relief hardbottom and sand. The Chicken Rock is approximately 37 m deep (Fig. 2) and is an ideal location for this study for three reasons. First, it has a relatively flat seafloor that allows for a high detection rate of acoustically tagged fish49. Second, a high-resolution bathymetric map was available for the area (C. Taylor, National Centers for Coastal Ocean Science). Third, many red snapper occupy the area, allowing us to catch and tag fish relatively easily. Recreational and commercial fishing occurs at the Chicken Rock year-round for a variety of species, but red snapper can only be retained during short open seasons that have occurred periodically since 2010.Data collectionWe quantified the fine-scale movements and distance off bottom for red snapper using VPS (Innovasea, Nova Scotia, Canada). VPS uses a time-difference-of-arrival algorithm to determine the location of coded acoustic transmitters that have been detected by at least three submersible acoustic receivers50. Highly precise fish positions (~ 1 m resolution) are possible if time is synchronized exactly across all receivers, which is accomplished by using sync tags that are either deployed independently throughout the receiver array or built into the receivers themselves. One downside of VPS is that data are not available in real time; receivers must be physically recovered to download data, and then data have to be sent to Vemco to determine fish positions. The advantages of VPS, however, are immense, especially in providing highly precise spatial positions each time acoustic signals are emitted from transmitters. VPS has been used many times to successfully quantify demersal fish movements27,28,49,50,54, and three-dimensional movements can be determined if pressure sensors are built into transmitters23,42.We deployed an array of 20 submersible VR2AR receivers at the Chicken Rock on 17 April 2019. Receivers were deployed in three rows of seven receivers, except for a single receiver in the northeast corner of the grid. Based on previously estimated detection distances of 200–400 m49,55, receivers were separated 200 m from each other, so the entire receiver grid occupied an area of approximately 400 × 1200 m (0.48 km2; Fig. 2). Each receiver was connected to a line between a 36-kg steel weight and a 28-cm diameter plastic float with 8.8 kg of buoyancy, with each receiver positioned approximately 3 m off the seafloor. Each VR2AR included its own sync tag for time synchronization and acoustic release so receivers could be retrieved at the end of the study. A TCM-1 current probe (Lowell Instruments) was attached to each of three receiver buoys spread out across our receiver array (Fig. 2) to collect minute-by-minute current speed and bottom water temperature.We also deployed a reference transmitter (Vemco V13T-1x) in the receiver array on 17 April 2019 (Fig. 2) to calculate sound speed velocity for VPS analyses and quantify positional error of transmitters in the receiver array by comparing its known location to its estimated positions over the course of the study. The reference transmitter was connected to a line with a weight at one end and a buoy at the other, had a 550–650 s random ping interval, and operated on a frequency of 69 kHz.A total of 44 red snapper were tagged in this study. Twenty-three red snapper were tagged on 7 May 2019, nineteen were tagged on 13 August 2019, one was tagged on 30 August 2019, and one was tagged on 22 September 2019 (Table 1). Most of these red snapper (N = 43) were caught via hook-and-line using either circle or J-style hooks, but one red snapper (tagged on 30 August 2019) was caught in a baited fish trap. Fish in good condition (i.e., no visible signs of barotrauma, jaw hooked, active) were tagged externally because external attachment is fast (i.e., greatly reducing surface time56) and externally attached transmitters are detected better than surgically implanted transmitters57. The downside is that transmitter retention is typically lower for externally attached transmitters compared to surgically implanted transmitters.We tagged red snapper with Vemco V13P-1 × transmitters that were 13 mm wide, 46 mm long, weighed 13 g in air, had a 130–230 s pulse interval, a 613 d battery life, and operated on a frequency of 69 kHz. Each transmitter also contained a pressure sensor, which was used to determine the depth of fish for each acoustic signal (accuracy = 1.7 m). Before field work began, stainless steel wire (0.89-mm diameter) was wrapped around the non-transmitting end of the transmitter, glued with marine adhesive (3 M 5200), and covered in heat shrink tubing. Approximately 15 cm of stainless steel wire that extended beyond the transmitter was straightened, and the end was sharpened.Upon