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.
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doi: 10.5061/dryad.5gn79
1. The 'habitat-specific species pool hypothesis' proposes that differences between habitats in the sizes of their species pools are the main drivers of diversity responses to habitat heterogeneity. Empirical tests of this hypothesis are not trivial since species might be missing from ecologically suitable habitats due to limited dispersal, while others may occur in unsuitable habitats by means of source-sink dynamics and mass effect. 2. We tested the habitat-specific species pool hypothesis in a local, environmentally heterogeneous community of annual plants using a novel 'ecological selection' experiment. Mixtures of seeds representing the whole community were sown in each habitat, and the emerging species were exposed to six generations of selection by environmental filtering and competition while being blocked from dispersal. A comparison of the total number of species that were able to survive in each habitat (i.e., to pass the selection test) with data on species richness in the natural community allowed us to test the degree to which observed differences in species richness between habitats could be explained by differences in the sizes of the respective species pools. 3. Results supported the species pool hypothesis, showing that differences in the sizes of the habitat-specific species pools were important in determining diversity responses to habitat heterogeneity. Moreover, species richness showed a unimodal response to local-scale gradients in habitat productivity, and this response could be attributed to underlying differences in species-pool sizes. Both results were robust to properties of the data and the method of analysis. 4. Synthesis. Our results provide a strong experimental evidence that differences in the sizes of habitat-specific species pools might be important in shaping the diversity of local communities. Future theoretical and empirical studies in community ecology should explore the potential sources and implications of such differences. Ron et al. 2017 JE dataplot level, habitat level and regional level species diversity for plots of four different soil depths
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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.
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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
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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.
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G-03Flowering time data from 2003 study in the greenhouse, performed at the Kellogg Biological Station near Kalamazoo MI. Populations: 9 Individuals per pop: 22-46 Total individuals in dataset: 306 These are also the parental plants from Sahli et al., (2008.2003QstParents.csvG-04Flowering time data from 2004 common garden in the greenhouse at Kellogg Biological Station near Kalamazoo MI. Populations: 9 Individuals per population: 58-142 Total individuals in experiment: 877. There are also the offspring from Sahli et al., (2008)2004QstOffspring.csvF-12Flowering time data from a common garden field experiment in 2012 at the Kellogg Biological Station near Kalamazoo MI. Populations:42, Individuals per population: 7-50, Total number of individuals: 5122012FieldData.csvF-13Flowering time measurements from 2013 common garden in field at Michigan State University. Populations: 27, individuals per population: 10, Total number of individuals: 2702013plantsSpring.csvG1-13Flowering time data from 2013 common garden experiment in greenhouse at Kellogg Biological Station. Populations: 15, individuals per population: 10 Total individuals: 150CultivarGH2013.csvG2-13Flowering time measurements from 2013 common garden experiment in the greenhouse at Kellogg Biological Station. Populations: 9, Individuals per population: 14-30, Total individuals: 254IsraelSpainPops2013GH.csvF-05Flowering time measurements from 2005 common garden experiment in the field at Kellogg Biological Station. Populations: 6, Individuals per population: 64-88, total individuals: 442. These are also the offspring from Sahli et al., (2008)LaleField2005.csvG-10Flowering time data from 2010 common garden experiment in the greenhouse at Kellogg Biological Station. Populations: 4, Individuals per population: 8-22, Total individuals: 55Summer2010dataSummary.csvWeedEvoAll the data files and metadata files required to replicate the results of this paper. Scripts and readme are available on github https://github.com/ACharbonneau/creepy-barnacle Approximately 200 weed species are responsible for more than 90% of crop losses and these comprise less than one percent of all named plant species, suggesting that there are only a few evolutionary routes that lead to weediness. Agricultural weeds can evolve along three main paths: they can be escaped crops, wild species, or crop-wild hybrids. We tested these three hypotheses in weedy radish, a weed of small grains and an emerging model for investigating the evolution of agricultural weeds, using 21 CAPS and SSR markers scored on 338 individuals from 34 populations representing all major species and sub-species in the radish genus Raphanus. To test for adaptation of the weeds to the agricultural environment, we estimated genetic differentiation in flowering time in a series of common garden experiments with over 2400 individuals from 43 populations (all but one of the genotyped populations plus 10 additional populations). Our findings suggest that the agricultural weed radish R.r. raphanistrum is most genetically similar to native populations of R.r. raphanistrum, and is likely not a feral crop or crop hybrid. We also show that weedy radish flowers more rapidly than any other Raphanus population or cultivar, which is consistent with rapid adaptation to the frequent and severe disturbance that characterizes agricultural fields.
