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We hand-collected adult toads (n = 8 individuals per site) from four sites across the toads’ tropical range within Australia, from Townsville, Qld in the east (GPS coordinates: − 19.26, 146.82, 14 m altitude) to Richmond, Qld (− 20.73, 143.14, 218 m altitude), Middle Point, NT (− 12.56, 131.33, 12 m altitude) and Kununurra, WA (− 15.78, 128.74, 49 m altitude) in the west. That transect spans the toads’ 80-year invasion history. Although both temperatures and precipitation exhibit a general east–west cline, the greatest disparities in the duration of hot dry conditions per year lie between the easternmost site (Townsville) and the three other sites (Fig. 1). We recorded toad mass (after gently squeezing the animal in a standardized manner to induce it to empty its bladder) and snout-vent length (SVL) immediately before conducting the trials.
Figure 1

Data from Australian Bureau of Meteorology7.

Mean climatic conditions in the four sites from which we collected cane toads (Rhinella marina) for use in laboratory trials. The red line connects mean monthly maximum air temperatures, the green line shows mean monthly air temperatures, and the blue line shows mean monthly minimum air temperatures. Histograms show mean monthly rainfall.

Full size image

Toads were not fed for three days prior to experiments, to ensure they would not defecate during the experiment and minimize variability in mass due to stomach contents. Toads from all four populations were housed in a room kept at 18 °C, then moved concurrently to a temperature-controlled room set at 37 °C. All toads were in separate containers (ventilated plastic boxes of 1-L capacity), half of which had dry paper towel as substrate whereas the other half had 40 mL of water, enough to keep the ventral portion of the body moist but not the rest of the body.
We measured toad body temperatures at the beginning of the trial, and after 20 min and 40 min, using an infrared thermometer (Digitech QM7215) held ~ 10 cm from the toad’s dorsal surface. At the beginning and end of the experiment we measured internal temperatures with a cloacal probe (Digitech QM7215 with probe attachment), to check that our measurements of external body temperature offer robust estimates of internal temperature also. Cloacal temperatures were taken within 10 s of each toad’s removal from the container. After a trial, toads were kept at a temperature of 25 °C, allowed to fully hydrate and monitored for wellbeing during recovery. No adverse effects of the trials were evident.
We used mixed model repeated measures analysis to identify factors affecting body temperatures of cane toads during the 40-min heating trials. Sex and body mass were used as covariates in the analysis with climate at each collection site (# consecutive months per year with average maximum temperature  > 30 °C and with  More

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Data input
ANTs can process two types of data: (1) data representing the directed interactions of individuals (e.g. grooming) or their associations (e.g. proximity), and (2) data representing individual attributes (sex, age, dominance rank, etc.).
Interactions and association data can be input in the form of a matrix or a data frame(s). The data frame structure depends on the type of protocol the user wants to follow. For network permutations, data frames must be in an edge list format with at least two columns, one of which indicates the actor and the other the receiver. An additional column may indicate weights of interactions. These data frames allow the user to directly input data collected in the field. For the data stream permutation approach, data can be presented in data frame format. In this case, data frames are not edge lists because they contain additional information in extra columns. For data stream permutations concerning focal observations16, i.e. data obtained from following a specific individual over a certain amount of time23, two extra columns are required in addition to those indicating the givers and receivers of a behaviour: one indicating the focal individual and another indicating the corresponding focal session. For data stream permutations on group follow observations6, i.e. recording individual associations at specific locations and time, data frames have to be in a ‘linear mode’, identical to SOCPROG (i.e. in which each line corresponds to the observation of an individual) with additional columns indicating the different ‘control’ factors (see “Permutations” section) such as the date, time of day or the geographical location associated with the interaction occurred.
It is also possible to use data frames for individual attributes (sex, age, dominance rank, hormone levels, etc.). These must be in a data frame format, with a row for every individual present in the data of individual interactions or associations. Each line represents the attribute(s) of a single individual.
Inputting these two types of data (interactions/associations and individual attributes) may enable the user to (1) permute and/or compute network measures on data representing individuals’ interactions or associations and (2) store node network measures with ANTs functions in the data frame(s) of individual attributes. This makes it possible to study how these node network measures are related to individuals’ attributes.
When performing the multiple networks analytical protocol, the user has to create an R list object where each element of the list stores interaction/association data representing a single network (list of data of interactions or associations). This list must contain a unique data format of interactions/associations (i.e. only edge lists, associations of group follow or associations of focal sampling). Optionally, the user can create a second R list object with the attributes of the individuals present in the corresponding list of interactions/associations (e.g. the data frame of individual attributes in element 1 corresponds to the individuals present in the list of interactions/associations in element 1, etc.). This way, permutations are generated independently in each network (e.g. 1,000 permutations in network 1, 1,000 permutations in network 2, etc.).
Testing data collection robustness
One of the main issues with regard to social network analysis and the study of animal groups is the quality of data collection (time of observation), as observation biases (e.g. some individuals are more frequently observed than others) can generate unreliable statistical results24, 25. Usually, data collection protocol has to be planned for the needs of the intended SNA before collecting data. The following questions must be answered: Do I observe all group members equally? Am I using the best method to limit the disturbance of animal behaviour and interactions? The choice of observation period is also a key factor, as some interindividual associations or interactions are rare and/or difficult to observe over the short term but are still important to attain the objectives of the study. However, this not always the case as scientists often collect data before carrying out analyses. ANTs meets the needs of these differing approaches by offering two different protocols to assess data collection robustness:
1.
Lusseau, et al.24 protocol to assess the robustness of node measures through bootstrapping.

