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Effects of pair migratory behaviour on breeding phenology and success in a partially migratory shorebird population

In migratory systems, variation in individual phenology can arise through differences in individual migratory behaviours, and this may be particularly apparent in partial migrant systems, where migrant and resident individuals are present within the same population. Links between breeding phenology and migratory behaviour or success are generally investigated at the individual level. However, for breeding phenology in particular, the migratory behaviours of each member of the pair may need to be considered simultaneously, as breeding phenology will likely be constrained by timing of the pair member that arrives last, and carry-over effects on breeding success may vary depending on whether pair members share the same migratory behaviour or not. We used tracking of marked individuals and monitoring of breeding success from a partially migrant population of Eurasian oystercatchers (Haematopus ostralegus) breeding in Iceland to test whether (a) breeding phenology varied with pair migratory behaviour; (b) within-pair consistency in timing of laying differed among pair migratory behaviours; and (c) reproductive performance varied with pair migratory behaviour, timing of laying and year. We found that annual variation in timing of laying differed among pair migratory behaviours, with resident pairs being more consistent than migrant and mixed pairs, and migrant/mixed pairs breeding earlier than residents in most years but later in one (unusually cold) year. Pairs that laid early were more likely to replace their clutch after nest loss, had higher productivity and higher fledging success, independent of pair migratory behaviour. Our study suggests that the links between individual migratory behaviour and reproductive success can vary over time and, to a much lesser extent, with mate migratory behaviour and can be mediated by differences in laying dates. Understanding these cascading effects of pair phenology on breeding success is likely to be key to predicting the impact of changing environmental conditions on migratory species.

Data from: What ecological factors favor parthenogenesis over sexual reproduction? A study on the facultatively parthenogenetic mayfly Alainites muticus in natural populations

Different reproductive modes are characterized by costs and benefits which depend on ecological contexts. For example, sex can provide benefits under complex biotic interactions, while its costs increase under mate limitation. Furthermore, ecological contexts often vary along abiotic gradients. Here, we study how these factors simultaneously influence the frequency of sex in the facultatively parthenogenetic mayfly Alainites muticus . We first verified that parthenogenesis translates into female-biased population sex ratios. We then measured the density of individuals (a proxy for mate limitation) and community diversity (biotic interaction complexity) for 159 A. muticus populations covering a broad altitudinal gradient and used structural equation modeling to investigate their direct and indirect influences on sex ratios. We found no effect of community diversity or altitude on sex ratios. Furthermore, even when females can reproduce parthenogenetically, they generally reproduce sexually, indicating that the benefits of sex exceed its costs in most situations. Sex ratios only become female-biased under low population densities, as expected if mate limitation was the main factor selecting for parthenogenesis. Mate limitation might be widespread in mayflies because of their short adult lifespan and limited dispersal, which can generate strong selection for reproductive assurance and may provide a stepping-stone towards obligate parthenogenesis.

opencc-by-4.0Mar 2020View details →

Fig. 5. A in Shell Morphology, Growth Pattern And Population Dynamics Of The Land Snail Xerolenta Obvia (Menke, 1828) In Two Areas Of Different Climatic Conditions Within A Temperate Climate Region

Fig. 5. A) Mean monthly whorl increment of Xerolenta obvia in two growth seasons in SW (solid line) and NE (dashed line) populations; B) mean (black lines), maxi- mum and minimum (grey lines) monthly temperature, and C) total monthly precipitation during the study period in SW (solid lines) and NE (dashed lines) sites. Data from nearest meteorological stations in Wrocław and Suwałki (IMGW-PIB data)

opencc-by-4.0Mar 2020View details →

Fig. 2 in Shell Morphology, Growth Pattern And Population Dynamics Of The Land Snail Xerolenta Obvia (Menke, 1828) In Two Areas Of Different Climatic Conditions Within A Temperate Climate Region

Fig. 2. Distribution of shell pattern types of Xero- lenta obvia snails in the two populations studied, SW (n = 781) and NE (n = 1387)

Data from: Genetic, maternal, and environmental influences on sociality in a pedigreed primate population

