Biology subjects

Childs, D.

Publications and source records attributed to Childs, D..

7 recordsLinked to original sources

bioRxiv2023

Nest site selection and productivity of a critically endangered parrot, the great green macaw (Ara ambiguus) in an anthropogenic landscape

Nest site selection is the principal way secondary cavity-nesting species mitigate the negative effects of factors such as predation, parasitism and exposure on productivity. High-quality cavities could then be expected to be selected in response to the primary threat to nest success. Understanding how demographic rates are affected by anthropogenic changes to ecosystems is vital if effective conservation management strategies are to be developed and implemented. Large-bodied secondary cavity-nesting birds rely on large cavities in mature trees that are often absent or reduced in anthropogenically disturbed forests. Thus, the availability of high-quality nest sites may be limited for these species, potentially reducing productivity. The aim of this study was to investigate nest-site selection and the effect of nest-site features on productivity in the critically endangered great green macaw (Ara ambiguus) in northern Costa Rica. We show that great green macaws select nest sites according to the characteristics of the cavity and of the tree in which they are located. Moreover, productivity was a function of certain cavity features. We conclude that great green macaws are not reliant on primary forest for nest sites and typically choose cavities in remnant, isolated trees in pasture or young secondary forests.

ecology↗

bioRxiv2022

Inadequate sampling of the soundscape leads to overoptimistic estimates of recogniser performance: a case study of two sympatric macaw species

Passive acoustic monitoring (PAM) - the use of autonomous recording units to record ambient sound - offers the potential to dramatically increase the scale and robustness of species monitoring in rainforest ecosystems. PAM generates large volumes of data that require automated methods of target species detection. Species-specific recognisers, which often use supervised machine learning, can achieve this goal. However, they require a large training dataset of both target and non-target signals, which is time-consuming and challenging to create. Unfortunately, very little information about creating training datasets for supervised machine learning recognisers is available, especially for tropical ecosystems. Here we show an iterative approach to creating a training dataset that improved recogniser precision from 0.12 to 0.55. By sampling background noise using an initial small recogniser, we can address one of the significant challenges of training dataset creation in acoustically diverse environments. Our work demonstrates that recognisers will likely fail in real-world settings unless the training dataset size is large enough and sufficiently representative of the ambient soundscape. We outline a simple workflow that can provide users with an accessible way to create a species-specific PAM recogniser that addresses these issues for tropical rainforest environments. Our work provides important lessons for PAM practitioners wanting to develop species-specific recognisers for acoustically diverse ecosystems.

ecology↗

bioRxiv2022

Antecedent effect models as an exploratory rather than predictive tool to understand the response of plant populations to climate drivers

Understanding mechanisms and predicting natural population responses to climate is a key goal of Ecology. However, studies explicitly linking climate to population dynamics remain limited. Antecedent effect models are a set of statistical tools that capitalize on the evidence provided by climate and population data to select time windows correlated with a response (e.g., survival, reproduction). Thus, these models can serve as both a predictive and exploratory tool. We compare the predictive performance of antecedent effect models against simpler models, and showcase their exploratory analysis potential by selecting a case study with high predictive power. We fit three antecedent effect models: (1) weighted mean models (WMM), which weigh the importance of monthly anomalies based on a Gaussian curve, (2) stochastic antecedent models (SAM), which weigh the importance of monthly anomalies using a Dirichlet process, and (3) regularized regressions using the Finnish Horseshoe prior (FHM), which estimate a separate effect size for each monthly anomaly. We compare these approaches to a linear model using a yearly climatic predictor and a null model with no predictors. We use demographic data from 77 natural populations of 34 plant species ranging between seven and 36 years of length. We then fit models to the asymptotic population growth rate ({lambda}) and its underlying vital rates: survival, development, and reproduction. We find that models including climate do not consistently outperform null models. We hypothesize that the effect of yearly climate is too complex, weak, and confounded by other factors to be easily predicted using monthly precipitation and temperature data. On the other hand, in our case study, antecedent effect models show biologically sensible correlations between two precipitation anomalies and multiple vital rates. We conclude that, in temporal datasets with limited sample sizes, antecedent effect models are better suited as exploratory tools for hypothesis generation. Open Research statementData and code to reproduce the analyses are available on zenodo at https://dx.doi.org/10.5281/zenodo.7839199.

ecology↗

bioRxiv2022

A novel role of 3,5-cAMP in the regulation of actin cytoskeleton in Arabidopsis.