2.
Balasubramaniam, et al.25 protocol to assess the robustness of global measures through observation deletion simulations.

For further information on the use of these different protocols, please refer to ANTs R documentation concerning functions in the ‘sampling.’ family.
Controlling for time heterogeneity
It is sometimes difficult to obtain the same number of observations per individual. ANTs enables users to control for time heterogeneity in different ways through the use of different association indices, namely the generalised affiliation index, the simple ratio index, the half-weight index or the square root index6. For further instructions on the use of these different indices, please refer to ANTs R documentation concerning the functions in the ‘assoc.’ family.
Computing network measures
Three types of network measures can be identified depending on the level of organisation: global measures, polyadic measures, and node measures. In ANTs, all these measures are grouped under the function family ‘met’. All the node measures available in ANTs are synthesised in Table 1. The measures we proposed in the package ANTs are the ones commonly used in Animal Social Network Analyses6, 22, 26,27,28.
Global measures (e.g. network diameter) are used to study the overall network and obtain valuable information regarding network efficiency, resilience, clusterisation, etc. Polyadic measures (e.g. assortativity) allow the study of interaction patterns between individuals. These measures provide information about how individuals interact according to their attributes. Node measures (e.g. strength) are the most frequently used measures in animal research. Among other things, node measures inform users about the centrality of an individual, the number of alters it has and/or its activity according to individual attributes, and reveal patterns that are common to individuals with similar attributes. By giving access to global, polyadic and node measures, we aim to enable users to adopt a multilevel approach and thereby understand the centrality of individuals in a group, the patterns of interaction between them and the impact of these two levels on the global network structure22, 29 .
For more details on the different types of measures, their mathematical formula, interpretation, limitations and past use in animal research, see Whitehead6, Sueur, et al.26, Sosa, et al.22, Sosa29 and refer to ANTs R documentation .
Permutations
When considering data robustness, permutations can be used to avoid observation biases and ensure the reliability of results obtained by SNA (i.e. results that have no type I and type II errors). Indeed, with the exception of some specific cases such as experiments in social insects, where individuals may be tracked continuously, it is usually assumed when examining inter-individual interactions within a group or a population that neither all the interactions nor all individuals are observed, that the times of observation vary from one individual to another, and that the data collected are intrinsically dependent. For these reasons, permutation tests are needed to control for data independency before performing inferential statistical tests, as inferential statistical tests assume data independency16.
The Null Model (NM) approach via permutation is one of the many current possibilities to test statistical hypotheses15. It allows users to perform analyses by creating random data sets from the observed data. The observed measure of interest X (e.g. coefficient of correlation) is compared to a posterior distribution obtained from the random data sets, and assesses whether X is significantly different from the random distribution by calculating the proportion of random values that differ from the observed value. The NM approach can be applied in different ways. ANTs allows for this by adapting the permutations (pre- or network permutations) according to the type of data collected ( i.e. pre- or network permutations for data on associations and interactions respectively) and the research question (i.e. permuting nodes when examining individual network measures or permuting links when examining individual polyadic or global measures).
Data stream and node network permutations are two of the most commonly used permutation methods to build null models in animal social network analysis. A description of these methods is presented by Puga-Gonzalez et al. (submitted). Data stream permutations were initially used to test whether individuals in a social population have a preference for association with certain partners rather than with others27, 30. One of the advantages of this method is that it can control for different factors such as location. It is therefore possible to test whether non-random associations are due to individuals’ social preference or result from a preference for the same habitat or location27.