capture, red snapper had their head and eyes covered in a wet towel and were measured for total length (mm). The sharpened transmitter wire was inserted laterally through the dorsal musculature of the fish approximately 2.5 cm posterior to, and 2.5 cm below, the insertion of the fish’s first dorsal spine. The wire was pushed laterally through the fish until the transmitter was pulled firmly against the fish’s left side, while the sharpened end emerged from the same spot on the right side of the fish. An aluminum washer was threaded onto the protruding wire, followed by a #1 double sleeve steel crimp, which was crimped onto the wire once the washer and crimp were held firmly on the right side of the fish. The wire beyond the crimp and wet towel were removed, the fish was attached to a weighted SeaQualizer fish release tool, and the fish was descended to a depth of approximately 31 m before being released by the device. The total surface time for each tagged red snapper was approximately 1.5 min.Data analysesWe first assessed whether potential error in red snapper positions could influence study results. For each reference tag position estimated by VPS, we calculated horizontal positional error as the difference between the known reference tag location and its estimated position based on VPS. We visualized daily horizontal positional error of the reference transmitter with a boxplot. Daily values were provided to determine if any changes in positional error occurred over time.Next, we used positional and depth data from fish that were monitored to determine the fate of each individual and classified them based on four events: tag loss, emigration, harvest, or predation48. Fish were assumed to have lost transmitters if the transmitter stopped moving; they were assumed to have emigrated if the transmitter moved to the edge of the receiver array before disappearing. Harvest was assumed if fish disappeared from within the receiver array. Predation (e.g., by sharks) was inferred from VPS data in one of three ways: (1) transmitters moved horizontally much faster than normal red snapper swimming speeds, (2) transmitters moved quickly across a wide range of depths, typically from the bottom to the surface and back, and (3) a reduced frequency of detections, as might be expected for transmitters in the abdominal cavity of a shark. VPS data were censored after the point at which any fish experienced tag loss, harvest, or predation, and only fish with 100 or more spatial positions were included in the analyses.We then estimated movement rates of each fish over time. Movement rate (m s−1) was quantified as the distance moved between each successive pair of spatial positions divided by the time between detections. One challenge with using movement rates is that straight-line movements are assumed between detections, when in reality fish may not move in straight lines. Red snapper were detected on average every 2–4 min, so this issue is less of a problem in our study compared to those using longer time intervals between detections51, but our movement rates can be considered minimum estimates. To further prevent negatively biased movement rate estimates, we excluded movement rate estimates for time intervals longer than 20 min; this decision had negligible effects on results (see Discussion).We also estimated the distance off the seafloor for all detections of acoustically tagged red snapper. We calculated distance off the bottom (m) for each fish position as the depth of the seafloor at that location minus the depth of the fish. We encountered an issue with some transmitters after tag loss whereby depth readings appeared to slowly drift towards shallower readings even though the transmitter was sitting on the bottom and not moving horizontally; in a few instances, this same depth drift issue was detected for transmitters attached to fish alive in the study area (i.e., distance off bottom was greater than zero for long periods of time, which never occurred for red snapper with working pressure sensors). We do not know the reason for these rare instances of depth drift by the pressure sensors, but out of caution we censored depth data for fish whose transmitters provided dubious depth data.We evaluated whether individual differences in movement rates or distance off the bottom were apparent. We created boxplots of movement rate and distance off bottom for each fish in the study, and tested for differences among individuals using a linear model where fish number was included as a categorical variable. We compared the Akaike information criterion (AIC) values of models including fish number with an intercept-only model where fish number was excluded, and models with the lowest AIC value (ΔAIC = 