More stories

  • in

    A convenient polyculture system that controls a shrimp viral disease with a high transmission rate

    Mathematical model 1—the relationship among the bodyweight of the initial WSSV-infected shrimp, number of deaths, and death time distributionThe experimental data show the time course of death for the infected shrimp satisfies the Laplacian distribution (Supplementary Tables 2–4). The relationship of the bodyweight of the initial infected shrimp number of deaths and death time distribution could be expressed by a mathematical model and the establishment of the mathematical model as shown below.Suppose that one dead shrimp could infect (n) healthy shrimp at the same day. These (n) infected shrimp do not die simultaneously but on different days (time course). The value of (n) is related to the weight of the dead shrimps—larger dead shrimp can infect more healthy shrimps of the same body weight. Our experimental results (Supplementary Tables 2–4) show the death time course for these (n) infected shrimp satisfies the Laplacian distribution, as follows:$$begin{array}{c}pleft(tright)=left{begin{array}{c}{b{{exp}}}left(-frac{left|t-aright|}{{c}_{1}}right),tle a\ {b{{exp}}}left(-frac{left|t-aright|}{{c}_{2}}right),t > aend{array}right.end{array}$$
    (1)
    where (a) is the peak time of number of dead shrimps, (b) is the maximal death percentage, ({c}_{1}) is related to the mortality increases of the infected shrimps, ({c}_{2}) is related to the mortality decreases of the infected shrimp, (p(t)) is the percentage of infected shrimp that die at time (t). The open bracket “{“ in formula (1) means the function is represented by two parallel expressions as described previously.Based on the Supplementary Tables 2–4, we can determine the value of (a), (b), ({c}_{1}), and ({c}_{2}) by the least square estimation method. As different weight corresponds to different distribution of death time, we can compute the relationship of weight of death shrimps with corresponding (a), (b), ({c}_{1}), and ({c}_{2}) (Supplementary Table 25).We found the relationship of (w) with (a), or (b), or ({c}_{1}) or ({c}_{2}) is quadratic (Eq. 2), with the data in Supplementary Table 25, we have$$begin{array}{c}left{begin{array}{c} a= -0.0918{w}^{2}+0.8772w+3.3449\ b=0.0029{w}^{2}-0.0369w+0.5849;;\ {c}_{1}=-0.0186{w}^{2}+0.1739w+0.7063\ {c}_{2}=0.0002{w}^{2}+0.0108w+1.0827;;,end{array}right.end{array}$$
    (2)
    Using Model 1, we can predict the effects of different body weights of dead WSSV-infected shrimp through the ingestion pathway of WSSV-infected dead shrimp on the WSSV transmission rate.Mathematical model 2—the dynamic changes of healthy, infected, and dead shrimp during WSSV transmissionWe derived and established Model 2 to simulate the WSS transmission dynamics in cultured shrimp. Using Model 2, we predicted the dynamic changes of three states (healthy, infected, and dead shrimps) in cultured shrimp as influenced by the WSS epidemic with the following:Now we can develop a model for the spread and break out of WSS. For any given weight (w) of shrimps, let ({s}_{h}(t)), ({s}_{i}(t)), and ({s}_{d}(t)) be the number of healthy shrimp, infected shrimp and dead shrimp respectively at time (t). Let (I(t)), (d(t)) be the number of daily infected shrimp, daily dead shrimp, respectively, at time (t).According to infection process, the decrement of healthy shrimp is caused by their infection, therefore we have (frac{d{s}_{h}}{{dt}}=-I(t)). The quantity change of infected shrimp includes the infection of healthy shrimp and the death of infected shrimp, we have (frac{d{s}_{i}}{{dt}}=I(t)-d(t)). The increment of dead shrimp is caused by the death of the infected shrimp; thus we have (frac{d{s}_{d}}{{dt}}=d(t)). We obtain the following system of ordinary differential equations:$$left{begin{array}{c}frac{d{s}_{h}}{{dt}}=-Ileft(tright)hfill\ ,frac{d{s}_{i}}{{dt}}=Ileft(tright)-dleft(tright)\ frac{d{s}_{d}}{{dt}}=d(t)hfillend{array}right.$$
    (3)
    where ({s}_{h}left(0right)={s}_{{h}_{0}}), ({s}_{i}left(0right)={s}_{{i}_{0}}), ({s}_{d}left(0right)={s}_{{d}_{0}}) are as the initial value, at (t=0).In the above system of ordinary differential equations, quantity (I(t)) can be expressed as follows$$begin{array}{c}Ileft(tright)={min }left{n{s}_{d}left(tright),{s}_{h}left(tright)-alpha {s}_{{h}_{0}}right}end{array},$$
    (4)
    (d(t)) can be expressed as$$begin{array}{c}dleft(tright)={int }_{0}^{T}{min }left{n{s}_{d}left(t-tau right),{s}_{h}left(t-tau right)-alpha {s}_{{h}_{0}}right}pleft(tau right)dtau end{array}$$
    (5)
    where (n) is the number of healthy shrimp infected by one dead shrimp on the first day. (p(tau )) is the death percentage of the (n) infected shrimp on the (tau) days, (T) is the longest survival time of infected shrimp.Now we explain how to set up the formulas (I(t)) and (d(t)). In the expression of (I(t)), (n{s}_{d}(t)) is the number of daily infected shrimp at time (t). But as the number of healthy shrimp decreases, there may not be as many as (n{s}_{d}(t)) healthy shrimp to be infected. Therefore, (I(t)) is the minimum of (n{s}_{d}(t)) and ({s}_{h}left(tright)-alpha {s}_{{h}_{0}}), where (alpha (0 , < , alpha, < , 1)) represents the percentage of healthy shrimp that may have resistance to viruses, (d(t)) is the number of shrimps infected from (0) to (t) die at time (t). We use this integral to express the number of shrimp die at time (t).To evaluate the performance of the model 2, we compare the simulated scenario and the biological experimental settings. Our experiments show the quantity change of dead shrimps and live shrimps with respect to time, which is consistent with the result of simulation (Supplementary Fig. 4).Mathematical model 3—use fish to control WSSWe established Model 3 for the prevention and control of WSS using fish. In Model 3, two parameters need to be determined before this model can be applied for evaluating the fish’s capability of WSS prevention and control. The two parameters are, (1) fish-feeding quantity of dead shrimp, and (2) fish-feeding ratio of dead shrimp over healthy shrimp. We obtained 1 kg grass carp’s feeding quantity of different body weights of shrimp and the feeding selectivity through experiments. The mathematical reasoning of Model 3 is as follows:To block the transmission of WSS, we apply fish to eat dead shrimp and infected shrimp. Let ({e}_{h}(t)), ({e}_{i}(t)), and ({e}_{d}(t)), respectively be the number of healthy shrimp, infected shrimp and dead shrimp eaten by fish daily at time (t), (f(t)) is the number of fish.The decrement of healthy shrimp is related to the number of infected healthy shrimp and the number of shrimp eaten by fish, as expressed in (frac{d{s}_{h}}{{dt}}=-I(t)-{e}_{h}(t)). Similarly, the dynamics of the infected shrimp is related to the number of infected healthy shrimp, the death number of infected shrimp, and the number of infected shrimp eaten by fish, as expressed in (frac{d{s}_{i}}{{dt}}=I(t)-d(t)-{e}_{i}(t)). The dynamics of dead shrimp is related to the death number of infected shrimp, and eaten by fish, as expressed in (frac{d{s}_{d}}{{dt}}=d(t)-{e}_{d}(t)). Combining the above formulae, we can write the model as follows:$$left{begin{array}{c}frac{d{s}_{h}}{{dt}}=-Ileft(tright)-{e}_{h}left(tright)quadhfill\ ,frac{d{s}_{i}}{{dt}}=Ileft(tright)-dleft(tright)-{e}_{i}left(tright)hfill\ frac{d{s}_{d}}{{dt}}=d(t)-{e}_{d}(t)hfillend{array}right.$$ (6) where ({s}_{h}left(0right)={s}_{{h}_{0}}), ({s}_{i}left(0right)={s}_{{i}_{0}}), ({s}_{d}left(0right)={s}_{{d}_{0}}) are as the initial value at (t=0). In the above model, (I(t)), (d(t)), ({e}_{h}(t)), ({e}_{i}(t)), and ({e}_{d}(t)) are respectively given as follows:$$left{begin{array}{c};, Ileft(tright)={min }left{n{s}_{d}left(tright),{s}_{h}left(tright)-alpha {s}_{{h}_{0}}right}hfill\ ;dleft(tright)={int }_{0}^{t}{min }left{n{s}_{d}left(t-tau right),{s}_{h}left(t-tau right)-alpha {s}_{{h}_{0}}right}pleft(tau right){exp }left{{int }_{t-tau }^{t}{{{{{rm{ln}}}}}}rleft(uright){du}right}dtau hfill\ {e}_{d}left(tright)={min }left{fleft(tright)cdot mcdot beta ,{s}_{d}left(tright)+dleft(tright)right}hfill\ ,{e}_{i}left(tright)={min }left{left(fleft(tright)cdot m-{e}_{d}left(tright)right)frac{{s}_{i}left(tright)+Ileft(tright)-dleft(tright)}{{s}_{i}left(tright)+{s}_{h}left(tright)-dleft(tright)},{s}_{i}left(tright)+Ileft(tright)-dleft(tright)right}hfill\ {e}_{h}left(tright)={min }left{fleft(tright)cdot m-{e}_{d}left(tright)-{e}_{i}left(tright),{s}_{h}left(tright)-Ileft(tright)right}hfill\ ;,rleft(tright)=1-frac{{e}_{i}left(tright)}{{s}_{i}left(tright)+Ileft(tright)-dleft(tright)}hfillend{array}right.$$ (7) where, (I(t)) is the same as in Eq. (4); for (d(t)), different from Eq. (5) is that we add an exponential item ({exp }left{{int }_{t-tau }^{t}{{{{{rm{ln}}}}}}rleft(uright){du}right}) to account for the infected shrimp that may be eaten by fish during the past (t) days. As for ({e}_{d}(t)) shown in Eq. (6), (m) is for that each fish eats (m) shrimps while (beta) accounts for a percentage of dead shrimp in (m) shrimp. In ({e}_{i}(t)), we introduce (frac{{s}_{i}left(tright)+Ileft(tright)-dleft(tright)}{{s}_{i}left(tright)+{s}_{h}left(tright)-dleft(tright)}) for the percentage of infected shrimp in live shrimp. ({e}_{h}(t)) accounts for the number of healthy shrimp eaten by fish. (r(t)) represents the percentage of infected shrimp not being eaten by fish. We performed the effects of 1 kg grass carps on shrimp with four different body weights. The simulated data agreed with the experimental results (Fig. 2c).The relationship among the bodyweight of one initial WSSV-infected shrimp, number of deaths, and death time distributionThree groups of 430 shrimp with a bodyweight of 1.98 ± 0.03, 6.13 ± 0.16, and 7.95 ± 0.13 g, respectively, were used. In each group, 30 shrimp were randomly selected and subjected to a two-step WSSV PCR assay. All the tested shrimp showed negative in the assay. The remaining 400 shrimps were divided equally and introduced to three experimental and one control ponds. All 12 aquariums (220 cm × 60 cm × 80 cm) were set up with a water volume of 0.5 m3 and a salinity of 8‰. Shrimp were quarantined for seven days before the experiment started. One piece of dead WSSV-infected shrimp was then introduced to each of the experimental aquariums. In addition, one piece of frozen dead shrimp (WSSV-free) was introduced to the control aquarium. Shrimp were fed once a day with artificial feed that is 2% of their body weight. Shrimp feces were timely removed, and 50% of the water in the aquarium was exchanged every day. To prevent healthy shrimps from eating the moribund shrimp but not the initial dead WSSV-infected shrimp, shrimp were observed every 10 min to identify and remove moribund shrimp from the second day of the experiment. Moribund shrimp were identified as the ones having pleopod activity, but no response to glass rod agitation. The experiment was continued until three days after the appearance of the last moribund shrimp in each aquarium. Five pieces each of moribund and survived shrimps in each aquarium were subjected to a one-step WSSV PCR assay. All moribund shrimps showed WSSV-positive, while survived shrimps showed WSSV-negative. A mathematical model (Model 1) describing the relationship among the bodyweight of one initial WSSV-infected shrimp, number of deaths, and death time distribution was established based on the experimental results.The dynamic changes of live, infected, and dead shrimps during WSSV transmissionTo determine the changes in numbers of live and dead shrimp during WSSV transmission, 9 cement ponds (315 cm × 315 cm × 120 cm) were set up with a water volume of 5 m3 and salinity of 8‰. Regarding the stocking quantity of 7.5 × 105/ha in shrimp farming production, 750 healthy shrimp with an average body weight of 7.9 g were cultured in each of the nine ponds.To prepare the WSSV acute-infected shrimp, healthy shrimp were starved for 3 days, and then fed with parts of dead WSSV-infected shrimp that are 20% of their body weights twice a day. Five shrimp were randomly selected and subjected to a one-step WSSV PCR assay. If the tested shrimp showed WSSV positive in the assay. The rest of the shrimp in the aquarium was used as the WSSV acute-infected shrimp in the following experiments.Healthy shrimp were quarantined for seven days before the experiment started. Thirty WSSV acute-infected shrimp were then introduced in each pond. Shrimps were fed once a day with artificial feed that is 2% of their body weight. The numbers of survived shrimp were counted in three ponds on the 2nd, 4th, 8th day after WSSV infection, respectively. Five dead shrimps in each pond were subjected to a one-step WSSV PCR assay, showing WSSV-positive. Based on model 1, we established a mathematical model (Model 2) to describe the dynamic changes of healthy, infected, and dead shrimps during WSSV transmission.The dead shrimp ingestion rate of fishTo determine the dead shrimp ingestion rate of grass carp (Ctenopharyngodon idellus). Three cement ponds (315 cm × 315 cm × 120 cm) were set up with a water volume of 5 m3 and a salinity of 5‰. Three grass carps with an average body weight of 0.5 kg, 1 kg, and 1.5 kg were released in each of the three ponds, respectively. The fish were raised for four days and then fed with dead shrimps with an average weight of 5.3 g. In addition, to determine the dead shrimp ingestion rate of African sharptooth catfish (Clarias gariepinus). Four cement ponds (315 cm × 315 cm × 120 cm) were set up with a water volume of 5 m3 and salinity of 3‰. One African sharptooth catfish with bodyweight of 0.262, 0.496, 0.731, and 1.502 kg was released in each of the four ponds, respectively. The fish were raised for four days and then fed with dead shrimps with an average body weight of 6.2 g. Finally, to determine the dead shrimp ingestion rate of red drum (Sciaenops ocellatus). Three cement ponds (315 cm × 315 cm × 120 cm) were set up with water volume of 5 m3 and a salinity of 5‰. One red drum with a bodyweight of 0.590, 0.654, and 0.732 kg was released in each of the three ponds, respectively. The fish were raised for four days and then fed with dead shrimps with an average body weight of 3.9 g.During the five days of the experiment, dead shrimp that were not ingested by fish were exchanged with new dead shrimps every day. Additionally, the total body weight of dead shrimp ingested by fishes was calculated by subtracting the total body weight of dead shrimp that remained in the pond from the total body weight of dead shrimp put in the pond. The shrimp ingestion rate of fish is quantified by the daily ingestion rate (total body weight of ingested shrimps per day/total body weight of fishes). The daily ingestion rate of fish was calculated for 5 days.The healthy shrimp ingestion rate of fishTo determine the healthy shrimp ingestion rate of grass carp, three experimental ponds and one control pond (315 cm × 315 cm × 120 cm) were set up with a water volume of 5 m3 and salinity of 5‰. In total, 750 healthy shrimp with an average body weight of 5.3 g were cultured in each pond. One grass carp weighting 0.956, 1.013, and 1.050 kg was released in each of the experiment ponds, respectively. No fish was released in the control pond. Every two days, 50% of the water in each pond was changed. Live shrimp that remained in each pond were counted and weighted after 10 days of the experiment.To determine the healthy shrimp ingestion rate of African sharptooth catfish, one experimental pond and one control pond (315 cm × 315 cm × 120 cm) were set up with a water volume of 5 m3 and salinity of 3‰. In total, 750 healthy shrimp with an average body weight of 2.2 g were cultured in each pond. One African sharptooth fish weighting 1.050 kg was released in the experiment pond. No fish was released in the control pond. Every 2 days, 50% of the water in each pond was changed. Live shrimp that remained in each pond were counted and weighted after 10 days of the experiment.To determine the healthy shrimp ingestion rate of red drum, three experimental ponds and one control pond (315 cm × 315 cm × 120 cm) were set up with a water volume of 5 m3 and salinity of 5‰. In total, 750 healthy shrimp with an average body weight of 2.7 g were introduced in each pond. One red drum weighting 0.519, 0.554, and 0.595 kg was released in each of the experiment ponds, respectively. No fish was released in the control pond. Every two days, 50% of the water in each pond was changed. Live shrimp that remained in each pond were counted and weighted after 10 days of the experiment.The feeding selectivity of fish on dead, infected, and healthy shrimpsTo determine the feeding selectivity of grass carp on dead, infected, and healthy shrimp, one aquarium (220 cm × 60 cm × 80 cm) was set up with a water volume of 0.5 m3 and a salinity of 5‰. Grass carp weighting 1.58 kg was cultured in the aquarium for four days before the experiment started. The diseased shrimp infected with WSSV died within two days, which makes it hard to distinguish the initial dead shrimp from the ones that were died from diseased shrimp. The diseased shrimp had reduced activity, and the activity of shrimp was reduced after the endopods and exopods were removed. Thus, shrimp with endopods and exopods removed were utilized to resemble WSSV-infected shrimp. Thirty pieces each of dead, WSSV-infected (endopods and exopods removed), and healthy shrimps were introduced in the aquarium. The mean weight of shrimp used in the experiment is 3.5 g.To determine the feeding selectivity of African sharptooth catfish on dead, infected, and healthy shrimps, one aquarium (220 cm × 60 cm × 80 cm) was set up with a water volume of 0.5 m3 and salinity of 3‰. African sharptooth catfish with body weight of 1.03 kg was cultured in the aquarium for four days before the experiment started. Thirty pieces each of dead, WSSV-infected (endopods and exopods removed), and healthy shrimps were introduced in the aquarium. The mean weight of shrimps used in the experiment is 8.4 g.During the 9 days of the experiment, the dead, infected (endopods and exopods removed), and healthy shrimp that remained in the aquarium were counted and weighed every day. New shrimps were added to ensure there are 30 pieces each of dead, infected (endopods and exopods removed), and healthy shrimp in the aquarium. The daily total body weight of shrimp that were ingested by fish in each pond was calculated by subtracting the total body weight of shrimp that remained in the pond from the total weight of shrimp put in the pond. The shrimp ingestion rate of fish is quantified by the daily ingestion rate (total body weight of ingested shrimp per day/bodyweight of fish).The suitable bodyweight of grass carp for controlling WSSTo determine the suitable bodyweight of grass carp for controlling WSS, four experimental groups and two control groups were set up. Each group consisted of three ponds (315 cm × 315 cm × 120 cm). In total, 600 healthy and 3 WSSV-infected shrimp with an average body weight of 5 g were cultured in each pond of experimental groups. One grass carp with a bodyweight of 0.3, 0.5, 1.0, 1.5 kg was released in the ponds of each experimental group, respectively. In the positive control group, 600 healthy and 3 WSSV-infected shrimp with an average body weight of 5.0 g were cultured in each of the three ponds without introducing grass carp. In the negative control group, 600 healthy shrimp with an average body weight of 5.0 g were cocultured with one grass carp weighting 1.0 kg in each of the three ponds. The numbers of live shrimp were counted after ten days of the experiment. If there were dead shrimp in the ponds, they were subjected to a one-step WSSV PCR assay. All dead shrimps showed positive for WSSV infection.The suitable bodyweight of African sharptooth catfish for controlling WSSTo determine the suitable bodyweight of African sharptooth catfish for controlling WSS, four experimental groups and two control groups were set up. Each group consisted of three ponds (315 cm × 315 cm × 120 cm). In total, 600 healthy and WSSV carrying shrimp and 3 WSSV-infected shrimp with an average body weight of 1.5 g were cultured in each pond of experimental groups. The WSSV carrying shrimp were determined as the ones that showed positive in a two-step WSSV assay. One African sharptooth catfish with a bodyweight of 0.25, 0.5, 0.75, 1.5 kg was released in the ponds of each experimental group, respectively. In the positive control group, 600 healthy and 3 WSSV-infected shrimp with an average body weight of 1.5 g were cultured in each of the three ponds without introducing African sharptooth catfish. In the negative control group, 600 healthy shrimps with an average body weight of 1.5 g were cocultured with one African sharptooth catfish weighting 1.0 kg in each pond. The numbers of live shrimp were counted after ten days of the experiment. If there were dead shrimps in the ponds, they were subjected to a one-step WSSV PCR assay. All dead shrimps showed positive for WSSV infection.The capacity of grass carp for controlling WSSTo determine the capacity of grass carp for controlling WSS, the number of WSSV-infected shrimp that could be ingested by one grass carp weighting 1 kg was evaluated. Four groups of shrimp with different body weights (1.3 ± 0.1, 2.5 ± 0.2, 5.0 ± 0.3, 7.8 ± 0.5 g) were cocultured with 1-kg grass carp in the ponds.In 1.3 ± 0.1 g group, 750 healthy shrimp were cultured in each of the nine cement ponds (315 cm × 315 cm × 120 cm). Healthy shrimps were cultured with 3, 6, 9, 12, 15, 18, and 21 pieces of WSSV-infected shrimp in each of the seven experimental ponds, respectively. One grass carp weighting 1 kg was released in each of the seven ponds. Healthy shrimp were cultured with 3 WSSV-infected shrimps in one pond as a positive control. Additionally, healthy shrimps were cultured without WSSV-infected shrimp nor grass carp in one pond as a negative control. In 2.5 ± 0.2 g group, 750 healthy shrimp were cultured with 10, 20, 30, 40, 50, 60, and 70 pieces of WSSV-infected shrimp in each of the seven experimental ponds, respectively. One grass carp weighting 1 kg was released in each of the seven ponds. Healthy shrimp were cultured with 10 WSSV-infected shrimp in one pond as a positive control. Additionally, healthy shrimps were cultured without WSSV-infected shrimp nor grass carp in one pond as a negative control. In 5.0 ± 0.3 g group, 750 healthy shrimp were cultivated with 50, 70, 90, 110, 120, 130, and 140 pieces of WSSV-infected shrimp in each of the seven experimental ponds, respectively. One grass carp weighting 1 kg was released in each of the seven ponds. Healthy shrimp were cultured with 50 WSSV-infected shrimps in one pond as a positive control. Additionally, healthy shrimps were cultured without WSSV-infected shrimps nor grass carp in one pond as a negative control. In 7.8 ± 0.5 g group, 750 healthy shrimp were cultured with 30, 40, 50, or 60 pieces of WSSV-infected shrimps in four experimental ponds, respectively. One grass carp weighting 1 kg was released in each of the four ponds. Healthy shrimp were cultured with 30 WSSV-infected shrimps in one pond as a positive control. In addition, healthy shrimps were cultured without WSSV-infected shrimps nor grass carp in one pond as a negative control.In all the ponds, shrimp were fed with artificial feed that is 2% of their body weight. And 50% of the water was changed every day. The numbers of the remaining live shrimp were counted after 15 days of the experiment. A mathematical model (Model 3) was established based on the relationship of healthy shrimp, infected shrimp, dead shrimp, and fish.Determine the numbers of grass carp and African sharptooth catfish required for controlling WSS in L. vanmamei cultivationThe number of grass carp required for controlling WSS in shrimp production was determined in Pinggang Aquaculture Base, Yangjiang, China in 2010. Forty ponds (0.34 ± 0.04 ha/pond) were divided into eight groups; each group consisted of 5 ponds. We cultured 675,000/ha of shrimp in the ponds. Shrimp were cultured for 20 days before 45, 150, 225, 300, 450, 600, 750/ha of grass carp with an average body weight of 1.0 kg were released in the ponds of group 2 to group 8. Shrimp were cultured without fish in the ponds of group 1. These 40 ponds were managed by using the same farming method. If the WSS outbreak occurred, shrimps were harvested immediately; if not, shrimps were harvested after 110 days of cultivation.The number of African sharptooth catfish required for controlling WSS in shrimp production was determined in Pinggang Aquaculture Base, Yangjiang, China in 2010. Thirty-five ponds (0.37 ± 0.06 ha/pond) were divided into seven groups; each group consisted of 5 ponds. We cultured 675,000/ha of shrimp in the ponds. Shrimp were cultured for 10 days before 150, 300, 450, 600, 750, 900/ha of African sharptooth catfish with an average body weight of 1.0 kg were released in the ponds of group 2 to group 7. Shrimp were cultured without fish in the ponds of group 1. These 35 ponds were managed by using the same farming method. If the WSS outbreak occurred, shrimps were harvested immediately; if not, shrimps were harvested after 110 days of cultivation.Validation of coculturing shrimp and grass carp for controlling WSS in L. vanmamei farmingIn 2011, the polyculture system of coculturing L. vanmamei and grass carps was validated at a farm in Maoming, Guangdong Province, China (Farm 1). Forty-six farm ponds (17.33 ha) were divided into zone A and zone B. Zone A consisted of 18 ponds with a total area of 6.03 ha, and zone B consisted of 28 ponds with a total area of 11.30 ha. The stocking quantity of shrimp in the ponds of zone A is 900,000/ha. Shrimp were cultured in the ponds for 20 days before releasing grass carps with an average body weight of 1.0 kg. The stocking quantity of fish is 317–450/ha. Shrimp were cultured without fish in the ponds of zone B, and the stocking quantity of shrimp is 900,000/ha. In 2012, we switched zones A and B, cultivating shrimp with grass carp in zone B but without fish in zone A. The stocking quantities of shrimp and fish were the same as in 2011. If a WSS outbreak occurred, shrimps were harvested immediately; if not, shrimps were harvested after 110 days of cultivation, and yields were measured.Validation of coculturing shrimp and African sharptooth catfish for controlling WSS in L. vanmamei farmingIn 2011, the polyculture system of coculturing L. vanmamei and African sharptooth catfish was validated at a farm in Qinzhou, Guangxi Province, China (Farm 2). Ninety-five farm ponds (88.2 ha) were divided into zone A and zone B. Zone A consisted of 38 ponds with a total area of 21.2 ha, and zone B consisted of 57 ponds with a total area of 67.0 ha. The stocking quantity of shrimp in the ponds of zone A is 750,000/ha. Shrimp were cultured in the ponds for 10 days before releasing African sharptooth catfish with an average body weight of 0.5 kg. The stocking quantity of fish is 525–750/ha. Shrimp were cultured without fish in the ponds of zone B, and the stocking quantity of shrimp is 750,000/ha. In 2012, we split zone B into zones B1 and B2. Shrimp were cultivated with catfish in 38 ponds of zone A and 25 ponds (27.00 ha) of zone B1, while shrimp were cultivated without fish in 32 ponds (40.00 ha) of zone B2. The stocking quantities of shrimp and fish were the same as in 2011. If WSS outbreak occurred, shrimps were harvested immediately; if not, shrimps were harvested after 110 days of cultivation, and yields were measured.Long-term validation of coculturing shrimp and fish for controlling WSS in L. vanmamei cultivationWe tested the effectiveness of using fish for controlling WSS in shrimp production at a farm in Maoming, Guangdong Province, China (Farm 1) from 2013 to 2019. In 2013, shrimp were co-cultured with African sharptooth catfish of body weight ranging from 0.5 to 0.6 kg in 13 ponds (3.73 ha). The stocking quantity of shrimp in these ponds ranges from 878,788/ha to 1,230,769/ha. And shrimp were co-cultured with grass carp of body weight ranges from 0.7 kg to 1.0 kg and African sharptooth catfish of body weight ranges from 0.5 kg to 0.6 kg in 10 ponds (3.7 ha). The stocking quantity of shrimp in these ponds ranges from 909,091/ha to 1,212,121/ha. Additionally, shrimp were cultured without fish in 11 ponds (3.63 ha). The stocking quantity of shrimp in these ponds ranges from 878,788/ha to 969,697/ha. If WSS outbreak occurred, shrimp were harvested immediately; if not, shrimp were harvested after 110 days of cultivation.In 2014, shrimp were cocultured with grass carp of body weight ranging from 0.7 to 1.0 kg in 8 ponds (2.76 ha). The stocking quantity of shrimp in these ponds ranges from 833,333/ha to 1,060,606/ha. And shrimp were co-cultured with grass carp of body weight ranges from 0.7 to 1.0 kg and African sharptooth catfish of body weight ranges from 0.5 to 0.6 kg in 12 ponds (4.03 ha). The stocking quantity of shrimp in these ponds ranges from 825,000/ha to 1,060,606/ha. Additionally, shrimp were cultured without fish in 5 ponds. The stocking quantity of shrimp in these ponds was 1,060,606/ha. If a WSS outbreak occurred, shrimp were harvested immediately; if not, shrimp were harvested after 110 days of cultivation.In 2015, shrimp were co-cultured with grass carp of body weight ranging from 0.7 to 1.0 kg in 19 ponds (7.4 ha). The stocking quantity of shrimp in these ponds ranges from 746,269 to 1,538,462/ha. In addition, shrimp were cultured without fish in 10 ponds (3.8 ha). The stocking quantity of shrimp in these ponds ranges from 750,000 to 909,091/ha. If a WSS outbreak occurred, shrimp were harvested immediately; if not, shrimp were harvested after 110 days of cultivation.In 2016, shrimp were co-cultured with grass carp of body weight ranging from 0.7 to 1.0 kg in 19 ponds (8.11 ha). The stocking quantity of shrimp in these ponds ranges from 488,372/ha to 636,364/ha. Additionally, shrimp were cultured without fish in 8 ponds (2.84 ha). The stocking quantity of shrimp in these ponds ranges from 543,478/ha to 636,364/ha. If a WSS outbreak occurred, shrimp were harvested immediately; if not, shrimp were harvested after 110 days of cultivation.In 2017, shrimp were cocultured with grass carp of body weight ranging from 0.7 to 1.0 kg in 6 ponds (1.56 ha). The stocking quantity of shrimp in these ponds was 961,538/ha. And shrimps were co-cultured with grass carp of body weight ranging from 0.7 kg to 1.0 kg and African sharptooth catfish of body weight ranges from 0.5 to 0.6 kg in 12 ponds (3.96 ha). The stocking quantity of shrimp in these ponds ranges from 848,485/ha to 909,091/ha. Additionally, shrimp were cultured without fish in 9 ponds (2.76 ha). The stocking quantity of shrimp in these ponds ranges from 848,485/ha to 961,538/ha. If a WSS outbreak occurred, shrimp were harvested immediately; if not, shrimp were harvested after 110 days of cultivation.In 2018, shrimp were cocultured with grass carp of body weight ranging from 0.7g to 1.0 kg in 22 ponds (9.24 ha). The stocking quantity of shrimp in these ponds ranges from 454,545/ha to 869,565/ha. Additionally, shrimp were cultured without fish in 9 ponds (3.36 ha). The stocking quantity of shrimp in these ponds ranges from 695,652/ha to 861,111/ha. If a WSS outbreak occurred, shrimp were harvested; if not, shrimp were harvested after 110 days of cultivation.In 2019, shrimp were cocultured with grass carp of body weight ranging from 0.7 to 1.0 kg in 30 ponds (11.31 ha). The stocking quantity of shrimp in these ponds ranges from 652,174/ha to 1,000,000/ha. Additionally, shrimp were cultured without fish in 10 ponds (3.57 ha). The stocking quantity of shrimp in these ponds ranges from 666,667/ha to 1,000,000/ha. If a WSS outbreak occurred, shrimp were harvested immediately; if not, shrimp were harvested after 110 days of cultivation.Validation of coculturing shrimp and brown-marbled grouper for controlling WSS in P. monodon farmingIn 2013, the polyculture system of coculturing P. monodon and brown-marbled grouper was validated at a farm in Changjiang, Hainan Province, China (Farm 3). We cultured 6 × 105/ha of non-SPF shrimp in 6 ponds (1.60 ha) for 30 days and then introduced 600~750/ha of brown-marbled grouper with an average body weight of 0.1 kg. Shrimp were also cultured without fish in 3 ponds (0.8 ha). The stocking quantity of shrimp in these ponds is 6 × 105/ha. If a WSS outbreak occurred, shrimp were harvested immediately; if not, shrimp were harvested after 150 days of cultivation, and yields were measured.In 2014, we cultured 6 × 105/ha of non-SPF shrimp in 6 ponds (1.60 ha) for 30 days and then introduced 600–750/ha of brown-marbled grouper with an average body weight of 0.1 kg. Shrimps were also cultured without fish in 3 ponds (0.8 ha). The stocking quantity of shrimp in these ponds is 6 × 105/ha. If a WSS outbreak occurred, shrimp were harvested immediately; if not, shrimp were harvested after 150 days of cultivation, and yields were measured.Validation of coculturing shrimp and branded gobies for controlling WSS in M. japonica farmingIn 2013, the polyculture system of coculturing M. japonica and branded gobies was validated at a farm in Qingdao, Shandong Province, China (Farm 4). We cultured 1.5 × 105/ha of non-SPF shrimp in 10 ponds (13.40 ha) for 30 days and then introduced 750~900/ha of branded gobies with an average body weight of 0.05 kg. Shrimp were also cultured without fish in 5 ponds (6.70 ha). The stocking quantity of shrimp in these ponds is 1.5 × 105/ha. If a WSS outbreak occurred, shrimp were harvested immediately; if not, shrimp were harvested after 100 days of cultivation, and yields were measured.In 2014, we cultured 1.5 × 105/ha of non-SPF shrimp in 10 ponds (13.40 ha) for 30 days and then introduced 750~900/ha of branded gobies with an average body weight of 0.1 kg. Shrimp were also cultured without fish in 5 ponds (6.70 ha). The stocking quantity of shrimp in these ponds is 1.5 × 105/ha. If a WSS outbreak occurred, shrimp were harvested immediately; if not, shrimp were harvested after 100 days of cultivation, and yields were measured.Promotion of the polyculture system at a farmers’ association in Nansha, ChinaWhen we promoted the polyculture system at the farmers’ association in 2015, only 6 farmers decided to adopt the system, as most of the farmers worried that fish would ingest healthy shrimp. Each of the 6 farmers introduced 225,000, 360,000, and 360,000 P.monodon postlarva to his/her earthen pond (3 ha) on March 28, May 8, and June 15, respectively. And 1350 grass carps with an average body weight of 1 kg were released in the ponds on April 30. These farmers harvested shrimp from May to November, and grass carp on December 14. The yields of shrimp and fish of these six ponds were recoded. The other farmers in the association introduced 225,000 and 360,000 of P.monodon postlarva to their ponds (3 ha) on March 28 and May 8, respectively. WSS outbreaks occur in their ponds from May 15 to May 23. Therefore, these farmers only harvested shrimp in May. Six ponds were randomly selected, and the yields of these ponds were recorded.Promotion of the polyculture system at a farmers’ association in Tanghai, ChinaFarmers at the farmers’ association used to culture 1500/ha of F. chinensis in earthen pond (5 ha) before the promotion of the polyculture system in 2015. The yields of 10 randomly selected ponds in 2014 were recorded. In 2015, farmers at the association started to culture 8,000/ha of F. chinensis in their ponds. The shrimp were cultured 20 days before 800/ha of branded gobies with an average body weight of 0.05 kg were released in the ponds. Branded gobies were cultivated for 15 days before introducing to the ponds. Shrimps were harvested after 120 days of cultivation. The yields of ten randomly selected ponds were recorded.Statistics and reproducibilityAlpha levels of 0.05 were regarded as statistically significant throughout the study. Three replicates were set up for each experiment to confirm the reproducibility of the data. All data are reported as the mean ± standard errors.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

