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Physa Acuta Transgenerational Plasticity

Exploring Morphology and Anti-Predator Behavioral Responses

@kaggle.thedevastator_physa_acuta_transgenerational_plasticity

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About this Dataset

Physa Acuta Transgenerational Plasticity


Physa acuta Transgenerational Plasticity

Exploring Morphology and Anti-Predator Behavioral Responses

By [source]


About this dataset

This dataset provides key insights into the effects of transgenerational plasticity on phenotypic traits of Physa acuta, a freshwater snail. Our observations and comparisons between and across generations revealed significant individual variation in how morphological traits expressed in the F2 snails exposed to predator cues. Exposure of the mothers to predators resulted in larger shells, greater crush resistance and anti-predator behavior within-generation plasticity such that F2 offspring reared with predator cues responded less to predation than those reared in control conditions. This data set is important for furthering our understanding of species interactions with their environment and is valuable for identifying potential evolutionary processes like natural selection and genetic drift. Additionally, this data set can be used along with other Physa acuta behavioral datasets or post-genomic resources to learn more about the implications of transgenerational plasticity on eco-evolutionary dynamics

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How to use the dataset

This dataset provides valuable insight into the evolutionary processes of natural selection and genetic drift, as well as the impact of transgenerational plasticity on phenotypic traits of Physa acuta, a freshwater snail. In this guide, we will provide an overview on how to use this dataset in order to gain a greater understanding of how species interact with their environment.

  • Start by downloading the appropriate CSV file and loading it into your statistical software program.
  • Looking at the columns included in this dataset, you will notice that some data points include labels such as “gen” and “line” related to information about the snail generation and lineage numbers respectively Other column headers detail characteristics such as “crush weight” or “response to predator cues (rw#) which measure variation in response between and within generations based on factors such as predator treatment or popualtion surrounding the snails habitat.
  • After familiarizing yourself with these variables, you may wish to determine any correlations exist between different morphological or behavioral traits over time - for example a correlation between rw1 (predator response 1) and Dvgen (difference in generation). To do so you can use linear regression techniques for studying trends over generations depending upon how many generations are still available for study.
  • You may also want explore whether any differences exist between adults exposed to predators an those not exposed when looking at anti-predator behavior – e..g comparing Antipredator 1 behaviors under various conditions (such control vs treatment). For this purpose Methods like Boxplotscan be used since they allow us define variation by providing information about outliers averages, etc which would give clear comparison of different types of behaviors across treatments ). Once again results can be compared historically alling one draw upon additional evidence concerning possible relationships ther conditions have hadon behavior characteristics concering our individuals over multiple generations

5 We hope that after reading through our guide you have enough knowledge now understanding better what specific predictions can be made , within limitations , using this datasetregarding eco-evolutionary dynamicsin relation with behavior/morphology parameters of Physa Acuta snails studied here

Research Ideas

  • To explore gene-environment interactions in species by comparing the expression of anti-predator traits between mothers exposed to predator cues and those with no exposure.
  • To analyze the difference in phenotype across generations and its effect on behavior, as well as to look for a correlation between morphological traits and anti-predator behaviors within a generation.
  • To identify how factor such as line, treatment parent or location can affect transgenerational plasticity in species like Physa acuta snails by looking at changes in population, crush weight and centroid among generations

Acknowledgements

If you use this dataset in your research, please credit the original authors.
Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication
No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: morphoanalysisF2DVs.csv

Column name Description
gen Generation of the snail (Categorical)
line Line of the snail (Categorical)
ttreatment Treatment of the snail (Categorical)
ptreatment Treatment of the snail's parent (Categorical)
numberincup Number of snails in the cup (Numerical)
**crush.weight.g ** Weight of the crushed snail (Numerical)
Dvpop Difference in shell size between generations (Numerical)
Dvcrush Difference in crush resistance between generations (Numerical)
Dvgen Difference in shell size between generations (Numerical)
Dvline Difference in shell size between lines (Numerical)
Dvpred Difference in shell size between predator treatments (Numerical)
Dvcent Difference in shell size between control treatments (Numerical)
Dvpptreat Difference in shell size between parent treatments (Numerical)
rw1 Response to predator cue 1 (Numerical)
rw2 Response to predator cue 2 (Numerical)
rw3 Response to predator cue 3 (Numerical)
rw4 Response to predator cue 4 (Numerical)
rw5 Response to predator cue 5 (Numerical)
rw6 Response to predator cue 6 (Numerical)