0) were considered the most parsimonious formulations58. Movement rate was positively skewed, so it was log-transformed to improve model fit. Model diagnostics (i.e., quantile–quantile, histogram of residuals, residuals versus linear predictions, response versus fitted values plots) were used to confirm that final models met assumptions of equal variance and normal residuals. We used R version 3.6.359 to carry out all statistical tests and to create all figures.Ideally, we would then test for the effects of environmental conditions and fish size on red snapper horizontal and vertical movements using a single, integrated analysis. However, models accounting for temporal autocorrelation and incorporating individual movement rate estimates from each fish as the response variable (i.e., including fish number as a random effect) did not converge, possibly due to large sample sizes (N = 346,363), so we used mean hourly values instead. The downside of this approach is that fish size had to be evaluated separately from the effects of environmental conditions, as described below. However, note that covariate relationships changed very little across a wide variety of model formulations.We tested for the effects of fish size on movement rate and distance off the bottom using generalized additive models60 (GAMs). GAMs are a regression modeling approach that relate a response variable to a single or multiple predictor variables using nonlinear, linear, or categorical functions. Mean log-transformed movement rate or distance off bottom were the response variables of these models and cubic-spline-smoothed fish total length (mm) was included as the predictor variable. As above, we compared the AIC values of models including fish size with an intercept-only model where fish size was excluded, and the model with the lowest AIC value was selected as the best model.We then assessed the influence of various environmental factors (see below) on red snapper movement rate and distance off bottom using GAMs. For these analyses, choosing the appropriate time scale for binning response and predictor data was critical. Longer time steps (i.e., day) were problematic because response and predictor variables frequently varied over much shorter time frames, while extremely short time steps (i.e., minute) were often lacking response and predictor variable information. Therefore, we used an hourly time step for this procedure. The main concern of using an hourly step is that any particular hourly time bin is likely to be more similar to the time bin nearest in time compared to a randomly selected time bin; in other words, time bins are not truly independent of one another61 (i.e., data are temporally autocorrelated). Not accounting for temporal autocorrelation that is present often leads to a negative bias in estimated regression coefficients and confidence intervals. To account for temporal autocorrelation, we used generalized additive mixed models (GAMMs) that included an autoregressive term for model errors. We used a likelihood ratio test to compare our GAMM to a GAM that did not include autoregressive errors, and in both cases GAMMs were selected over GAMs so they were used for movement and distance off bottom models.We limited our GAMMs to five predictor variables based on previous work. The first predictor variable was time of day, which we included because red snapper movements have been shown to vary over diel periods29. We included time of day (tod) as a categorical variable with three levels: day, crepuscular period, and night. Because sunrise and sunset times varied over the course of our 8-mo study, we defined crepuscular periods as a one hour period of time spanning 30 min before sunrise or sunset to 30 min after sunrise or sunset for each day of the study. Day was defined as 30 min after sunrise to 30 min before sunset, and night was defined as 30 min after sunset to 30 min before sunrise.Bottom water temperature has been shown to be strongly correlated with red snapper movements and home range size28,29, so it was included as our second predictor variable. We calculated bottom water temperature (temp; °C) as the mean bottom temperature measured across the three current probes deployed in the receiver array. Cold bottom water temperatures were observed near the conclusion of our study (December 2019) due to declining air temperatures and water column mixing, but also during periodic upwelling events that occurred from late May through early August. Upwelling is a common oceanographic feature of the region, occurring when upwelling-favorable winds are observed concurrent with the Gulf Stream being in a relatively inshore position62,63. Upwelled water that is cold and nutrient-rich is generally only found near