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Deciphering deterministic factors of predation pressures in deep time.

ABSTRACT: Predation pressure occurs as a result of predation frequency and prey vulnerability. Although quantifying these factors individually is essential to precisely understand predation effects on evolution, they have been generally less accessible. Here, using a modified form of Poisson function, we quantified the frequencies and vulnerabilities, as well as the resulting predation pressures, concerning the shell drillers versus prey interactions from the Eocene and Miocene periods. Our analysis quantitatively revealed that low-spired shells tend to show increased vulnerability except for two planispiral species that exhibit an unexpectedly low vulnerability. We then identified septal structures within the two species that resemble those in nautiloids and ammonoids but which provided a defensive role against the predators, enhancing the mean lifetime by approximately 20%. The current approach enables us to quantitatively trace how predation frequency and prey vulnerability have interacted, been transformed spatio-temporally, and been a driving force of evolution at geological time scales.

SUBMITTER: Ishikawa M

PROVIDER: S-EPMC6277388 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

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Deciphering deterministic factors of predation pressures in deep time.

Ishikawa Makiko M Kase Tomoki T Tsutsui Hidekazu H

Scientific reports 20181203 1

Predation pressure occurs as a result of predation frequency and prey vulnerability. Although quantifying these factors individually is essential to precisely understand predation effects on evolution, they have been generally less accessible. Here, using a modified form of Poisson function, we quantified the frequencies and vulnerabilities, as well as the resulting predation pressures, concerning the shell drillers versus prey interactions from the Eocene and Miocene periods. Our analysis quant ...[more]

PMID: 30510248

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Project description:BackgroundAlthough DNA sequence plays a crucial role in establishing the unique epigenome of a cell type, little is known about the sequence determinants that lead to the unique epigenomes of different cell types produced during cell differentiation. To fill this gap, we employed two types of deep convolutional neural networks (CNNs) constructed for each of differentially related cell types and for each of histone marks measured in the cells, to learn the sequence determinants of various histone modification patterns in each cell type.ResultsWe applied our models to four differentially related human CD4+ T cell types and six histone marks measured in each cell type. The cell models can accurately predict the histone marks in each cell type, while the mark models can also accurately predict the cell types based on a single mark. Sequence motifs learned by both the cell or mark models are highly similar to known binding motifs of transcription factors known to play important roles in CD4+ T cell differentiation. Both the unique histone mark patterns in each cell type and the different patterns of the same histone mark in different cell types are determined by a set of motifs with unique combinations. Interestingly, the level of sharing motifs learned in the different cell models reflects the lineage relationships of the cells, while the level of sharing motifs learned in the different histone mark models reflects their functional relationships. These models can also enable the prediction of the importance of learned motifs and their interactions in determining specific histone mark patterns in the cell types.ConclusionSequence determinants of various histone modification patterns in different cell types can be revealed by comparative analysis of motifs learned in the CNN models for multiple cell types and histone marks. The learned motifs are interpretable and may provide insights into the underlying molecular mechanisms of establishing the unique epigenomes in different cell types. Thus, our results support the hypothesis that DNA sequences ultimately determine the unique epigenomes of different cell types through their interactions with transcriptional factors, epigenome remodeling system and extracellular cues during cell differentiation.

Project description:Accurate detection and classification of predation events is important to determine predation and consumption rates by predators. However, obtaining this information for large predators is constrained by the speed at which carcasses disappear and the cost of field data collection. To accurately detect predation events, researchers have used GPS collar technology combined with targeted site visits. However, kill sites are often investigated well after the predation event due to limited data retrieval options on GPS collars (VHF or UHF downloading) and to ensure crew safety when working with large predators. This can lead to missing information from small-prey (including young ungulates) kill sites due to scavenging and general site deterioration (e.g., vegetation growth). We used a space-time permutation scan statistic (STPSS) clustering method (SaTScan) to detect predation events of grizzly bears (Ursus arctos) fitted with satellite transmitting GPS collars. We used generalized linear mixed models to verify predation events and the size of carcasses using spatiotemporal characteristics as predictors. STPSS uses a probability model to compare expected cluster size (space and time) with the observed size. We applied this method retrospectively to data from 2006 to 2007 to compare our method to random GPS site selection. In 2013-2014, we applied our detection method to visit sites one week after their occupation. Both datasets were collected in the same study area. Our approach detected 23 of 27 predation sites verified by visiting 464 random grizzly bear locations in 2006-2007, 187 of which were within space-time clusters and 277 outside. Predation site detection increased by 2.75 times (54 predation events of 335 visited clusters) using 2013-2014 data. Our GLMMs showed that cluster size and duration predicted predation events and carcass size with high sensitivity (0.72 and 0.94, respectively). Coupling GPS satellite technology with clusters using a program based on space-time probability models allows for prompt visits to predation sites. This enables accurate identification of the carcass size and increases fieldwork efficiency in predation studies.

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Deciphering deterministic factors of predation pressures in deep time.

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Deciphering deterministic factors of predation pressures in deep time.

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