Various aspects of sociality in mammals (e.g., dyadic connectedness) are linked with measures of biological fitness (e.g., longevity). How within- and between-individual variation in relevant social traits arises in uncontrolled wild populations is challenging to determine but is crucial for understanding constraints on the evolution of sociality. We use an advanced statistical method, known as the 'animal model', which incorporates pedigree information, to look at social, genetic, and environmental influences on sociality in a long-lived wild primate. We leverage a longitudinal database spanning 20 years of observation on individually recognized white-faced capuchin monkeys (Cebus capucinus imitator), with a multi-generational pedigree. We analyze two measures of spatial association, using repeat sampling of 376 individuals (mean: 53.5 months per subject, range: 6-185 months per subject). Conditioned on the effects of age, sex, group size, seasonality , and El Niño–Southern Oscillation phases, we show low to moderate long-term repeatability (across years) of the proportion of time spent social (posterior mode [95% Highest Posterior Density interval]: 0.207 [0.169, 0.265]) and of average number of partners (0.144 [0.113, 0.181]) (latent scale). Most of this long-term repeatability could be explained by modest heritability (h2social: 0.152 [0.094, 0.207]; h2partners: 0.113 [0.076, 0.149]) with small long-term maternal effects (m2social: 0.000 [0.000, 0.045]; m2partners: 0.000 [0.000, 0.041]). Our models capture the majority of variance in our behavioral traits, with much of the variance explained by temporally changing factors, such as group of residence, highlighting potential limits to the evolvability of our trait due to social and environmental constraints.

Data and code from: Cooperation and coordination in heterogeneous populations

One landmark application of evolutionary game theory is the study of social dilemmas. This literature explores why people cooperate even when there are strong incentives to defect. Much of this literature, however, assumes that interactions are symmetric. Individuals are assumed to have the same strategic options and the same potential payoffs. Yet many interesting questions arise once individuals are allowed to differ. Here, we study asymmetry in simple coordination games. In our setup, human participants need to decide how much of their endowment to contribute to a public good. If a group's collective contributions reach a pre-defined threshold, all group members receive a reward. To account for possible asymmetries, individuals either differ in their endowments or their productivities. According to our theoretical equilibrium analysis, such games tend to have many possible solutions. In equilibrium, group members may contribute the same amount, different amounts, or nothing at all. According to the behavioral experiment, however, humans favor the equilibrium in which everyone contributes the same proportion of their endowment. We use these experimental results to highlight the nontrivial effects of inequality on cooperation, and we discuss to which extent models of evolutionary game theory can account for these effects.

Data From: Population-specific responses in eastern oysters exposed to low salinity in the northern Gulf of Mexico

Eastern oysters, Crassostrea virginica, are facing rapid environmental changes in the northern Gulf of Mexico and can respond to these changes via plasticity or evolution. Plastic responses can immediately buffer against environmental changes, although this buffering may impact the organism's ability to evolve in subsequent generations. While plasticity and evolution are not mutually exclusive, the relative contribution and interaction between them remain unclear. In this study, we investigate the roles of plastic and evolved responses to low salinity in C. virginica using a common garden experiment with four populations exposed to two salinities. We use three transcriptomic analyses (edgeR, PERMANOVA, and WGCNA) combined with physiology data to identify the effect of genotype (population), environment (salinity), and genotype-by-environment interaction on both whole organism and molecular phenotypes. We demonstrate that variation in gene expression is mainly driven by population, with relatively small changes in response to salinity. In contrast, the morphology and physiology data reveal that salinity has a larger influence on oyster performance than the population of origin. All analyses lacked signatures of genotype-by-environment interaction, and in contrast to previous studies, we find no evidence for population-specific responses to low salinity. However, individuals from the highest salinity estuary displayed highly divergent gene expression from other populations, which could potentially drive population-specific responses to other stressors. Our findings suggest that C. virginica largely rely on plasticity in physiology to buffer the effects of low salinity, but that these changes in physiology do not rely on large persistent changes in gene expression.