  • in

    Indigenous sex-selective salmon harvesting demonstrates pre-contact marine resource management in Burrard Inlet, British Columbia, Canada

    1.Drucker, P. Indians of the Northwest Coast (McGraw-Hill, 1955).Book 

    Google Scholar 
    2.Kroeber, A. Culture and natural areas of Native North America (University of California Press, 1939).
    Google Scholar 
    3.Introduction, S. W. In Handbook of North American Indians volume 7: Northwest Coast (ed. Suttles, W.) 1–15 (Smithsonian Institution, 1990).
    Google Scholar 
    4.Suttles, W. Coping with abundance: subsistence on the Northwest Coast. In Coast Salish Essays (ed. Suttles, W.) (Talon Books, 1987).
    Google Scholar 
    5.Barnett, H. G. The Coast Salish of British Columbia (University of Oregon Press, 1955).
    Google Scholar 
    6.Ames, K. The Northwest Coast: Complex hunter-gatherers, ecology, and social evolution. Annu. Rev. Anthropol. 23, 209–229 (1994).Article 

    Google Scholar 
    7.Carlson, R. L., Szpak, P. & Richards, M. The Pender Canal site and the beginnings of the Northwest Coast cultural system. Can. J. Archaeol. 41, 1–29 (2017).
    Google Scholar 
    8.Cannon, A. & Yang, D. Y. Early storage and sedentism on the Pacific Northwest Coast: ancient DNA analysis of salmon remains from Namu, British Columbia. Am. Antiquity 71, 123–140 (2006).Article 

    Google Scholar 
    9.Matson, R. G. The evolution of Northwest Coast subsistence. In Research in Economic Anthropology Supplement 6: Long-Term Subsistence Change in Prehistoric North America (eds Croes, D. et al.) 366–428 (JAI Press Inc., 1992).
    Google Scholar 
    10.Caldwell, M. et al. A bird’s eye view of northern Coast Salish intertidal resource management features, southern British Columbia. J. Island Coast. Archaeol. 7, 219–233 (2012).Article 

    Google Scholar 
    11.Caldwell, M. & Lepofsky, D. Indigenous marine resource management on the Northwest Coast of North America. Ecol. Process. 2(1), 12 (2013).
    Google Scholar 
    12.Croes, D. R. (ed.). The Qwu?gwes Archaeological Site and Fish Trap (45TN240), and Tested Homestead (45TN396), Eleven-year South Puget Sound Community College Summer Field School Investigations with the Squaxin Island Tribe—Final Report. Report on file, Washington State Department of Archaeology and Historic Preservation, Olympia (2013).13.Lepofsky, D. et al. Shellfish mariculture on the Northwest Coast of North America. Am. Antiq. 80, 236–259 (2015).Article 

    Google Scholar 
    14.Mathews, D. L. & Turner, N. J. Ocean cultures: northwest coast ecosystems and indigenous management systems. In Conservation for the Anthropocene Ocean: Interdisciplinary Science in Support of Nature and People (eds Levin, P. S. & Poe, M. R.) 169–201 (Academic Press, 2017).Chapter 

    Google Scholar 
    15.Williams, J. Clam gardens: aboriginal mariculture on Canada’s West Coast (New Star Books, 2006).
    Google Scholar 
    16.Campbell, S. & Butler, V. Archaeological evidence for resilience of Pacific Northwest salmon populations and the socioecological system over the last~7,500 years. Ecol. Soc. 15(1), 17 (2000).Article 

    Google Scholar 
    17.Thornton, T., Deur, D. & Kitka, H. Cultivation of salmon and other marine resources on the Northwest Coast of North America. Hum. Ecol. 43, 189–199 (2015).Article 

    Google Scholar 
    18.Thornton, T. The ideology and practice of Pacific Herring cultivation among the Tlingit and Haida. Hum. Ecol. 43, 213–223 (2015).Article 

    Google Scholar 
    19.Petersen, J. R. et al. Use of the traditional halibut hook of the Makah Tribe, the čibu⋅d, reduces bycatch in recreational halibut fisheries. PeerJ 8, e9288 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    20.Ritchie, M. & Angelbeck, B. “Coyote broke the dams”: Power, reciprocity, and conflict in fish weir narratives and implications for traditional and contemporary fisheries. Ethnohistory 67(2), 191–220 (2020).Article 

    Google Scholar 
    21.Royle, T. C. A. et al. An efficient and reliable DNA-based sex identification method for archaeological Pacific salmonid (Oncorhynchus spp.) remains. PLoS ONE 13(3), e0193212 (2018).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    22.Royle, T. C. A. et al. Investigating the sex-selectivity of a Middle Ontario Iroquoian Atlantic salmon (Salmo salar) and lake trout (Salvelinus namaycush) fishery through ancient DNA analysis. J. Archaeol. Sci. Rep. 31, 102301 (2020).
    Google Scholar 
    23.George, G. National Energy Board Hearing Order OH-001-2014. Trans Mountain Pipeline ULC. Trans Mountain Expansion Project. Volume 6 (2014).24.Morin, J. Tsleil-Waututh Nation’s History, Culture and Aboriginal Interests in Eastern Burrard Inlet. Report on file, Gowlings, Lafleur, Henderson LLP, Vancouver (2015).25.Suttles, W. Central Coast Salish. In Handbook of North American Indians Volume 7: Northwest Coast (ed. Suttles, W.) 453–475 (Smithsonian Institution, 1990).26.Hancock, M. J. & Marshall, D.E. Catalogue of Salmon Streams and Spawning Escapements of Statistical Area 28 Howe Sound-Burrard Inlet. Canadian Data Report of Fisheries and Aquatic Sciences No. 557 (1986).27.Harris, G. The salmon and trout streams of Vancouver. Waters J. Vanc. Aquar. 3, 4–23 (1978).
    Google Scholar 
    28.Ricker, W. E. Effects of the Fishery and of Obstacles to Migration on the Abundance of Fraser River Sockeye Salmon (Oncorhynchus nerka). Canadian Technical Report of Fisheries and Aquatic Sciences No. 1522 (1987).29.Charlton, A. S. The Belcarra Park Site. (Department of Archaeology, Simon Fraser University, 1980).30.Lepofsky, D., Trost, D. & Morin, J. Coast Salish interaction: a view from the inlets. Can. J. Archaeol. 31, 190–223 (2007).
    Google Scholar 
    31.Morin, J., Lepofsky, D., Ritchie, M., Porcic, M. & Edinborough, K. Assessing continuity in the ancestral territory of the Tsleil-Waututh-Coast Salish, southwest British Columbia, Canada. J. Anthropol. Archaeol. 51, 77–87 (2018).Article 

    Google Scholar 
    32.Morin, J., Muir, B., Ritchie, M. & Sellers, I. Tsleil-Waututh and Simon Fraser University Archaeological Investigations at Port Moody (Reed Point, Shoreline Park, Old Orchard Park, Slaughterhouse Creek, Carraholly Point, and Barnet Beach). Permit 2014–344. Report on file, British Columbia Archaeology Branch, Victoria (2020).33.Harris, C. Voices of disaster: smallpox around the Strait of Georgia in 1782. Ethnohistory 41, 591–626 (1994).Article 

    Google Scholar 
    34.Chisholm, B. S. Reconstructions of Prehistoric Diet in British Columbia Using Stable-Carbon Isotopic Analysis. PhD dissertation. (Simon Fraser University, 1986).35.Hanson, D. K. Late Prehistoric Subsistence in the Strait of Georgia Region of the Northwest Coast. Master’s thesis. (Simon Fraser University, 1991).36.Trost, T. Forgotten Waters: A Zooarchaeological Analysis of the Cove Cliff Site (DhRr 18), Indian Arm, British Columbia. Master’s thesis. (Simon Fraser University, 2005).37.Pierson, N. Bridging Troubled Waters: Zooarchaeology and Marine Conservation on Burrard Inlet, Southwest British Columbia. Master’s thesis. (Simon Fraser University, 2011).38.Morin, J. et al. DNA-based species identification of ancient salmonid remains provides new insight into pre-contact Coast Salish salmon fisheries in Burrard Inlet, British Columbia, Canada. J. Archaeol. Sci. Rep. 37, 102956 (2021).
    Google Scholar 
    39.Sproat, G. June 15, 1877. Copy of minute of decision, Joint Indian Reserve Commission. Signed by Dominion Commissioner Alex Anderson, Provincial Commissioner Arch. McKinley and Joint Commissioner G.M. Sproat. Federal set of JIRC’s Minutes and plans, surveyor’s copy. Aboriginal and Northern Affairs Canada, BC Regional Office Specific Claims Branch, Resource Library, Vancouver. AAND Lands and Trusts registration #15215. Also LAC, RG10, Volume 3612, File 3756-23, Reel C10106 (1877).40.Mortimer, H. & George, D. You Call Me Chief: Impressions of the Life of Dan George (Doubleday, 1981).
    Google Scholar 
    41.Talbot, M. Old Legends and Customs of the British Columbia Coast Indians. s.n., New Westminster (1952).42.Thornton, M. Indian Lives and Legends (Mitchell Press, 1966).
    Google Scholar 
    43.MacDonald, C., Drake, D., Doerksen, J. & Cotton, M. Between Forest and Sea: Memories of Belcarra (Belcarra Historical Group, 1998).
    Google Scholar 
    44.Romanoff, S. Fraser Lillooet Fishing. In A Complex Culture of the British Columbia Plateau, Vancouver (ed. Hayden, B.) 222–265 (University of British Columbia Press, 1992).
    Google Scholar 
    45.Kennedy, D. & Bouchard, R. Sliammon Life, Sliammon Lands (Talonbooks, 1983).
    Google Scholar 
    46.Mathisen, O. A. The effect of altered sex ratios on the spawning of red salmon. In Studies of Alaska Red Salmon (ed. Koo, T.) 137–246 (University of Washington Press, 1962).
    Google Scholar 
    47.Reed, W. J. Sex-selective harvesting of Pacific salmon: a theoretically optimal solution. Ecol. Model. 14, 261–271 (1982).Article 

    Google Scholar 
    48.Salo, E. O. Life history of chum salmon (Oncorhynchus keta). In Pacific Salmon Life Histories (eds Margolis Groot, C. & Margolis, L.) 231–310 (UBC Press, 1991).
    Google Scholar 
    49.Jenness, D. The Faith of a Coast Salish Indian (British Columbia Provincial Museum, 1955).50.Richling, B. (ed.) The W̲SÁNEĆ and Their Neighbours: Diamond Jenness on the Coast Salish of Vancouver Island, 1935 (Rock’s Mills Press, 2016).51.Dale, C. & Natcher, D. C. What is old is new again: the reintroduction of Indigenous fishing technologies in British Columbia. Local Environ. 20(11), 1309–1321 (2015).Article 

    Google Scholar 
    52.Ritchie, M. P. & Springer, C. Harrison River Chum Fishery: The Ethnographic and Archaeological Perspective. Report on file, Sts′ailes, Agassiz (2010).53.Simeone, W. E. & Valentine, E. M. Ahtna Knowledge of Long-Term Changes in Salmon runs in the Upper Copper River Drainage, Alaska (Alaska Department of Fish and Game, Division of Subsistence, 2007).54.Langdon, S. J. Traditional Knowledge and Harvesting of Salmon by Huna and Hinyaa Tlingit. (U.S. Fish and Wildlife Service, Office of Subsistence Management, Fisheries Resource Monitoring Program, 2006).55.Ratner, N. C. et al. Local Knowledge, Customary Practices, and Harvest of Sockeye salmon from the Klawock and Sarkar Rivers, Prince of Wales Island, Alaska (Alaska Department of Fish and Game, Division of Subsistence, 2006)56.Curtis, E. The North American Indian: being a series of volumes picturing and describing the Indians of the United States, the Dominion of Canada, and Alaska, Vol. 13 (Plimpton Press, 1924).
    Google Scholar 
    57.Kennedy, D. & Bouchard, R. Stl’atl’imx fishing. In A Complex Culture of the British Columbia Plateau (ed. Hayden, B.) 266–354 (UBC Press, 1992).
    Google Scholar 
    58.Yang, D. Y. & Watt, K. Contamination controls when preparing archaeological remains for ancient DNA analysis. J. Archaeol. Sci. 32(3), 331–336 (2005).Article 

    Google Scholar 
    59.Speller, C. F. et al. High potential for using DNA from ancient herring bones to inform modern fisheries management and conservation. PLoS ONE 7, e51122 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 
    ADS 

    Google Scholar 
    60.Yang, D. Y., Eng, B., Waye, J. S., Dudar, J. C. & Saunders, S. R. Technical note: improved DNA extraction from ancient bones using silica-based spin columns. Am. J. Phys. Anthropol. 105(4), 539–543 (1998).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    61.Yang, D. Y., Liu, L., Chen, X. & Speller, C. F. Wild or domesticated: DNA analysis of ancient water buffalo remains from North China. J. Archaeol. Sci. 35(10), 2778–2785 (2008).Article 

    Google Scholar 
    62.Bertho, S. et al. The unusual rainbow trout sex determination gene hijacked the canonical vertebrate gonadal differentiation pathway. Proc. Natl. Acad. Sci. U.S.A. 115(50), 12781–12786 (2008).Article 
    CAS 

    Google Scholar 
    63.Yano, A. et al. An immune-related gene evolved into the master sex-determining gene in rainbow trout, Oncorhynchus mykiss. Curr. Biol. 22(15), 1423–1428 (2012).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    64.Yano, A. et al. The sexually dimorphic on the Y-chromosome gene (sdY) is a conserved male-specific Y-chromosome sequence in many salmonids. Evol. Appl. 6(3), 486–496 (2013).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    65.Yang, D., Cannon, A. & Sanders, S. R. DNA species identification of archaeological salmon bone from the Pacific Northwest Coast of North America. J. Archaeol. Sci. 31, 619–631 (2004).Article 

    Google Scholar 
    66.R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2019).67.Kim, K. et al. A real-time PCR-based amelogenin Y allele dropout assessment model in gender typing of degraded DNA samples. Int. J. Legal Med. 127, 55–61 (2013).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    68.Sinding, M. et al. Sex determination of baleen whale artefacts: Implications for ancient DNA use in zooarchaeology. J. Archaeol. Sci. Rep. 10, 345–349 (2016).
    Google Scholar 
    69.Cooper, A. & Poinar, H. Ancient DNA: do it right or not at all. Science 289(5482), 1139 (2000).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    70.McKechnie, I. Investigating the complexities of sustainable fishing at a prehistoric village on western Vancouver Island, British Columbia, Canada. J. Nat. Conserv. 15(3), 208–222 (2007).Article 

