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Data for: Weak, but not strong, ties support coalition formation among wild female chimpanzees
<div> <div> <div> <div> <p>In social species, individuals may be able to overcome competitive constraints on cooperation by leveraging relationships with familiar, tolerant partners. While strong social ties have been linked to cooperation in several social mammals, it is unclear the extent to which weak social ties can support cooperation, particularly among non-kin. We tested the hypothesis that weakly affiliative social relationships support cooperative coalition formation using 10 years of behavioural data on wild female chimpanzees. Female chimpanzees typically disperse and reside with non-kin as adults. Their social relationships are differentiated but often relatively weak, with few dyads sharing strong bonds. Females occasionally form aggressive coalitions together. Three measures of relationship quality - party association, five-meter proximity, and whether a dyad groomed - positively predicted coalitions, indicating that relationship quality influenced coalition partnerships. However, dyads that groomed frequently did not form more coalitions than dyads that groomed occasionally, and kin did not cooperate more than expected given their relationship quality. Thus, strong bonds and kinship did not bolster cooperation. We conclude that cooperative coalitions among female chimpanzees depend on social tolerance but do not require strong bonds. Our findings highlight social tolerance as a distinct pathway through which females can cultivate cooperative relationships.</p> </div> </div> </div> </div>
Object neophilia in wild herring gulls in urban and rural locations
<p>Living with increasing urbanisation and human populations requires resourcefulness and flexibility in wild animals' behaviour. Animals have to adapt to anthropogenic novelty in habitat structure and resources that may not resemble, or be as beneficial as, natural resources. Herring gulls (Larus argentatus) increasingly reside in towns and cities to breed and forage, yet how gulls are adjusting their behaviour to life in urban areas is not yet fully understood. This study investigated wild herring gulls' responses to novel and common anthropogenic objects in urban and rural locations. We also examined whether gulls' age influenced their object response behaviour. We found that, out of the 126 individual gulls presented with objects, 34% approached them. This suggests that the majority of targeted gulls were wary or lacked interest in the experimental set-up. Of the 43 gulls that approached the objects, we found that those tested in urban locations approached more slowly than their rural counterparts. Overall, gulls showed no preference for either novel or common anthropogenic objects, and age did not influence likelihood of approach, approach speed or object choice. Individuals paid most attention to the object they approached first, potentially indicative of individual preferences. Our findings indicate that most herring gulls are not as attracted to anthropogenic objects as anecdotal reports have suggested. Covering up obvious food rewards may thus help mitigate human-gull conflict over anthropogenic food sources.</p>
High rates of vessel noise disrupt foraging in wild harbour porpoises (Phocoena phocoena) - scripts and example dataset
<p>This upload contains Matlab scripts used to compute third-octave levels from audio recorded with DTAG-3 tags on free-ranging harbour porpoises. It also contains examples of results, outputs of such scripts (hp12_272a_noisedata.mat and hp12_293a_noisedata.mat), for two of the seven animals in the study, as well as sensor data for all the animals (e.g. hp12_272a_prh625.nc). The metadata for all the uploaded data are stored in netCDF files (.nc) and the overview plots show noise, vessel presence and foraging data for all study animals. Finally, the upload contains scripts that use the results to perform a series of permutation tests to compare foraging buzz count and total buzz duration in minutes with high- and low-level noise.</p>
The CWR richness dataset of manuscript "ENHANCING IN SITU CONSERVATION OF CROP WILD RELATIVES FOR FOOD AND AGRICULTURE IN LITHUANIA"
<p>The CWR National Inventory database has been created by combining data from the Database of EU Habitat Mapping in Lithuania (BIGIS), the Herbarium Database of the Nature Research Centre (BILAS), the Lithuanian Vegetation Database (EU-LT-001), and the Global Biodiversity Information Facility (GBIF). It was used to calculate CWR richness in 4 by 4 kilometres grid cell. The dataset contains three GIS files (.shp) - Boundaries of Lithuania, CWR richness in grid cells and 45 potential genetic reserve sites.</p>