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doi: 10.5061/dryad.hv1kv
1. Movement based indices such as Moves Per Minute (MPM) and Proportion Time Moving (PTM) are common methodologies to quantify foraging behavior. We explore fundamental drawbacks of these indices, question the ways scientists have been using them, and propose new solutions. 2. To do so, we combined analytical and simulation models with lizards foraging data at the individual and species levels. 3. We found that the maximal value of MPM is constrained by the minimal durations of moves and stops. As a result, foragers that rarely move and those that rarely stop are bounded to similar low MPM values. This implies that (a) MPM has very little meaning when used alone, (b) MPM and PTM are interdependent, and (c) certain areas in the MPM-PTM plane cannot be occupied. We also found that MPM suffers from inaccuracy and imprecision. 4. We introduced a new bias correction formula for already published MPM data, and a novel index of Changes Per Minute (CPM) that uses the frequency of changes between move and stop bouts. CPM is very similar to MPM, but does not suffer from bias. Finally, we suggested a new foraging plane of Average Move and Average Stop durations. We hope that our guidelines of how to use (and not to use) movement- based indices will add rigor to the study of animals' foraging behavior. Lizards' foraging behavior indicesThese file contain already published lizards' foraging behavior indices (MPM and PTM) and additional information about the duration of the original observation as reported by the authors. We corrected the MPM values to reduce the inherent bias and report how the new calculated MPM' values deviate from the reported ones.Halperin_etal_appendix_4.xlsx
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Determining large-scale patterns of plant elemental concentrations and stoichiometry along environmental gradients is critical for understanding plant adaptive strategies and predicting ecosystem biogeochemistry processes. However, it remains unclear as to how plant carbon (C), nitrogen (N), and phosphorus (P) concentrations and their stoichiometry in different organs (leaves, stems, and roots) respond to large-scale environmental gradients in drylands. We determined C, N, and P concentrations and their ratios in leaves, stems, and roots of plants growing in the dryland ecosystems of China. Using threshold indicator taxa analyses, we identified indicator species of plant C, N, and P responses to aridity and soil properties. The arithmetic averaged concentrations of C, N, and P in drylands were 414, 18.7, and 1.38 mg/g for leaves, respectively; 445, 12.1, and 1.08 mg/g for stems, respectively; and 418, 10.5, and 0.89 mg/g for roots, respectively. The C : N, C : P, and N : P ratios were 25.2, 386, and 16.3 for leaves, respectively; 42.8, 592, and 14.8 for stems, respectively; and 46.8, 658, and 15.6 for roots, respectively. Aridity and soil pH generally exerted positive effects on plant N and negative effects on C and P concentrations and, thus, were related negatively to C : N ratios and positively to C : P and N : P ratios. The C, N, and P concentrations in organs generally increased with increasing corresponding soil C, N, and P concentrations. Shrubs were mainly positive indicators of plant C, N, and P concentrations in response to aridity and soil pH, and negative indicators of soil nutrients. In contrast, herbaceous species were mainly positive indicators of soil nutrients and negative indicators of aridity and soil pH. These findings indicate that plants tend to accumulate N rather than C and P with increasing aridity and soil pH. The identification of indicator species for plant elements in response to aridity and soil traits informs our understanding of species-specific biogeographic patterns of organ elements and potential adaptive strategies of plants in drylands.
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doi: 10.5061/dryad.pm0n4
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
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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.
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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.
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doi: 10.5061/dryad.5gn79
1. The 'habitat-specific species pool hypothesis' proposes that differences between habitats in the sizes of their species pools are the main drivers of diversity responses to habitat heterogeneity. Empirical tests of this hypothesis are not trivial since species might be missing from ecologically suitable habitats due to limited dispersal, while others may occur in unsuitable habitats by means of source-sink dynamics and mass effect. 2. We tested the habitat-specific species pool hypothesis in a local, environmentally heterogeneous community of annual plants using a novel 'ecological selection' experiment. Mixtures of seeds representing the whole community were sown in each habitat, and the emerging species were exposed to six generations of selection by environmental filtering and competition while being blocked from dispersal. A comparison of the total number of species that were able to survive in each habitat (i.e., to pass the selection test) with data on species richness in the natural community allowed us to test the degree to which observed differences in species richness between habitats could be explained by differences in the sizes of the respective species pools. 3. Results supported the species pool hypothesis, showing that differences in the sizes of the habitat-specific species pools were important in determining diversity responses to habitat heterogeneity. Moreover, species richness showed a unimodal response to local-scale gradients in habitat productivity, and this response could be attributed to underlying differences in species-pool sizes. Both results were robust to properties of the data and the method of analysis. 4. Synthesis. Our results provide a strong experimental evidence that differences in the sizes of habitat-specific species pools might be important in shaping the diversity of local communities. Future theoretical and empirical studies in community ecology should explore the potential sources and implications of such differences. Ron et al. 2017 JE dataplot level, habitat level and regional level species diversity for plots of four different soil depths
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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.
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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
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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.