    Google Scholar 
    71.Cannon, A., Yang, D. Y. & Speller, C. Site-specific salmon fisheries on the central coast of British Columbia. In The Archaeology of North Pacific Fisheries (eds Moss, M. & Cannon, A.) 57–74 (University of Alaska Press, 2011).
    Google Scholar 
    72.McKechnie, I. & Moss, M. Meta-analysis in zooarchaeology expands perspectives on Indigenous fisheries of the Northwest Coast of North America. J. Archaeol. Sci. Rep. 8, 470–485 (2016).
    Google Scholar 
    73.Orchard, T. J. & Szpak, P. Zooarchaeological and isotopic insights into locally variable economic patterns: a case study from late Holocene southern Haida Gwaii, British Columbia. BC Stud. 187, 107–147 (2015).
    Google Scholar 
    74.Rodrigues, A. T., McKechnie, I. & Yang, D. Y. Ancient DNA analysis of indigenous rockfish use on the Pacific Coast: implications for marine conservation areas and fisheries management. PLoS ONE 13(2), e0192716 (2018).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    75.McGregor, D. Coming full circle: Indigenous knowledge, environment, and our future. Am. Indian Q. 28(3), 385–410 (2004).Article 

    Google Scholar 
    76.Suttles, W. Economic Life of the Coast Salish of Hario and Rosario Straits. PhD Dissertation. (University of Washington, 1951).77.Caldwell, M. E. Northern Coast Salish Marine Resource Management. PhD dissertation. (University of Alberta, 2015).78.Deur, D. Tending the garden, making the soil: Northwest Coast estuarine gardens as engineered environments. In Keeping It Living: Traditions of Plant Use and Cultivation on the Northwest Coast of North America (eds Deur, D. & Turner, N.) (UBC Press, 2005).
    Google Scholar 
    79.Hoffmann, T. et al. Engineered feature used to enhance gardening at a 3800-year-old site on the Pacific Northwest coast. Sci. Adv. 2(12), e1601282 (2016).PubMed 
    PubMed Central 
    Article 
    ADS 

    Google Scholar 
    80.Lepofsky, D. et al. Documenting pre-contact plant management on the Northwest Coast: an example of prescribed burning in the Central and Upper Fraser Valley, British Columbia. In Keeping It Living: Traditions of Plant Use and Cultivation on the Northwest Coast of North America (eds Deur, D. & Turner, N.) 218–239 (UBC Press, 2005).
    Google Scholar 
    81.Turner, N. J., Deur, D. & Lepofsky, D. Plant management systems of British Columbia’s First Peoples. BC Stud. 179, 107–133 (2013).
    Google Scholar 
    82.Turner, N. J., Smith, R. & Jones, J. A fine line between two nations: ownership patterns for plant resources among Northwest Coast indigenous peoples. In Keeping It Living: Traditions of Plant Use and Cultivation on the Northwest Coast of North America (eds Deur, D. & Turner, N. J.) 151–180 (UBC Press, 2005).
    Google Scholar 
    83.Turner, N. J. & Peacock, S. Solving the perennial paradox: ethnobotanical evidence for plant resource management on the Northwest Coast. In Keeping It Living: Traditions of Plant Use and Cultivation on the Northwest Coast of North America (eds Deur, D. & Turner, N. J.) 101–151 (UBC Press, 2005).
    Google Scholar 
    84.Limburg, K. E., Walther, Y., Hong, B., Olson, C. & Stora, J. Prehistoric versus modern Baltic Sea cod fisheries: selectivity across the millennia. Proc. R. Soc. B 275, 2659–2665 (2008).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    85.Sanchez, G. Indigenous stewardship of marine and estuarine fisheries? Reconstructing the ancient size of Pacific herring through linear regression models. J. Archaeol. Sci. Rep. 29, 102061 (2020).
    Google Scholar 
    86.Slaney, T. L., Hyatt, K. D., Northcote, T. G. & Fielden, R. J. Status of anadromous salmon and trout in British Columbia and Yukon. Fisheries 21(10), 20–35 (1996).Article 

    Google Scholar 
    87.Kope, R. & Wainwright, T. Trends in the status of Pacific salmon populations in Washington, Oregon, California, and Idaho. N. Pac. Anadr. Fish Comm. Bull. 1, 1–12 (1998).
    Google Scholar 
    88.Gustafson, R. G. et al. Pacific salmon extinctions: Quantifying lost and remaining diversity. Conserv. Biol. 21(4), 1009–1020 (2007).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    89.Price, M. H., English, K. K., Rosenberger, A. G., MacDuffee, M. & Reynolds, J. D. Canada’s wild salmon policy: an assessment of conservation progress in British Columbia. Can. J. Fish. Aquat. Sci. 74(10), 1507–1518 (2017).Article 

    Google Scholar 
    90.Gayeski, N. J. et al. The failure of wild salmon management: need for a place-based conceptual foundation. Fisheries 43(7), 303–309 (2018).Article 

    Google Scholar 
    91.Morales, Q. E., Lepofsky, D. & Berkes, F. Ethnobiology and fisheries: Learning from the past for the present. J. Ethnobiol. 37(3), 369–379 (2017).Article 

    Google Scholar 
    92.Reid, A. J. et al. Two-eyed seeing: an Indigenous framework to transform fisheries research and management. Fish Fish. 00, 1–19 (2020).
    Google Scholar 
    93.Atlas, W. I. et al. Indigenous systems of management for culturally and ecologically resilient Pacific salmon (Oncorhynchus spp.) fisheries. Bioscience 71(2), 1–19 (2021).Article 

    Google Scholar  More

  • in

    Spatial regulation of cell motility and its fitness effect in a surface-attached bacterial community

    1.Flemming H-C, Wingender J, Szewzyk U, Steinberg P, Rice SA, Kjelleberg S. Biofilms: an emergent form of bacterial life. Nat Rev Microbiol. 2016;14:563–75.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    2.Nadell CD, Xavier JB, Foster KR. The sociobiology of biofilms. FEMS Microbiol Rev. 2009;33:206–24.CAS 
    PubMed 

    Google Scholar 
    3.Rumbaugh KP, Sauer K. Biofilm dispersion. Nat Rev Microbiol. 2020;18:571–86.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    4.Costerton JW, Stewart PS, Greenberg EP. Bacterial biofilms: a common cause of persistent infections. Science. 1999;284:1318–22.CAS 
    PubMed 

    Google Scholar 
    5.Drenkard E, Ausubel FM. Pseudomonas biofilm formation and antibiotic resistance are linked to phenotypic variation. Nature. 2002;416:740–3.CAS 
    PubMed 

    Google Scholar 
    6.de Carvalho CCCR. Marine biofilms: a successful microbial strategy with economic implications. Front Mar Sci. 2018;5:126.7.McDougald D, Rice SA, Barraud N, Steinberg PD, Kjelleberg S. Should we stay or should we go: mechanisms and ecological consequences for biofilm dispersal. Nat Rev Microbiol. 2012;10:39–50.CAS 

    Google Scholar 
    8.Nathan R, Getz WM, Revilla E, Holyoak M, Kadmon R, Saltz D, et al. A movement ecology paradigm for unifying organismal movement research. Proc Natl Acad Sci USA. 2008;105:19052–9.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    9.Yan J, Monaco H, Xavier JB. The ultimate guide to bacterial swarming: an experimental model to study the evolution of cooperative behavior. Annu Rev Microbiol. 2019;73:293–312.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    10.Gokhale S, Conwill A, Ranjan T, Gore J. Migration alters oscillatory dynamics and promotes survival in connected bacterial populations. Nat Commun. 2018;9:5273.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    11.Hallatschek O, Fisher DS. Acceleration of evolutionary spread by long-range dispersal. Proc Natl Acad Sci USA. 2014;111:E4911–9.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    12.Birzu G, Hallatschek O, Korolev KS. Fluctuations uncover a distinct class of traveling waves. Proc Natl Acad Sci USA. 2018;115:E3645–54.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    13.Ping D, Wang T, Fraebel DT, Maslov S, Sneppen K, Kuehn S. Hitchhiking, collapse, and contingency in phage infections of migrating bacterial populations. ISME J. 2020;14:2007–18.PubMed 
    PubMed Central 

    Google Scholar 
    14.Chen L, Noorbakhsh J, Adams RM, Samaniego-Evans J, Agollah G, Nevozhay D, et al. Two-dimensionality of yeast colony expansion accompanied by pattern formation. PLoS Comput Biol. 2014;10:e1003979.PubMed 
    PubMed Central 

    Google Scholar 
    15.Patra P, Kissoon K, Cornejo I, Kaplan HB, Igoshin OA. Colony expansion of socially motile Myxococcus xanthus cells is driven by growth, motility, and exopolysaccharide production. PLoS Comput Biol. 2016;12:e1005010.PubMed 
    PubMed Central 

    Google Scholar 
    16.Chapman BB, Brönmark C, Nilsson J-Å, Hansson L-A. The ecology and evolution of partial migration. Oikos. 2011;120:1764–75.
    Google Scholar 
    17.Lundberg P. Partial bird migration and evolutionarily stable strategies. J Theor Biol. 1987;125:351–60.
    Google Scholar 
    18.Kokko H. Directions in modelling partial migration: how adaptation can cause a population decline and why the rules of territory acquisition matter. Oikos. 2011;120:1826–37.
    Google Scholar 
    19.Singh NJ, Leonardsson K. Partial migration and transient coexistence of migrants and residents in animal populations. PloS One. 2014;9:e94750.PubMed 
    PubMed Central 

    Google Scholar 
    20.Armbruster CE, Mobley HLT. Merging mythology and morphology: the multifaceted lifestyle of Proteus mirabilis. Nat Rev Microbiol. 2012;10:743.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    21.Schaffer JN, Pearson MM. Proteus mirabilis and urinary tract infections. Microbiol Spectr. 2015;3. https://doi.org/10.1128/microbiolspec.UTI-0017-2013.22.Jones BV, Young R, Mahenthiralingam E, Stickler DJ. Ultrastructure of Proteus mirabilis swarmer cell rafts and role of swarming in catheter-associated urinary tract infection. Infect Immun. 2004;72:3941–50.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    23.Li X, Zhao H, Lockatell CV, Drachenberg CB, Johnson DE, Mobley HL. Visualization of Proteus mirabilis within the matrix of urease-induced bladder stones during experimental urinary tract infection. Infect Immun. 2002;70:389–94.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    24.Stickler DJ. Bacterial biofilms in patients with indwelling urinary catheters. Nat Clin Pr Urol. 2008;5:598–608.CAS 

    Google Scholar 
    25.Jacobsen SM, Stickler DJ, Mobley HLT, Shirtliff ME. Complicated catheter-associated urinary tract infections due to Escherichia coli and Proteus mirabilis. Clin Microbiol Rev. 2008;21:26–59.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    26.Harshey RM. Bacterial motility on a surface: many ways to a common goal. Annu Rev Microbiol. 2003;57:249–73.CAS 
    PubMed 

    Google Scholar 
    27.Verstraeten N, Braeken K, Debkumari B, Fauvart M, Fransaer J, Vermant J, et al. Living on a surface: swarming and biofilm formation. Trends Microbiol. 2008;16:496–506.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    28.Kearns DB. A field guide to bacterial swarming motility. Nat Rev Microbiol. 2010;8:634–44.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    29.Wu Y, Jiang Y, Kaiser AD, Alber M. Self-organization in bacterial swarming: lessons from myxobacteria. Phys Biol. 2011;8:055003.PubMed 

    Google Scholar 
    30.Howery KE, Şimşek E, Kim M, Rather PN. Positive autoregulation of the flhDC operon in Proteus mirabilis. Res Microbiol. 2018;169:199–204.CAS 
    PubMed 

    Google Scholar 
    31.Little K, Austerman J, Zheng J, Gibbs KA. Cell shape and population migration are distinct steps of Proteus mirabilis swarming that are decoupled on high-percentage agar. J Bacteriol. 2019;201:e00726–18.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    32.Furness RB, Fraser GM, Hay NA, Hughes C. Negative feedback from a Proteus class II flagellum export defect to the flhDC master operon controlling cell division and flagellum assembly. J Bacteriol. 1997;179:5585–8.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    33.Claret L, Hughes C. Functions of the subunits in the FlhD(2)C(2) transcriptional master regulator of bacterial flagellum biogenesis and swarming. J Mol Biol. 2000;303:467–78.CAS 
    PubMed 

    Google Scholar 
    34.Deegan RD, Bakajin O, Dupont TF, Huber G, Nagel SR, Witten TA. Capillary flow as the cause of ring stains from dried liquid drops. Nature. 1997;389:827–9.CAS 

    Google Scholar 
    35.Andac T, Weigmann P, Velu SKP, Pinçe E, Volpe G, Volpe G, et al. Active matter alters the growth dynamics of coffee rings. Soft Matter. 2019;15:1488–96.CAS 
    PubMed 

    Google Scholar 
    36.Nellimoottil TT, Rao PN, Ghosh SS, Chattopadhyay A. Evaporation-induced patterns from droplets containing motile and nonmotile bacteria. Langmuir. 2007;23:8655–8.CAS 
    PubMed 

    Google Scholar 
    37.Clemmer KM, Rather PN. Regulation of flhDC expression in Proteus mirabilis. Res Microbiol. 2007;158:295–302.CAS 
    PubMed 

    Google Scholar 
    38.Howery KE, Clemmer KM, Rather PN. The Rcs regulon in Proteus mirabilis: implications for motility, biofilm formation, and virulence. Curr Genet. 2016;62:775–89.CAS 
    PubMed 

    Google Scholar 
    39.Howery KE, Clemmer KM, Şimşek E, Kim M, Rather PN. Regulation of the min cell division inhibition complex by the Rcs phosphorelay in Proteus mirabilis. J Bacteriol. 2015;197:2499–507.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    40.Wang Q, Zhao Y, McClelland M, Harshey RM. The RcsCDB signaling system and swarming motility in Salmonella enterica Serovar Typhimurium: dual regulation of flagellar and SPI-2 virulence genes. J Bacteriol. 2007;189:8447–57.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    41.Samanta P, Clark ER, Knutson K, Horne SM, Prüß BM. OmpR and RcsB abolish temporal and spatial changes in expression of flhD in Escherichia coli biofilm. BMC Microbiol. 2013;13:182.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    42.Girgis HS, Liu Y, Ryu WS, Tavazoie S. A comprehensive genetic characterization of bacterial motility. PLoS Genet. 2007;3:e154.PubMed Central 

    Google Scholar 
    43.Francez-Charlot A, Laugel B, Van Gemert A, Dubarry N, Wiorowski F, Castanié-Cornet MP, et al. RcsCDB His-Asp phosphorelay system negatively regulates the flhDC operon in Escherichia coli. Mol Microbiol. 2003;49:823–32.CAS 
    PubMed 

    Google Scholar 
    44.Rieck VT, Palumbo SA, Witter LD. Glucose availability and the growth rate of colonies of Pseudomonas fluorescens. J Gen Microbiol. 1973;74:1–8.CAS 
    PubMed 

    Google Scholar 
    45.Shao X, Mugler A, Kim J, Jeong HJ, Levin BR, Nemenman I. Growth of bacteria in 3-d colonies. PLoS Comput Biol. 2017;13:e1005679.PubMed 
    PubMed Central 

    Google Scholar 
    46.Warren MR, Sun H, Yan Y, Cremer J, Li B, Hwa T. Spatiotemporal establishment of dense bacterial colonies growing on hard agar. Elife. 2019;8:e41093.PubMed 
    PubMed Central 

    Google Scholar 
    47.Lavrentovich MO, Koschwanez JH, Nelson DR. Nutrient shielding in clusters of cells. Phys Rev E Stat Nonlin Soft Matter Phys. 2013;87:062703. -PubMed 
    PubMed Central 

    Google Scholar 
    48.Dal Co A, van Vliet S, Ackermann M. Emergent microscale gradients give rise to metabolic cross-feeding and antibiotic tolerance in clonal bacterial populations. Philos Trans R Soc Lond B Biol Sci. 2019;374:20190080.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    49.Huang YH, Ferrières L, Clarke DJ. The role of the Rcs phosphorelay in Enterobacteriaceae. Res Microbiol. 2006;157:206–12.CAS 
    PubMed 

    Google Scholar 
    50.Majdalani N, Gottesman S. The Rcs phosphorelay: a complex signal transduction system. Annu Rev Microbiol. 2005;59:379–405.CAS 
    PubMed 

    Google Scholar 
    51.Fraebel DT, Mickalide H, Schnitkey D, Merritt J, Kuhlman TE, Kuehn S. Environment determines evolutionary trajectory in a constrained phenotypic space. Elife. 2017;6:e24669.PubMed 
    PubMed Central 

    Google Scholar 
    52.Yi X, Dean AM. Phenotypic plasticity as an adaptation to a functional trade-off. Elife. 2016;5:e19307.PubMed 
    PubMed Central 

    Google Scholar 
    53.van Ditmarsch D, Boyle KE, Sakhtah H, Oyler JE, Nadell CD, Déziel É, et al. Convergent evolution of hyperswarming leads to impaired biofilm formation in pathogenic bacteria. Cell Rep. 2013;4:697–708.PubMed 
    PubMed Central 

