DOI: 10.5061/dryad.573n5tbkh
title: Environmental heterogeneity across an urban gradient influences detritus and nutrients within artificial containers and their associated vector Aedes sp. larvae in San Juan, Puerto Rico

description: Detrital inputs from the surrounding terrestrial environment provide
 essential nutrients that sustain mosquito populations in aquatic
 containers. The larvae of Aedes aegypti often develop in artificial
 habitats in urban areas but little is known about how that environment
 shapes their life history or phenotypic traits. We sampled mosquito
 larvae, container detritus, and suspended particulate organic matter in 44
 locations across the San Juan Metropolitan Area urban gradient in Puerto
 Rico. We characterized the surrounding environment in terms of land cover,
 land use, and vegetation α diversity. We show that container detritus and
 nutrients are influenced by fine-scale environmental variations
 environment, affecting Ae. aegypti and its competitor Ae. mediovittatus
 larvae phenotypic traits and nutrient composition.
description: # Environmental heterogeneity across an urban gradient influences detritus
 and nutrients within artificial containers and their associated vector
 Aedes sp. larvae in San Juan, Puerto Rico Dataset DOI:
 [10.5061/dryad.573n5tbkh](10.5061/dryad.573n5tbkh) ## Description of the
 data and file structure We sampled mosquito larvae (density, biomass,
 nutrients), container detritus (type, biomass, nutrients), and suspended
 particulate organic matter (SPOM) in 44 locations and characterized the
 surrounding environment in terms of land cover, land use, and vegetation α
 diversity. Percent impervious cover (%) and canopy cover (%) were
 estimated in a 200 m buffer. Canopy cover directly over larval containers
 was also measured with a handheld densitometer. A 1.52 m radius visual
 survey from each container was conducted to determine vegetation α
 diversity. A foot survey along a 25 m transect from each container was
 conducted while recording number of structures (e.g., houses), green
 spaces, and house abandonment. ### Files and variables #### File:
 SJMA\_gradient\_and\_containers.csv **Description:**  ##### Variables *
 Container: Container identification number * H: Number of houses along 25
 m transect * GS: Number of green spaces along 25 m transect * AB: Number
 of abandoned structures along 25 m transect * Adb: animal detritus biomass
 (g) in container * Pdb: plant detritus biomass (g) in container * flb:
 flower detritus biomass (g) in container * lvb: Leaves detritus biomass
 (g) in container * sdb: seed detritus biomass (g) in container * FCN:
 container suspended particulate organic matter Carbon:Nitrogen * detN:
 container detritus Nitrogen (%) * detCN: container
 detritus Carbon:Nitrogen * detNi: container detritus d15N * detC:
 container detritus Carbon (%) * detCi: container detritus d13C * FN:
 container suspended particulate organic matter Nitrogen (%) *
 Fni: container suspended particulate organic matter d15N * FC: container
 suspended particulate organic matter Carbon (%) * Fci: container suspended
 particulate organic matter d13C * canb: container canopy cover % on a 200m
 buffer area * imp: container impervious cover % on a 200m buffer area *
 cano: canopy cover over container measured with densitometer * alphadiv:
 container vegetation alpha diversity in a 1.52 m radius * AMd: *Aedes
 mediovittatus *density * AMb: *Aedes mediovittatus *biomass * AEd: *Aedes
 aegypti* density * AEb: *Aedes aegypti* biomass * AMCN: *Aedes
 mediovittatus *Carbon:Nitrogen * AECN: *Aedes aegypti *Carbon:Nitrogen *
 AEN: *Aedes aegypti *nitrogen (%) * AENi: *Aedes aegypti *d15N *
 AEC: *Aedes aegypti *carbon (%) * AECi: *Aedes aegypti *d13C * AMN: *Aedes
 mediovittatus* nitrogen (%) * AMNi: *Aedes mediovittatus *d15N *
 AMC: *Aedes mediovittatus *carbon (%) * AMCi: *Aedes mediovittatus *d13C *
 NA: Variable not measured due to absence i.e., not a true zero. ##
 Code/software Statistical analyses were conducted using RStudio
 2023.12.0+369 "Ocean Storm". We performed an iterative PCA
 algorithm using the impute PCA function from the missMDA package. We also
 selected important variables contributing to each principal component
 using the fviz_contrib function from the factoextra package.