Recovered frog populations coexist with endemic Batrachochytrium dendrobatidis despite load-dependent mortality

Novel infectious diseases, particularly those caused by fungal pathogens, pose considerable risks to global biodiversity. The amphibian chytrid fungus (Batrachochytrium dendrobatidis, Bd) has demonstrated the scale of the threat, having caused the greatest recorded loss of vertebrate biodiversity attributable to a pathogen. Despite catastrophic declines on several continents, many affected species have experienced population recoveries after epidemics. However, the potential ongoing threat of endemic Bd in these recovered or recovering populations is still poorly understood. We investigated the threat of endemic Bd to frog populations that recovered after initial precipitous declines, focusing on the endangered rainforest frog Mixophyes fleayi. We conducted extensive field surveys over four years at three independent sites in eastern Australia. First, we compared Bd infection prevalence and infection intensities within frog communities to reveal species-specific infection patterns. Then, we analyzed capture-recapture data of M. fleayi to estimate the impact of Bd infection intensity on apparent mortality rates and Bd infection dynamics. We found that M. fleayi had lower infection intensities than sympatric frogs across the three sites, and cleared infections at higher rates than they gained infections throughout the study period. By incorporating time-varying individual infection intensities, we show that healthy M. fleayi populations persist despite increased apparent mortality associated with infrequent high Bd loads. Infection dynamics were influenced by environmental conditions, with Bd prevalence, infection intensity, and rates of gaining infection associated with lower temperatures and increased rainfall. However, mortality remained constant year-round despite these fluctuations in Bd infections, suggesting major mortality events did not occur over the study period. Together, our results demonstrate that while Bd is still a potential threat to recovered populations of M. fleayi, high rates of clearing infections and generally low average infection loads likely minimize mortality caused by Bd. Our results are consistent with pathogen resistance contributing to the coexistence of M. fleayi with endemic Bd. We emphasize the importance of incorporating infection intensity into disease models rather than infection status alone. Similar population and infection dynamics likely exist within other recovered amphibian-Bd systems around the globe, promising longer-term persistence in the face of endemic chytridiomycosis.

opencc-by-4.0Jun 2022View details →

Data and code for "Changing allometric relationships among fossil and Recent populations in two colonial species"

MEPS.plus.xlsx (dataset from Di Martino & Liow 2021) Microporella_allometry_22.03.2022.xlsx (Sheet 1: Measurement data; Sheet 2: Fossil sample metadata; Sheet 3: Recent samples metadata) allo.10.R (code)

opencc-by-4.0Jul 2022View details →

Data Release: Population properties and multimessenger prospects of neutron star-black hole mergers following GWTC-3

Neutron star-black hole (NSBH) mergers detected in gravitational waves have the potential to shed light on supernova physics, the dense matter equation of state, and the astrophysical processes that power their potential electromagnetic counterparts. We use the population of four candidate NSBH events detected in gravitational waves so far with a false alarm rate  ≤1 yr−1 to constrain the mass and spin distributions and multimessenger prospects of these systems. We find that the black holes in NSBHs are both less massive and more slowly spinning than those in black hole binaries. We also find evidence for a mass gap between the most massive neutron stars and least massive black holes in NSBHs at 98.6% credibility. We consider both a Gaussian and a power-law pairing function for the distribution of the mass ratio between the neutron star and black hole masses but find no statistical preference between the two. Using an approach driven by gravitational-wave data rather than binary simulations, we find that fewer than 14% of NSBH mergers detectable in gravitational waves will have an electromagnetic counterpart. Finally, we propose a method for the multimessenger analysis of NSBH mergers based on the nondetection of an electromagnetic counterpart and conclude that, even in the most optimistic case, the constraints on the neutron star equation of state that can be obtained with multimessenger NSBH detections are not competitive with those from gravitational-wave measurements of tides in binary neutron star mergers and radio and X-ray pulsar observations.