    Google Scholar 
    54.Auer GK, Oliver PM, Rajendram M, Lin T-Y, Yao Q, Jensen GJ, et al. Bacterial swarming reduces Proteus mirabilis and Vibrio parahaemolyticus cell stiffness and increases β-Lactam susceptibility. mBio. 2019;10:e00210–19.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    55.Kaiser D. Bacterial swarming: a re-examination of cell-movement patterns. Curr Biol. 2007;17:R561–R70.CAS 
    PubMed 

    Google Scholar 
    56.Inoue T, Shingaki R, Hirose S, Waki K, Mori H, Fukui K. Genome-wide screening of genes required for swarming motility in Escherichia coli K-12. J Bacteriol. 2007;189:950–7.CAS 
    PubMed 

    Google Scholar 
    57.Dong T, Joyce C, Schellhorn H. The role of RpoS in bacterial adaptation. In: El-Sharoud W, editor. Bacterial physiology. Heidelberg: Springer, Berlin; 2008. pp 313-37.58.Phaiboun A, Zhang Y, Park B, Kim M. Survival kinetics of starving bacteria is biphasic and density-dependent. PLoS Comput Biol. 2015;11:e1004198.PubMed 
    PubMed Central 

    Google Scholar 
    59.Majdalani N, Hernandez D, Gottesman S. Regulation and mode of action of the second small RNA activator of RpoS translation, RprA. Mol Microbiol. 2002;46:813–26.CAS 
    PubMed 

    Google Scholar 
    60.Peterson CN, Carabetta VJ, Chowdhury T, Silhavy TJ. LrhA regulates rpoS translation in response to the Rcs phosphorelay system in Escherichia coli. J Bacteriol. 2006;188:3175–81.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    61.Lok T, Overdijk O, Piersma T. The cost of migration: spoonbills suffer higher mortality during trans-Saharan spring migrations only. Biol Lett. 2015;11:20140944.PubMed 
    PubMed Central 

    Google Scholar 
    62.Flack A, Fiedler W, Blas J, Pokrovsky I, Kaatz M, Mitropolsky M, et al. Costs of migratory decisions: a comparison across eight white stork populations. Sci Adv. 2016;2:e1500931.PubMed 
    PubMed Central 

    Google Scholar 
    63.Rankin MA, Burchsted JCA. The cost of migration in insects. Annu Rev Entomol. 1992;37:533–59.
    Google Scholar 
    64.Ni B, Colin R, Link H, Endres RG, Sourjik V. Growth-rate dependent resource investment in bacterial motile behavior quantitatively follows potential benefit of chemotaxis. Proc Natl Acad Sci USA. 2020;117:595–601.CAS 
    PubMed 

    Google Scholar 
    65.Amsler CD, Cho M, Matsumura P. Multiple factors underlying the maximum motility of Escherichia coli as cultures enter post-exponential growth. J Bacteriol. 1993;175:6238–44.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    66.Yokota T, Gots JS. Requirement of adenosine 3’, 5’-cyclic phosphate for flagella formation in Escherichia coli and Salmonella typhimurium. J Bacteriol. 1970;103:513–6.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    67.Soutourina O, Kolb A, Krin E, Laurent-Winter C, Rimsky S, Danchin A, et al. Multiple control of flagellum biosynthesis in Escherichia coli: role of H-NS protein and the cyclic AMP-catabolite activator protein complex in transcription of the flhDC master operon. J Bacteriol. 1999;181:7500–8.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    68.Silverman M, Simon M. Characterization of Escherichia coli flagellar mutants that are insensitive to catabolite repression. J Bacteriol. 1974;120:1196–203.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    69.Mitrophanov AY, Groisman EA. Positive feedback in cellular control systems. Bioessays. 2008;30:542–55.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    70.Raj A, van Oudenaarden A. Nature, nurture, or chance: stochastic gene expression and its consequences. Cell. 2008;135:216–26.71.Ferrières L, Clarke DJ. The RcsC sensor kinase is required for normal biofilm formation in Escherichia coli K-12 and controls the expression of a regulon in response to growth on a solid surface. Mol Microbiol. 2003;50:1665–82.PubMed 

    Google Scholar 
    72.Guttenplan SB, Kearns DB. Regulation of flagellar motility during biofilm formation. FEMS Microbiol Rev. 2013;37:849–71.CAS 

    Google Scholar  More

  • in

    Hotspots for rockfishes, structural corals, and large-bodied sponges along the central coast of Pacific Canada

    The Wuikinuxv, Kitasoo/Xai’xais, Heiltsuk and Nuxalk First Nations hold Indigenous rights to their territories, where all data were collected. Scientific staff who are members of these Nations or who work directly for them had direct approvals from Indigenous rights holders and were exempt from other research permit requirements. Collaborating DFO scientists worked in partnership with the First Nations to collect data in their territories..Sampling targeted rocky reefs, the preferred habitat for most Sebastidae38, which we located through local Indigenous knowledge or a bathymetric model49. Data were collected by four fishery-independent methods—shallow diver transects, mid-depth video transects, deep video transects, and hook-and-line sampling—detailed in earlier publications32,33,34,35,50,51 and summarized in Table 1. Data had a spatial resolution of ≤ 130 m2 and each sampling location (N = 2936 for Sebastidae, 2654 for sponges, 2321 for corals) was ascribed to a 1-km2 planning unit within the standardized grid used to design the MPA network (N = 632 for Sebastidae, 525 for sponges, 529 for corals, 516 inclusive of surveys for all taxonomic groups).Table 1 Survey methods used for data collection.Full size tableAlthough sampling encompassed 11 years (2006–2007, 2013–2021: Table 1), 84% of 1-km2 planning units were sampled during only one year (Appendix S2). Analyses, therefore, focus on spatial variability in species distributions and do not address temporal variability within planning units. When all years and methods are combined, 1-km2 planning units had a median of 3 samples (range = 1 to 80, Q1 = 2, Q3 = 6) (i.e., sum of dive transects, video sub-transects, and hook-and-line sessions). Supplementary Data Set 1 reports sampling effort by 1-km2 planning unit, survey type, and year (see Data Availability for link to these data).For each 1-km2 planning unit, u, we calculated hotspot indices for Sebastidae (BSEB,u), structural corals (BCor,u), and large-bodied sponges (BSp,u). These indices did not consider cup corals, whip-like corals or encrusting corals or sponges.As detailed below (Eqs. 1–4), each species of Sebastidae or genera of corals contributed to BSEB,u or BCor,u, according to their abundance weighted by Wt: a conservation prioritization score based on taxon characteristics. For the 26 species of Sebastidae that we observed, Wt equaled the sum of scores for (1) fishery vulnerability, using intrinsic population growth rate, r, as a proxy variable52,53, (2) depletion level, using the ratio of recent biomass to unfished biomass as a proxy variable, (3) ecological role, with trophic level as proxy, and (4) evolutionary distinctiveness14 (Table 2; Appendix S3). Because several rockfishes are very long-lived (i.e., have low values for r) and depleted, maximum potential scores were twice as large for fishery vulnerability and depletion level than for ecological role and evolutionary distinctiveness. Data for depletion level and evolutionary distinctiveness were unavailable for some species, and score calculations (detailed in Table 2) account for missing values (Appendix S3).Table 2 Criteria and equations used to calculate the conservation prioritization score, Wt, for each species of Sebastidae and for each taxa of structural corals.Full size tableFor the 6 genera of structural corals analyzed (Appendix S4), Wt depended on mean height (estimated from video transect images: Table 1), which correlates positively with vulnerability to physical damage from bottom-contact fishing gear (including longer time to recovery)20,54,55 and with strength of ecological role (e.g., amount of biogenic habitat and carbon sequestration increases with height)44,56 (Table 2, Appendix S4). Wt for corals did not include depletion level due to lack of data.The hotspot index for large-bodied sponges, BSp,u did not differentiate between species characteristics (i.e., ({W}_{t}=1)) and we pooled the abundances of all observed species of Hexactinellidae (Aphrocallistes vastus, Farrea occa, Heterochone calyx, Rhabdocalyptus dawsoni, Staurocalyptus dowlingi) and Demospongiae (Mycale cf loveni). This approach is consistent with regional fishery bodies worldwide, which treat large-bodied sponges as a single functional group57.To derive hotspot indices for each taxonomic group (Sebastidae, structural corals, or large-bodied sponges), we first developed a set of candidate generalized linear mixed models (GLMM) to explain relative abundance data for rockfish, corals, and sponges. For each GLMM, we estimated ({lambda }_{t,i,l}), the expected counts (or expected percent cover) for taxa t obtained with survey method i at point location l. (Point locations are individual dive transects, video transect bins, or hook-and-line timed sessions: Table 1.) Specifically,$${lambda }_{t,i,l}=gleft(beta {X}_{t,i,l}right)$$
    (1)
    $${C}_{t,i,l}mathrm {, or ,} {D}_{t,i,l}sim fleft({lambda }_{t,i,l}right)$$
    (2)
    where g was the link function for the GLMM and f the distribution for the likelihood function modelling either the observed counts C (negative binomial) for Sebastidae and structural corals or a combination of counts (negative binomial) and percent cover D (beta distribution) for large-bodied sponges. We used multiple GLMMs to model large-bodied sponges because deep video transects recorded actual counts whereas dive or mid-depth video transects recorded percent cover categories (Table 1).For each taxonomic group, we estimated a set of coefficients (beta) for the vector of X covariates that best estimated counts or percent cover. Our hypothesized covariates included the 1-km2 planning unit (modelled as a random intercept to control for repeated measures within a given planning unit), survey method, depth (including both linear or a 2nd order polynomial), and taxa. Each GLMM controlled for sample effort as an offset—effort was measured either as area covered by dive transects or video bins, or the duration of hook-and-line sessions. We also tested for possible covariate’s effects on the dispersion parameter (for the negative binomial GLMMs) and zero-inflation terms (for both the negative binomial and beta GLMMs). The best set of covariates to predict counts or percent cover were then chosen based on AIC model selection criteria. All models were fitted using ‘glmmTMB’58 in R version 4.0.259, and simulated residuals and diagnostic tests performed for each best-fit model using the package ‘DHARMa’60. For example, our best model for Sebastidae counts predicted 2% fewer zero counts than were observed.We applied depth and survey method selectivity criteria to reduce excessive zeroes in the count data that may be biologically unjustified (Appendix S5). For all taxon, if i detected t, then the method was valid for that taxon. If i did not detect t and t is a Sebastidae, then the method was valid (i.e., count = 0) only if the overall 10th and 90th percentiles of depths sampled by that method encompassed the expected depth range of t (Appendix S5). If i did not detect t and t is a coral or sponge (which are rarer than Sebastidae), then the method is valid only if the depth of the sampling event exceeded or equaled the minimum expected depth of t. Also, hook-and-line gear cannot systematically sample sessile benthic organisms or planktivores and this method was valid only for non-planktivorous Sebastidae (Appendix S5).Using the best-fit models from above, we calculated the expected count (or percent cover) per unit of effort, (mu), for taxa t observed with method i at each planning unit u:$${mu }_{t,i,u}=frac{{sum }_{l=1}^{{n}_{i,u}}left({lambda }_{t,i,l}right)}{{sum }_{l=1}^{{n}_{i,u}}left({mathrm{E}}_{t,i,l}right)}$$
    (3)
    where ({n}_{i,u}) was the total number of point locations sampled by that method within the planning unit and effort was either the cumulative area covered by dive or video surveys or the cumulative duration of hook-and-line sampling sessions within the planning unit. Because survey methods differed in their maximum values and potential biases (e.g., field of view is greater for divers than for video cameras; hook-and-line gear samples one fish at a time while visual methods can observe multiple fish simultaneously),({mu }_{t,i,u}) was rescaled as a min–max normalization,({mu }_{t,i,u}^{^{prime}}) (i.e., difference between the observed value and the minimum value across all u, divided by the range of values across all u).The hotspot index for each of Sebastidae, structural corals, and large-bodied sponges (denoted as taxonomic group g) was then calculated for each planning unit as:$${B}_{g,u }={sum }_{t=1}^{{n}_{s,g}}{sum }_{i=1}^{{n}_{m,g}}{mu }_{t,i,u}^{^{prime}}{W}_{t}$$
    (4)
    where Wt was the taxon-specific weighing factor (Table 2, Appendices S3, S4), ({n}_{s,g}) was the number of species in taxonomic group g, and ({n}_{m,g}) was the number of valid methods to sample group g.For each 1-km2 planning unit where all taxonomic groups were surveyed (N = 518), we then calculated the overall hotspot index:$${B}_{o,u }=H{sum }_{g=1}^{G}{B}_{g,u}.$$
    (5)
    where H is Shannon’s evenness index, with proportional abundance of each taxonomic group represented by BSEB,u, BCor,u, and BSp,u.Hotspot index values were normalized as the proportion of the maximum value and converted to decile ranks. Relationships between decile ranks and index values were nonlinear (Appendix S6).For Sebastidae, large-bodied sponges, and the overall hotspot index, we defined hotspots as planning units containing decile ranks 9 or 10: criterion which we deemed appropriate for the small spatial scales of conservation planning being used for the central portion of the Northern Shelf Bioregion (16-km2 planning units in Fig. 2). We are aware that other studies define hotspots based on a narrower range of values (e.g., top 10%26; top 2.5%28) but their context is generally one in which conservation planning is done at a much greater scale (e.g., ≈50,000-km2 grid cells26;1° latitude × 1° longitude grid cells28). For structural corals, which had near-zero index values in all but the top-ranking planning units (Appendix S6), we defined hotspots as planning units containing decile rank 10.Maximum depths sampled within planning units were deepest in the Mainland Fjord and shallowest in the Aristazabal Banks Upwelling Upper Ocean Subregion (Appendix S7). Accordingly, we used multiple logistic regression implemented with the ‘glm’ function in R to estimate the probabilities hotspot occurrence within 1-km2 planning units in relation to maximum depth sampled (including a 2nd-order polynomial) and Upper Ocean Subregion. Competing models were compared with AIC model selection procedures.Following the directive of Central Coast First Nations, decile rank distributions were mapped as 16-km2 planning units, u16 (N = 283 for Sebastidae, 264 for sponges, 263 for corals, 260 inclusive of surveys for all taxonomic groups), thereby protecting sensitive locations that would be revealed at smaller scales. To do so, we took the average between the maximum index value and the mean of the remainder of index values among the 1-km2 planning units, u, contained within each u16, and converted these values into decile ranks. This approach balances conservation prioritization among u16 that may have good average index values for multiple u, and u16 with a single high-ranking u among multiple low-scoring u. Relationships between decile ranks and hotspot index values also were nonlinear at this scale (Appendix S6). The same hotspot definitions developed for u apply to u16.Eighty one percent of 16-km2 planning units were sampled during only one or two years (Appendix S2). When all years and methods are combined, 16-km2 planning units had a median of 6 samples (range = 1 to 110, Q1 = 3, Q3 = 13). Supplementary Data Set 2 reports sampling effort by 16-km2 planning unit, survey type, and year (see Data Availability for link to these data). More

  • in

    Factors influencing the global distribution of the endangered Egyptian vulture

    1.BirdLife International. Neophron percnopterus, Egyptian vulture. http://www.iucnredlist.org/details/22695180/0 (2017) https://doi.org/10.2305/IUCN.UK.2017-3.RLTS.T22695180A118600142.en2.Gradev, G., Garcia, V., Ivanov, I., Zhelev, P. & Kmetova, E. Data from Egyptian vultures (Neophron percnopterus) tagged with GPS/GSM transmitters in Bulgaria. Acta Zool. Bulg. 64, 141–146 (2012).
    Google Scholar 
    3.Green, R. E. et al. Diclofenac poisoning as a cause of vulture population declines across the Indian subcontinent. J. Appl. Ecol. 41, 793–800 (2004).CAS 
    Article 

    Google Scholar 
    4.Arkumarev, V., Dobrev, V., Abebe, Y. D., Popgeorgiev, G. & Nikolov, S. C. Congregations of wintering Egyptian Vultures Neophron percnopterus in Afar, Ethiopia: Present status and implications for conservation. Ostrich 85, 139–145 (2014).Article 

    Google Scholar 
    5.Grubač, B., Velevski, M. & Avukatov, V. Long-term population decrease and recent breeding performance of the Egyptian vulture Neophron percnopterus in Macedonia. North. West. J. Zool. 10, 25–35 (2014).
    Google Scholar 
    6.Angelov, I., Hashim, I. & Oppel, S. Persistent electrocution mortality of Egyptian vultures neophron percnopterus over 28 years in East Africa. Bird Conserv. Int. 23, 1–6 (2013).Article 

    Google Scholar 
    7.Zuberogoitia, I., Zabala, J., Martínez, J. A., Martínez, J. E. & Azkona, A. Effect of human activities on Egyptian vulture breeding success. Anim. Conserv. 11, 313–320 (2008).Article 