the bottom, which tends to cause phytoplankton blooms near the bottom that decrease water clarity. From preliminary analyses of red snapper VPS data, we observed differing behaviors of fish during periods of upwelling than periods lacking upwelling. Therefore, we developed an upwelling index as our third predictor variable, which was calculated as the difference between the surface water temperature and mean bottom water temperature (upwel; °C). Surface water temperature was not available at the study site, so we obtained hourly surface temperature data from the nearest NOAA buoy (#41159), which was located ~ 85 km southwest of the study site in a similar water depth (Fig. 1). We assume that surface water temperature at the study site could be approximated with data from this buoy, which is a reasonable assumption given surface water temperature and wave heights from this buoy were strongly correlated with values from another buoy (NOAA buoy #41025) ~ 70 km northeast of the study site.The last two predictor variables involved properties of water movement at the seafloor in the study area. The fourth predictor variable was wave orbital velocity (wov; m s−1), which is a measure of the wave-generated oscillatory flow (“sloshing”) of water at the seabed. Wave orbital velocity was included because it was much more strongly correlated with gray triggerfish (Balistes capriscus) movement rates at the Chicken Rock area than either barometric pressure or bottom water temperature43, the latter of which have been shown to be more important for organisms in shallow water64,65. Wave orbital velocity was calculated following Bacheler et al.43 using the properties of surface wave period and height, which were also obtained from NOAA buoy 41159. The last predictor variable included in models was current speed (cur; cm s−1), which was calculated as the mean horizontal current speed from the three current probes deployed on the bottom in the receiver array.The GAMMs were formulated as:$$y = upalpha + f(tod) + s_{1} (temp) + s_{2} (upwel) + s_{3}(wov) + s_{4} (cur) + varepsilon ,$$
(1)
where y is either acoustically tagged red snapper log-transformed movement rate (m s−1) or distance off the bottom (m), α is the intercept, f is a categorical function, s1-4 are cubic spline smoothing functions, and (varepsilon) is the autoregressive error term accounting for temporal autocorrelation in the data.We employed model selection techniques to assess the importance of predictor variables. Specifically, we compared full models that included all five predictor variables to reduced models that included fewer predictor variables. Model comparisons were made using AIC, and models with the lowest AIC value (ΔAIC = 0) were again considered the most parsimonious. Various diagnostics of final models were examined using the “gam.check” function in the mgcv library to ensure model fit was suitable.Given the importance of upwelling to the vertical movements of red snapper (see Results section), we last include results from a conductivity-temperature-depth (CTD) cast taken in the study area from the NOAA Ship Pisces on 29 June 2019 (07:40 EDT), which occurred during a time when bottom upwelling was present. This CTD cast was conducted using a Sea-Bird SBE 9 deployed from the surface to within 1.5 m of the bottom, and depth-specific water temperature and beam transmission data were provided to highlight the vertical extent of upwelling on this particular day. Beam transmission is the fraction of a light source reaching a light detector set a distance away and is a quantitative measure of water clarity; a common feature of upwelling in the region (in addition to cold water) is declining clarity due to increased production within nutrient-rich, upwelled water near the bottom. We combine these water temperature and beam transmission data with a boxplot of red snapper distances off the bottom by hour throughout the same day the CTD cast was taken (29 June 2019).Ethics approvalThe tagging protocol was approved by the Institutional Animal Care and Use Committee (# NCA19-002) of the North Carolina Aquariums on 20 March 2019. All research activities were carried out under a Scientific Research Permit issued to Nathan Bacheler on 10 April 2017 by the Southeast Regional Office of the U.S. National Marine Fisheries Service, in accordance with the relevant guidelines and regulations on the ethical use of animals as experimental subjects. More

