Ecology

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Study siteSocial hermit crabs (Coenobita compressus) were studied in Osa Peninsula, Costa Rica, at a long-term field site (Osa Conservation’s Piro Biological Station), where the population has been under study since 200817. Experiments were carried out from January to March 2019 at the beach-forest interface (Fig. 1A), an area where ‘fission–fusion’ social groupings30 continuously form and dissolve31 and where free-roaming individuals regularly travel17. All studies were undertaken during daylight hours (06:30–11:30 h) during periods of peak social activity.Figure 1Study site and experimental areas. (A) Satellite view of study site: a section of Piro beach, Osa Peninsula, Costa Rica. Dashed red squares indicate areas where experiments were carried out and schematic versions are shown below in (B) and (C) (Satellite image: created using Google Earth Version 9, https://earth.google.com/). (B) Overhead view of the section of the beach where free-roam experiments were carried out. Arrows denoting left and right correspond to stimulus directions during free-roam experiments. (C) Overhead view of the beach-forest interface where the handled experiments were carried out. Arrows denoting left, right, forest, and ocean correspond to stimulus directions during handled experiments. The solid red box represents the platform on which the artificial beach was created. For (B) and (C), environment is color coded: blue = ocean, yellow = beach sand, dark green = rainforest, light green = open grassy area with sparse trees. Compass in the bottom left of each panel shows cardinal directions.Full size imageWe conducted two separate sets of experiments, both involving a similar stimulus design (below). First, to determine whether free-roaming individuals were biased in their movement decisions by a collective, we performed a set of free-roam experiments (see “Experiment 1: Free-roam”). The free-roam experiments were conducted directly on the beach (Fig. 1B; 8° 23′ 39.5″ N, 83° 20′ 10.2″ W). Second, to determine whether an increase in danger influenced the relative independence versus social bias in individual movement, we performed a set of handled experiments (see “Experiment 2: Handled”). The handled experiments were conducted on a platform (Fig. 1C; 8° 23′ 33.2″ N, 83° 19′ 50.6″ W), which was immediately adjacent to the beach and situated within the range of the crabs’ normal daily movements. All reported compass bearings are relative to magnetic North (0°) unless otherwise specified.Stimulus designAs conspecific ‘stand-ins’, we used N = 60 Nerita scabricosta shells (C. compressus’ preferred shell species23), spanning a natural range of sizes (9–32 mm) within this population (Table S1; Fig. S1). To create a group of these stand-ins that we could manoeuvre as a collective, each shell was affixed using epoxy to one of four strands of clear fishing line, which were each 4 m long. These lines were spaced approximately 30 cm apart on a long wooden dowel (Figs. 2A,B, 3A,B). An equal number of shells (N = 15 shells per line) were distributed randomly along the 2 m of each fishing line furthest from the dowel. To allow the experimenter to manoeuvre the stimuli, without disturbing live crabs’ behaviour, another fishing line (4 m in length) was attached to the top of the dowel. With this line, the entire apparatus could be pulled by the experimenter from a distance, thereby simulating synchronised movement of the entire collective. To control for any influence the apparatus might have on focal individuals (other than that produced by the movement of the shell ‘stand-ins’), the entire apparatus—dowels and fishing lines—was replicated, just without any attached shells, for use as a control (Figs. 2C, 3C).Figure 2Free-roam experiments: stimuli and experimental design. (A) Photograph of a free-roam experiment in progress, with a drone hovering above and one of the authors (CD) pulling the simulated collective (Photo: Jakob Krieger). Schematics of stimuli are shown in B and C, with N = 3 free-roaming crabs also pictured. (B) Experimental stimuli: consisting of N = 60 shells arranged in four lines of fifteen shells each, attached to clear fishing line and fixed to a wooden dowel. (C) Control stimuli: four empty lines of clear fishing line, fixed to a wooden dowel. An experimenter moved the stimuli from a distance, by pulling another clear