    Google Scholar 
    8.Sen, B., Avares, J. P. & Bilgin, C. C. Nest site selection patterns of a local Egyptian Vulture Neophron percnopterus population in Turkey. Bird Conserv. Int. 27, 568–581 (2017).Article 

    Google Scholar 
    9.Ceballos, O. & Donázar, J. A. Factors influencing the breeding density and nest-site selection of the Egyptian vulture (Neophron percnopterus). J. Ornithol. 130, 353–359 (1989).Article 

    Google Scholar 
    10.Sarà, M. & Vittorio, M. Factors influencing the distribution, abundance and nest-site selection of an endangered Egyptian vulture (Neophron percnopterus) population in Sicily. Anim. Conserv. 6, 317–328 (2003).Article 

    Google Scholar 
    11.KC, K. B. et al. Factors influencing the presence of the endangered Egyptian vulture Neophron percnopterus in Rukum, Nepal. Glob. Ecol. Conserv. 20, e00727 (2019).Article 

    Google Scholar 
    12.Mateo-Tomás, P. & Olea, P. P. Livestock-driven land use change to model species distributions: Egyptian vulture as a case study. Ecol. Indic. 57, 331–340 (2015).Article 

    Google Scholar 
    13.García-RIPOLLÉS, C., López-LÓPEZ, P. & Urios, V. First description of migration and wintering of adult Egyptian vultures neophron percnopterus tracked by GPS satellite telemetry. Bird Study 57, 261–265 (2010).Article 

    Google Scholar 
    14.Oppel, S. et al. Landscape factors affecting territory occupancy and breeding success of Egyptian vultures on the Balkan Peninsula. J. Ornithol. 158, 443–457 (2017).Article 

    Google Scholar 
    15.Bhusal, K. Population status and breeding success of Himalayan Griffon, Egyption vulture and Lammergeier in Gherabhir, Arghakhanchi, Nepal. (MSc thesis. Institute of Science and Technology, Tribuvan University, Kritipur, Nepal, 2011). https://doi.org/10.13140/RG.2.2.18494.69447.16.López-lópez, A. P. et al. Food predictability determines space use of endangered vultures: Implications for management of supplementary feeding. Ecol. Appl. 24, 938–949 (2014).PubMed 
    Article 

    Google Scholar 
    17.Cortés-avizanda, A., Ceballos, O. & Donázar, J. Long-term trends in population size and breeding success in the Egyptian Vulture (Neophron percnopterus) in Northern Spain. J. Raptor Res. 43, 43–49 (2009).Article 

    Google Scholar 
    18.Rosenblatt, E. Neophron percnopterus Egyptian vulture. Animal Diversity Web https://animaldiversity.org/accounts/Neophron_percnopterus/ (2007).19.ESRI. ArcGIS Desktop: Release 10.5. Environmental systems research Redlands, California, USA https://www.arcgis.com/features/index.html (2017).20.Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005).Article 

    Google Scholar 
    21.USGS/EarthExplorer. Data Sets. United States Geological Survey https://earthexplorer.usgs.gov/ (2017).22.JAXA EORC. Global PALSAR-2/PALSAR/JERS-1 Mosaic and Forest/Non-forest Map. Earth Observation Research Center https://www.eorc.jaxa.jp/ALOS/en/palsar_fnf/data/index.htm (2017).23.CIESIN. Gridded population of the world (GPW), v4. http://sedac.ciesin.columbia.edu/data/collection/gpw-v4 (2000).24.Robinson, T. P. et al. Mapping the global distribution of livestock. PLoS ONE 9, e96084 (2014).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    25.FAO/GeoNetwork. Global land cover share database. http://www.fao.org/geonetwork/srv/en/main.home (2014).26.Elith, J. et al. Novel methods improve prediction of species’ distributions from occurrence data. Ecography (Cop.) 29, 129–151 (2006).Article 

    Google Scholar 
    27.Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modelling of species geographic distributions. Ecol. Modell. 190, 231–259 (2006).Article 

    Google Scholar 
    28.Lobo, J. M., Jiménez-valverde, A. & Real, R. AUC: a misleading measure of the performance of predictive distribution models. Glob. Ecol. Biogeogr. 17, 145–151 (2008).Article 

    Google Scholar 
    29.Allouche, O., Tsoar, A. & Kadmon, R. Assessing the accuracy of species distribution models : Prevalence, kappa and the true skill statistic (TSS). J. Appl. Ecol. 43, 1223–1232 (2006).Article 

    Google Scholar 
    30.Pearce, J. & Ferrier, S. Evaluating the predictive performance of habitat models developed using logistic regression. Ecol. Modell. 133, 225–245 (2000).Article 

    Google Scholar 
    31.Barbet-Massin, M., Jiguet, F., Albert, C. H. & Thuiller, W. Selecting pseudo-absences for species distribution models: how, where and how many?. Methods Ecol. Evol. 3, 327–338 (2012).Article 

    Google Scholar 
    32.Liu, C., White, M. & Newell, G. Selecting thresholds for the prediction of species occurrence with presence-only data. J. Biogeogr. 40, 778–789 (2013).Article 

    Google Scholar 
    33.Cortés-Avizanda, A., Martín-López, B., Ceballos, O. & Pereira, H. M. Stakeholders perceptions of the endangered Egyptian vulture: Insights for conservation. Biol. Conserv. 218, 173–180 (2018).Article 

    Google Scholar 
    34.Hernández, M. & Margalida, A. Poison-related mortality effects in the endangered Egyptian vulture (Neophron percnopterus) population in Spain. Eur. J. Wildl. Res. 55, 415–423 (2009).Article 

    Google Scholar 
    35.Mateo-Tomás, P., Olea, P. P. & Fombellida, I. Status of the Endangered Egyptian vulture Neophron percnopterus in the Cantabrian Mountains, Spain, and assessment of threats. Oryx 44, 434–440 (2010).Article 

    Google Scholar 
    36.Carrete, M. et al. Habitat, human pressure, and social behavior : Partialling out factors affecting large-scale territory extinction in an endangered vulture. Biol. Conserv. https://doi.org/10.1016/j.biocon.2006.11.025 (2007).Article 

    Google Scholar 
    37.Zuberogoitia, I., Zabala, J., Martínez, J. E., González-Oreja, J. A. & López-López, P. Effective conservation measures to mitigate the impact of human disturbances on the endangered Egyptian vulture. Anim. Conserv. 17, 410–418 (2014).Article 

    Google Scholar 
    38.Garcia-Ripolles, C. & Lopez-Lopez, P. Population size and breeding performance of Egyptian vultures (Neophron percnopterus) in eastern Iberian Peninsula. J. Raptor Res. 40, 217–221 (2006).Article 

    Google Scholar 
    39.Velevski, M., Grubac, B. & Tomovic, L. Population viability analysis of the Egyptian vulture Neophron percnopterus in Macedonia and Implications for Its Conservation. Acta Zool. Bulg. 66, 43–58 (2014).
    Google Scholar 
    40.Arkumarev, V. et al. Breeding performance and population trend of the Egyptian vulture Neophron percnopterus in Bulgaria conservation implications. Ornis Fenn. 95, 115–127 (2018).
    Google Scholar 
    41.Dobrev, V. et al. Habitat of the Egyptian vulture (Neophron percnopterus) in Bulgaria and Greece (2003–2014). (2016).42.Milchev, B., Spassov, N. & Popov, V. Diet of the Egyptian vulture (Neophron percnopterus) after livestock reduction in Eastern Bulgaria. N. West. J. Zool. 8, 315–323 (2012).
    Google Scholar 
    43.Milchev, B. & Georgiev, V. Extinction of the globally endangered Egyptian vulture Neophron percnopterus breeding in SE Bulgaria. N. West. J. Zool. 10, 266–272 (2014).
    Google Scholar 
    44.Poirazidis, K., Goutner, V., Skartsi, T. & Stamou, G. Modelling nesting habitat as a conservation tool for the Eurasian black vulture (Aegypius monachus) in Dadia Nature Reserve, northeastern Greece. Biol. Conserv. 118, 235–248 (2004).Article 

    Google Scholar 
    45.Sanchis Serra, A. et al. Towards the identification of a new taphonomic agent: An analysis of bone accumulations obtained from modern Egyptian vulture (Neophron percnopterus) nests. Quat. Int. 330, 136–149 (2014).Article 

    Google Scholar 
    46.Vittorio, M. D., Lopez-Lopez, P., Cortone, G. & Luiselli, L. The diet of the Egyptian vulture (Neophron percnopterus) in Sicily: Temporal variation and conservation implications. Vie Milieu Life Environ. 67, 1–8 (2017).
    Google Scholar 
    47.Di Vittorio, M. et al. Successful fostering of a captive-born Egyptian Vulture (Neophron Percnopterus) in Sicily. J. Raptor Res. 40, 247–248 (2006).Article 

    Google Scholar 
    48.Sarà, M., Grenci, S. & Vittorio, M. D. Status of Egyptian vulture (Neophron percnopterus) in Sicily. J. Raptor Res. 43, 66–69 (2009).Article 

    Google Scholar 
    49.Vittorio, M. D. et al. Dispersal of Egyptian vultures Neophron percnopterus: the first case of long-distance relocation of an individual from France to Sicily. Ringing Migr. 31, 111–114 (2016).Article 

    Google Scholar 
    50.García-Heras, M. S., Cortés-Avizanda, A. & Donázar, J. A. Who are we feeding? Asymmetric individual use of surplus food resources in an insular population of the endangered Egyptian vulture Neophron percnopterus. PLoS ONE 8, e80523 (2013).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    51.Gangoso, L. et al. Susceptibility to infection and immune response in insular and continental populations of Egyptian vulture: Implications for conservation. PLoS ONE 4, e6333 (2009).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    52.Donazar, J. A. et al. Conservation status and limiting factors in the endangered population of Egyptian vulture (Neophron percnopterus) in the Canary Islands Conservation status and limiting factors in the endangered population of Egyptian vulture ( Neophron percnopterus ) in. Biol. Conserv. 107, 89–97 (2002).Article 

    Google Scholar 
    53.Rodríguez, B., Rodríguez, A., Siverio, F. & Siverio, M. Factors affecting the spatial distribution and breeding habitat of an insular cliff-nesting raptor community. Curr. Zool. 64, 173–181 (2018).PubMed 
    Article 

    Google Scholar 
    54.Kret, E. et al. First documented case of the killing of an egyptian vulture (Neophron Percnopterus) for belief-based practices in Western Africa. Life Environ. 68, 45–50 (2018).
    Google Scholar 
    55.Thouless, C. R., Fanshawe, J. H. & Bertram, B. C. R. Egyptian vultures Neophron percnopterus and Ostrich Struthio camelus eggs: the origins of stone-throwing behaviour. Ibis (Lond.) 131, 9–15 (1989).Article 

    Google Scholar 
    56.Cuthbert, R. et al. Rapid population declines of Egyptian vulture (Neophron percnopterus) and red-headed vulture (Sarcogyps calvus) in India. Anim. Conserv. 9, 349–354 (2006).Article 

    Google Scholar 
    57.Samson, A. & Ramakarishnan, B. Observation of a population of Egyptian Vultures Neophron percnopterus in Ramanagaram Hills, Karnataka, southern India. Vulture News 71, 36–49 (2016).Article 

    Google Scholar 
    58.Farashi, A. & Alizadeh-Noughani, M. Niche modelling of the potential distribution of the Egyptian Vulture Neophron percnopterus during summer and winter in Iran, to identify gaps in protected area coverage. Bird Conserv. Int. 29, 423–436 (2019).Article 

    Google Scholar 
    59.Tauler-Ametller, H., Hernández-Matías, A., Pretus, J. L. L. & Real, J. Landfills determine the distribution of an expanding breeding population of the endangered Egyptian vulture Neophron percnopterus. Ibis (Lond). 159, 757–768 (2017).Article 

    Google Scholar 
    60.Mateo-Tomás, P. & Olea, P. P. Diagnosing the causes of territory abandonment by the Endangered Egyptian vulture Neophron percnopterus: The importance of traditional pastoralism and regional conservation. Oryx 44, 424–433 (2010).Article 

    Google Scholar 
    61.Galligan, T. H. et al. Have population declines in Egyptian vulture and Red-headed vulture in India slowed since the 2006 ban on veterinary diclofenac?. Bird Conserv. Int. 24, 272–281 (2014).Article 

    Google Scholar 
    62.Lieury, N., Gallardo, M., Ponchon, C., Besnard, A. & Millon, A. Relative contribution of local demography and immigration in the recovery of a geographically-isolated population of the endangered Egyptian vulture. Biol. Conserv. 191, 349–356 (2015).Article 

    Google Scholar 
    63.Porter, R. F. & Suleiman, A. S. the Egyptian Vulture Neophron percnopterus on Socotra, Yemen: Population, ecology, conservation and ethno-ornithology. Sandgrouse 34, 44–62 (2012).
    Google Scholar  More

  • in

    Coordination during group departures and progressions in the tolerant multi-level society of wild Guinea baboons (Papio papio)

    1.Conradt, L. & Roper, T. J. Group decision-making in animals. Nature 421, 155–158 (2003).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    2.King, A. J. & Cowlishaw, G. Leaders, followers and group decision-making. Commun. Integr. Biol. 2, 147–150 (2009).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    3.Couzin, I. D. & Franks, N. R. Self-organized lane formation and optimized traffic flow in army ants. Proc. R. Soc. B Biol. Sci. 270, 139–146 (2003).CAS 
    Article 

    Google Scholar 
    4.Ballerini, M. et al. Empirical investigation of starling flocks: a benchmark study in collective animal behaviour. Anim. Behav. 76, 201–215 (2008).Article 

    Google Scholar 
    5.Couzin, I. D., Krause, J., James, R., Ruxton, G. D. & Franks, N. R. Collective memory and spatial sorting in animal groups. J. Theor. Biol. 218, 1–11 (2002).ADS 
    MathSciNet 
    PubMed 
    Article 

    Google Scholar 
    6.Dyer, J. R. G., Johansson, A., Helbing, D., Couzin, I. D. & Krause, J. Leadership, consensus decision making and collective behaviour in humans. Philos. Trans. R. Soc. B Biol. Sci. 364, 781–789 (2009).7.Brent, L. J. N. et al. Ecological knowledge, leadership, and the evolution of menopause in killer whales. Curr. Biol. 25, 746–750 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    8.Lee, H. C. & Teichroeb, J. A. Partially shared consensus decision making and distributed leadership in vervet monkeys: older females lead the group to forage. Am. J. Phys. Anthropol. 161, 580–590 (2016).PubMed 
    Article 

    Google Scholar 
    9.Smith, J. E. et al. Collective movements, leadership and consensus costs at reunions in spotted hyaenas. Anim. Behav. 105, 187–200 (2015).Article 

    Google Scholar 
    10.Fischhoff, I. R. et al. Social relationships and reproductive state influence leadership roles in movements of plains zebra Equus burchellii. Anim. Behav. 73, 825–831 (2007).Article 

    Google Scholar 
    11.Conradt, L. & Roper, T. J. Consensus decision making in animals. Trends Ecol. Evol. 20, 449–456 (2005).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    12.Stueckle, S. & Zinner, D. To follow or not to follow: decision making and leadership during the morning departure in chacma baboons. Anim. Behav. 75, 1995–2004 (2008).Article 

    Google Scholar 
    13.Sueur, C. & Petit, O. Shared or unshared consensus decision in macaques?. Behav. Processes 78, 84–92 (2008).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    14.Strandburg, P, Eshkin, A., Farine, D. R., Couzin, I. D. & Crofoot, M. C. Shared decision-making drives collective movement in wild baboons. Science 348, 1358–1361 (2015).15.Fischer, J. & Zinner, D. Communication and cognition in primate group movement. Int. J. Primatol. 32, 1279–1295 (2011).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    16.Pyritz, L. W., King, A. J., Sueur, C. & Fichtel, C. Reaching a consensus: terminology and concepts used in coordination and decision-making research. Int. J. Primatol. 32, 1268–1278 (2011).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    17.Raveling, D. G. Preflight and flight behavior of Canada geese. Auk 86, 671–681 (1969).Article 

    Google Scholar 
    18.Byrne, R. W., Whiten, A. & Henzi, S. P. Social relationships of mountain baboons: leadership and affiliation in a non-female-bonded monkey. Am. J. Primatol. 20, 313–329 (1990).CAS 
    PubMed 
    Article 

    Google Scholar 
    19.Boinski, S. & Garber, P. A. On the move: how and why animals travel in groups: on the move: how and why animals travel in groups. Am. Anthropol. 104, 669–670 (2002).Article 

    Google Scholar 
    20.Ramseyer, A., Thierry, B., Boissy, A. & Dumont, B. Decision-making processes in group departures of cattle. Ethology 115, 948–957 (2009).Article 