African swine fever has breached the island of Borneo, where it is wiping out populations of the wild bearded pig Sus barbatus. First confirmed in early February, the outbreak has driven a precipitous decline in this species in less than two months. Field sites in the east of the Sabah region are reporting a complete absence of live pigs in forests. Local extinctions across swathes of Borneo are a realistic prospect.Bearded pigs are listed as vulnerable by the International Union for Conservation of Nature. They are seen as ‘ecosystem engineers’ in the Bornean rainforest, where they are one of the most abundant species of mammal. Bearded pigs can be legally hunted under permit, and are an important source of animal protein for many communities.The African swine fever virus is already island-hopping across southeast Asia, threatening 11 species of endemic pig, including the Sulawesi warty pig (Sus celebensis). Opportunities to control the disease in wild-pig populations are limited. Vaccines for domestic pigs are still in development, so the best hope for stemming loss of the wild animals could be to protect isolated populations in geographically defensible locations. More

Morphological changes of the leavesThe influence of NO2 stress on the plants was first reflected by the morphological changes of the leaves (Fig. 1)20,22. Slight NO2 injury was manifested by slight green deficiency and light color. Moderate NO2 injury was manifested by irregular watery spots between leaf veins, which gradually developed into yellow necrotic spots followed by lesions at the leaf stalk and margins. When the exposure time extended to 72 h, the leaves turned yellow, and irreversible injury occurred, which led to leaf death. The damaged areas of the leaves of the two species at different time points of NO2 exposure are summarized in Table 1.Figure 1Leaf injury symptoms of Carpinus betulus (A) and Carpinus putoensis (B) under different NO2 exposure time and after recovery.Full size imageTable 1 The damaged areas (percentage) of the leaves of Carpinus betulus and Carpinus putoensis at different time points of NO2 stress.Full size tableChanges in MDA contentThe changes in the MDA content of C. betulus and C. putoensis at different time points of NO2 stress are shown in Fig. 2. With the prolongation of NO2 stress, the MDA content of C. betulus showed an increasing tendency with the variation range from 0.016 to 0.029 µmol g−1 fw. However, no significant differences were observed at different time points of NO2 exposure.Figure 2Changes in the MDA content of C. betulus and C. putoensis at different time points of NO2 stress and after self recovery. Letters or letter combinations containing the same letter indicate no significant difference between the corresponding NO2 exposure time points in the same plant species according to ANOVA or nonparametric Kruskal–Wallis ANOVA followed by Bonferroni tests. Capital letters for C. putoensis and lower letters for C. betulus.Full size imageAs NO2 fumigation time extended, the MDA content of C. putoensis also showed an increasing tendency, with the variation range from 0.015 to 0.034 µmol g−1 fw. Compared with the control group, a significant difference was observed in the MDA content from 24 h, which peaked at 72 h. In C. putoensis, although the recover group and the control group did not show a significant difference, the MDA content of the former lay between 0.015 (6 h) and 0.019 µmol g−1 fw (12 h), which suggests that the plant did not recovered completely from the stress damage.Compared with C. putoensis, C. betulus exhibited a smaller variation amplitude in the MDA content under NO2 stress. The MDA content of C. betulus did not show noticeable changes at 1, 6, and 12 h, and it was till 24 h when a rapid increase occurred. These findings indicate a delayed injury response of C. betulus. In contrast, with the prolongation of NO2 stress, the MDA content of C. putoensis noticeably increased, which indicates an increase in the membrane lipid peroxidation activity of C. putoensis under NO2 stress.Changes in POD activityThe changes in POD activity of C. betulus and C. putoensis at different time points of NO2 stress are shown in Fig. 3. With the prolongation of NO2 stress, the POD activity of C. betulus showed an increasing tendency, with a variation range from 323 to 663 U (g * min)−1 fw. After 30-d self recovery, the POD activity returned to 409 U (g * min)−1 fw, which was comparable to that of the control.Figure 3Changes in POD activity of C. betulus and C. putoensis at different time points of NO2 stress and after self recovery. Letters or letter combinations containing the same letter indicate no significant difference between the corresponding NO2 exposure time points in the same plant species according to ANOVA or nonparametric Kruskal–Wallis ANOVA followed by Bonferroni tests. Capital letters for C. putoensis and lower letters for C. betulus.Full size imageAs NO2 fumigation time extended, the POD value of C. putoensis also showed an increasing tendency, with