Node network permutation is the other commonly used method to test network-related hypotheses in animal research. Node permutations have mainly been used to compare two matrices (or networks) involving the same group of individuals, i.e. matrix correlations. In this case, the values entered in the cell of the matrices are (un)directed behaviours (e.g. grooming or playing). In contrast to the gambit of the group, (un)directed behaviours are usually collected via focal sampling, scan sampling, or ad libitum sampling23. During node permutations, the identity of the nodes is redistributed at each permutation whilst the node metric is kept constant. This allows users to test whether a specific network metric is associated with a specific node attribute (e.g. whether females groom more than males), or whether behaviours are reciprocated or directed to individuals with a specific trait (e.g. grooming directed up the dominance hierarchy). All of the permutation approaches available in ANTs are in the family function ‘perm’ with two subclasses, ‘perm.ds’ and ‘perm.net’ for data stream and network permutations, respectively. ANTs can perform data stream permutations for group follow and focal sampling data collection protocols. Network permutations can be performed on (1) node label(s) (with labels’ dependency maintained or not), (2) links, (3) link weights, and (4) link weights swap between categories. Among those different types of permutations, node label (ESM Appendix 1) and data stream (ESM Appendix 2) permutations are probably the most commonly used standard approaches in animal network analysis. For this reason, we developed a specific workflow to allow their use (ESM Appendix 1 and ESM Appendix 2) in ANTs for the study of single31 or multiple networks9, 13 (for network comparisons or time-aggregated analyses). To date, ANTs is the only software permitting the use of these approaches in an all-in-one environment and their application for the analysis of multiple networks.
For more details on the different permutations and their applications according to the data collection protocol, the type of behavioural data collected and the research question, see Bejder, et al.30, Whitehead27, Whitehead, et al.32, Croft, et al.28, Farine16,Momigliano, et al.33, Sosa29, ANTs R documentation, ESM Appendix 1 and ESM Appendix 2.
Statistical tests based on data permutations
All the statistical tests available in ANTs are in the family function ‘stat’. The available tests are correlation test ‘stat.cor’, t-test ‘stat.t’, Linear Model (LM) ‘stat.lm’, Generalised Linear Model (GLM) ‘stat.glm’, Generalised Linear Mixed Models (GLMMs) ‘stat.glmm’, assortativity test ‘stat.assortativity’, TaurK correlation ‘stat.Taurk’ and deletion simulation ‘stat.deletion’. ANTs stat. function returns an object with the posterior distribution of the variable tested.
1.
Once the permutation test has been performed, the function ‘ant’, allows the user to obtain the statistical results from any output object of any function ‘stat’. The ‘ant’ function returns a data frame with statistics specific to the type of statistical test run. However, some of these statistics are common to all tests, namely the P-values on the right or left of the distribution and the two-side p-values.

2.
Measures of the ‘effect size’ of the posterior distribution according to the statistics of interest: 95% confidence interval and the mean of the distribution a posteriori (see Farine and Whitehead34). The histograms of the post-distribution of the statistics of interest obtained from the permutations.

Network visualization
ANTs allows network visualisation with a data frame containing node information and a matrix of interactions/associations. Nodes and links can be parametrised to modify their size and colour and highlight differences (e.g. females showing higher eigenvectors than males). Network layouts are currently based on Barnes Hut repulsion, Hierarchical Repulsion and Force Atlas 2. For more details on network visualisation, see ANTs function ‘net.vis’ in the package instructions document. These layouts are commonly used in animal social network analyses9, 35,36,37 as for instance, Force Atlas 2 arranges the visualisation graph with the distance between nodes is inversely proportional to their association, giving a nice view of who is close to whom. More