File: F2_Behavior_WMorphology.csv

Column name Description
numberincup Number of snails in the cup (Numerical)
rw1 Response to predator cue 1 (Numerical)
rw2 Response to predator cue 2 (Numerical)
rw3 Response to predator cue 3 (Numerical)
rw4 Response to predator cue 4 (Numerical)
rw5 Response to predator cue 5 (Numerical)
rw6 Response to predator cue 6 (Numerical)
Dvpop Difference in shell size between generations (Numerical)
Dvgen Difference in shell size between generations (Numerical)
Dvline Difference in shell size between lines (Numerical)
Dvpred Difference in shell size between predator treatments (Numerical)
Dvcent Difference in shell size between control treatments (Numerical)
Dvpptreat Difference in shell size between parent treatments (Numerical)
Line The line of the snail (Categorical)
Treatment.parent The treatment of the parent (Categorical)
Condition The condition of the offspring (Categorical)
check.number The check number of the snail (Numerical)
batch The batch of the snail (Categorical)
location The location of the snail (Categorical)
Antipredator.1 The anti-predator behavior of the snail (Numerical)
Location.change The change in location of the snail (Categorical)
rw7 Response to predator cue 7 (Numerical)
rw8 Response to predator cue 8 (Numerical)
rw9 Response to predator cue 9 (Numerical)
rw10 Response to predator cue 10 (Numerical)

Acknowledgements

If you use this dataset in your research, please credit the original authors.
If you use this dataset in your research, please credit .

Tables

F2 Behavior Wmorphology

@kaggle.thedevastator_physa_acuta_transgenerational_plasticity.f2_behavior_wmorphology
  • 59.85 KB
  • 5500 rows
  • 29 columns
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CREATE TABLE f2_behavior_wmorphology (
  "snail_id" VARCHAR,
  "line" BIGINT,
  "treatment_parent" VARCHAR,
  "treatment" VARCHAR,
  "condition" VARCHAR,
  "check_number" BIGINT,
  "batch" BIGINT,
  "location" VARCHAR,
  "antipredator_1" BIGINT,
  "location_change" DOUBLE,
  "numberincup" BIGINT,
  "centroid" DOUBLE,
  "rw1" DOUBLE,
  "rw2" DOUBLE,
  "rw3" DOUBLE,
  "rw4" DOUBLE,
  "rw5" DOUBLE,
  "rw6" DOUBLE,
  "rw7" DOUBLE,
  "rw8" DOUBLE,
  "rw9" DOUBLE,
  "rw10" DOUBLE,
  "dvpop" DOUBLE,
  "centroid_1" DOUBLE,
  "dvgen" DOUBLE,
  "dvline" DOUBLE,
  "dvpred" DOUBLE,
  "dvcent" DOUBLE,
  "dvpptreat" DOUBLE
);

Morphoanalysisf2dvs

@kaggle.thedevastator_physa_acuta_transgenerational_plasticity.morphoanalysisf2dvs
  • 209.57 KB
  • 566 rows
  • 69 columns
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CREATE TABLE morphoanalysisf2dvs (
  "tpsdigid" BIGINT,
  "id" VARCHAR,
  "gen" BIGINT,
  "line" BIGINT,
  "ttreatment" VARCHAR,
  "treatment" VARCHAR,
  "ptreatment" VARCHAR,
  "numberincup" BIGINT,
  "centroid" DOUBLE,
  "crush_weight_g" DOUBLE,
  "dvpop" DOUBLE,
  "dvcrush" DOUBLE,
  "dvgen" DOUBLE,
  "dvline" DOUBLE,
  "dvpred" DOUBLE,
  "dvcent" DOUBLE,
  "dvpptreat" DOUBLE,
  "rw1" DOUBLE,
  "rw2" DOUBLE,
  "rw3" DOUBLE,
  "rw4" DOUBLE,
  "rw5" DOUBLE,
  "rw6" DOUBLE,
  "rw7" DOUBLE,
  "rw8" DOUBLE,
  "rw9" DOUBLE,
  "rw10" DOUBLE,
  "rw11" DOUBLE,
  "rw12" DOUBLE,
  "rw13" DOUBLE,
  "rw14" DOUBLE,
  "rw15" DOUBLE,
  "rw16" DOUBLE,
  "rw17" DOUBLE,
  "rw18" DOUBLE,
  "rw19" DOUBLE,
  "rw20" DOUBLE,
  "rw21" DOUBLE,
  "rw22" DOUBLE,
  "rw23" DOUBLE,
  "rw24" DOUBLE,
  "rw25" DOUBLE,
  "rw26" DOUBLE,
  "rw27" DOUBLE,
  "rw28" DOUBLE,
  "rw29" DOUBLE,
  "rw30" DOUBLE,
  "rw31" DOUBLE,
  "rw32" DOUBLE,
  "rw33" DOUBLE,
  "rw34" DOUBLE,
  "rw35" DOUBLE,
  "rw36" DOUBLE,
  "rw37" DOUBLE,
  "rw38" DOUBLE,
  "rw39" DOUBLE,
  "rw40" DOUBLE,
  "rw41" DOUBLE,
  "rw42" DOUBLE,
  "rw43" DOUBLE,
  "rw44" DOUBLE,
  "rw45" DOUBLE,
  "rw46" DOUBLE,
  "rw47" DOUBLE,
  "rw48" DOUBLE,
  "rw49" DOUBLE,
  "rw50" DOUBLE,
  "rw51" DOUBLE,
  "rw52" DOUBLE
);

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