Ecology

arising from: P. B. Ngor et al.; Scientific Reports https://doi.org/10.1038/s41598-018-27340-1 (2018).
As one of the richest sources of fisheries-related data in the lower Mekong basin, the Tonle Sap dai fishery has received considerable attention in the literature in recent years as concerns grow over the impacts of hydropower dams on fisheries, which are important for livelihoods and food security1,2,3.
Ngor et al.4 reported a decline since 2000 in the catch of larger species which tend to occupy higher trophic levels; compensatory increases in the catch of smaller species; and declines in the mean body weight (and length) of common species in the Tonle Sap dai fishery, as evidence of the effects of indiscriminate fishing or “fishing-down” of the multi-species fish assemblage in the lower Mekong basin. We provide evidence below that suggest that these apparent recent changes are more likely to reflect changing hydrological conditions than fishing-down effects, possibly caused by climate change and recently also by hydropower development.
The dai fishery has been reliably monitored since 1997–98. Without explanation, Ngor et al. excluded the first three seasons (1997–98 to 1999–2000) of monitoring data which include one of the driest fishing seasons on record (1998–99). The authors thereby created a time series beginning with the three wettest seasons (largest floods) since monitoring began (2000–1 to 2002–3) that were followed by 12 seasons of variable, but decreasing flows caused by hydropower dam construction, low rainfalls possibly resulting from climate change, and abstractions for agriculture5,6 (Fig. 1).
Figure 1

Source: Mekong River Commission Secretariat.

The flood index (FI) or flood pulse14 in the Tonle Sap Great Lake System (1997/08–2014/15). The FI is a measure of the flood extent and duration, calculated as the sum of the flooded area days above the mean flooded area from April to March of the following year2. Whilst highly variable, a downward decline (p-value = 0.06) in the FI is observed between 2000/01 (Year 2001) and 2014/15 (Year 2015) shown by solid circles. Adding the most recent data for 2016–2018 (not shown here), confirmed that a downward linear trend in the FI since the 2000/01 season is statistically significant (p-value  45 cm) excluding those with zero catch in any year. These 28 species formed approximately 16% of the total catch during the study period. We also found negative regression coefficients for all 28 species, supporting the findings of Ngor et al. However, the combined annual catch of these 28 species did not decline significantly through time (R2 = 0.22; p-value = 0.07).
We did however find that the combined annual catch of these 28 larger species varied significantly with the annual flood index (FI)—a measure of flood extent and duration (R2 = 0.46; p-value  45 cm) species and the flood index (R2 = 0.46; p-value  More

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Continuous and discrete quantity information are important in guiding animal behaviour in virtually all aspects of life. The capacity to evaluate continuous (uncountable) quantities, such as length, area, weight, or duration, is widespread and can be found in organisms with relatively simple nervous systems, such as annelids, crustaceans, or gastropds1,2,3. This quantitative information takes part in decision-making processes in different contexts. For example, animals may gauge body sizes of rivals or prospective mates, assess distances from home, or estimate the extent of a food patch4,5,6.
Several vertebrates, from teleost fishes to primates, can also process discrete (countable) information. For example, many species are capable of accurately estimating the number of elements in a set and comparing the numerosity of different sets7,8. Studies conducted in nature or in the laboratory have shown that numerical abilities serve important adaptive functions. For example, in guppies, New Zealand robins, and macaques, quantity discrimination is used to select the patch containing the larger number of food items9,10,11. Conversely, some predators, namely lions and striped field mice, use this ability to select the smallest prey groups because they are more vulnerable to predation12,13. Various group-living mammals, including chimpanzees, lions, and hyenas, gauge the relative number of opponents before deciding whether to attack or withdraw14,15,16. Gregarious fish use the same ability to select the social group that provides the best protection from predators17,18,19. Some species, including eastern mosquitofish, brown-headed cowbirds, and American coots, use their quantitative abilities to increase reproductive success20,21,22.
Cognitive psychologists have shown that in these cases it is not necessary to assume the existence of a true numerical estimation system because an animal can use continuous cues, such as the amount of movement, the cumulative surface occupied by items, or the convex hull of the set as a proxy for number23,24. Inferring the existence of a numerical system requires a series of careful laboratory control experiments in which the animal is subjected to numerical tasks, while the access to non-numerical information is simultaneously prevented8,25. This process is not always straightforward and studies often fail to reach a firm conclusion even after numerous experiments are performed. In fact, convincing evidence of the presence of a numerical system exists only for a small fraction of the species investigated (e.g., guppy26, chicken27, and rhesus monkeys10).
It is not known whether numerical abilities have similar selective advantages in other phyla and whether numerical systems are widespread outside the vertebrate group. To date, this issue has been investigated only in a handful of species, and there is convincing evidence of a true numerical system for only one of them, the honeybee28,29,30. Honeybees, Apis mellifera, can be trained to discriminate different numbers of dots to obtain a food reward31,32. They are able to accomplish this task even when main continuous cues are controlled, thus it is suggested that they possess a numerical system analogous to that of vertebrates. Honeybees can also use ordinal information and learn the correct position in a sequence of artificial flowers when distance cues are made irrelevant33. Similar evidences have been recently provided for another social bee, the bumblebee, Bombus terrestris34,35. The function of cardinal and ordinal numerical abilities in social bees is unclear, but it has been suggested that they mainly serve to recognise flowers from the number of petals and to learn the location of food around their hives, respectively.
Circumstantial evidence suggests the ability to estimate the quantity of conspecifics in three other arthropod species. The juvenile spiders of Portia africana have been reported to take into account the number of competitors present when choosing between two patches of food36. Males of the coleopteran Tenebrio molitor are able to discriminate different numbers of females based on the odours they emit37. Ants (Formica xerophila) perceiving themselves as part of a large group are more aggressive towards another species than ants perceiving themselves as isolated individuals38. Controls are difficult to perform in these types of experiments, and it is unknown whether these three species are actually estimating the number of individuals or they are using other types of information as a proxy of number.
Recently, a mollusc, the cuttlefish, was observed to prefer the larger quantity of shrimps up to 4 versus 5 items39. Although authors manipulate some continuous cues (i.e., density and total activity of preys), it is unclear whether cuttlefish are really counting prey or are using other cues, such as the cumulative area occupied by shrimps or the convex hull of the groups.
Theba pisana is a small terrestrial snail inhabiting the dunes of the Mediterranean coasts. Similar to most snails, it is active mainly at night. This species has a considerable thermal tolerance, with an upper lethal limit that lies, depending on exposure time, between 46 °C and 50 °C40. However, during sunny days, the sandy ground can reach temperatures that largely exceed this lethal limit (up to 75 °C). To avoid these adverse conditions at sunrise, dune snails climb the stem of tall vegetation, where the temperature rarely exceeds 30 °C, and remain inactive until night. If placed on the ground during the day, these snails rapidly regain an elevated position by orienting towards nearby stems and climbing on them (Fig. 1a; Supplementary Video S1). At our site of capture, snails were collected mainly from vertical, unbranched stems of live or dead inedible plants and herbs (e.g., Puccinellia palustris, P. distans, and Juncus maritimus).
Figure 1