Sea urchins
Experimental sea urchins were produced in November 2018, fed Ulva pertusa ad libitum for ~ 2 months until the test diameter reached 0.3–0.4 cm diameter and subsequently fed S. japonica for culture1,4. Three hundred healthy S. intermedius (~ 3 cm of test diameter) were randomly selected from an aquaculture farm in Huangnichuan, Dalian (121° 45′ N, 38° 82′ E) and then were transported to the Key Laboratory of Mariculture and Stock Enhancement in North China’s Sea, Ministry of Agriculture at Dalian Ocean University (121° 56′ N, 38° 87′ E) on 9 July 2019. Sea urchins were maintained in a large fiberglass tank (length × width × height: 180 × 100 × 80 cm) of the recirculating system (Huixin Co., Dalian, China) to acclimatize to laboratory conditions and fed S. japonica ad libitum for 1 week with aeration. Water quality parameters were measured daily. Water temperature was 23.55 ± 0.07 °C, pH 7.72 ± 0.02 and salinity 33.76 ± 0.04. They were then fasted for another week until the experiment began.
Test diameter, wet body weight and wet gonad weight were evaluated for the initial conditions of sea urchins before the experiments started (N = 20 for test diameter and wet body weight; N = 10 for wet gonad weight).
Crude protein, fiber, fat and ash of S. horneri and S. japonica
Samples were taken from each dried brown alga to investigate their organic composition (crude protein, crude fiber and crude fat) and ash on 20 August 2019 (N = 3). Semi-micro Kjeldahl nitrogen was used to determine the crude protein concentration of the dried brown algae44. In order to measure the crude fiber concentration of brown algae, about 10 g of each sample of the dried brown algae was boiled with a mixed solution (1.25% dilute acid and dilute alkali) for 30 min and ashed at 550 °C to remove the minerals45. Five grams of each dried sample and 15 mL petroleum ether were added to the Soxhlet extractor and refluxed at constant temperature (45 ± 1 °C) for eight hours to assess the crude fat concentration of the brown algae46. To investigate the ash concentration of the brown algae, approximate two g dried samples were placed in a constant weight fritted glass and burned in a muffle furnace (M110, Thermo CO., U.S) at 550 °C for 48 h45.
Experiment I
Experimental design
Diet was the experimental factor, either S. horneri or S. japonica. Fresh S. horneri were collected from a farm in Huangnichuan Dalian (121° 45′ N, 38° 82′ E) and S. japonica from Dalian Bay (120° 37′ E 38° 56′ N) in July 2019. Individuals were fed dried S. horneri (experimental group) and dried S. japonica (control group) ad libitum for ~ 9 weeks during the experiment (from 23 July 2019 to 25 September 2019). One large fiberglass tank was used for each experimental treatment. One hundred sea urchins were haphazardly chosen and put into 100 individual cylindrical cages (length × width × height: 10 × 10 × 20 cm; 1.5 cm of mesh size) in each tank (length × width × height: 150 × 100 × 60 cm) of the recirculating system (Huixin Co., Dalian, China) with aeration, according to the experimental design. Diseased sea urchins were removed timely from the tanks to avoid the potential spread of infectious diseases in experimental treatments and were transported into new tanks (length × width × height: 75 × 45 × 35 cm) for individual culture and observation following with the previous management.
Water temperature was not controlled, ranging from 21.3 to 25.6 °C during the experiment. Water quality parameters were measured weekly as pH 7.59–7.85 and salinity 32.69–32.13. One-half of the seawater was renewed daily.
Number of survived and diseased sea urchins
Black-mouth disease refers to the perioral membrane turns black (Fig. 4A) with the decreased ability of attaching and feeding in sea urchins47. Sea urchin with spotting disease is indicated by the spotting lesions with red, purple or blackish color on the body wall followed by the detachment of local spines48 (Fig. 4B). The enlarging spotting lesions commonly cause ulceration on the body wall and finally result in death8. Sea urchin without disease performance is shown in Fig. 4C. The number of survived and diseased sea urchins (either black-mouth or spotting diseased) was recorded during the experiment.
Figure 4

The conceptual diagrams show the black-mouth disease (A), spotting disease (B) and without disease performance (C) of sea urchin as well as the devices for righting behavior (D) and Aristotle’s lantern reflex (E).