Data from: Longitudinal gut microbiome dynamics in relation to age and senescence in a wild animal population
<p>In humans, gut microbiome (GM) differences are often correlated with, and sometimes causally implicated in, ageing. However, it is unclear how these findings translate in wild animal populations. Studies that investigate how GM dynamics change within individuals, and with declines in physiological condition, are needed to fully understand links between chronological age, senescence, and the GM, but have rarely been done. Here, we use longitudinal data collected from a closed population of Seychelles warblers (<em>Acrocephalus sechellensis</em>) to investigate how bacterial GM alpha diversity, composition, and stability are associated with host senescence. We hypothesised that GM diversity and composition will differ, and become more variable, in older adults, particularly in the terminal year prior to death, as the GM becomes increasingly dysregulated due to senescence. However, GM alpha diversity and composition remained largely invariable with respect to adult age and did not differ in an individual's terminal year. Furthermore, there was no evidence that the GM became more heterogenous in senescent age groups (individuals older than 6 years), or in the terminal year. Instead, environmental variables such as season, territory quality, and time of day, were the strongest predictors of GM variation in adult Seychelles warblers. These results contrast with studies on humans, captive animal populations, and some (but not all) studies on non-human primates, suggesting that GM deterioration may not be a universal hallmark of senescence in wild animal species. Further work is needed to disentangle the factors driving variation in GM-senescence relationships across different host taxa.</p>
Infection-nutrition feedbacks: fat supports pathogen clearance but pathogens reduce fat in a wild mammal
<p>Though far less obvious than direct effects (clinical disease or mortality), the indirect influences of pathogens are difficult to estimate but may hold fitness consequences. Here, we disentangle the directional relationships between infection and energetic reserves, evaluating the hypotheses that energetic reserves influence infection status of the host and that infection elicits costs to energetic reserves. Using repeated measures of fat reserves and infection status in individual bighorn sheep (<em>Ovis canadensis</em>) in the Greater Yellowstone Ecosystem, we documented that fat influenced ability to clear pathogens (<em>Mycoplasma ovipneumoniae</em>) and infection with respiratory pathogens was costly to fat reserves. Costs of infection approached, and in some instances exceeded, costs of rearing offspring to independence in terms of reductions to fat reserves. Fat influenced probability of clearing pathogens, pregnancy, and over-winter survival; from an energetic perspective, an animal could survive for up to 23 days on the amount of fat that was lost to high levels of infection. Cost of pathogens may amplify tradeoffs between reproduction and survival. In the absence of an active outbreak, the influence of resident pathogens often is overlooked. Nevertheless, the energetic burden of pathogens likely has consequences for fitness and population dynamics, especially when food resources are insufficient.</p> <p><span> </span></p>
Figure 2 in Adaptations in wild radish (Raphanus raphanistrum) flowering time, Part 1: Individual-based modeling of a polygenic trait
Figure 2. The relationship between the number of semidominant larger M1 alleles and days to first flower (DFF), needed to model the long-day selections. Note that six smallereffect M2 genes will add another 0 to 18 days to DFF. If each M1 allele added the same amount in the long-day selections, the relationship would be linear.
Figure 1 in Adaptations in wild radish (Raphanus raphanistrum) flowering time, Part 1: Individual-based modeling of a polygenic trait
Figure 1. Goodness of fit of the modeled data (A) when compared with the recorded glasshouse data (B). Graphs show cumulative days to first flowering (DFF) adaptations in Raphanus raphanistrum populations as a result of repeated early and late days to flowering selection. In both graphs, the basal population is the black solid line in the center,the darker lines are the matched generations of early flowering (EF1, EF3, FE4,and EF5; colored orange), and late flowering (LF2 and LF3; colored purple), with and late flowering (LF2 and LF3),with the two unmatched generations (EF2 and LF2) shown in lighter tones.The far-left population (early flowering EF5) displays very little phenotypic variability, whereas the far-right population (late flowering 3) is very diverse.
