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Study sitesOur study sites are located on the Yatta Plateau in south-eastern Kenya. This region is characterized by dry savannahs. Annual rainfall (average: 810 mm) occurs during two periods, from March to May (average: 330 mm) and from October to January (average 480 mm) (c.f. Jaetzold et al.37). The commonest soil types are ferralsols and luvisols, which are of low fertility37. 97.1% of the human population in our study region depend on subsistence crop farming38, and the population has almost doubled in number from 1999 to 200938. Consequently, fallow periods for fields are omitted, which further decreases soil fertility, and increases pressure on pristine habitats.The dry savannah landscape is traversed by temporary (seasonal) rivers. These rivers are bordered by riparian vegetation, consisting of a diverse and unique plant community. However, this vegetation is frequently exploited for timber, charcoal and brick production39,40. The region is further affected by climate change, with an increase in rainfall variability and mean temperature37. These factors lower the reliability of agricultural production and food security, hence leading to severe destruction of pristine habitats.We selected two study sites, affected by different anthropogenic pressures, but which are subject to identical biotic and abiotic preconditions (including seasonality): Firstly, a highly degraded anthropogenic landscape along Nzeeu River, south of Kitui city. Secondly, a largely intact dryland environment along Kainaini River located near the university campus of the South Eastern Kenya University, north of Kitui city (Fig. 1). The landscape along Nzeeu River is densely populated by subsistence farmers. Thus, the original riparian and savannah vegetation has been mostly transformed into arable fields for the cultivation of maize, sorghum, peas, and mangos. Furthermore, the riparian vegetation, where it still exists, has largely been replaced by invasive exotic plant species (e.g. Lantana camara)12. The landscape of our second study site along Kainaini River represents a still largely intact riparian forest with adjoining dry savannahs. It remains mostly undisturbed, except for some moderate live-stock pasturing by nearby subsistence settlers.Butterfly assessmentsWe counted butterflies in both habitat types along line-transects, each 150 m long. We set 24 transects along each of the two rivers, with eight transects along the river bank, eight 250 m distant to the river, and another eight 500 m distant to the river (in total: 2 × 24 transects = 48 transects). The minimum distance between transects was at least 200 m, to minimize spatial autocorrelation. Exact GPS coordinates of each transect are given in Appendix S2.We recorded all butterflies encountered during transect counts (species, number of individuals of each species). Each transect was visited eight times during the dry season (August/September 2019) and eight times during the rainy season (January/February 2020). Data collection was performed between 9 a.m. and 4 p.m. Each butterfly individual within 5 m of the transect line (horizontally to vertically) was recorded by visual observation and, if needed, a butterfly net (see Pollard15, with modifications). While recording butterflies, the observers walked very slowly and spent about 15 min per transect. Species were identified either immediately while the butterfly was on the wing, or individuals were netted and then determined in the field. Individuals of species for which ad hoc identification was critical (e.g. many blues and skippers) were caught with the net, photographed (upper and under wing side) and released again. The photograph-based identification of these individuals was performed later using literature25. Apart from species and number of individuals per species, we recorded cloud cover during each transect walk (classified as: clear, slightly cloudy, mostly cloudy, overcast), exact time, and date. Field teams comprised two observers and one person making notes of all observations. Transects are displayed in Fig. 1. All butterfly data collected are compiled in Appendix S3.TraitsThe occurrence of a species in a specific environment strongly depends on its ecology, behaviour, and life-history41. Therefore, we considered these characteristics for each butterfly species recorded in the field. These trait data were compiled from Larsen25 and web-sites (e.g. www.gbif.org, www.lepiforum.de/non-eu.pl). We considered the following characteristics: wing span (mm), ratio length/width of the forewing (relative), ratio forewing length/thorax width (relative), geographic distribution (4 categories), savannah index (5 categories), forest index (5 categories), tree index (3 categories), wetness index (3 categories), habitat specialisation (3 