DOI: 10.5061/dryad.jm63xsjhd
title: Microhabitat conditions drive uncertainty of risk and shape neophobic responses in Trinidadian guppies, Poecilia reticulata

description: In response to uncertain risks, prey may rely on neophobic phenotypes to
 reduce the costs associated with the lack of information regarding local
 conditions. Neophobia has been shown to be driven by information
 reliability, ambient risk, and predator diversity, all of which shape
 uncertainty of risk. We similarly expect environmental conditions to shape
 uncertainty by interfering with information availability. In order to test
 how environmental variables might shape neophobic responses in Trinidadian
 guppies (Poecilia reticulata), we conducted an in situ field experiment of
 two high-predation risk guppy populations designed to determine how the
 “average” and “variance” of several environmental factors might influence
 the neophobic response to novel predator models and/or novel foraging
 patches. Our results suggest neophobia is shaped by water velocity,
 microhabitat complexity, pool width and depth, as well as substrate
 diversity and heterogeneity. Moreover, we found differential effects of
 the “average” and “variance” environmental variables on food- and
 predator-related neophobia. Our study highlights that assessment of
 neophobic drivers should consider predation risk, various microhabitat
 conditions, and neophobia being tested. Neophobic phenotypes are expected
 to increase the probability of prey survival and reproductive success
 (i.e. fitness), and are therefore likely linked to population health and
 species survival. Understanding the drivers and consequences of
 uncertainty of risk is an increasingly pressing issue, as ecological
 uncertainty increases with the combined effects of climate change,
 anthropogenic disturbances, and invasive species. 
description: # Microhabitat conditions drive uncertainty of risk and shape neophobic
 responses in Trinidadian guppies, <i>Poecilia reticulata</i>
 [https://doi.org/10.5061/dryad.jm63xsjhd](https://doi.org/10.5061/dryad.jm63xsjhd) This dataset contains three CSV files, including: 1. Response variables: a dataset of the average of two neophobic behavioural responses (latency to inspect a novel predator model, and latency to enter a novel foraging arena). We observed these behaviours three times each within each testing pool, such that each pool generally had 6 separate locations where behavioural assays were conducted. In order to avoid pseudo-replication, we calculated an average of each response latency within each pool. We had three exceptions to this, where we were only able to sample latency to forage once in one pool, and twice in two pools. 2. Explanatory variables: this dataset includes measures of pool depth and width, pool area, water velocity, and substrate complexity (rugosity). Our dataset contains the singular measure of pool length, in addition to calculated average and variance of depth, width, surface velocity, mid-depth velocity, and rugosity for each pool. There were six exceptions in the Lopinot river, where we failed to collect velocity data. 3. Substrate data: We categorized the substrate type in a pool systematically up to 5 times, depending on pool size, using a grid frame. We categorized substrate type as sand, fine (&lt;1 cm), coarse (1-3 cm), cobble (&gt;3cm), hard (smooth rock/granite), or leaf litter. The dataset includes the count values of how many grids (in 0.5 grid increments) out of the 25 total from the grid frame were covered by each substrate type, in each sample within each pool. From these, we calculated substrate alpha diversity and heterogeneity (Hurlbert's Probability of Interspecific Encounter), which were included as additional explanatory variables in our models. ## Description of the data and file structure All datasets correspond to data collected from 27 pools in the Lopinot river, and 15 pools in the Acono River in the Trinidadian Northern Range. --Column Names--- Each pool is identified using "Pool\_ID" in dataset 1, "pool.ID" in dataset 2, and "Pool.ID" for dataset 3. Dataset 1 Each pool is identified using "Pool\_ID" Average latencies to inspect a novel predator model (measured in seconds) for each pool are identified as "LAT\_INSP". Average latencies to enter a novel foraging arena (measured in seconds) for each pool are identified as "LAT\_FOR". Dataset 2 Each pool is identified using "pool.ID" Each population is identified using "population" Average rugosity values are identified using "rugosity", whereas rugosity variance values are identified as "rugosity.var". Given that the rugosity measure is a ratio, these values have no unit. Length of each pool is identified using "length", in units of meters. Average width values are identified using "width", whereas width variance values are identified as "width.var", in units of meters. Average depth values are identified using "depth", whereas depth variance values are identified as "depth.var", in units of centimeters. Average surface velocity values are identified using "surf.vel", whereas surface velocity variance values are identified as "surf.vel.var", in units of meters/second. Average mid-depth (40% depth) velocity values are identified using "mid.vel", whereas mid-depth velocity variance values are identified as "mid.vel.var", in units of meters/second. Dataset 3 Each pool is identified using "Pool.ID" Each substrate category is identified as "Sand", "Fine" (substrate &lt;1 cm in size), "Coarse" (substrate 1-3 cm in size), "Cobble" (substrate &gt;3cm in size), "Hard" (smooth rock/granite), or "Leaf" (leaf litter). Data corresponding to each column represent the grid counts (in 0.5 increments) of each substrate, given a 25 grid frame.