a variation range from 385 to 596 U (g * min)−1 fw. The recovery group did not show a significant difference compared with the control group.In C. betulus, the POD activity value rapidly increased at 72 h of NO2 stress, which showed a significant difference compared with any other group (adjusted p  0.05).Figure 4Changes in the soluble protein content of C. betulus and C. putoensis under NO2 stress at different time points and after self recovery. Letters or letter combinations containing the same letter indicate no significant difference between the corresponding NO2 exposure time points in the same plant species according to ANOVA or nonparametric Kruskal–Wallis ANOVA followed by Bonferroni tests. Capital letters for C. putoensis and lower letters for C. betulus.Full size imageIn C. putoensis, the soluble protein content also showed an increasing trend as the fumigation time prolonged. The variations ranged from 2.61 to 3.27 mg g−1 fw. Compared with the control group, the recovery group exhibited a lower soluble protein content, although no significant difference was observed between them.As shown in Fig. 4, the maximum difference in the soluble protein content of C. betulus was 2.33 mg g−1 fw, which was greatly larger than that of C. putoensis (0.66 mg g−1 fw). Particularly, C. betulus exhibited a rapid increase in the soluble protein content from 12 h of fumigation, which indicates that C. betulus increased protein synthesis when encountered with NO2 stress, whereas C. putoensis showed only weak resistance against the stress.Changes in NRAt 0 h of NO2 treatment, the NR activity of C. betulus was 1.43 ± 0.14 µmol NO2−·g−1fw·h−1. With the prolongation of NO2 exposure, the NR activity of C. betulus exhibited a gradual increase followed by a gradual decrease, and a significant difference (adjusted p  0.05). In C. putoensis, the NR activity of the control group was 0.58 ± 0.06 µmol NO2−·g−1fw·h−1. As the NO2 exposure time prolonged, NR activity exhibited a rapid increase (adjusted p  0.05). The results were shown in Fig. 5.Figure 5Changes in the NR activity of C. betulus and C. putoensis under NO2 stress at different time points and after self recovery. Letters or letter combinations containing the same letter indicate no significant difference between the corresponding NO2 exposure time points in the same plant species according to ANOVA or nonparametric Kruskal–Wallis ANOVA followed by Bonferroni tests. Capital letters for C. putoensis and lower letters for C. betulus.Full size imageChanges in NO3
−NAs the NO2 treatment time extended, the NO3−N content of C. betulus exhibited an increase followed by a gradual decrease, and a significant difference (adjusted p  0.05). In C. putoensis, the NO3−N content also exhibited an increase followed by a decrease after NO2 exposure. However, a significant difference was observed from 12 h. After 30-d recovery, the index returned to a normal level (adjusted p  > 0.05). The results were shown in Fig. 6.Figure 6Changes in the NO3−N content of C. betulus and C. putoensis under NO2 stress at different time points and after self recovery. Letters or letter combinations containing the same letter indicate no significant difference between the corresponding NO2 exposure time points in the same plant species according to ANOVA or nonparametric Kruskal–Wallis ANOVA followed by Bonferroni tests. Capital letters for C. putoensis and lower letters for C. betulus.Full size imageChanges in mineral elementsThe changes in the mineral elements of C. betulus and C. putoensi under NO2 stress and after self recovery are summarized in Table 2.Table 2 Changes in the mineral element contents of C. betulus and C. putoensis under NO2 stress and after self recovery.Full size tableMacroelements(1) N. At 1 h of NO2 stress, the total nitrogen content of C. betulus increased slightly to 1.68 ± 0.17 g/kg; this value was higher than that of control (1.4 ± 0.13 g/kg), but no significant difference was observed (adjusted p  > 0.05). With the prolongation of the stress, the content decreased, with the variations ranging from 0.84 to 1.68 g/kg and the maximum difference of 0.84 g/kg. The recovery group did not show a significant difference compared with the control group (1.53 ± 0.15 vs. 1.4 ± 0.13; adjusted p = 1.00).Overall, the changes in the total nitrogen content of C. putoensis showed a similar trend with those of C. betulus. At 1 h of NO2 stress, the total nitrogen content of C. putoensis significantly increased compared with that of the control (1.68 ± 0.15 g/kg vs. 1.12 ± 0.11 g/kg; adjusted p  0.05).