categories), larval foodplant specialisation (3 categories), larval food plant type (dicotyledonous, monocotyledonous), and hemeroby index (4 categories). Detailed classifications are provided in Appendix S4.Habitat parametersHabitat structures impact species´ occurrence, abundances and community structures42. In our study, we considered habitat structures for each transect. Habitat parameters were recorded (counted and estimated) every 20 m along each transect. We estimated the following habitat parameters: Canopy cover (percentage of leaf cover vs. sky measured with the CanopeoApp); herb, shrub and tree cover (percentage coverage of each layer within a radius of 3 m); flowers on herbs, shrubs and trees (estimated within a radius of 3 m, and subsequently allocated to the classes 0, 1–10, 11–50, 51–100 and  > 100 flowers); occurrence of Lantana camara shrub, and exotic trees (estimated coverage within a radius of 3 m, and subsequently allocated to the classes 0 (no), 1 (rare), 2 (present) and 3 (dominant), respectively); and water availability (presence/absence) within a radius of 3 m. All raw data of habitat parameters are provided in Appendix S5.StatisticsWe first arranged the raw data in three matrices: a 71 × 14 species × trait matrix T, a 71 × 96 species × transect matrix M, and a 6 × 96 habitat characteristics × transect matrix H. Matrix multiplication of E = T−1MA−1, where A is the vector of total abundances in the transects, returned a matrix E of average trait expression in each transect.To answer the first research question, we compared species richness, abundances, and trait expression between the transects and used general linear modelling (glm) to detect differences in richness and trait expression with respect to the study sites (i.e. the two river systems with their different land-use patterns), season, distance from the rivers, as well as to environmental variables. Some of the habitat variables and trait expressions were highly positively correlated (Appendix S1). Consequently, the glm included only variables correlated by less than r = 0.7 (i.e. shrub cover, tree cover, habitat specialisation, savannah index, larval foodplant specialisation, and hemeroby).To infer differences in community structure between transects (second research question), we first calculated the two most dominant eigenvectors, which explained 91.5% and 3.5% of variance, of a principal components analysis of the M matrix. These eigenvectors cover differences in species composition between and within transects. We used glm and two-way Permanova to relate these differences to season, distance to river, and study sites (i.e. different land-use types in the two river systems). Additionally, we assessed the degree of β-diversity among sets of transects with the proportional turnover metric of Tuomisto43: (beta =1-frac{alpha }{gamma }); where α denotes the average species richness per transect and γ the corresponding total richness.To infer species spill-over effects from the riparian forests into the adjoining savannah (third research question), we calculated the Bray–Curtis similarities for three groups of transects within each season and study site. First, we compared average pairwise Bray–Curtis values between transects of intermediate and greater distance with the near-river transects within each study site. Second, we calculated the average Bray–Curtis similarities between all transects within each study site (2)—season (2)—distance class to river (3) combination. Third, we calculated the average within-transect Bray–Curtis similarity for the rainy season, to infer small scale compositional variability. The latter calculations were impossible for the dry season, due to the overall low number of recorded species. Calculations were done with Statistica 12. More

Sea urchins and experimental designSeven hundred small S. intermedius (31.9 ± 0.4 mm of test diameter, mean ± SD) were chosen from an aquaculture farm in Changhai County, Dalian (122° 63′ N, 39° 25′ E) on 23 July 2020. They were subsequently transported to the Key Laboratory of Mariculture and Stock Enhancement in North China’s Sea, Ministry of Agriculture and Rural Affairs at Dalian Ocean University (121° 56′ N, 38° 87′ E) and maintained in a fiberglass tank (a closed culture system, length × width × height: 150 × 100 × 60 cm) with aeration for 7 days to acclimatize to laboratory conditions. The kelp Saccharina japonica, which is the most common food used for S. intermedius culture58, was fed ad libitum under the neutral photoperiod (12 h light:12 h dark). One-half of the seawater was changed daily. Water temperature, pH and salinity were 22.6 ± 0.2 °C, 7.7 ± 0.3 and 30.7 ± 0.1 ‰ (Mean ± SD) according to the daily measurement using a portable water quality monitor (YSI Incorporated, OH, USA), respectively.The rearing space was defined as the ratio of