Full size image

Dried food consumption
The measurement of food consumption was conducted for six consecutive days (from 7 August 2019 to 12 August 2019). The total supplemented and remained diets were weighed (G & G Co., San Diego, USA) after removing the water on their surface. The samples of uneaten diets were collected, weighed and dried for 4 days at 80 °C and then reweighed (N = 5). To avoid the loss of uneaten food, a fine silk net (mesh size 260 μm) was set outside the cage to collect the fragments of uneaten brown algae7.
Dried food consumption was calculated as follows (according to Zhao et al.49 with some revisions):

$$text{F } = {text{ W}}_{1}times ({1}-frac{{text{B}}_{text{s}}-{text{B}}_{text{u}}}{{text{B}}_{text{s}}}text{)}-{text{W}}_{2} times (1-frac{{text{C}}_{text{s}}-{text{C}}_{text{u}}}{{text{C}}_{text{s}}}text{)}$$

F = dried food consumption (g), W1 = wet weight of total supplement diets (g), W2 = wet weight of total uneaten diets (g), Bs = wet weight of sample supplemented diets (g), Bu = dry weight of sample supplemented diets (g), Cs = wet weight of sample uneaten diets (g), Cu = dry weight of sample uneaten diets (g).
Growth
Test diameters, Aristotle’s lantern length were measured using a digital vernier caliper (Mahr Co., Ruhr, Germany). Body, Aristotle’s lantern and gut were weighted wet using an electric balance (G & G Co., San Diego, USA) on 25 September 2019 (N = 27 for test diameter and wet body weight; N = 6 for Aristotle’s lantern length, wet weight of Aristotle’s lantern and gut).
Specific growth rate (SGR) was calculated according to the following formula:

$${text{SGR}}text{ (}{%}text{)} , text{=}frac{ln{text{P}}_{2}-ln{text{P}}_{1}}{text{D}},times,{100}$$

SGR = specific growth rate, P2 = final wet body weight, P1 = initial wet body weight, D = experimental duration.
Gonad yield
Gonads were carefully collected from each treatment and weighed using an electric balance (G & G Co., San Diego, USA) on 25 September 2019 (N = 6). Gonad index was calculated according to the following formula:

$$text{GI } (%)= text{ } frac{text{GW}}{{text{BW}}},times,{100}$$

GI = gonad index, GW = wet gonad weight, BW = wet body weight.
Gonadal development
One of five pieces of each gonad was preserved in the Bouin’s solution (saturated picric acid solution: formaldehyde: glacial acetic acid = 15: 5: 1) for 48 h between the treatments (N = 6). Standard histology technique, including embedment, infiltration, section and stain, was performed to make the gonad tissue slices50. Sections were classified according to the stage of development of germinal cells and nutritive phagocytes: stage I, recovering; stage II, growing; stage III, premature; stage IV, mature; stage V, partly spawned; stage VI, spent51,52,53.
Experiment II
Experimental design
Experiment II lasted for 4 weeks (from 25 September 2019 to 23 October 2019). Eighty healthy sea urchins were haphazardly selected from each treatment at the end of experiment I. They were then distributed into 80 cylindrical cages (5 × 10 × 10 cm) in each fiberglass tanks (length × width × height: 77.5 × 47.0 × 37.5 cm) of the temperature-controlled system (Huixin Co., Dalian, China) with aeration in both treatments. Sea urchins were maintained at 23.5 °C for 2 weeks (the average water temperature of experiment I) to eliminate the past thermal history, following the previous diet strategy of experiment I. Water quality was recorded daily as pH 7.83–7.85 and salinity 32.44–32.62. One-third of the seawater was renewed daily.
Subsequently, to investigate whether S. horneri and S. japonica contribute to the resistance abilities of small S. intermedius at moderately elevated temperatures, 40 individuals were haphazardly chosen from each treatment and placed into 40 cylindrical cages (5 × 10 × 10 cm) in each tank (length × width × height: 77.5 × 47.0 × 37.5 cm) of a temperature-controlled system (Huixin Co., Dalian, China) with aeration for both groups on 9 October 2019. They were subsequently exposed to the moderately elevated temperatures (rose from 23.5 to 26.5 °C at a rate of 0.5 °C per day and maintained at 26.5 °C for 1 week), according to the records of water temperature in Heishijiao sea area (~ 2 m water depth, 38° 51′ N, 121° 33′ E) in the summer of 2017 and 2018 (Fig. 5). Righting behavior, tube feet extension and Aristotle’s lantern reflex were assessed on 23 October 2019.
Figure 5

Daily records of the water temperature in Heishijiao sea area, Dalian (~ 2 m water depth, 38° 51′ N, 121° 33′ E) in the summers of 2017 and 2018.