fishing line along an open strip of sandy beach in the presence of free roaming crabs. Each experiment was video recorded from above by an overhead drone.Full size imageFigure 3Handled experiments: stimuli and experimental design. (A) Photograph of the artificial beach created on a platform adjacent to the natural beach (Photo: Mark Laidre). Photo shows experimental stimulus and an opaque plastic cup in the center, under which a focal crab was placed prior to the start of each experiment. Schematics of stimuli are shown in (B) and (C). (B) Experimental stimuli: consisting of 60 shells arranged in four lines of fifteen, attached to clear fishing line and fixed to a wooden dowel. (C) Control stimuli: four empty lines of clear fishing line, fixed to a wooden dowel. The cup was removed by one experimenter from a distance via an attached clear fishing line on a pulley system; the stimulus was then maneuvered by a second experimenter, also from a distance, via another clear fishing line.Full size imageExperiment 1: Free-roamTo test whether the movement of the collective influenced free-roaming individuals’ travel direction, the stimuli were pulled across the beach at a uniform speed (1 m per min), within the natural range of the walking speed of social hermit crabs17,22,23. Each trial lasted 1 min. A total of N = 80 free-roam trials were conducted, N = 40 experimental (with the full collective, represented by all the shells) and N = 40 controls (with only the raw materials, but no shell collective). For each of the N = 80 trials, the movement of a single free-roaming focal individual was recorded.It is not uncommon to see multiple crabs moving parallel to (or perpendicular to) the shore, since many individuals will often be collectively attracted to eviction sites, injured conspecifics, or food items, with all the attracted individuals travelling in a roughly parallel formation16,17. For each trial in the free-roam experiments, the stimuli were pulled parallel to the shore (Fig. 1B), either to the right (116.1°) or to the left (296.1°). We did not pull the stimuli perpendicular to the shore, given the substantial slope from the forest down to the ocean, which would have confounded any such comparisons. Condition (experimental or control) and stimulus direction (right or left) were selected randomly, with balanced sample sizes (N = 20 for each). To ensure there was a free-roaming focal individual, whose movement we could measure in response to the stimulus, a trial was only carried out when at least one live crab was walking within approximately 30 cm of the stationary stimulus. Then pulling was initiated.To avoid disturbing live individuals by moving through or near the vicinity, we gathered overhead video footage of all experiments using a drone (Phantom advanced model GL300C). Drone video recorded all interactions between the focal individual and the simulated collective while the drone hovered at a height of approximately 2 m above the beach. At this height, there was no disturbance to natural behaviour or movement of the crabs, and the drone remained positioned overhead for at least 1 min prior to the start of a trial. Minor adjustments to position were then made between trials due to drone drift (i.e., slight movement of the drone due to wind).To randomly select focal individuals for video coding, we first split an image of the starting frame of each video file into a 4 × 4 matrix, with N = 16 equally-sized sections, and then used a random number generator to choose one section (repeating this step if no crabs were present in the selected section). Second, we numbered all individuals in the selected section and again used a random number generator to select the individual.To calculate bearings relative to magnetic North for the direction each focal crab moved, we first measured the angle of divergence (°) between the stimulus trajectory and the focal crabs’ trajectory. Focal crab trajectory—a proxy for the overall direction of the crab’s movement—was measured by drawing a straight line from the start-to-end position of that individual (see Fig. S2 and Vid. S1 for further explanation). Stimulus trajectory was measured in the same manner, using the shell closest to the focal at the beginning of the trial. Using Google Maps and the IGIS Map bearing angle calculator, we calculated the bearing of our stimuli (right and left) relative to true North (right: 114°, left: 294°). To