culture volume to the number of sea urchins (cm3 ind−1). Rearing assemblage is the main factor being tested in this study. To simulate the currently used rearing assemblage in longline culture, 24 individuals were placed at plastic devices without layer divisions (length × width × height: 24.5 × 16.8 × 6 cm for culture volume; 25 holes of 0.5 cm diameter/100 cm2) as group A (the control group, 102.9 cm3 ind−1 of initial rearing space, Fig. 7a). To investigate whether multi-layer rearing assemblage improves the survival, food utilization and growth, 24 sea urchins were equally put into the cages where were evenly divided into three layers (8 sea urchins in each layer and length × width × height: 24.5 × 16.8 × 6 cm for each layer, 308.7 cm3 ind−1 of initial rearing space; 25 holes of 0.5 cm diameter/100 cm2; group B; Fig. 7b). Further, to evaluate whether eliminating interaction further contributes to the improvement of these commercially important traits of sea urchins in multi-layer rearing assemblage, 8 sea urchins were divided into eight divisions for each layer in the cages as group C (length × width × height: 8.3 × 5.9 × 6 cm for each division, 297.36 cm3 ind−1 of initial rearing space; 25 holes of 0.5 cm diameter/100 cm2; Fig. 7c). Each treatment had 8 replicates. All devices were placed in a fiberglass tank (length × width × height: 150 × 100 × 60 cm) and immersed in water for ~ 30 cm with aeration. They were easily disassembled for the experimental management.Figure 7Diagrams of the experimental cages used for the groups A (a), B (b) and C (c), the sea urchin with the spotting disease (d) and without the disease (e) and the devices used for measuring the Aristotle’s lantern reflex (f).Full size imageThe experimental period was about ~ 7 weeks (from 31 July 2020 to 20 September 2020) under the neutral photoperiod (12 h light: 12 h dark). The kelp, which was regularly collected in the intertidal waters at Heishijiao, Dalian (121° 58′ E, 38° 87′ N), was daily provided to sea urchins in abundance for all the groups. The remained kelp, feces and dead sea urchins were removed daily. One-half of the seawater was replaced daily by the fresh and filtered seawater which was pumped from the coast of Heishijiao, Dalian. Water temperature was not controlled, ranging from 22.2 to 24.5 °C (the natural seasonal cycle of increasing temperature during summer in the region). Water quality parameters were measured weekly as salinity 29.3 ± 0.6 ‰, pH 7.8 ± 0.2 (mean ± SD) using a portable water quality monitor (YSI Incorporated, OH, USA).To ensure the random sampling, sea urchins were taken out from the experimental device and placed in 24 plastic boxes (labeled from number 1 to number 24, length × width × height: 6 × 6 × 4 cm for each box). Individuals were chosen corresponding to the number (within 24) generated by the “sample” function in R studio (1.1.463). Sampling was re-conducted if the number corresponds to empty, dead or diseased sea urchins.Mortality and morbiditySpotting disease, which appears as spotting lesions with red, purple or blackish color on the test (Fig. 7d), is the most common lethal disease in S. intermedius aquaculture12. Sea urchin without disease is shown in Fig. 7e. Dead sea urchins were removed daily and the number of survivor and diseased sea urchins was recorded weekly for each cage during the experiment (N = 8).Food consumptionThe measurement of food consumption (g dry weight) was conducted once a week (24 h from Tuesday to Wednesday) (N = 8). The total supplied and remained diets were weighted wet by an electric balance (G & G Co., San Diego, USA) after the removal of the surface moisture. The dried weights of feces and samples of supplied and uneaten kelp were determined after 4 days at 80 °C in a convection oven (Yiheng Co., Shanghai, China).Food consumption was calculated as follows (revised from Hu et al.9 for being more concise):$${text{F}} = frac{{{text{A}}_{0} times frac{{{text{A}}_{1} }}{{{text{A}}_{2} }} – {text{B}}_{0} times frac{{{text{B}}_{1} }}{{{text{B}}_{2} }}}}{{text{N}}}$$F = dry food intake per sea urchin (g ind−1 day−1), A0 = wet weight of total supplied diets (g), B0 = wet weight of total uneaten diets (g), A1 = dried weight of sample supplied diets (g), A2 = wet weight of sample supplied diets (g), B1 = dry weight of sample uneaten diets (g), B2 = wet weight of sample uneaten diets (g), N = the number of sea urchins.GrowthTest diameter and lantern length were measured using a digital vernier caliper (Mahr Co., Ruhr, Germany). Body, lantern and gut were weighted wet using an electric balance (G & G Co., San Diego, USA). Test diameter and body weight were evaluated every Wednesday. The average value of the three individuals was considered as the trait value for each replicate (N = 8). Lantern length, wet lantern weight and wet gut weight were recorded