1. Monthly series of abundance indexes for the English Channel squid stock, based on fishery statistics of the United Kingdom (1980–93) and France (1986–96), were compared with water temperature data. The two objectives of the study were to test empirical predictive models and to analyse the stock–environment relationship at various time scales; both correlation and time‐series statistical techniques were applied. Sea surface temperature (SST) showed inter‐annual fluctuations and month‐to‐month auto‐correlation in addition to the annual cycle. 2. Trends in squid landings and temperature at the annual scale were found to be related, whatever the statistical method used (moving averages, cumulative functions or regression using averaged data). 3. Variable selection applied in a ‘multi‐month’ model suggested that fishing season indexes could be predicted from temperatures observed in the previous winter. The link between mild winter conditions and cohort success in winter/spring spawning species suggested that early life survival (and/or growth) was involved. This empirical model is a first step in the development of environment‐predicted recruitment indexes useful for management advice. 4. Seasonal decomposition was performed on both the squid resource data and SST data in search of short‐term relationships. In spite of the flexibility of the loliginid life‐cycle, no significant relationship was found between squid seasonally adjusted indexes and temperature anomalies in the previous months. This underlined the conclusion that temperature effect on cohort success was not constant throughout the year.
Journal of Applied Ecology – Wiley
Published: Feb 1, 1999
It’s your single place to instantly
discover and read the research
that matters to you.
Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.
All for just $49/month
Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly
Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.
Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.
Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.
All the latest content is available, no embargo periods.
“Hi guys, I cannot tell you how much I love this resource. Incredible. I really believe you've hit the nail on the head with this site in regards to solving the research-purchase issue.”Daniel C.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud
“I must say, @deepdyve is a fabulous solution to the independent researcher's problem of #access to #information.”@deepthiw
“My last article couldn't be possible without the platform @deepdyve that makes journal papers cheaper.”@JoseServera