    Google Scholar 
    21.Petit, O. & Bon, R. Decision-making processes: the case of collective movements. Behav. Processes 84, 635–647 (2010).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    22.King, A. J., Johnson, D. D. P. & Van Vugt, M. The origins and evolution of leadership. Curr. Biol. 19, R911–R916 (2009).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    23.Krause, J., Hoare, D., Krause, S., Hemelrijk, C. K. & Rubenstein, D. I. Leadership in fish shoals. Fish Fish. 1, 82–89 (2000).Article 

    Google Scholar 
    24.Allen, C. R. B., Brent, L. J. N., Motsentwa, T., Weiss, M. N. & Croft, D. P. Importance of old bulls: leaders and followers in collective movements of all-male groups in African savannah elephants (Loxodonta africana). Sci. Rep. 10, 1–9 (2020).Article 
    CAS 

    Google Scholar 
    25.Pettit, B., Ákos, Z., Vicsek, T. & Biro, D. Speed determines leadership and leadership determines learning during pigeon flocking. Curr. Biol. 25, 3132–3137 (2015).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    26.Mutinda, H., Poole, J. H. & Moss, C. F. Decision making and leadership in using the ecosystem. in The Amboseli Elephants: A Long-Term Perspective on a Long-Lived Mammal (Chicago Scholarship, 2011).27.Kummer, H. Social Organization of Hamadryas Baboons: A Field Study. Bibliotheca Primatologica (University of Chicago Press, 1968).28.Holekamp, K. E., Boydston, E. E., & Smale, L. Group travel in social carnivores. in On the move: How and why animals travel in groups 587–627 (University of Chicago Press, 2000).29.Pyritz, L. W., Kappeler, P. M. & Fichtel, C. Coordination of group movements in wild red-fronted lemurs (Eulemur rufifrons): processes and influence of ecological and reproductive seasonality. Int. J. Primatol. 32, 1325–1347 (2011).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    30.Jacobs, A., Maumy, M. & Petit, O. The influence of social organisation on leadership in brown lemurs (Eulemur fulvus fulvus) in a controlled environment. Behav. Processes 79, 111–113 (2008).CAS 
    PubMed 
    Article 

    Google Scholar 
    31.Farine, D. R., Strandburg-Peshkin, A., Couzin, I. D., Berger-Wolf, T. Y. & Crofoot, M. C. Individual variation in local interaction rules can explain emergent patterns of spatial organization in wild baboons. Proc. R. Soc. B Biol. Sci. 284, 25–29 (2017).
    Google Scholar 
    32.Kappeler, P. M. A framework for studying social complexity. Behav. Ecol. Sociobiol. 73, 13 (2019).Article 

    Google Scholar 
    33.Papageorgiou, D. & Farine, D. R. Shared decision-making allows subordinates to lead when dominants monopolize resources. Sci. Adv. 6, 1–8 (2020).Article 

    Google Scholar 
    34.Conradt, L., Krause, J., Couzin, I. D. & Roper, T. J. ‘Leading according to need’ in self-organizing groups. Am. Nat. 173, 304–312 (2009).CAS 
    PubMed 
    Article 

    Google Scholar 
    35.Rodriguez-Santiago, M. et al. Behavioral traits that define social dominance are the same that reduce social influence in a consensus task. Proc. Natl. Acad. Sci. U. S. A. 117, 18566–18573 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    36.Grueter, C. C. et al. Multilevel organisation of animal sociality. Trends Ecol. Evol. 35, 834–847 (2020).PubMed 
    Article 

    Google Scholar 
    37.Kummer, H. In Quest of the Sacred Baboon: a Scientist’s Journey. (Princeton University Press, 1995).38.Fischer, J. et al. Charting the neglected West: The social system of Guinea baboons. Am. J. Phys. Anthropol. 162, 15–31 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    39.Whitehead, H. et al. Multilevel societies of female sperm whales (Physeter macrocephalus) in the Atlantic and Pacific: Why Are they so different?. Int. J. Primatol. 33, 1142–1164 (2012).Article 

    Google Scholar 
    40.Kummer, H. Two variations in the social organization of baboons. in Primates: studies in adaptation and variability 293–312 (Holt, Rinehart & Winston, 1968).41.Fischer, J. et al. The Natural History of Model Organisms: Insights into the evolution of social systems and species from baboon studies. Elife 8, e50989 (2019).42.Swedell, L. African Papionins: Diversity of social organization and ecological flexibility. in Primates in perspective 241–277 (Oxford University Press, 2011).43.Anandam, M., Bennett, E. & Davenport, T. Species accounts of Cercopithecidae. in Handbook ofthe mammals of the world Vol. 3 primates 628–753 (Lynx Edicions, 2013).44.Barrett, L. & Henzi, S. P. Baboons. Curr. Biol. 18, 404–406 (2008).Article 
    CAS 

    Google Scholar 
    45.Ransom, T. W. Beach troop of the Gombe. (Bucknell University Press, 1981).46.Norton, G. Leadership: decision processes of group movement in yellow baboons. in Primate ecology and conservation. 145–156 (Cambridge University Press, 1986).47.Stoltz, L. & Saayman, G. S. Ecology and behaviour of baboons in the northern transvaal. Nature 26, 99–142 (1970).
    Google Scholar 
    48.Buskirk, W. H., Buskirk, R. E. & Hamilton, W. J. Troop-mobilizing behavior of adult male chacma baboons. Folia Primatol. 22, 9–18 (1974).CAS 
    Article 

    Google Scholar 
    49.Collins, D. A. Spatial pattern in a troop of yellow baboons (Papio cynocephalus) in Tanzania. Anim. Behav. 32, 536–553 (1984).Article 

    Google Scholar 
    50.Rhine, R. J., Hendy, H. M., Stillwell-Barnes, R., Westlund, B. J. & Westlund, H. D. Movement Patterns of YeIIow Baboons (Papio cynocephaius): Central Positioning of Walking Infants. Am. J. Phys. Anthropol. 53, 159–167 (1980).Article 

    Google Scholar 
    51.Rhine, R. J. & Owens, N. W. The order of movement of adult male and black infant baboons (Papio anubis) entering and leaving a potentially dangerous clearing. Folia Primatol. 18, 276–283 (1972).CAS 
    Article 

    Google Scholar 
    52.Rhine, R. J. & Westlund, B. J. Adult Male positioning in baboon progressions: order and chaos revisited. Folia Primatol. 35, 77–116 (1981).CAS 
    Article 

    Google Scholar 
    53.Rhine, R. J., Bioland, P. & Lodwick, L. Progressions of adult male chacma baboons (Papio ursinus) in the moremi wildlife reserve. Int. J. Primatol. 6, 115–122 (1985).Article 

    Google Scholar 
    54.Rowell, T. Long-term changes in a population of ugandan baboons. Folia Primatol. 11, 241–254 (1969).CAS 
    Article 

    Google Scholar 
    55.Sigg, H. & Stolba, A. Home range and daily march in a Hamadryas baboon troop. Folia Primatol. (Basel) 36, 40–75 (1981).CAS 
    Article 

    Google Scholar 
    56.Schweitzer, C., Gaillard, T., Guerbois, C., Fritz, H. & Petit, O. Participant profiling and pattern of crop-foraging in chacma baboons (Papio hamadryas ursinus) in Zimbabwe: Why Does Investigating Age-Sex Classes Matter?. Int. J. Primatol. 38, 207–223 (2017).Article 

    Google Scholar 
    57.Stolba, A. Entscheidungstindung in verbanden von papio hamadryas. (University of Zurich, 1979).58.Strandburg-Peshkin, A., Papageorgiou, D., Crofoot, M. C. & Farine, D. R. Inferring influence and leadership in moving animal groups. Philos. Trans. R. Soc. B Biol. Sci. 373, (2018).59.Harding, R. S. O. Patterns of movement in open country baboons. Am. J. Phys. Anthropol. 47, 349–353 (1977).Article 

    Google Scholar 
    60.DeVore, I. & Washburn, S. L. Baboon Ecology and Human Evolution. in African Ecology and Human Evolution 335–367 (Routledge, 2017).61.Altmann, S. A. Baboon progressions: Order or chaos? A study of one-dimensional group geometry. Anim. Behav. 27, 46–80 (1979).Article 

    Google Scholar 
    62.Goffe, A. S., Zinner, D. & Fischer, J. Sex and friendship in a multilevel society : behavioural patterns and associations between female and male Guinea baboons. Behav. Ecol. Sociobiol. 70, 323–336 (2016).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    63.Patzelt, A. et al. Male tolerance and male – male bonds in a multilevel primate society. PNAS 111, 14740–14745 (2014).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    64.Pines, M., Saunders, J. & Swedell, L. Alternative routes to the leader male role in a multi-level society: Follower vs. solitary male strategies and outcomes in hamadryas baboons. Am. J. Primatol. 73, 679–691 (2011).65.Schreier, A. L. & Swedell, L. The fourth level of social structure in a multi-level society: Ecological and social functions of clans in Hamadryas Baboons. Am. J. Primatol. 71, 948–955 (2009).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    66.Dal Pesco, F., Trede, F., Zinner, D. & Fischer, J. Kin bias and male pair-bond status shape male-male relationships in a multilevel primate society. Behav. Ecol. Sociobiol. 75, 1–14 (2021).Article 

    Google Scholar 
    67.Strandburg-peshkin, A., Farine, D. R., Couzin, I. D. & Crofoot, M. C. Shared decision-making drives collective movement in wild baboons. Science 348, 1358–1361 (2015).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    68.Leca, J. B., Gunst, N., Thierry, B. & Petit, O. Distributed leadership in semifree-ranging white-faced capuchin monkeys. Anim. Behav. 66, 1045–1052 (2003).Article 

    Google Scholar 
    69.Rhine, R. J. The order of movement of yellow baboons. Folia Primatol 23, 72–104 (1975).CAS 
    Article 

    Google Scholar 
    70.Rhine, R. J. & Tilson, R. Reactions to fear as a proximate factor in the sociospatial organization of baboon progressions. Am. J. Primatol. 13, 119–128 (1987).PubMed 
    Article 

    Google Scholar 
    71.Bonnell, T. R., Clarke, P. M., Henzi, S. P. & Barrett, L. Individual-level movement bias leads to the formation of higher-order social structure in a mobile group of baboons. R. Soc. Open Sci. 4, (2017).72.Strandburg-Peshkin, A., Farine, D. R., Crofoot, M. C. & Couzin, I. D. Habitat and social factors shape individual decisions and emergent group structure during baboon collective movement. Elife 6, (2017).73.King, A. J., Douglas, C. M. S., Huchard, E., Isaac, N. J. B. & Cowlishaw, G. Dominance and affiliation mediate despotism in a social primate. Curr. Biol. 18, 1833–1838 (2008).CAS 
    PubMed 
    Article 

    Google Scholar 
    74.King, A. J., Sueur, C., Huchard, E. & Cowlishaw, G. A rule-of-thumb based on social affiliation explains collective movements in desert baboons. Anim. Behav. 82, 1337–1345 (2011).Article 

    Google Scholar 
    75.Harel, R., Loftus, C. J. & Crofoot, M. C. Locomotor compromises maintain group cohesion in baboon troops on the move. bioRxiv (2020).76.Wang, C. et al. Decision-making process during collective movement initiation in golden snub-nosed monkeys (Rhinopithecus roxellana). Sci. Rep. 10, 1–10 (2020).Article 
    CAS 

    Google Scholar 
    77.Whitehead, H. Consensus movements by groups of sperm whales. Mar. Mammal Sci. 32, 1402–1415 (2016).Article 

    Google Scholar 
    78.Crook, J. H. Gelada baboon herd structure and movement a comparative report. Symp. Zool. Soc. London 18, 237–258 (1966).
    Google Scholar 
    79.Grueter, C. C., Li, D., Ren, B., Wei, F. & Li, M. Deciphering the social organization and structure of wild yunnan snub-nosed monkeys (Rhinopithecus bieti). Folia Primatol. 88, 358–383 (2017).Article 

    Google Scholar 
    80.Zinner, D. et al. Comparative ecology of Guinea baboons (Papio papio). Primate Biol. 8, 19–35 (2021).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    81.Altmann, J. Observational study of behavior: sampling methods. Behaviour 49, 227–266 (1974).CAS 
    PubMed 
    Article 

    Google Scholar 
    82.Sueur, C. & Petit, O. Organization of group members at departure is driven by social structure in Macaca. Int. J. Primatol. 29, 1085–1098 (2008).Article 

    Google Scholar 
    83.Seltmann, A., Majolo, B., Schülke, O. & Ostner, J. The Organization of Collective Group Movements in Wild Barbary Macaques (Macaca sylvanus): Social Structure Drives Processes of Group Coordination in Macaques. PLoS One 8, (2013).84.Core Team, R. R: A Language and Environment for Statistical Computing. (2018).85.Baayen, R. H. Analyzing linguistic data: A practical introduction to statistics using R. Anal. Linguist. Data A Pract. Introd. to Stat. Using R 1–353 (2008).86.Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Software 67, 1–48 (2015).Article 

    Google Scholar 
    87.Dobson, A. An introduction to generalized linear models. (CRC Press, 2002).88.Forstmeier, W. & Schielzeth, H. Cryptic multiple hypotheses testing in linear models: Overestimated effect sizes and the winner’s curse. Behav. Ecol. Sociobiol. 65, 47–55 (2011).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    89.Bolker, B. M. et al. Generalized linear mixed models: a practical guide for ecology and evolution. Trends Ecol. Evol. 24, 127–135 (2009).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    90.Barr, D. J., Levy, R., Scheepers, C. & Tily, H. J. Random effects structure for confirmatory hypothesis testing: Keep it maximal. J. Mem. Lang. 68, 255–278 (2013).Article 

    Google Scholar 
    91.Fahrmeir, L., Kneib, T., Lang, S. & Marx, B. Regression Modesl (Springer, 2013).MATH 
    Book 

    Google Scholar 
    92.Hadfield, J. D. MCMCglmm: MCMC Methods for Multi-Response GLMMs in R. J. Stat. Softw. 33, 1–22 (2010).Article 

    Google Scholar  More

  • in

    Adaptation strategies and collective dynamics of extraction in networked commons of bistable resources

    Agent-resource affiliation networksWe consider games involving populations of agents that extract from multiple common-pool sources (which term we use for nodes representing resources in accord with previous related work15,16). Agents’ access to sources is defined by bipartite networks, wherein a link between an agent and a source indicates that the agent can access that source. This access is determined by some exogenous factors and remains fixed in time. The set of agents affiliated with a particular source (s) is denoted as ({mathbf{A}}_{s}), while the set of sources affiliated with a particular agent (a) is denoted as ({mathbf{S}}_{a}). The degree of an agent (a) is denoted by (m(a)), and the degree of a source (s) by (n(s)).To explore the effects of network topology upon extraction dynamics and wealth distributions, we generate ensembles of ({10}^{3}) networks, each having (50) agents and (50) sources and sharing mean agent degree (langle mrangle =5) and mean source degree (langle nrangle =5). All networks thus share the same total numbers of agents, sources, and links, but differ in how these links are distributed among agents and sources. We generate 9 network ensembles, each generated to represent a particular combination of one of three types of degree heterogeneity in its source degree distribution (U: uniform-degree, L: low-heterogeneity, or H: high-heterogeneity) with one of three similar distributions of agent degree (u, l, or h39) (Supplementary Information S1.1). Degree histograms, averaged over each ensemble, provide a representative source degree distribution ({P}_{mathbf{S}}(n)) and agent degree distribution ({P}_{mathbf{A}}(m)) for each network type (Fig. 2a and b). It is worth noting that the results of the simulations depend primarily on the degree distributions of agents and sources rather than on the overall size of the networks used (Supplementary Information S3.1).Figure 2(a) Source degree distributions and (b) Agent degree distributions for 9 network ensembles, each representing a combination of a Uniform-degree (U), Low-heterogeneity (L), or High-heterogeneity (H) source degree distribution with a uniform-degree (u), low-heterogeneity (l), or high-heterogeneity (h) agent degree distribution. Ensemble mean time-averaged quantities from pure free adaptation dynamics: (c) Agent payoffs (f(a)) as a function of agent degree (m(a)); (d) Collective extraction (overrightarrow{q}(s)) as a function of source degree (n(s)); (e) Source quality (b(s)) as a function of source degree (n(s)); and (f) Period of oscillation (T(s)) as a function of source degree (n(s)). Means are computed from simulations on ({10}^{3}) networks of each type.Full size imageNetworked CPR extraction gameOn these networks, we simulate iterative games in which agents vary the extraction effort that they apply to their affiliated sources, altering the quality of these sources; in turn, these changes in source quality then influence how agents adapt their extraction levels in subsequent rounds. The extraction effort exerted by agent (a) upon its affiliated source (s) is denoted as (q(a,s)). The total effort exerted by an agent (a), its individual extraction, is denoted by (overleftarrow{q}left(aright)=sum_{sin {mathbf{S}}_{a}}q(a,s)). The total effort exerted upon source (s), or its collective extraction, is denoted by (overrightarrow{q}left(sright)=sum_{ain {mathbf{A}}_{s}}q(a,s)). The quality of a source (s) is quantified by the benefit (b(s)) per unit extraction effort applied that the source provides. The cost associated with extraction is given by a convex (quadratic) function of (overleftarrow{q}left(aright)), such that marginal costs increase with individual extraction15,16. In addition to modelling the increasing costs (i.e., diminishing returns) associated with the physical act of extraction itself, this could also reflect escalating, informal social penalties that result from increasing extraction (i.e., “graduated sanctions”1,40). The net payoff accumulated by an agent (a) in a game iteration is thus$$fleft( a right) = left[ {mathop sum limits_{{s in {mathbf{S}}_{a} }} qleft( {a,s} right) cdot bleft( s right)} right] – frac{gamma }{2}{ }mathop{q}limits^{leftarrow} left( a right)^{2} ,$$
    (1)

