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Learning is an adaptation that allows individuals to respond to environmental stimuli in ways that improve their reproductive outcomes. The degree of sophistication in learning mechanisms potentially explains variation in behavioural responses. Here, we present a model of learning that is inspired by documented intra- and interspecific variation in the performance in a simultaneous two-choice task, the ‘biological market task’. The task presents a problem that cleaner fish often face in nature: the decision of choosing between two client types; one that is willing to wait for inspection and one that may leave if ignored. The cleaners’ choice hence influences the future availability of clients, i.e. it influences food availability. We show that learning the preference that maximizes food intake requires subjects to represent in their memory different combinations of pairs of client types rather than just individual client types. In addition, subjects need to account for future consequences of actions, either by estimating expected long-term reward or by experiencing a client leaving as a penalty (negative reward). Finally, learning is influenced by the absolute and relative abundance of client types. Thus, cognitive mechanisms and ecological conditions jointly explain intra and interspecific variation in the ability to learn the adaptive response. All the data for the paper was generated by running individual based simulations written in c++ laguange. The code both for generating the data and for figure of the published article can be found in here: DOI: 10.5281/zenodo.3361665 Files names starting with the identifier "FAA" correspond to Fully Aware Agents, while files named starting with "PAA" correspond to Partially Aware Agents. After the identifier, file names contain 7 numbers, each one preceeded by the information of the simulation they communicate. The first number gives the alpha parameter, it is followed by the word "alph". The second number provides the value used in parameter gamma. The third number correspond to the value of parameter tau (this parameter is only relevant for simulation run for algorithm SARSA). The fourth number provides the value for a boolean variable that determines whether penalty is used in the simulation. The fifth and sixth numbers give the value for the probability of a visitor, and the probability of a resident, respetively. Finally, the last number gives the seed used in the random number generator. Columns, in the files, have headers that correspond to values or parameters in the models. All other parameter values can be found in the associated json files that are contained within the same folder. 

Population reduction and disturbances may alter dispersal, mating patterns and gene flow. Rather than taking the common approach of comparing different populations or sites, here we studied gene flow via wind-mediated effective pollen dispersal on the same plant individuals before and after a fire-induced population drop, in a natural stand of Pinus halepensis. The fire killed 96% of the pine trees in the stand and cleared the vegetation in the area. Thirteen trees survived in two groups separated by ~80 m, and seven of these trees had serotinous (closed) pre-fire cones that did not open despite the fire. We analyzed pollen from closed pre- and post-fire cones using microsatellites. The two groups of surviving trees were highly genetically differentiated, and the pollen they produced also showed strong among-group differentiation and very high kinship both before and after the fire, indicating limited and very local pollen dispersal. The pollen not produced by the survivors also showed significant pre-fire spatial genetic structure and high kinship, indicating mainly within-population origin and limited gene flow from outside, but became spatially homogeneous with random kinship after the fire. We suggest that post-fire gene flow via wind-mediated pollen dispersal increased by two putative mechanisms: 1) a drastic reduction in local pollen production due to population thinning, effectively increasing pollen immigration; 2) an increase in wind speeds in the vegetation-free post-fire landscape. This research shows that dispersal can alleviate negative genetic effects of population size reduction, and that disturbances might enhance gene flow, rather than reduce it. Trees, seeds and pollen genotypic dataDryad-Shohami&Nathan.txt

Stylophora pistillata is a widely used coral “lab-rat” species with highly variable morphology and a broad biogeographic range (Red Sea to western central Pacific). Here we show, by analysing Cytochorme Oxidase I sequences, from 241 samples across this range, that this taxon in fact comprises four deeply divergent clades corresponding to the Pacific-Western Australia, Chagos-Madagascar-South Africa, Gulf of Aden-Zanzibar- Madagascar, and Red Sea-Persian/Arabian Gulf-Kenya. On the basis of the fossil record of Stylophora, these four clades diverged from one another 51.5-29.6 Mya, i.e., long before the closure of the Tethyan connection between the tropical Indo-West Pacific and Atlantic in the early Miocene (16–24 Mya) and should be recognised as four distinct species. These findings have implications for comparative ecological and/or physiological studies carried out using Stylophora pistillata as a model species, and highlight the fact that phenotypic plasticity, thought to be common in scleractinian corals, can mask significant genetic variation. Stylophora sequence files for DryadAligned sequence files used for phylogeny analysis. ReadMe files also included.