Full size image

Similarly, to explore the effects of S. horneri and S. japonica on the resistance abilities of small S. intermedius under acute changes of water temperature, another 40 individuals were randomly selected and placed into 40 cylindrical cages (5 × 10 × 10 cm) in each tank (length × width × height: 77.5 × 47.0 × 37.5 cm) of the temperature-controlled system (Huixin Co., Dalian, China) with aeration for both treatments on 9 October 2019. The water temperature was set at 23.5 °C. A tank of seawater was prepared at 15 °C. To simulate the changes of water temperature in Haiyang island near Dalian (39° 03′ N, 123° 09′ E) where water temperature frequently fluctuates from 22 to 16 °C instantly by the cold water mass17, sea urchins were transferred directly from 23.5 to 15 °C, maintained at 15 °C for an hour and subsequently quickly returned to 23.5 °C for another hour to finish one cycle of the acute change of water temperature. After four cycles, righting behavior, tube feet extension and Aristotle’s lantern reflex of sea urchins were observed.
Righting behavior
Sea urchins were placed with the aboral side down on the bottom of an experimental tank (length × width × height: 60 × 40 × 16 cm, Fig. 4D). Righting response time is the time required for individuals in the inverted posture to right themselves with the aboral side up22. The righting response time in seconds was recorded during 10 min. If individuals did not right themselves within 10 min, the time was counted as 600 s (N = 15).
Tube feet extension
The method of assessing tube feet extension was established according to You et al.27, with some revisions. Sea urchins were maintained in a tank (length × width × height: 12 × 10 × 10 cm) with fresh seawater for ~ 5 min before the observation (N = 15). The subjective assessment of tube feet extension was evaluated by a well-trained team (5 persons) that was familiar with tube feet extension analysis of sea urchins. The ranking method was quantified based on the quantity and length of tube foot.
Tube feet extension (rating 1–5):

1 = extremely abnormal (not extending)

2 = severe abnormality (extremely low quantity and extremely short length)

3 = moderate anomaly (low quantity and short length)

4 = mild abnormality (slight decrease in quantity and length)

5 = normal (normal quantity and length)

Aristotle’s lantern reflex
A simple device, which has two small compartments (length × width × height: 4.8 × 5.6 × 4.5 cm) with a food film on the bottom, was used to measure Aristotle’s lantern reflex according to our previous study29. Food film was made by a mixture of ~ 2.5 g agar and 50 ml seawater in order to avoid the potential impacts of the food palatability on sea urchins. The number of Aristotle’s lantern reflex were counted within 5 min using a digital camera (Canon Co., Shenzhen, China) under the device (N = 7 for sea urchins fed S. horneri and N = 10 for individuals fed S. japonica under moderately elevated temperatures; N = 10 for both groups under acutely changed temperatures; Fig. 4E).
Statistical analysis
Normal distribution and homogeneity of variance of the data were analyzed using the Kolmogorov–Smirnov test and Levene test, respectively. The number of survived and diseased S. intermedius were compared using the Fisher′s exact test. Food consumption was analyzed using one-way repeated measured ANOVA. Kruskal–Wallis test was performed to compare the difference of righting behavior and tube feet extension between the treatments and also used to analyze Aristotle’s lantern reflex under acutely changed temperatures. Independent-samples t test was carried out to compare the difference between the final and initial conditions of sea urchins. Test diameter, wet body weight, SGR, Aristotle’s lantern length, wet Aristotle’s lantern weight, wet gut weight, gonad index and Aristotle’s lantern reflex (under moderately elevated temperatures) were analyzed using the independent-samples t test. All data analyses were performed using SPSS 19.0 statistical software. A probability level of P  More

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Affiliations

Sustainable Agroecosystems Group, Department of Environmental Systems Science, ETH Zurich, Zurich, Switzerland
W. J. Blaser, J. Landolt & J. Six

Council for Scientific and Industrial Research – Soil Research Institute, Kwadaso, Kumasi, Ghana
J. Oppong & E. Yeboah

Institute of Integrative Biology, Department of Environmental Systems Science, ETH Zurich, Zurich, Switzerland
S. P. Hart

Authors
W. J. Blaser

J. Oppong

S. P. Hart

J. Landolt

E. Yeboah

J. Six

Corresponding author
Correspondence to W. J. Blaser. More