10.5061/DRYAD.NZS7H44PF
Roy, Lise
0000-0001-8833-1717
Centre d'Ecologie Fonctionnelle et Evolutive
Taudière, Adrien
0000-0003-1088-1182
Centre d'Ecologie Fonctionnelle et Evolutive
Bonato, Olivier
Centre d'Ecologie Fonctionnelle et Evolutive
Data from: Evaluating the link between predation and pest control services
in the mite world
Dryad
dataset
2020
European Commission
https://ror.org/00k4n6c32
RRHA 160116CR0820011
French region Rhône-Alpes-Auvergne*
French region Rhône-Alpes-Auvergne
2021-08-03T00:00:00Z
2021-05-10T00:00:00Z
en
https://doi.org/10.1002/ece3.6655
27191290 bytes
4
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
Pest regulation by natural enemies has a strong potential to reduce the
use of synthetic pesticides in agroecosystems. However, the effective role
of predation as an ecosystem service remains largely speculative,
especially with minute organisms such as mites. Predatory mites are
natural enemies for ectoparasites in livestock farms. We tested for an
ecosystem-level control of the poultry pest Dermanyssus gallinae by other
mites naturally present in manure in poultry farms, and investigated
differences among farming practices (conventional, free-range and
organic). We used a multiscale approach involving (i) in-vitro behavioural
predation experiments, (ii) arthropod inventories in henhouses with
airborne DNA, (iii) a statistical model of covariations in mite abundances
comparing farming practices. Behavioural experiments revealed that three
mites are prone to feed on D. gallinae. Accordingly, we observed
covariations between the pest and these three taxa only, in airborne DNA
at the henhouse level, and in mites sampled from manure. In most
situations, covariations in abundances were high in magnitude and their
sign was positive. Predation on a pest happens naturally in livestock
farms due to predatory mites. However, the complex dynamics of mite
trophic network prevents the emergence of a consistent assemblage-level
signal of predation. Based on these results, we suggest perspectives for
mite-based pest control and warn against any possible disruption of
ignored services through the application of veterinary drugs or
pesticides.
The study was focused on the acarofauna of poultry manure. It was
conducted in 20 barn henhouses distributed among 3 types of management
practices (6 conventional, 8 free-range and 6 organic) and located half in
eastern France down to Jura mounts (Ain region) and half in the South of
the Rhône valley (Drôme region). We conducted four successive sampling
campaigns in 2016 at 3-month intervals: March (13–22nd), June (9–15th),
September (19–22nd) and December (12-15th). Henhouses not operating at the
time of a given campaign (empty period for sanitation once a year) were
excluded from this campaign and one henhouse was not sampled in September
due to sanitary impediments. The start dates of the flocks (introduction
of a new group of producing hens into a henhouse after a sanitary empty
period) varied amongst farms; thus, the age of the flock varied between
the farms at each sampling campaign (hereafter “flock age”). In addition,
airborne DNA particles were sampled from 10 and 13 randomly selected
points at once in two of the present henhouses (F4 and F7 resp.) to build
accumulation curves and thus state how data from two points are
representative of the whole henhouse communities. During each sampling
campaign and in each henhouse, we sampled airborne particles from two
randomly selected points from ca. 30 cm above the slatted floor using a
Coriolis® µ air sampler (Bertin Instruments, Montigny-le-Bretonneux,
France). Airborne particles were collected into a PBS + 0.01% Tween32
medium at a rate of 0.1 m3 per min for 10 minutes. A 100-110 bp long DNA
fragment of the variable region V7 in the gene coding the 18S rRNA from
all Eukaryotes was amplified by PCR using the following primer pair:
forward: 5′-TTTGTCTGSTTAATTSCG-3′ and reverse 5′-CACAGACCTGTTATTGC-3′
(Guardiola et al., 2015). The PCR products were sequenced via Illumina
MiSeq by Spygen (Le Bourget-du-Lac, France). The obtained sequences were
analyzed using the bioinformatics pipeline described in Supplementary
material S2. In short, sequences were quality-filtered using Sickle
(https://github.com/najoshi/sickle) and clustered into operational
taxonomic units (OTUs) using vsearch (Rognes et al., 2016). Finally, each
OTU was taxonomically classified using RDP-classifier (Wang et al., 2007).
We provide a file presenting the entire pipeline developed for the
analysis of data obtained by ILLUMINA sequencing (carried out by Spygen,
Le Bourget du Lac) on our samples (Pipeline_air_DNA_analysis.pdf). In this
file the different steps of the analysis are detailed, with information on
the files generated during the analysis. We associate some of these files,
so as to allow access to the main part of the data, from the filtered and
de-replicated sequences file to the OTUs files associated with the
classical taxonomic assignments and the assignments to the morphoespecies
considered in the study.