(a) Example of a dune snail T. pisana climbing on the stem of tall vegetation. (b) The circular arena used for investigating quantity discrimination ability in laboratory.

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Zanforlin showed that it is possible to simulate this behaviour in the laboratory41. After placing dune snails on a brightly lit arena, they rapidly orient towards a black cardboard shape on a white background and climb on it. With this setup, it was possible to study shape preference by placing two shapes at 60° angle from the centre and releasing the snail from the centre of the arena. He found that, confronted with similar geometric figures (e.g., two rectangles), snails oriented consistently towards the stimulus with the largest area. When area was kept constant, no particular preference for shape was observed, although there was a tendency to prefer the figure with a longer perimeter or with wider axes.
In all the experiments of the former study, snails were required to choose between two single shapes. In nature, however, stems are frequently arranged in clusters. All things being equal, there are several potential advantages in heading towards a large cluster of stems. In a cluster, there is greater probability of finding the stem with the most suitable features, such as a correct diameter or an optimal orientation to shelter from wind and sun40. In addition, not all the stems are accessible due to the presence of intricate or thorny vegetation at the base. Heading towards a group of stems increases the chances that at least one stem can be reached and climbed. Furthermore, most predators (mainly passerine birds, wall lizards, and rats) are small and catch only one or few preys at a time, and hence, sheltering in clusters could determine a dilution effect on predation risk42,43.
Based on the above considerations, we made the prediction that natural selection in T. pisana should favour the ability to discriminate between a single stem and a cluster and discriminate among clusters, based on the quantity of stems. The aim of the first experiment was to test this hypothesis. In the laboratory, we simulated stems used by dune snails as refuges by using black vertical bars on a white background (Fig. 1b; Supplementary Video S2). As we found that dune snails discriminate rather accurately between quantities of stems, in a series of subsequent experiments, we investigated the mechanism involved. Specifically, we tried to figure out if snails were using a true numerical system or if they used continuous quantitative information that co-varied with numerosity, such as the cumulative area occupied by items, their density or the convex hull they spanned.
Experiment 1: discrimination of the quantity of refuges
A previous study on dune snails investigated the choice between single objects that differed in shape and size41. However, based on their ecology, we predict that snails searching for protection from the heat also should focus on number and should move towards the largest available group of stems. In Experiment 1a, we studied whether dune snails prefer a group of refuges to a single one (Fig. 2a), and in Experiment 1b, we measured their accuracy to discriminate among groups of refuges differing in numerosity (Fig. 2b). To obtain reference data about snails’ general discriminatory abilities, in Experiment 1c, we measured the accuracy of dune snails to discriminate two equally shaped objects that differ in surface area (Fig. 3c).
Figure 2

(a–c) Stimuli used in Experiment 1a, 1b and 1c, respectively.