The role of cyclic adenosine monophosphate (3,5-cAMP) in plants is not well understood, and here, we report a novel role of 3,5-cAMP in regulating the actin cytoskeleton. The 3,5-cAMP treatment affects the thermal stability of 51 proteins, including a vegetative actin isoform, ACTIN2. Consistent with the above results, the increase in 3,5-cAMP levels, obtained either by feeding or by chemical modulation of 3,5-cAMP metabolism, is sufficient to partially rescue the short hypocotyl phenotype of the actin2 actin7 mutant, severely compromised in actin level. No such complementation was measured for a positional isomer of 3,5-cAMP, 2,3-cAMP, attesting to the specificity of 3,5-cAMP treatment. Moreover, supplementation of 3,5-cAMP partly counters the activity of an actin-depolymerizing drug latrunculin B. In vitro characterization of the 3,5-cAMP - actin interaction argues against the direct binding. Instead, based on the proteomics characterization of the act2act7 mutant supplemented with 3,5-cAMP, we hypothesize that 3,5-cAMP affects cytoskeleton dynamic by modulation of calcium signaling, and actin binding proteins.

plant biology↗

bioRxiv2019

Seasonal Effects of Environmental Change on Yellow-Bellied Marmot Population Dynamics

Natural populations are exposed to seasonal variation in environmental factors that simultaneously affect several demographic rates (survival, development, reproduction). The resulting covariation in these rates determines population dynamics, but accounting for its numerous biotic and abiotic drivers is a significant challenge. Here, we use a factor-analytic approach to capture partially unobserved drivers of seasonal population dynamics. We use 40 years of individual-based demography from yellow-bellied marmots (Marmota flaviventer) to fit and project population models that account for seasonal demographic covariation using a latent variable. We show that this latent variable, by producing positive covariation among winter demographic rates, depicts a measure of environmental quality. Simultaneous, negative responses of winter survival and reproductive-status change to declining environmental quality result in a higher risk of population quasi-extinction, regardless of summer demography where recruitment takes place. We demonstrate how complex environmental processes can be summarized to understand population persistence in seasonal environments.

ecology↗

bioRxiv2019

Reduced proteasome activity in the aging brain results in ribosome stoichiometry loss and aggregation

A progressive loss of protein homeostasis is characteristic of aging and a driver of neurodegeneration. To investigate this process quantitatively, we characterized proteome dynamics during brain aging in the short-lived vertebrate Nothobranchius furzeri combining transcriptomics and proteomics. We detected a progressive reduction in the correlation between protein and mRNA, mainly due to post-transcriptional mechanisms that account for over 40% of the age-regulated proteins. These changes cause a progressive loss of stoichiometry in several protein complexes, including ribosomes, which show impaired assembly / dis-assembly and are enriched in protein aggregates in old brains. Mechanistically, we show that reduction of proteasome activity is an early event during brain aging and is sufficient to induce proteomic signatures of aging and loss of stoichiometry in vivo. Using longitudinal transcriptomic data, we show that the magnitude of early life decline in proteasome levels is the major risk factor for mortality. Our work defines causative events in the aging process that can be targeted to prevent loss of protein homeostasis and delay the onset of age-related neurodegeneration. HighlightsO_LIProgressive loss of stoichiometry affects multiple protein complexes C_LIO_LIRibosomes aggregate in old brains C_LIO_LIPartial reduction of proteasome activity is sufficient to induce loss of stoichiometry C_LIO_LIReduced proteasome levels are a major risk factor for early death in killifish C_LI

systems biology↗

bioRxiv2018

Non-Parametric Analysis of Thermal Proteome Profiles Reveals Novel Drug-Binding Proteins

Detecting the targets of drugs and other molecules in intact cellular contexts is a major objective in drug discovery and in biology more broadly. Thermal proteome profiling (TPP) pursues this aim at proteome-wide scale by inferring target engagement from its effects on temperature-dependent protein denaturation. However, a key challenge of TPP is the statistical analysis of the measured melting curves with controlled false discovery rates at high proteome coverage and detection power. We present non-parametric analysis of response curves (NPARC), a statistical method for TPP based on functional data analysis and nonlinear regression. We evaluate NPARC on five independent TPP datasets and observe that it is able to detect subtle changes in any region of the melting curves, reliably detects the known targets, and outperforms a melting point-centric, single-parameter fitting approach in terms of specificity and sensitivity. NPARC can be combined with established analysis of variance (ANOVA) statistics and enables flexible, factorial experimental designs and replication levels. To facilitate access to a wide range of users, a freely available software implementation of NPARC is provided.

bioinformatics↗