On the impermanence of species: The collapse of genetic incompatibilities in hybridizing populations

Species pairs often become genetically incompatible during divergence, which is an important source of reproductive isolation. An idealized picture is often painted where incompatibility alleles accumulate and fix between diverging species. However, recent studies have shown both that incompatibilities can collapse with ongoing hybridization, and that incompatibility loci can be polymorphic within species. This paper suggests some general rules for the behavior of incompatibilities under hybridization. In particular, we argue that redundancy of genetic pathways can strongly affect the dynamics of intrinsic incompatibilities. Since fitness in genetically redundant systems is unaffected by introducing a few foreign alleles, higher redundancy decreases the stability of incompatibilities during hybridization, but also increases tolerance of incompatibility polymorphism within species. We use simulations and theories to show that this principle leads to two types of collapse: in redundant systems, exemplified by classical Dobzhansky-Muller incompatibilities, collapse is continuous and approaches a quasi-neutral polymorphism between broadly sympatric species, often as a result of isolation-by-distance. In non-redundant systems, exemplified by coevolution among genetic elements, incompatibilities are often stable, but can collapse abruptly with spatial traveling waves. As both types are common, the proposed principle may be useful in understanding the abundance of genetic incompatibilities in natural populations.

Model Zoo: A Dataset of Diverse Populations of Resnet-18 Models - CIFAR-10

Abstract In the last years, neural networks have evolved from laboratory environments to the state-of-the-art for many real-world problems. Our hypothesis is that neural network models (i.e., their weights and biases) evolve on unique, smooth trajectories in weight space during training. Following, a population of such neural network models (refereed to as “model zoo”) would form topological structures in weight space. We think that the geometry, curvature and smoothness of these structures contain information about the state of training and can be reveal latent properties of individual models. With such zoos, one could investigate novel approaches for (i) model analysis, (ii) discover unknown learning dynamics, (iii) learn rich representations of such populations, or (iv) exploit the model zoos for generative modelling of neural network weights and biases. Unfortunately, the lack of standardized model zoos and available benchmarks significantly increases the friction for further research about populations of neural networks. With this work, we publish a novel dataset of model zoos containing systematically generated and diverse populations of neural network models for further research. In total the proposed model zoo dataset is based on six image datasets, consist of 24 model zoos with varying hyperparameter combinations are generated and includes 47’360 unique neural network models resulting in over 2’415’360 collected model states. Additionally, to the model zoo data we provide an in-depth analysis of the zoos and provide benchmarks for multiple downstream tasks as mentioned before. Dataset This dataset is part of a larger collection of model zoos and contains the zoo of 1000 ResNet18 models trained on CIFAR10. All zoos with extensive information and code can be found at www.modelzoos.cc. The complete zoo is 2.3TB large. Due to the size, this repository contains the checkpoints of epochs 1, 10 and 50. For a link to the full dataset as well as more information on the zoos and code to access and use the zoos, please see www.modelzoos.cc.

Generation time in stage-structured populations under fluctuating environments (Simulations)

The study is about the computation of the generation time when individuals are classified based on their stage and the environment stochastically fluctuates with time. This folder contains simulation code in R to plot Figure 2 of the manuscript.

opencc-by-4.0May 2022View details →

Data to accompany the publication "Combined biophysical and genetic modelling approaches reveal complementary information about population connectivity of New Zealand green-lipped mussels"

Data to accompany the publication "Combined biophysical and genetic modelling approaches reveal complementary information about population connectivity of New Zealand green-lipped mussels".  migrationmatrix14.txt contains the particle tracking matrix, with the total number of particles that migrated from row i to column j (out of a total of 2217864 particles released per population). mussel_microsat_Genepop.txt contains the microsatellite data for each population in Genepop format.

Model Zoo: A Dataset of Diverse Populations of Resnet-18 Models - CIFAR-100

Abstract In the last years, neural networks have evolved from laboratory environments to the state-of-the-art for many real-world problems. Our hypothesis is that neural network models (i.e., their weights and biases) evolve on unique, smooth trajectories in weight space during training. Following, a population of such neural network models (refereed to as “model zoo”) would form topological structures in weight space. We think that the geometry, curvature and smoothness of these structures contain information about the state of training and can be reveal latent properties of individual models. With such zoos, one could investigate novel approaches for (i) model analysis, (ii) discover unknown learning dynamics, (iii) learn rich representations of such populations, or (iv) exploit the model zoos for generative modelling of neural network weights and biases. Unfortunately, the lack of standardized model zoos and available benchmarks significantly increases the friction for further research about populations of neural networks. With this work, we publish a novel dataset of model zoos containing systematically generated and diverse populations of neural network models for further research. In total the proposed model zoo dataset is based on six image datasets, consist of 27 model zoos with varying hyperparameter combinations are generated and includes 50’360 unique neural network models resulting in over 2’585’360 collected model states. Additionally, to the model zoo data we provide an in-depth analysis of the zoos and provide benchmarks for multiple downstream tasks as mentioned before. Dataset This dataset is part of a larger collection of model zoos and contains the zoo of 1000 ResNet18 models trained on CIFAR100. All zoos with extensive information and code can be found at www.modelzoos.cc. The complete zoo is 2.6TB large. Due to the size, this repository contains the checkpoints of the last epoch 60. For a link to the full dataset as well as more information on the zoos and code to access and use the zoos, please see www.modelzoos.cc.