We conducted a study on interpopulation variation of color patterns in two congeneric chameleon species, which have an analogous life history. Both species are able to rapidly change color pattern, and context-dependent color patterns often vary across a wide geographic range. Specifically, we tested four hypotheses that can explain the observed interpopulation variation of color patterns by a series of behavioral field trials where the color patterns of individuals were recorded and later analyzed by a deep neural network algorithm. We used redundancy analysis (dbRDA) to relate genetic, spectral, and behavioral predictors to interpopulation color pattern distance. Our results showed that both isolation by distance and alternative mating tactics were significant predictors for interpopulation color pattern variation in Chamaeleo chamaeleon males. In contrast, in C. dilepis, the interpopulation color pattern variation was largely explained by isolation by distance, and the evidence for alternative mating tactics was absent. In both chameleon species, the environmental colors showed no evidence of influencing chameleon interpopulation color pattern variation, regardless of sex or behavioral context. This contrasting finding suggests that interpopulation context-dependent color pattern variations in each species are maintained under a different set of selective pressures or circumstances. Genetic and behavioral factors affecting interpopulation color pattern variation in two congeneric chameleon species Tammy Keren-Rotem, Devon C. Main, Adi Barocas, David Donaire-Barroso, Michal Haddas-Sasson, Carles Vila, Tal Shaharabany, Lior Wolf, Krystal A. Tolley, and Eli Geffen Image library of the color patterns on the lateral side of Chamaeleo chamaeleon and Chamaeleo dilepis. The white balance of all images was standardized prior the analysis using the spectral reflectance of the color board (GretagMacBeth ColorChecker chart) and the Photoshop software. Location, sex, body weight and snout-vent length are provided for each specimen in Excel tables. The images are sorted by species, location, and social context (female-female (FF), female-male (MF), and male-male (MM) matches, sand ingle individual on a pole). In the Excel data files, NA = data is not available Image library of the color patterns on the lateral side of Chamaeleo chamaeleon and Chamaeleo dilepis. The white balance of all images was standardized prior to the analysis using the spectral reflectance of the color board (GretagMacBeth ColorChecker chart) and the Photoshop software. Location, sex, body weight and snout-vent length are provided for each specimen in Excel tables. The images are sorted by species, location, and social context (female-female (FF), female-male (MF), and male-male (MM) matches, and single individual on a pole).  Any software that can handle raster images.

To survive, organisms must extract information from the past that is relevant for their future. How this process is expressed at the neural level remains unclear. We address this problem by developing a novel approach from first principles. We show here how to generate low-complexity representations of the past that produce optimal predictions of future events. We then illustrate this framework by studying the coding of ‘oddball’ sequences in auditory cortex. We find that for many neurons in primary auditory cortex, trial-by-trial fluctuations of neuronal responses correlate with the theoretical prediction error calculated from the short-term past of the stimulation sequence, under constraints on the complexity of the representation of this past sequence. In some neurons, the effect of prediction error accounted for more than 50% of response variability. Reliable predictions often depended on a representation of the sequence of the last ten or more stimuli, although the representation kept only few details of that sequence. fnames.csvfnames.csv relates unit index with the original data filename, to link back to the raw data. The format of the file is described in readData.m.stim.csvstim.csv contains the sequence of stimuli. The format of the file is described in readData.m.resp.csvresp.csv is the main data file. It contains spike times for all trials of all neurons. Each trial is represented by 700 ms, with stimulus onset at 200 ms after trial onset. Tone duration is 230 ms. The format of the file is described in readData.m.readData.mThis file contains a description of the three files comprising this data set, as well as example matlab code for reading and processing them.

Aim To determine how reptile populations respond to anthropogenic habitat modification and determine if species traits and environmental factors influence such responses. Location Global. Time period 1981–2018. Major taxa studied Squamata. Methods We compiled a database of 56 studies reporting how habitat modification affects reptile abundance, and calculated standardised mean differences in abundance (Hedges’ g). We used Bayesian meta-analytical models to test whether responses to habitat modification depended on body size, clutch size, reproductive mode, habitat specialisation, range size, disturbance type, vegetation type, temperature and precipitation. Results Based on 815 effect sizes from 376 species, we found an overall negative effect of habitat modification on reptile abundance (mean Hedges’ g = -0.43, 95% credible intervals = -0.61 to -0.26). Reptile abundance was, on average, one-third lower in modified compared to unmodified habitats. Small range sizes and small clutch sizes were associated with more negative responses to habitat modification, although the responses were weak and the credible intervals overlapped zero. We detected no effects of body size, habitat specialisation, reproductive mode (egg-laying or live-bearing), temperature, or precipitation. Some families exhibited more negative responses than others, although overall there was no phylogenetic signal in the data. Mining had the most negative impacts on reptile abundance, followed by agriculture, grazing, plantations and patch size reduction, whereas the mean effect of logging was neutral. Main conclusions Habitat modification is a key cause of reptile population declines, although there is variability in responses both within and between species, families, and vegetation types. The effect of disturbance type appeared to be related to intensity of habitat modification. Ongoing development of environmentally sustainable practices that ameliorate anthropogenic impacts is urgently needed to prevent reptile population declines. Based on published literature, we compiled a database of 56 studies reporting how habitat modification affects reptile abundance. We extracted data from the text, tables, figures and appendices of papers. We used the means, standard deviations and sample sizes to calculate standardised mean differences (Hedges’ g and log response ratio) and sampling variances. For each data point, we recorded a number of ecological and environmental traits predicted to be important determinants of population sensitivity to habitat disturbance. The ecological traits were body mass, clutch size, reproductive mode, habitat specialisation and range size. We calculated an index of habitat specialisation by counting the number of major habitat types (e.g. forest, savanna, wetlands, rocky areas) listed in each species’ IUCN Red List profile. We derived range size from species distribution maps. A full set of trait data was not available for all study species. We recorded vegetation types as either forest, woodland, shrubland or grassland. We calculated the mean temperature of the warmest quarter of the year and mean annual precipitation within a 5-km radius around each study location.