(4) Ca. With the prolongation of NO2 exposure, the Ca content of C. betulus exhibited an increase followed by a gradual decrease, and the variations ranged from 84 to 243 µg L−1 dw. A significant difference was observed at 72 h of NO2 exposure. In C. putoensis, significant differences in the Ca content were observed during NO2 exposure, except at 12 h. In both species, the Ca content of the recovery group did not show a significant difference compared with the control group. The variation amplitude of the Ca content of C. betulus (159 µg L−1 dw) was noticeably greater than that of C. putoensis (68 µg L−1 dw).(5) Mg. As the NO2 stress prolonged, the Mg content of C. betulus did not show a significant drop, except at 48 h. The variations ranged from 21.4 to 31.3 µg L−1 dw. In C. putoensis, the variations ranged from 12.2 to 32.2 µg L−1 dw. In both species, the Ca content of the recovery group did not show a significant difference compared with the control group. The variation amplitude of the Ca content of C. betulus (9.9 µg L−1 dw) was remarkably smaller than that of C. putoensis (20 µg L−1 dw).Microelements(1) Zn. With the prolongation of NO2 exposure, the Zn content of C. betulus exhibited an increase followed by a gradual decrease. Compared with the control, significant differences were observed at 1, 6, and 12 h. The variations anged from 7.1 to 10.6 µg L−1 dw. In C. putoensis, significant differences in the Zn content were observed at 6 h and 48 h, and the variations ranged from 5.7 to 11.2 µg L−1 dw. The variation amplitude of the Zn content of C. betulus (3.5 µg L−1 dw) was smaller than that of C. putoensis (5.5 µg L−1 dw). In each species, the Zn content of the recovery group showed a significant difference compared with the control group.(2) Mn. At 1 h of NO2 fumigation, a sharp drop was observed, compared with the control. Afterwards, the Mn content of C. betulus exhibited a general increase followed by a gradual decrease. However, at any time point during NO2 exposure, a significant lower Mn content was observed when compared to the control. The variations of the Mn content ranged from 11.2 to 78.1 µg L−1 dw. In C. putoensis, the Mn content during NO2 exposure significantly decreased compared with control, and the variations ranged from 9.4 to 85.5 µg L−1 dw. The variation amplitude of the Mn content of C. betulus (66.9 µg L−1 dw) was slightly smaller than that of C. putoensis (76.1 µg L−1 dw). In each species, the Mn content of the recovery group did not show a significant difference compared with the control group.Correlation analysisThe correlations between the investigated indices and NO2 exposure time were analyzed using the Pearson’s method (Table 3). POD and soluble protein had a strong positive correlation with NO2 exposure time (correlation coefficient: 0.891 and 0.799, respectively), and NR, NO3−N, N, K, and Ca had a strong negative correlation with NO2 exposure time (correlation coefficient: -0.691, -0.805, -0.744, -0.606 and -0.696, respectively). MDA and the Zn content were not correlated with the exposure time.Table 3 Correlations of the investigated indices with NO2 exposure time.Full size table More

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Mist wafts through the trees at the Monteverde Cloud Forest Biological Preserve in Costa Rica. Cloud forests around the world are threatened by development, wood collection and climate change. Credit: Stefano Paterna/Alamy

Conservation biology
04 May 2021
Forests that float in the clouds are drifting away

Tropical cloud forests are safe havens for a vast range of creatures and plants, but they are under siege around the globe.

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Remote habitats called tropical cloud forests, which cling to misty mountains and tap humid air for water, are in decline. So says a global analysis also reporting that cloud forests, despite occupying just 0.4% of Earth’s land, harbour around 15% of the global biodiversity of birds, mammals, amphibians and tree ferns.Dirk Karger at the Swiss Federal Institute for Forest, Snow and Landscape Research in Birmensdorf, Walter Jetz at Yale University in New Haven, Connecticut, and their colleagues created habitat-prediction models that incorporate remote-sensing data on cloud cover and other conditions to predict the coverage of tropical cloud forests worldwide. They then studied satellite imagery of land cover from 2001 to 2018 to determine the rate of cloud-forest loss and analysed how this loss would affect 3,700 species living in this ecosystem.The team estimates that more than 15,000 square kilometres of tropical cloud forest — 2.4% of the global total — were lost during the 18-year period. Africa and the Americas had the greatest losses. The authors note that the establishment of protected areas did little to halt the loss of habitat and its biodiversity, highlighting the urgent need for other safeguards.