determine bearings for our stimuli relative to magnetic North, we then used the Enhanced Magnetic Model (EMM) magnetic field calculator, provided by NOAA, to calculate the relevant declination (− 2.1°) for our coordinates on the dates the experiments were carried out, subtracting this value from true North. Thus, for the free-roam experiments, the bearing of a stimulus moving to the right, relative to magnetic North, was 116.1°, and the bearing of a stimulus moving to the left, relative to magnetic North, was 296.1°. Lastly, bearings for focal crabs’ directions, relative to magnetic North, could then be calculated using the new bearings of the stimuli and the angle of divergence between stimulus and crab trajectories.To gauge the level of interaction that focal individuals had with the collective, we recorded whether or not individuals initiated contact with shells in the experimental condition. An individual was classed as having initiated contact if it climbed onto a shell or touched a shell with its claws (Vid. S2). Additionally, we noted whether individuals were bumped by passing shells. An individual was classed as having been bumped if a moving shell hit it while the individual was withdrawn, stationary, or facing away from the moving shell (Vid. S3).To assess whether drone drift during experiments was a problem, we examined a random sample (N = 20) of the videos, both control (N = 10) and experimental (N = 10). We took N = 40 images from these 20 videos (i.e., two images from each video: one at the start of the 1-min trial and one at the end of the 1-min trial) and used a system wherein we marked the same two distinguishable fixed points on the landscape in each pair of images. We then overlaid the images in each pair, allowing us to see any longitudinal or latitudinal movement as well as any potential rotation of the drone. Nineteen of the N = 20 pairs of images showed virtually identical overlap of the markers, with just one image showing a minor gap between 1 of the 2 landmarks, suggesting slight rotation of the drone. We were therefore confident that drone drift was not an issue in our analyses.All videos were coded by CD. To measure inter-observer reliability for the angle of divergence (°) between stimulus trajectory and focal crabs’ trajectory (see Fig. S2), a random sample of videos (N = 41 total, N = 22 of experimental and N = 19 of control) were also coded by a second observer (MP) who was naïve to the competing hypotheses. There was strong inter-observer reliability in the measurements (F1,39 = 142.8, p  More

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Effects of the application of different improved materials on properties of reclaimed soilSoil organic matter (SOM) and total nitrogen (TN)After the application of different improved materials, the SOM and TN contents in both 0–0.15 m and 0.15–0.30 m layers of the hollow village reclaimed soil showed an overall increasing trend (Fig. 1). In the 0–0.15 m layer, the organic matter content increased by 9.6%, 79.0%, 90.0%, 61.4%, 120.1%, and 131.7% respectively under TM, TF, TO, TMF, TMO and TFO treatments compared with CK treatment, indicating that different improved materials all played important roles in improving the organic matter content of reclaimed soil (Fig. 1a). The improvement of organic matter content in the 0–0.15 m layer of reclaimed soil by the treatments of different improved materials showed as follows: TFO  > TMO  > TO  > TF  > TMF  > TM  > CK, and TO, TMO and TFO treatments with organic fertilizer addition could significantly improve the organic matter content of the reclaimed soil (P  2 mm water-stable aggregates was increased by 88.1%, 194.5%, 203.7%, 376.2%, and 781.7% respectively under TF, TO, TMF, TMO and TFO compared with CK. The proportion of water-stable macroaggregates under different treatments showed as follows: TFO (35.8%)  > TMO (20.7%)  > TO (16.9%)  > TMF (16.3%)  > TF (12.3%)  > TM (10.1%)  > CK (9.0%), and the water-stable macroaggregates were increased by 328.2%, 130.0%, 87.8%, 81.1%, 36.7%, and 12.2% respectively compared with CK, with the maximum increase of 328.2%. In general, all six different amendment material treatments increased the proportion of water-stable macroaggregates in reclaimed soil and promoted the aggregation and cementation of water-stable microaggregates ( 0.25 mm). And the TFO showed the best effect on the increase of water-stable