The global breeding population of Eleonora’s Falcon (Falco eleonorae) is distributed from the Canary Islands in the west, across the Mediterranean Sea, to Cyprus in the east. The remoteness of nesting colonies, which are predominantly located on sea cliffs and islets, renders breeding success estimation a challenging task, requiring a composite approach to assess each of the breeding stages. Early estimates of the breeding success of Eleonora’s Falcon suggested that the Akrotiri colony in Cyprus had the lowest breeding success among all the colonies throughout the species’ breeding range, at a level seemingly unsustainable, suggesting the colony might have been in danger of gradual extinction. Here we use a diversity of survey methods including boat, ground, and aerial surveys, with the incorporation of photography and photogrammetry, to reassess the breeding success and the effect of nest characteristics on the Eleonora’s Falcon breeding population in Cyprus. During a 6-yr study, we found that Cyprus hosts ~138 ± 8 breeding pairs and that breeding success equals 1.54 ± 0.85 fledglings per breeding pair, and thus is considerably higher than previous estimates. In addition, by analyzing temporal variation in breeding and nest characteristics, we found that early breeding and reuse of nests positively influence breeding success, but physical nest characteristics have a limited effect on colony productivity. The range of survey methods employed, as well as the array of photography techniques utilized, enhanced the efficiency and accuracy of this study, allowing us to overcome the challenge of inaccessibility of nesting cliffs. The raw data used in statistical analyses are all provided along with the R code. The data have all been combined here into one dataset though analyses were performed on subsets of the data as described in the manuscript. The script to produce the digital surface model is provided but we do not provide exact coordinates because of sensitivity of falcon nest sites to disturbance. The dataset is raw survey data from monitoring Eleonora's falcon nest sites using a variety of methods described in the paper. Also included in separate sheets are the code used to analyse the data - R code for statistical analyses and python code to produce a digital surface model of the nesting cliffs.

Social learning is widespread but the causes for variation in the use of social versus private information are not always clear. Alongside adaptive explanations, suggesting that animals learn socially only when it is indeed adaptive to do so, it is also possible that the use of social learning is limited by mechanistic constraints. A common, but frequently overlooked challenge for social learning mechanisms is the need to allow learners to solve a problem through watching it being solved by others. This requires animals to be able to shift between contexts: from the context of the observed solution, to the context of the unsolved problem. For instance, for the social learning of cues associated with hidden food, an individual that merely sees a conspecific exploiting the food must, in the later absence of demonstrators or visible rewards, also learn to explore the cue for itself. Here we show that this shift in context can indeed be difficult. In two experiments involving sand colors, house sparrows trained with hidden seeds learned to search for hidden seeds (based on food-color association) better than sparrows trained with exposed seeds. However, the latter showed color preference when tested with seeds exposed on both sand colors. These results demonstrate that context-specific learning makes it difficult to generalize reward-cue association from “exposed” to “hidden” conditions, which may explain why social learning is often more effective when it is based on socially facilitated active search (for hidden food), similar to that used in the context of independent foraging. Experiment 1 datasheetSparrows' pecks in the training and test phases of experiment 1Context paper - experiment 1 data.xlsxExperiment 2 datasheetThis file contains two tables: 1. A summed up table of sparrow's choices in different phases of the two tests. 2. A table depicting the sequence of choice made by each bird in each of the testsContext paper - experiment 2 data.xlsx