Nature Ecol. Evol. (2021)

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Among the many groups of terrestrial species, our understanding of mammal visual signalling might be hampered by the fact that most research on mammals has focused on chemical (e.g., scat, urine, and glands) and acoustic (e.g., howling) signalling1,2. Instead2,3, visual communication might be an overlooked communication channel2,4, despite being perhaps as important as the others, if we consider that: (1) mammal coloration has evolved for inter- and intraspecific communication2,4,5,6,7, which means that mammals use visual signals to communicate; and (2) visual signalling through physical marks (e.g., bites and scratches) is permanent and, thus, has the obvious advantages of (a) being long-lasting, i.e., environmental factors such as rain or snow are less likely to affect the detectability of visual marks as compared to, e.g., chemical signalling8, although mammals have found strategies to make chemical signalling last as long as possible9, and (b) functioning remotely, i.e., even when the signaller is away from the marked location2. Visual marking may also allow individuals to reduce repeated visits to strategic marking points, and thus save time and energy, which would otherwise detract animals from other activities, like foraging and reproduction10. Therefore, visual signalling may represent a reliable and advantageous communication channel8.Solitary species like bears may benefit from advertising their location, size, and reproductive status to expedite mate selection during the breeding season. Moreover, brown bears usually occur at low densities across their range, making direct interactions with one another infrequent11,12. Thus, long-lasting visual signalling may be particularly effective and considerably time saving. To date, studies on bear communication have highlighted two main forms of communication10,13,14,15,16,17: (1) olfactory communication, i.e., the marking of focal trees by rubbing the body against the trunk and/or by urination and deposition of anogenital gland secretions; and (2) pedal marking, by which bears mark the ground with their scent by grinding their feet into the substrate. Auditory communication, e.g., vocalizations used as threats during agonistic encounters, to advertise sexual receptivity, or for communication between females and their cubs, is considered as the least important channel through which bears signal, whereas visual communication has always been considered limited to different forms of body postures or behavioural displays (but see18).Since the beginning of the 1980s, bear marks on trees have puzzled researchers8. The function of, and motivation behind, tree biting and clawing have prompted a variety of theories related to glandular scent deposition (i.e., chemical signalling), but none of these hypotheses has been considered satisfactory, nor have they ever been tested8.
The debarking behaviour of brown bears Ursus arctos, which leaves bright and conspicuous marks on tree trunks (see Extended Data Fig. 1 and Extended Data Fig. 2), presents a unique yet unexplored opportunity to investigate new ways of visual communication in terrestrial mammals, and to better understand both bear and carnivore communication broadly. The hypothesis behind this experimental work is that brown bears may rely on visual communication via the conspicuous marks that they produce on trees.Figure 1Brown bear response to trunk mark manipulation. The behavioural sequence of an adult male brown bear removing the pieces of bark that we used to conceal the visual markings on an ash tree during the mating season in the Cantabrian Mountains, Spain (12/06/2020, 15h37). The whole sequence is shown in the video footage Extended Data Fig. 5.Full size imageAfter manipulating bear tree marks in the Cantabrian Mountains (north-western Spain), we found that bears removed the bark strips that we used to cover their marks during the mating season (Extended Data Figs. 3 and 4), suggesting that bear debarking may represent a visual communication channel used for intraspecific communication.Brown bear responses to marked tree manipulationsAfter concealing bear marks due to trunk debarking with bark strips from the same tree species (see “Methods”), our manipulations on 20 trees triggered a rapid reaction from brown bears. Between the 16th of May and the end of September 2020 (overlapping part of the brown bear mating period in the Cantabrian Mountains19), brown bears removed the strips of bark that we used to cover the trunk marks in 9 (45%) out of the 20 manipulated trunks (Fig. 1 and Extended Data Fig. 5). However, if we consider that these nine trees were also the ones that we could manipulate (because of field work restrictions due to COVID-19) from the start of the mating season (beginning of May), 100% of the bark strips used to cover tree marks were removed by bears when the manipulation occurred at the commencement of the mating season. In only one case, a bear removed the bark strips covering marks on a tree that was manipulated later in the mating season (end of June). Control bark strips fixed to (a) the same trunk as the manipulated bear mark, (b) the nearest neighbouring tree to the manipulated one showing bear marks, and (c) the nearest rubbing trees with no bear marks, were never removed by bears. In two cases (50%), after the first removal of the manipulated mark by a bear, which was subsequently covered again with new strips (n = 4), a bear removed the strips a second time. Further, camera traps showed that: (1) bears uncovered the manipulated marks the first time they visited the tree after our manipulation; (2) bark strips that were not removed were always the result of bears not visiting the site after tree manipulations; and (3) the shortest lapse of time between a mark manipulation and a bear visiting the tree for the first time and uncovering the mark was seven days. Thus, manipulations always triggered a rapid response from bears when adult males, probably the same individuals that debarked the trunks, came back and check on marked trees.Conspicuousness of brown bear visual marksThe conspicuousness of a visual signal is not only increased by its position in a noticeable location, but also by the contrast between the signal and its background20,21. A remarkable difference (pixel intensity: mean (± SD) = 85.09 ± 26.77, range = 20.27–177.06) exists between bark and sapwood brightness for all tree species (t = 19.07, p =   More