macroaggregates, followed by TMO, TO, and TMF, while TF and TM treatments showed little effect.Figure 2Percentage (%) of soil water-stable aggregates under the application of different improved materials at 0.15–0.30 m Depth. CK: no improved material; TM: maturing agent (ferrous sulfate); TF: fly ash; TO: organic fertilize; TMF: maturing agent + fly ash, TMO: maturing agent + organic fertilizer; TFO: fly ash + organic fertilizer. Different lowercase letters represent significant differences among different improved material treatments in the same particle-size aggregates.Full size imageFigure 3Percentage (%) of soil water-stable aggregates under the application of different improved materials at 0.15–0.30 m Layer. CK: no improved material; TM: maturing agent (ferrous sulfate); TF: fly ash; TO: organic fertilize; TMF: maturing agent + fly ash, TMO: maturing agent + organic fertilizer; TFO: fly ash + organic fertilizer. Different lowercase letters represent significant differences among different improved material treatments in the same particle-size aggregates.Full size imageIn the 0.15–0.30 m layer, the change of water-stable aggregates showed a similar trend to that in the 0–0.15 m layer compared with CK treatment. TF, TO, TMF, TMO and TFO treatments all significantly increased the proportion of  > 2 mm and 1–2 mm water-stable aggregates, and decreased the proportion of water-stable microaggregates (P  2 mm water-stable aggregates by 130.3%, 94.5%, 133.9%, 151.4%, and 309.2% respectively compared with CK, of which TFO treatment showed the most significant effect on the increase of the proportion of water-stable macroaggregates. Compared with the 0–0.15 m layer, the proportion of water-stable macroaggregates in the 0.15–0.30 m layer showed a gradual decrease with the increase of soil depth.Water-stable aggregates structure stabilityThe mean weight diameter (MWD), geometric mean diameter (GMD), unstable aggregate index (ELT), and fractal dimension (D) are important indicators reflecting the structural geometry and stability of soil aggregates, and it has been indicated in this research that the higher the MWD and GMD and the smaller the ELT and D, the better the structural stability of the aggregates and the soil structure27,28. Compared with CK treatment, the MWD and GMD showed a trend of significant increase while the D and ELT showed a trend of significant decrease (P  TF  > TMF  > TM  > CK. The combination of organic–inorganic improved materials can effectively reduce the BD of reclaimed soil, and the BD under TFO treatment was the smallest, 1.19 g cm−3. In the 0.15–0.30 m layer, through variance analysis, the effect of different improved materials on the BD showed a similar decreasing trend to that in the 0–0.15 m layer.Figure 4Effects of the application of different improved materials on BD and SMC. CK: no improved material; TM: maturing agent (ferrous sulfate); TF: fly ash; TO: organic fertilize; TMF: maturing agent + fly ash, TMO: maturing agent + organic fertilizer; TFO: fly ash + organic fertilizer; BD, soil bulk density; SMC, soil moisture content. Different lowercase letters represent significant differences among different improved material treatments in the same soil layer.Full size imageThe soil moisture content (SMC) of the reclaimed soil in the 0–0.15 m and 0.15–0.30 m layers increased significantly after the application of different improved materials (P  TMO  > TMF  > TO  > TF≈TM  > CK (Fig. 4b). In the 0–0.15 m soil layer, the SMC under TM, TF, TO, TMF, TMO and TFO treatments was increased by13.5%, 13.8%, 21.4%, 21.9%, 32.4% and 38.3% respectively compared with CK. The TMO and TFO showed the most significant positive effect on the SMC of reclaimed soil, and the mass water content was 17.4% and 18.2% respectively. In conclusion, compared with CK, these improved materials increased the SOM content and porosity, promoted the formation and stability of aggregates, and increased the retention and transmission of water, which was helpful to maintain more water. Among them, the coupling treatment of organic and inorganic improved materials can hold more soil moisture, and the most significant increase was observed under TFO and TMO.Correlation analysis between soil organic matter and water-stable aggregates parametersTo further explore the correlation between the parameters of the reclaimed soil after the application of six different improved materials, a