Impact of infectious diseases on wild bovidae populations in Thailand: Insights from population modelling and disease dynamics
<p>The wildlife and livestock interface is vital for wildlife conservation and habitat management. Infectious diseases maintained by domestic species may impact threatened species such as Asian bovids, as they share natural resources and habitats. To predict the population impact of infectious diseases with different traits, we used stochastic mathematical models to simulate the population dynamics over 100 years for 100 times a model gaur (<em>Bos gaurus</em>) population with and without disease. We simulated repeated introductions from a reservoir, such as domestic cattle. We selected six bovine infectious diseases; anthrax, bovine tuberculosis, hemorrhagic septicaemia, lumpy skin disease, foot and mouth disease and brucellosis, all of which have caused outbreaks in wildlife populations. From a starting population of 300, the disease-free population increased by an average of 228% over 100 years. Brucellosis with frequency-dependent transmission showed the highest average population declines (-97%), with population extinction occurring 16% of the time. Foot and mouth disease with frequency-dependent transmission showed the lowest impact, with an average population increase of 200%. Overall, acute infections with very high or low fatality had the lowest impact, whereas chronic infections produced the greatest population decline. These results may help disease management and surveillance strategies support wildlife conservation.</p>
Figure 1 in Exploring chemical control of 2,4-D-resistant wild radish (Rophonus rophonistrum) with auxin-related compounds
Figure 1. Compounds used as potential 2,4-D synergists or substitutes,and how they fit into the schemes of auxin biology.The compounds used in the current study are shown in shaded ovals, with blue representing the auxin biosynthesis pathway, green representing auxin signaling and transport, and orange representing auxin response. Abbreviations: ACC, 1-aminocyclopropane-1-carboxylic acid; BAP, 6-benzylaminopurine; CdRP, 1-(O-carboxylphenylamino)1-deoxyribulose-5-phosphate; EGTA, ethylene glycol bis(2-aminoethyl)tetraacetic acid; IAD, indole-3-acetaldehyde; IAM, indole-3-acetamide; IAN, indole-3-acetonitrile; IAOx, indole-3-acetaldoxime; IGP, indole glycerol phosphate; IPyA, indole-3-pyruvic acid; MA, methylanthranilate; PRA, phosphoribosylanthranilate; SAM, S-adenosylmethionine.
Figure 2 in Adaptations in wild radish (ROphOnus rOphOnistrum) flowering time, Part 2: Harvest weed seed control shortens flowering by twelve days
Figure 2. Changes in the number of seeds m−2 (A, D, G), the probability of seed capture by harvest weed seed control (HWSC) (B, E, H), and days to first flower (DFF) (C, F, I). (A–C) Varying herbicide efficiencies, in the absence of HWSC, over 30 yr. (D–F) When knockdown herbicide was less effective (-ρ-), when postemergence herbicide was less effective (-○-), and when both herbicides were effective, without HWSC (___) over 20 yr. (G–I) Model used management system P and started with either a smaller seedbank (___) or a larger seedbank (-○-) over 20 yr.
Figure 4 in Exploring chemical control of 2,4-D-resistant wild radish (Rophonus rophonistrum) with auxin-related compounds
Figure 4. Assessment of auxinic herbicides applied preemergence to suspected and confirmed 2,4-D–resistant Rophonus rophonistrum populations. Field-collected populations with suspected resistance to 2,4-D (11 populations) and the confirmed resistant,2,4-D–selected populations (populations R1–R11) were sprayed preemergence with 560 g ha−1 2,4- D, 570 g ha−1 MCPA, or 750 g ha−1 dicamba, and the field-collected populations were also sprayed postemergence as part of the same experiment (dark blue bars). Values are means ± SE (n = 11, with each population representing one replicate), and different letters above bars denote significant (P <0.05) differences between means. For visual comparison, the averaged data for populations R1–R11 sprayed postemergence with 500 g ha−1 2,4-D or dicamba or 600 g ha−1 MCPA were also included, along with the pre- and postemergence data for the pooled susceptible (S1 and S2) populations (light blue bars). Data for the postemergence 2,4-D and dicamba treatments of populations R1–R11 were taken from Goggin et al. (2018: supplementary table S3) and from the current glasshouse study for the postemergence MCPA treatment.
Figure 3 in Exploring chemical control of 2,4-D-resistant wild radish (Rophonus rophonistrum) with auxin-related compounds
Figure 3. Response of 2,4-D–resistant Rophonus rophonistrum populations to MCPA and mecoprop. Populations at the 2-leaf stage were sprayed with MCPA, mecoprop, or a 1:1 mix of each herbicide, and their survival was assessed after 21 d. (A) Survival in the glasshouse following treatment with 1,200 g ai ha−1 MCPA or mecoprop standalone, or 600 þ 600 g ha−1 MCPA þ mecoprop.As there were no significant differences among treatments within each population, the data were pooled,and means are shown as wide gray bars behind the blue bars that represent each individual herbicide treatment.Different letters above bars denote significant (P <0.05) differences among populations in response to the pooled treatments (values are means ± SE; n = 3). (B) Dose of MCPA or mecoprop or a 1:1 mix required to kill 50% of individuals (ED50) in an outdoor dose–response experiment. Different letters above bars denote significant differences in ED50 values within and among populations.