Sparsified Model Zoo Twins: A Dataset of Sparsified Populations of Neural Network Models - MNIST

Abstract In the last years, neural networks have evolved from laboratory environments to the state-of-the-art for many real-world problems. Our hypothesis is that neural network models (i.e., their weights and biases) evolve on unique, smooth trajectories in weight space during training. Following, a population of such neural network models (refereed to as “model zoo”) would form topological structures in weight space. We think that the geometry, curvature and smoothness of these structures contain information about the state of training and can be reveal latent properties of individual models. With such zoos, one could investigate novel approaches for (i) model analysis, (ii) discover unknown learning dynamics, (iii) learn rich representations of such populations, or (iv) exploit the model zoos for generative modelling of neural network weights and biases. Unfortunately, the lack of standardized model zoos and available benchmarks significantly increases the friction for further research about populations of neural networks. With this work, we publish a novel dataset of model zoos containing systematically generated and diverse populations of neural network models for further research. In total the proposed model zoo dataset is based on six image datasets, consist of 27 model zoos with varying hyperparameter combinations are generated and includes 50’360 unique neural network models resulting in over 2’585’360 collected model states. Additionally, to the model zoo data we provide an in-depth analysis of the zoos and provide benchmarks for multiple downstream tasks as mentioned before. Dataset This dataset is part of a larger collection of model zoos and contains the sparsified twins of models trained on MNIST. The original population is made available at https://doi.org/10.5281/zenodo.6632086. Sparsification is done using Variational Dropout, starting from the last epoch of the original population. The zip file contains the sparsification trajectory for 25 epochs for all 1000 models. All zoos with extensive information and code can be found at www.modelzoos.cc.

Model Zoo: A Dataset of Diverse Populations of Resnet-18 Models - Tiny ImageNet

Abstract In the last years, neural networks have evolved from laboratory environments to the state-of-the-art for many real-world problems. Our hypothesis is that neural network models (i.e., their weights and biases) evolve on unique, smooth trajectories in weight space during training. Following, a population of such neural network models (refereed to as “model zoo”) would form topological structures in weight space. We think that the geometry, curvature and smoothness of these structures contain information about the state of training and can be reveal latent properties of individual models. With such zoos, one could investigate novel approaches for (i) model analysis, (ii) discover unknown learning dynamics, (iii) learn rich representations of such populations, or (iv) exploit the model zoos for generative modelling of neural network weights and biases. Unfortunately, the lack of standardized model zoos and available benchmarks significantly increases the friction for further research about populations of neural networks. With this work, we publish a novel dataset of model zoos containing systematically generated and diverse populations of neural network models for further research. In total the proposed model zoo dataset is based on six image datasets, consist of 27 model zoos with varying hyperparameter combinations are generated and includes 50’360 unique neural network models resulting in over 2’585’360 collected model states. Additionally, to the model zoo data we provide an in-depth analysis of the zoos and provide benchmarks for multiple downstream tasks as mentioned before. Dataset This dataset is part of a larger collection of model zoos and contains the zoo of 1000 ResNet18 models trained on Tiny Imagenet. All zoos with extensive information and code can be found at www.modelzoos.cc. The complete zoo is 2.6TB large. Due to the size, this repository contains the checkpoints of the first 115 models at their last epoch 60. For a link to the full dataset as well as more information on the zoos and code to access and use the zoos, please see www.modelzoos.cc.