regression analysis was conducted in this paper on the correlation between the parameters of organic matter and water-stable aggregates with different particle sizes. From Table 2, it could be seen that the organic matter content had a highly significant positive correlation with MWD, GMD and  > 2 mm water-stable aggregates content and a highly significant negative correlation with ELT, D and water-stable microaggregates content ( 2 mm, 1–2 mm, and 0.5–1 mm) content had a significant positive correlation with MWD and GMD values and a highly significant negative correlation with ELT and D values; water-stable microaggregates ( TMO  > TO  > TMF  > TF  > TM  > CK, and different improved materials all significantly increased maize yield compared with CK (P  More

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RBD severity in different rice ecosystems of KarnatakaBased on the observations made during the exploratory surveys of 2018 and 2019 (Table 1 and Fig. 1), it was found that RBD severity significantly varied across studied areas and districts (Fig. 2). The disease severity was highest in Chikmagalur, followed by Kodagu, Shivamogga, Mysore, and Mandya districts which belong to Hilly and Kaveri ecosystems. At the same time, the lowest severity was documented in Udupi, Gulbarga, Gadag, Dakshin Kannad, Raichur, and Bellary districts of coastal, UKP, and TBP ecosystems (Fig. 3A).Table 1 Details of diverse rice-growing ecosystems selected for the study.Full size tableFigure 1Featured map of South-East Asia (A), India (B), and Karnataka (C). A total of 18 administrative districts of Karnataka were considered to gather data on rice blast disease. The area of different districts under study is shown (D). The maps were created using R software (version R-4.0.3).Full size imageFigure 2Distribution map indicating the sampling sites and the severity of rice blast disease in different rice ecosystems of Karnataka during 2018 and 2019. The maps were created using R software (version R-4.0.3).Full size imageFigure 3(A) Bar graph repressing the severity of rice blast disease (RBD) in different districts of Karnataka during 2018 and 2019. (B) Clustering of districts based on the severity of RBD in different districts of Karnataka by hclust method.Full size imageHierarchical cluster analysis using the average linkage method for RBD severity among the 18 administrative districts of diverse rice ecosystems of Karnataka identified two main clusters, namely, cluster I and cluster II (Fig. 3B). Cluster I consist of two subclusters, cluster IA and IB. Subcluster IA consists of Mandya, Dharwad, Mysore, Hassan, Shivamogga, Haveri, and Belgaum; While, Kodagu, and Chikmagalur districts were clustered in IB. Similarly, Cluster II was divided into cluster IIA and cluster IIB. Subcluster IIA comprises Udupi, Gulbarga, Gadag, Raichur, Dakshin Kannad, Uttar Kannad, Koppal and Bellary, and Davanagere district was grouped under cluster IIB.Spatial point pattern analysis of RBDThe cluster and outlier analysis was done using Local Moran’s I and p-values. The analyses have identified RBD cluster patterns at the district level during 2018 and 2019, representing dispersed and aggregated clusters of severity (Fig. 4). Based on positive I value, most of the districts were clustered together (at I  > 0), except the coastal districts such as Uttar Kannad, Udupi, Dakshin Kannad, and interior districts such as Dharwad, Davanagere, and Chikmagalur, which exhibited negative I value (at I  More

Sea anemones turn oxybenzone into a light-activated agent that can bleach and kill corals.Credit: Georgette Douwma/Getty

A common but controversial sunscreen ingredient that is thought to harm corals might do so because of a chemical reaction that causes it to damage cells in the presence of ultraviolet light. Researchers have discovered that sea anemones, which are similar to corals, make the molecule oxybenzone water-soluble by tacking a sugar onto it. This inadvertently turns oxybenzone into a molecule that — instead of blocking UV light — is activated by sunlight to produce free radicals that can bleach and kill corals. “This metabolic pathway that is meant to detoxify is actually making a toxin,” says Djordje Vuckovic, an environmental engineer at Stanford University in California, who was part of the research team. The animals “convert a sunscreen into something that’s essentially the opposite of a sunscreen”.Oxybenzone