1. According to the threat-sensitivity hypothesis, prey avoidance behaviour should reflect the magnitude of predation risk. Since predation can strongly affect reproduction success, ovipositing females are expected to adaptively adjust their predator-avoidance response, or local breeding patch selectivity, in accordance with the perceived level of threat posed for their progeny by specific predators. However, association between avoidance and predation can be disrupted when the prey and the predator lack spatio-temporal opportunities to co-evolve, such as in cases of non-native predator introductions. 2. We examined the interactions between mosquitoes (from the genus Culex) and three species of sympatric predaceous freshwater fish, a native cyprinid (Barbus paludinosus), a cichlid (Pseudocrenilabrus philander), and an introduced poeciliid, the Western mosquitofish (Gambusia affinis). 3. In an outdoor mesocosm experiment we quantified patterns of Culex oviposition site selection across fish species using free-roaming, caged, and fish-free treatments. In a complementary laboratory experiment we tested the effectiveness of each fish species as predators of mosquito eggs and larvae. 4. Synthesis and applications. We found evidence for: (i) mosquito egg raft predation by free- roaming fish; (ii) fish-specific avoidance by ovipositing Culex and (iii) a positive association between fish-specific oviposition avoidance and fish-specific efficiency as an egg predator. These results contribute towards a better understanding of predator-prey coevolution, predator-borne cue recognition, and suggest local native fish, the Southern mouthbrooder (Pseudocrenilabrus philander), as an alternative to Gambusia for the biocontrol of Culex mosquitoes. Culex egg raft distribution in mesocosm experimentFile include data on the distribution of Culex egg rafts between treatments. The data was collected in an oviposition habitat selection outdoor mesocosm experiment. Columns A-C describe the treatment code. Columns E-F describe the distribution of treatments among experimental tubs. Columns I-K describe the number of egg rafts deposited per day in each of the experimental tubs.Dryad_1.csvPrey items count data from laboratory feeding experimentFile include data on the number of prey items consumed through time in a laboratory feeding experiment. Column A describe the fish ID number. Column B describe the fish species. Column C describe the mosquito stage. Columns D-AB describe the number of prey item counted per hour over 24 h.Dryad_2.csv

1. Protein quantification is a routine procedure in ecological studies despite the inherent limitations of well-acknowledged protein determination methods which have been largely overlooked by ecologists. Thus, we want to bridge this knowledge gap, in hopes of improving the way ecologists quantify proteins and interpret findings. 2. We surveyed the ecological literature to determine how and why ecologists quantify proteins. To determine whether different quantification methods produce comparable results across taxa, and between populations of a single species, we estimated the protein content of eight phylogenetically diverse taxa, and of desert isopods fed different diets, using various derived protocols of the ‘crude protein’, Bradford and BCA methods. 3. We found that ecologists use many protein quantification procedures, often without reporting the crucial information needed to evaluate and repeat their methods. Our empirical work demonstrated that the three quantification methods examined, and their derived protocols, resulted in highly divergent protein estimations that were inconsistent in rank across taxa, preventing conversion between methods. We also found that different quantification methods yielded different answers to whether isopod protein content is affected by diet. 4. We conclude that commonly used quantification techniques yield distinct protein estimations with varying precision, and no single method is likely to be more accurate than another across taxa which may lead to inconsistent results across taxa and between conspecifics. Inaccurate protein quantification may explain the observed mismatch between organismal N and protein that has plagued some recent studies and that contradicts the principles of ecological stoichiometry. We recommend using a single BCA protocol to reduce inconsistencies across studies, until the promising Amino Acid Analysis becomes more affordable, accurate, and accessible to ecologists. Until then, ecologists should consider the above-mentioned drawbacks of protein quantification methods and interpret their results accordingly. Files are separated based on the experiment ('Multi-taxa' and 'Conspecifics') and the quantification method ('Crude protein, Bradford and BCA), and the two are specified in the file name. Crude protein files (for both experiments) – Contains columns that specify the sample, a column with the measured nitrogen content (% from dry wt.) and a column with the calculated Crude protein based on a 6.25 Nitrogen-to-protein factor. Bradford & BCA files – Multi-taxa experiment: The first sheet describes the order in which the 96-well plates were organized. The sheet also contains: 1) the concentration of the standard proteins in each well, 2) the weight used for extraction for each sample, and 3) the time it took us to handle the reagents (i.e., the time since adding the reagent until the first wavelength reading) and accordingly what measurement out of the 12 was used for calculation (relates only to Bradford). All the additional data needed to calculate the protein content is found in the 'Methods' section of the paper. The following ten sheets contain the ten plates' wavelength readings (i.e., ten replicates) throughout time. Conspecifics experiment: For each of the four plates, a sheet with the plate's order and data about its samples is followed by a sheet with the wavelength readings. See 'Methods' section in the original paper.