Sparsified Model Zoo Twins: A Dataset of Sparsified Populations of Neural Network Models - SVHN

Abstract In the last years, neural networks have evolved from laboratory environments to the state-of-the-art for many real-world problems. Our hypothesis is that neural network models (i.e., their weights and biases) evolve on unique, smooth trajectories in weight space during training. Following, a population of such neural network models (refereed to as “model zoo”) would form topological structures in weight space. We think that the geometry, curvature and smoothness of these structures contain information about the state of training and can be reveal latent properties of individual models. With such zoos, one could investigate novel approaches for (i) model analysis, (ii) discover unknown learning dynamics, (iii) learn rich representations of such populations, or (iv) exploit the model zoos for generative modelling of neural network weights and biases. Unfortunately, the lack of standardized model zoos and available benchmarks significantly increases the friction for further research about populations of neural networks. With this work, we publish a novel dataset of model zoos containing systematically generated and diverse populations of neural network models for further research. In total the proposed model zoo dataset is based on six image datasets, consist of 27 model zoos with varying hyperparameter combinations are generated and includes 50’360 unique neural network models resulting in over 2’585’360 collected model states. Additionally, to the model zoo data we provide an in-depth analysis of the zoos and provide benchmarks for multiple downstream tasks as mentioned before. Dataset This dataset is part of a larger collection of model zoos and contains the sparsified twins of models trained on SVHN. The original population is made available at https://doi.org/10.5281/zenodo.6632120. Sparsification is done using Variational Dropout, starting from the last epoch of the original population. The zip file contains the sparsification trajectory for 25 epochs for all 1000 models. All zoos with extensive information and code can be found at www.modelzoos.cc.

Population models used in: Method to assess potential magnitude of terrestrial European avian population reductions from ingestion of lead ammunition

Current estimates of terrestrial bird losses across Europe from ingestion of lead ammunition are based on uncertain or generic assumptions. A method is needed to develop defensible European-specific estimates compatible with available data that does not require long-term field studies. We propose a 2-step method using carcass data and population models. The method estimates percentage of deaths diagnosed as directly caused by lead poisoning as a lower bound and, as an upper bound, the percentage of possible deaths from sublethal lead poisoning that weakens birds, making them susceptible to death by other causes. We use these estimates to modify known population-level annual mortality. Our method also allows for potential reductions in reproduction from lead shot ingestion because reductions in survival and reproduction are entered into population models of species with life histories representative of the most groups of susceptible species. The models estimate the sustainability and potential population decreases from lead poisoning in Europe. Using the best available data, we demonstrate the method on two taxonomic groups of birds: gallinaceous birds and diurnal raptors. The direction of the population trends affects the estimate, and we incorporated such trends into the method. Our midpoint estimates of the reduction in population size of the European gallinaceous bird (< 2%) group and raptor group (2.9 – 7.7%) depend on the species life history, maximum growth rate, population trend, and if reproduction is assumed to be reduced. Our estimates can be refined as more information becomes available in countries with data gaps. We advocate use of this method to improve upon or supplement approaches currently being used. As we demonstrate, the method also can be applied to individual species of concern if enough data across countries are available.

Novel host plant unmasks heritable variation in plant preference within an insect population

Introductions of novel plant species can disturb the historical resource environment of herbivorous insects, resulting in strong selection to either adopt or exclude the novel host. However, an adaptive response depends on heritable genetic variation for preference or performance within the targeted herbivore population, and it is unclear how heritability of host-use preference may differ between novel and historical hosts. Pieris macdunnoughii butterflies in the Rocky Mountains lay eggs on the nonnative mustard Thlaspi arvense, which is lethal to their offspring. Heritability analyses revealed considerable sex-linked additive genetic variation in host preference within a population of this butterfly. This was contrary to general predictions about the genetic basis of preference variation, which are hypothesized to be sex-linked between populations but autosomal within populations. Evidence of sex-linkage disappeared when butterflies were tested on methanol-based chemical extracts, suggesting these chemicals in isolation may not be the primary driver of female choice among available host plants. Although unexpected, evidence for within-population sex-linked genetic variation in preference for T. arvense over native hosts indicates that persistent maladaptive oviposition on this lethal plant must be maintained by alternative evolutionary dynamics such as migration- or drift-selection balance or pleiotropic constraints.