is the sun-blocking agent in many suncreams. Its chemical structure causes it to absorb UV rays, preventing damage to skin cells. But it has attracted controversy in recent years after studies reported that it can damage coral DNA, interfere with their endocrine systems and cause deformities in their larvae2. These concerns have led to some beaches in Hawaii, Palau and the US Virgin Islands, banning oxybenzone-containing sunscreens. Last year, the US National Academies of Sciences, Engineering, and Medicine convened a committee to review the science on sunscreen chemicals in aquatic ecosystems; its report is expected in the next few months.The latest study, published on 5 May in Science1, highlights that there has been little research into the potentially toxic effects of the by-products of some substances in sunscreens, says Brett Sallach, an environmental scientist at the University of York, UK. “It’s important to track not just the parent compound, but these transformed compounds that can be toxic,” he says. “From a regulatory standpoint, we have very little understanding of what transformed products are out there and their effects on the environment.”But other factors also threaten the health of coral reefs; these include climate change, ocean acidification, coastal pollution and overfishing that depletes key members of reef ecosystems. The study does not show where oxybenzone ranks in the list.Simulated seaTo understand oxybenzone’s effects, Vuckovic, environmental engineer William Mitch at Stanford and their colleagues turned to sea anemones, which are closely related to corals, and similarly harbour symbiotic algae that give them colour.The researchers exposed anemones with and without the algae to oxybenzone in artificial seawater, and illuminated them with light — including the UV spectrum — that mimicked the 24-hour sunlight cycle. All the animals exposed to both the chemical and sunlight died within 17 days. But those exposed to sunlight without oxybenzone or to oxybenzone without UV light lived.Oxybenzone alone did not produce dangerous reactive molecules when exposed to sunlight, as had been expected, so the researchers thought that the molecule might be metabolized in some way. When they analysed anemone tissues, they found that the chemical bound to sugars accumulated in them, where it triggered the formation of oxygen-based free radicals that are lethal to corals. “Understanding this mechanism could help identify sunscreen molecules without this effect,” Mitch says.The sugar-bound form of oxybenzone amassed at higher levels in the symbiotic algae than in the anemones’ own cells. Sea anemones lacking algae died around a week after exposure to oxybenzone and sunlight, compared with 17 days for those with algae. That suggests the algae protected the animals from oxybenzone’s harmful effects.Corals that have been subject to environmental stressors such as changing temperatures often become bleached, losing their symbiotic algae. “If they’re weaker in this state, rising sea water temperature or ocean acidification might make them more susceptible to these local, anthropogenic contaminants,” Mitch says.Greater dangerIt’s not clear how closely these laboratory-based studies mimic the reality of reef ecosystems. The concentration of oxybenzone at a coral reef can vary widely, depending on factors such as tourist activity and water conditions. Sallach points out that the concentrations used in the study are more like “worst-case exposure” than normal environmental conditions.The study lacks “ecological realism”, agrees Terry Hughes, a marine biologist at James Cook University in Townsville, Australia. Coral-bleaching events on Australia’s Great Barrier Reef, for example, have been linked more closely to trends in water temperature than to shifts in tourist activity. “Mass bleaching happens regardless of where the tourists are,” Hughes says. “Even the most remote, most pristine reefs are bleaching because water temperatures are killing them.”Hughes emphasizes that the greatest threats to reefs remain rising temperatures, coastal pollution and overfishing. Changing sunscreens might not do much to protect coral reefs, Hughes says. “It’s ironic that people will change their sunscreens and fly from New York to Miami to go to the beach,” he says. “Most tourists are happy to use a different brand of sunscreen, but not to fly less and reduce carbon emissions.” More