in week 4 (29 August 2020) and week 7 (20 September 2020) (N = 8).Aristotle’s lantern reflexAristotle’s lantern reflex, which refers to one cycle from the opening to the closing of the teeth59, was measured using a simple device according to the method of Ding et al.38. There were small compartments (length × width × height: 4.8 × 5.6 × 4.5 cm) with a film (made by 3 g agar and 2 g kelp powder) on the bottom of the device38 (Fig. 7f). The frequency of Aristotle’s lantern reflex was counted within 5 min using a digital camera (Canon Co., Shenzhen, China) under the device in week 4 (29 August 2020) and week 7 (20 September 2020). The average value of all the 5 individuals was considered as Aristotle’s lantern reflex for each replicate (N = 8).5-HT concentrationThe 5-HT is a signaling molecule, playing an important role in regulating feeding behavior52. To evaluate whether 5-HT is involved in Aristotle’s lantern reflex, 5-HT concentration of muscle in lantern was measured for each treatment in week 4 and week 7. 5-HT concentration was considered as the average value of all the 3 healthy individuals for each replicate (N = 8).The concentration of 5-HT was measured using ELISA kits (Nanjing Jiancheng Bio-engineering Institute, Nanjing, China) according to the instructions of the manufacturer. After adding the enzyme-labeled antibody, the substrate became a colored product that was directly related to the amount of the substance tested. The concentrations of 5-HT were calculated by comparing the optical density (O.D.) value of the samples to the standard curve and calculated according to the following formula (according to the kit’s instructions):$${text{Y}} = frac{1}{{({text{a }} + {text{bx}}^{{text{c}}} )}}$$Y = the concentration of 5-HT (ng mL−1), x = the O.D. value of the samples, a = 0.00027, b = 0.12086, c = 1.36806.Pepsin activityPepsin is important for sea urchins to digest protein-rich algae40,60. Pepsin activity was analyzed using the pepsin kits (Nanjing Jiancheng Bio-engineering Institute, Nanjing, China) in week 4 and week 7, following the instructions of the manufacturer. The average value of all the 3 individuals was considered as the pepsin activity for each replicate (N = 8). The procedures include enzyme reaction and color development reaction39. The temperature of reaction was 37 °C and pepsin activities were counted as U mg protein−1. The formula of pepsin activity is shown as follows (according to the kit’s instructions):$${text{P}} = frac{{{text{M}}_{0} – {text{M}}_{1} }}{{{text{M}}_{2} – {text{M}}_{3} }} times frac{{{text{S}}_{0} }}{{{text{S}}_{1} }} times frac{{{text{V}}_{1} times {text{V}}_{2} }}{{{text{V}}_{3} }}$$P = pepsin activity (U/mg prot), M0 = the O.D. value of the sample, M1 = the O.D. value of comparison, M2 = the standard O.D. value, M3 = blank O.D. value, S0 = the standard concentration (50 μg mL−1), S1 = reaction time (10 min), V1 = total volume of reaction solution (0.64 mL), V2 = sample protein concentration (0.04 mL), V3 = sampling volume (mg prot/mL).Gut morphological examinationAfter sea urchins were dissected on week 4 and week 7, all gut tissue samples (~ 1 g for each sample) were fixed in Bouin’s solution (glacial acetic acid: formaldehyde: saturated picric acid solution = 1:5:15) according to the method of Wu et al.61. They were subsequently transferred for gradient dehydration, embedding, cutting, staining and observation62 (N = 24).Statistical analysisKolmogorov–Smirnov test and Levene test were used to analyze the normal distribution and homogeneity of the data, respectively. Rearing assemblage was set as the main factor in the one-way ANOVA with three levels: the control system without layer divisions (group A), a second system with divisions in the cages to simulate the three layers cages (group B) and a third system with individual divisions for each sea urchin (group C). One-way ANOVA was used to analyze the mortality (in weeks 3, 4, 5, 6, 7), morbidity (in weeks 3, 6, 7), food consumption (in weeks 2, 5, 7), test diameter (in weeks 1, 2, 3, 4, 5, 6), body weight (in weeks 1, 4, 5, 7), 5-HT, pepsin activity, lantern length, lantern weight and gut weight. Duncan multiple comparison analysis was performed when significant differences were found in the one-way ANOVA. Kruskal–Wallis test was carried out to compare the differences of mortality (weeks 1, 2), morbidity (weeks 1, 2, 4, 5), food consumption (weeks 1, 3, 4, 6), test diameter (week 7), body weight (weeks 2, 3, 6) and Aristotle’s lantern reflex, because of non-normal distribution and/or heterogeneity of variance. A non-parametric post-hoc test was carried out when significant differences were found in the Kruskal–Wallis test. All data analyses were performed using SPSS 19.0 statistical software. A probability level of P  More

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