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Figure 3

(a) Percentage of snails choosing the stimulus with larger quantity of bars in Experiment 1a and 1b. Snails showed a significant preference for larger quantity up to 4 versus 5 bars. There was a significant difference amongst the numerical ratios (P  More

Data
Data were collected in 2015 through a survey deployed as part of the Feed the Future Buena Milpa project (http://www.cimmyt.org/project-profile/buena-milpa/). The project’s goal was to reduce food insecurity and malnutrition by fostering sustainable, resilient, and innovative maize-based farming systems in the WHG.
We conducted the survey, with support from local researchers and agronomy students of the University of San Carlos (USAC), in summer of 2015 in 989 maize-growing farm households in 64 communities of 16 municipalities of the WHG. The number of surveys at the departmental level was: Totonicapán (226), Quiche (350), Quetzaltenango (187) and Huehuetenango (226) (See Supplementary Table 1 and Supplementary Fig. 1 for details). Criteria to select communities were: location within the targeted municipalities in the Buena Milpa project, the active work of project partners within the communities, and their distribution within four selected watersheds that included farming systems at different altitudes, ranging from 1400 to 3200 masl. For household selection, enumerators walked radial transects to survey household members, choosing only households that explicitly agreed to participate and had agricultural land on at least part of which maize was grown. The survey was a closed questionnaire with 139 questions in 18 sections including 5 project themes: milpa-maize germplasm improvement, natural resource conservation in farming system, farming system diversification, agricultural innovation systems and social inclusion.
Milpa diversity and extent
We used survey findings regarding the previous year’s crop production (2014) for all 989 surveyed households to understand the importance and diversity of the milpa system. The 989 households reported crop production on a total of 1,541 plots, 1,324 of which included maize. The other 217 plots grew potato (85 plots), coffee (50), vegetables (31), bean (19), fruit trees (12), faba bean (7), forestry trees (6), pea (2), oats, wheat, etc. Given the study’s focus on smallholder farmers, we discarded other 119 plots that had unrealistically large values for plot size or maize production levels for the region (i.e. more than ten times the average plot size or calculated maize yield,  > 2 ha and  > 11 ton ha−1 year−1).
For the resulting 1205 plots we constructed a tree depicting maize-system diversity in the WHG, with each node indicating the main cropping associations. The tree was nested, so the first node displays all maize plots, with successive splits for monocrop vs intercropped maize and with crop names presented according to how often they are grown (overall: maize  > common beans  > potatoes  > squash  > faba beans  > fruit trees  > vegetables). So, in plots where maize is grown with potatoes and common beans, the association is termed maize-bean-potatoes.
Milpa and food security
We assessed maize yield differences for monocropping and intercropping to detect a yield penalty or advantage for maize (see below), including survey information only for households from which we had available yield data for all crops. In the survey, total production for each crop was recorded regardless of the number of plots on which it was grown and therefore, for 398 households (40.2% of the sample) that had more than one plot under maize or any of the milpa crops, it was impossible to calculate the yield and had to be discarded from the analysis due to a lack of available plot-level information. The results were further screened for complete crop information and unrealistic values on crop production levels (i.e. ten times higher than the average yield for the different crops—See Supplemental Table 2), resulting in a usable sample of 368 plots.
For statistical analysis, only those crop combinations with a sample size equal to or greater than 9 plots were selected, resulting in 357 plots with, in descending order, the following numbers of 300 plots sown to each cropping combination: maize monocrop (163), maize-bean (109), maize-bean-squash (30), maize-bean/faba (12), maize-potato (13), maize-squash (11), maize-bean-potato (10), maize-faba (9). Other crop combinations with very low sample sizes were maize-squash-faba (4), maize-potato-faba (3), maize-bean-squash-faba (2), maize-bean-potato-faba (1) and maize-bean-potato-squash (1), making it difficult to include them in further statistical analyses.
Maize yields for plots under monocropped maize (163) were first compared to maize yields from intercropped plots (194). To choose the most appropriate statistical test, we checked if the outcome variable, maize yield, met the assumptions required for a parametric test. Although we had independent samples, large sample sizes, and homogeneous variances (F = 1.1809, p-value = 0.267), maize yield proved to be non-normally distributed after Shapiro-Wilks normality test was significant (W = 0.911, p-value  1]{ }}}{{text{N}}}{ } times {overline{text{s}}},{[18]}$$

where (N) is the number of nutrients considered (14), (s_{i}) is the fraction of potentially nourished male adults by a given nutrient, and (overline{s}) is the average of potentially nourished persons for all nutrients. To calculate s we multiplied the yield of each crop reported in the survey (kg ha−1) by the nutrient composition of each crop, using the content per 100 g of the 14 different nutrients for each crop, as per the INCAP Food Composition Table for Central America32 and using the INCAP RDA for an adult male33 (See Supplementary Tables 3 and 4). All values were calculated per hectare and year. PNA levels across cropping associations were also compared. First, we checked if PNA data met the assumptions required for a parametric test. PNA variances proved to be non-homogeneous (F = 14.5, p-value =   More