Figure 2. Interaction between 2,4-D in Exploring chemical control of 2,4-D-resistant wild radish (Rophonus rophonistrum) with auxin-related compounds
Figure 2. Interaction between 2,4-D and other auxin-related compounds in 2,4-D–susceptible (S1) or 2,4-D–resistant (R2 or R3) Rophonus rophonistrum populations. Seedling radicle elongation on agar in the presence of 2,4-D, a potential synergist, or both, was measured, and the interaction between chemicals was assessed using a Colby analysis. Values are means ± SE of three replicates. Asterisks denote an interaction (Iij) significantly different from an additive interaction, with negative values indicating synergism and positive values indicating antagonism. Abbreviations: ABA, abscisic acid; ACC, 1-aminocyclopropane-1-carboxylic acid; Aden, adenosine; BAP, 6-benzylaminopurine; 1-But, 1- butanol; Cyclan, cyclanilide; EGTA, ethylene glycol-bis(β-aminoethyl ether)-N,N,N 0,N 0-tetraacetic acid; IAM, indole-3-acetamide; DL-Met, DL-methionine; MA, methyl anthranilate; Trypt, tryptamine.
Figure 3 in Adaptations in wild radish (ROphOnus rOphOnistrum) flowering time, Part 2: Harvest weed seed control shortens flowering by twelve days
Figure 3. Changes in the number of seeds m−2 (A, D), the probability of seed capture by harvest weed seed control (HWSC) (B, E), and changes in days to first flower (DFF) (C, F) under weed management system P; HWSC efficacy (up to 20 yr) was increased from 75% (___) to 95% (-○-) (A–C); the level of fruit abscission (up to 20 yr) was changed from low (37%) (-○-) to high (74%) (—) (D–F). Note the variation between replicates was small as long as seed numbers are above 1 m−2; below that level, genetic changes in one or two plants had a more significant effect on the results.
Figure 1 in Adaptations in wild radish (ROphOnus rOphOnistrum) flowering time, Part 2: Harvest weed seed control shortens flowering by twelve days
Figure 1. Showing the number of seeds produced by weeds from each cohort (y axis), dependent on the evolved days to first flower (DFF) (x axis). The peak of each curve indicates the ideal DFF, with earlier cohorts taking longer to flower. (A) The standard farming system, before introduction of harvest weed seed control (HWSC). The range in ideal DFF across the different cohorts is approximately 15 d for Figure 1A. (B–D) How the various weed management systems affect the number of live seeds (avoiding HWSC) for weed management systems E (B), D (C), and P (D). In Figure 1B–D, the range in ideal DFF across the different cohorts is approximately 50 d.
Fig. 3 in Repeatability Analysis Of Egg Shape In A Wild Tree Sparrow (Passer Montanus) Population: A Sensitive Method For Egg Shape Description
Fig. 3. The effect of egg-photographing on the description of outline. Panel a shows ten outlines described following the photos of ten randomly chosen eggs, panel b shows ten outlines described fol-
Figure 6 in Prevalence of Spiroplasma and interaction with wild Glossina tachinoides microbiota
Figure 6. Neighbor-Joining consensus tree (A) and Haplotype network analysis (B) of the Spiroplasma in G. tachinoides in Burkina Faso and Ghana. (A) Neighbor-Joining consensus tree was built after alignment of all the concatenated sequences. The method used to calculate the distance was Tamura-Nei. (B) Haplotype network generated based on the ML tree which was generated based on Spiroplasma sequences. The black lineaments on the lines represent mutation events between the haplotypes. The different colors represent the locations. The reference sequence of Spiroplasma in G. fuscipes fuscipes species (KX159391) was used as the outgroup for construction of both phylogenetic tree and haplotype network.
Figure 2 in Prevalence of Spiroplasma and interaction with wild Glossina tachinoides microbiota
Figure 2. Prevalence of Spiroplasma according to location. Bars marked with the same lower-case letter do not differ significantly at the 0.05 level.
Figure 5 in Prevalence of Spiroplasma and interaction with wild Glossina tachinoides microbiota
Figure 5. Normalized density of Spiroplasma (A), Trypanosoma (B), and Wigglesworthia (C) according to Spiroplasma-Trypanosoma co-infection in wild G. tachinoides. Bars marked with the same lower-case letter do not differ significantly at the 0.05 level.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.