    where (gamma) is a positive cost parameter.Bistable model of CPR depletion and remediationSources are bistable, meaning that at any time they can occupy one of two states: (1) a viable state, during which the source provides a benefit of magnitude (alpha) in return for each unit of extraction effort, and (2) a depleted state, during which this benefit is reduced by (beta) ((0 vec{q}_{{text{D}}} left( s right)} \ {0, } & {{text{if }} chi_{t – 1} left( s right) = 1{text{ and }}vec{q}_{t} left( s right) le vec{q}_{R} left( s right)} \ {chi_{t – 1} left( s right),} & {text{otherwise }} \ end{array} } right.$$
    (3)
    In the results that follow, we focus upon a uniform capacity scenario, wherein all sources share identical threshold values (vec{q}_{{text{D}}} left( s right) equiv vec{q}_{{text{D}}}) and (vec{q}_{{text{R}}} left( s right) equiv vec{q}_{{text{R}}} left( s right)). An alternative degree-proportional capacity scenario, in which threshold values increase with source degree, is discussed in the Supplementary Information (S3.4.2).Free adaptationUnder the free adaptation strategy, an agent updates its extraction levels independently at each of its affiliated sources depending on the state of each (Fig. 1b). As in the replicator rule often applied in networked evolutionary game models17,41,42, the rate at which an agent adapts its extraction levels within a time interval ({Delta }t) is proportional to the marginal payoff that the agent expects to attain thereby:$$frac{{{Delta }qleft( {a,s} right)}}{{{Delta }t}} = kfrac{partial fleft( a right)}{{partial qleft( {a,s} right)}},$$
    (4)
    where (k) is a rate constant. So, each extraction level (qleft( {a,s} right)) is updated according to$$q_{t + 1} left( {a,s} right) = q_{t} left( {a,s} right) + kleft[ {alpha – beta chi_{t} left( s right) – gamma {mathop{q}limits^{leftarrow}}_{t} left( a right)} right].$$
    (5)
    The higher an agent’s individual extraction (overleftarrow{q}(a)), the more slowly it will increase its extraction from viable sources, and the more rapidly it will reduce its extraction from depleted sources.Uniform adaptationWhen applying the uniform adaptation strategy, an agent adjusts each of its extraction levels by the same magnitude (Delta qleft(a,sright)equivDelta overleftarrow{q}(a)/mleft(aright)) (Fig. 1c). Assuming again that the rate at which an agent enacts this update is proportional to the associated marginal payoff, an agent adapts its extraction levels at all of its affiliated sources (s) by$$q_{t + 1} left( {a,s} right) = q_{t} left( {a,s} right) + kleft[ {alpha – beta overline{chi }left( a right) – gamma {mathop{q}limits^{leftarrow}}_{t} left( a right)} right],$$
    (6)
    where (overline{chi }left( a right) = left[ {mathop sum nolimits_{{s^{prime} in {mathbf{S}}_{a} }} chi left( {s^{prime}} right)} right]/mleft( a right)) is the mean state of the agent’s affiliated sources.ReallocationWhen practicing reallocation, an agent shifts an increment of extraction effort from a depleted source to a viable source such that its overall individual extraction (mathop{q}limits^{leftarrow} left( a right)) remains unchanged (Fig. 1d). The agent thus randomly selects one depleted source (s_{{text{D}}} in {mathbf{S}}_{a}) and one viable source (s_{{text{V}}} in {mathbf{S}}_{a}), if available. Since the marginal payoff per unit reallocated is (beta), updates its extraction levels such that$$q_{t + 1} left( {a,s} right) = left{ {begin{array}{*{20}c} {q_{t} left( {a,s} right) – kbeta , } & {{text{if}} s = s_{{text{D}}} } \ {q_{t} left( {a,s} right) + kbeta , } & {{text{if}} s = s_{{text{V}}} } \ {q_{t} left( {a,s} right),} & {text{otherwise }} \ end{array} } right.$$
    (7)

    When an agent’s affiliated sources all share the same quality value, no such reallocation is possible, and so the agent retains its present extraction levels: (q_{t + 1} left( {a,s} right) = q_{t} left( {a,s} right)) for all (s in {mathbf{S}}_{a}).Mixed strategiesAn agent’s adaptation strategy (({p}_{0},{p}_{updownarrow },{p}_{leftrightarrow })) comprises the probabilities that it will practice each of these update rules in any given round: its free adaptation propensity (({p}_{0})), its uniform adaptation propensity (({p}_{updownarrow })), and its reallocation propensity (({p}_{leftrightarrow })). An agent’s choice of a particular update rule is thus based only on its own innate inclinations, but the rate at which it enacts the selected rule is influenced by current resource conditions. We first simulate dynamics in which the same adaptation strategy is shared by all members of a population throughout the entire course of a simulation. We then consider games in which agents’ individual adaptation strategies are each allowed to independently evolve under generalized reinforcement learning38,43 (Supplementary Information S1.3.4). That is, after enacting a chosen update rule in an iteration (t), each agent (a) observes the payoff change (Delta {f}_{t}left(aright)={f}_{t}left(aright)-{f}_{t-1}(a)). If (Delta {f}_{t}left(aright) >0), then the agent’s relative propensity to practice this update rule in subsequent rounds is increased. If the agent’s payoffs decreased ((Delta {f}_{t}left(aright)0) or remediation threshold ({overrightarrow{q}}_{mathrm{R}}left(sright)). That is, all resource depletion events are assumed to be extreme enough to motivate agents to continuously “self-regulate” by remediating depleted sources (see Supplementary Information S5 for a more thorough discussion of these parameter settings).In simulations where reinforcement learning is applied, all agents are initialized with ({p}_{updownarrow }={p}_{leftrightarrow }=.333). For pure free adaptation simulations (({p}_{0}=1)), initial extraction levels were randomized (({q}_{t=0}left(a,sright)in [0,frac{{overrightarrow{q}}_{mathrm{D}}left(sright)}{nleft(sright)}])). All other simulations (({p}_{0} More

  • in

    Linking gut microbiome with the feeding behavior of the Arunachal macaque (Macaca munzala)

    1.Muegge, B. D. et al. Diet drives convergence in gut microbiome functions across mammalian phylogeny and within humans. Science 6032(332), 970–974 (2011).ADS 
    Article 
    CAS 

    Google Scholar 
    2.McKenzie, V. J. et al. The effects of captivity on the mammalian gut microbiome. Integr. Comp. Biol. 57, 690–704 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    3.Flint, H. J., Scott, K. P., Duncan, S. H., Louis, P. & Forano, E. Microbial degradation of complex carbohydrates in the gut. Gut Microbes 3, 289–306 (2012).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    4.Chen, T. et al. Fiber-utilizing capacity varies in Prevotella- versus Bacteroides-dominated gut microbiota. Sci. Rep. 7, 2594 (2017).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    5.Costello, E. K., Stagaman, K., Dethlefsen, L., Bohannan, B. J. M. & Relman, D. A. The application of ecological theory toward an understanding of the human microbiome. Science 6086(336), 1255–1262 (2012).ADS 
    Article 
    CAS 

    Google Scholar 
    6.Campbell, C. J., Fuentes, A., MacKinnon, K. C., Bearder, S. K. & Stumpf, R. Primates in Perspective (Oxford University Press, 2010).
    Google Scholar 
    7.Chivers, D. J. Functional anatomy of the gastrointestinal tract. Colobine Monkeys Ecol. Behav. Evol. 205–227 (1994).8.Davies, G. E. Colobine Monkeys: Their Ecology, Behaviour and Evolution (Cambridge University Press, 1994).
    Google Scholar 
    9.Neish, A. S. Microbes in gastrointestinal health and disease. Gastroenterology 136, 65–80 (2009).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    10.Hanya, G. & Chapman, C. A. Linking feeding ecology and population abundance: A review of food resource limitation on primates. Ecol. Res. 28, 183–190 (2013).Article 

    Google Scholar 
    11.Fan, P., Ni, Q., Sun, G., Huang, B. & Jiang, X. Gibbons under seasonal stress: the diet of the black crested gibbon (Nomascus concolor) on Mt. Wuliang, Central Yunnan, China. Primates 50, 37 (2009).PubMed 
    Article 

    Google Scholar 
    12.Burrows, A. M. & Nash, L. T. The Evolution of Exudativory in Primates (Springer Science & Business Media, 2010).Book 

    Google Scholar 
    13.Amato, K. R. et al. Gut microbiome, diet, and conservation of endangered langurs in Sri Lanka. Biotropica 52, 981–990 (2020).Article 

    Google Scholar 
    14.Amato, K. R. et al. The gut microbiota appears to compensate for seasonal diet variation in the wild black howler monkey (Alouatta pigra). Microb. Ecol. 69, 434–443 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    15.Amato, K. R. et al. Variable responses of human and non-human primate gut microbiomes to a Western diet. Microbiome 3, 53 (2015).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    16.Clayton, J. B. et al. Captivity humanizes the primate microbiome. Proc. Natl. Acad. Sci. U. S. A. 113, 10376–10381 (2016).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    17.Frankel, J. S., Mallott, E. K., Hopper, L. M., Ross, S. R. & Amato, K. R. The effect of captivity on the primate gut microbiome varies with host dietary niche. Am. J. Primatol. 81, 1–9 (2019).Article 

    Google Scholar 
    18.Lee, W., Hayakawa, T., Kiyono, M., Yamabata, N. & Hanya, G. Gut microbiota composition of Japanese macaques associates with extent of human encroachment. Am. J. Primatol. 81, 1–14 (2019).Article 

    Google Scholar 
    19.Amato, K. R. et al. Habitat degradation impacts black howler monkey (Alouatta pigra) gastrointestinal microbiomes. ISME J. 7, 1344–1353 (2013).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    20.Moeller, A. H. et al. Sympatric chimpanzees and gorillas harbor convergent gut microbial communities. Genome Res. 23, 1715–1720 (2013).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    21.Suzuki, T. A. & Worobey, M. Geographical variation of human gut microbial composition. Biol. Lett. 10, (2014).22.Sinha, A., Datta, A., Madhusudan, M. D. & Mishra, C. Macaca munzala: a new species from western Arunachal Pradesh, northeastern India. Int. J. Primatol. 26, 977–989 (2005).Article 

    Google Scholar 
    23.Sinha, A., Kumar, R. S., Gama, N., Madhusudan, M. D. & Mishra, C. Distribution and conservation status of the Arunachal macaque, Macaca munzala, in western Arunachal Pradesh, northeastern India. Primate Conserv. 2006, 145–148 (2006).Article 

    Google Scholar 
    24.Mendiratta, U., Kumar, A., Mishra, C. & Sinha, A. Winter ecology of the Arunachal macaque Macaca munzala in Pangchen Valley, western Arunachal Pradesh, northeastern India. Am. J. Primatol. 71, 939–947 (2009).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    25.Kumar, R. S., Mishra, C. & Sinha, A. Foraging ecology and time-activity budget of the Arunachal macaque Macaca munzala: A preliminary study. Curr. Sci. 93, 532–539 (2007).
    Google Scholar 
    26.Ghosh, A., Thakur, M., Singh, S. K., Sharma, L. K. & Chandra, K. Gut microbiota suggests dependency of Arunachal Macaque (Macaca munzala) on anthropogenic food in Western Arunachal Pradesh, Northeastern India: Preliminary findings. Glob. Ecol. Conserv. 22, e01030 (2020).Article 

    Google Scholar 
    27.Song, S. J., Amir, A., Metcalf, J. L. & Amato, K. R. Preservation methods differ in fecal microbiome stability, affecting suitability for field studies. mSystems 1, 1–12 (2016).Article 

    Google Scholar 
    28.Li, Q. & Zhang, Y. A molecular phylogeny of macaca based on mtochondrial corntrol region sequeryces. Zool. Res. 25, 385–390 (2004).
    Google Scholar 
    29.Kumar, S., Stecher, G., Li, M., Knyaz, C. & Tamura, K. MEGA X: Molecular evolutionary genetics analysis across computing platforms. Mol. Biol. Evol. 35, 1547–1549 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    30.Kanthaswamy, S. et al. Microsatellite markers for standardized genetic management of captive colonies of rhesus macaques (Macaca mulatta). Am. J. Primatol. Off. J. Am. Soc. Primatol. 68, 73–95 (2006).CAS 

    Google Scholar 
    31.Peakall, R. O. D. & Smouse, P. E. GENALEX 6: Genetic analysis in Excel. Population genetic software for teaching and research. Mol. Ecol. Notes 6, 288–295 (2006).Article 

    Google Scholar 
    32.Kalinowski, S. T., Wagner, A. P. & Taper, M. L. ML-RELATE: A computer program for maximum likelihood estimation of relatedness and relationship. Mol. Ecol. Notes 6, 576–579 (2006).CAS 
    Article 

    Google Scholar 
    33.Takai, K. & Horikoshi, K. Rapid detection and quantification of members of the archaeal community by quantitative PCR using fluorogenic probes. Appl. Environ. Microbiol. 66, 5066–5072 (2000).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    34.Herlemann, D. P. R. et al. Transitions in bacterial communities along the 2000 km salinity gradient of the Baltic Sea. ISME J. 5, 1571–1579 (2011).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    35.Muyzer, G., De Waal, E. C. & Uitterlinden, A. G. Profiling of complex microbial populations by denaturing gradient gel electrophoresis analysis of polymerase chain reaction-amplified genes coding for 16S rRNA. Appl. Environ. Microbiol. 59, 695–700 (1993).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    36.Caporaso, J. G. et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc. Natl. Acad. Sci. U. S. A. 108, 4516–4522 (2011).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    37.Schloss, P. D. et al. Introducing mothur: Open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75, 7537–7541 (2009).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    38.Schloss, P. D. Reintroducing mothur: 10 years later. Appl. Environ. Microbiol. 86, 1–13 (2020).
    Google Scholar 
    39.Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    40.Quast, C. et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013).CAS 
    Article 

    Google Scholar 
    41.McMurdie, P. J. & Holmes, S. Phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, (2013).42.Cao, Y. microbiomeMarker: microbiome biomarker analysis. R package version 0.0.1.9000 https://github.com/yiluheihei/microbiomeMarker (2020).43.Lozupone, C., Lladser, M. E., Knights, D., Stombaugh, J. & Knight, R. UniFrac: An effective distance metric for microbial community comparison. ISME J. 5, 169–172 (2011).PubMed 
    Article 

    Google Scholar 
    44.Segata, N. et al. Metagenomic biomarker discovery and explanation. Genome Biol. 12, R60 (2011).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    45.Mao, S., Zhang, R., Wang, D. & Zhu, W. The diversity of the fecal bacterial community and its relationship with the concentration of volatile fatty acids in the feces during subacute rumen acidosis in dairy cows. BMC Vet. Res. 8, 1 (2012).CAS 
    Article 

    Google Scholar 
    46.Wang, B., Yao, M., Lv, L., Ling, Z. & Li, L. The human microbiota in health and disease. Engineering 3, 71–82 (2017).Article 

    Google Scholar 
    47.Carding, S., Verbeke, K., Vipond, D. T., Corfe, B. M. & Owen, L. J. Dysbiosis of the gut microbiota in disease. Microb. Ecol. Health Dis. 26, 26191 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    48.Kumar, R. S., Gama, N., Raghunath, R., Sinha, A. & Mishra, C. In search of the munzala: distribution and conservation status of the newly-discovered Arunachal macaque Macaca munzala. Oryx 42, 360–366 (2008).Article 

    Google Scholar 
    49.Sarania, B., Devi, A., Kumar, A., Sarma, K. & Gupta, A. K. Predictive distribution modeling and population status of the endangered Macaca munzala in Arunachal Pradesh, India. Am. J. Primatol. 79, 2592 (2017).Article 

    Google Scholar 
    50.Aiyadurai, A., Singh, N. J. & Milner-Gulland, E. J. Wildlife hunting by indigenous tribes: A case study from Arunachal Pradesh, north-east India. Oryx 44, 564–572 (2010).Article 

    Google Scholar 
    51.Mishra, C., Madhusudan, M. D. & Datta, A. Mammals of the high altitudes of western Arunachal Pradesh, eastern Himalaya: an assessment of threats and conservation needs. Oryx 40, 29–35 (2006).Article 

    Google Scholar  More