
Temperature and hen harrier productivity: from local mechanisms to geographical patterns
Author(s) -
Redpath S. M.,
Arroyo B. E.,
Etheridge B.,
Leckie F.,
Bouwman K.,
Thirgood S. J.
Publication year - 2002
Publication title -
ecography
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.973
H-Index - 128
eISSN - 1600-0587
pISSN - 0906-7590
DOI - 10.1034/j.1600-0587.2002.250503.x
Subject(s) - ecology , brood , range (aeronautics) , productivity , population , nest (protein structural motif) , climate change , geography , biology , demography , biochemistry , materials science , sociology , economics , composite material , macroeconomics
Climate is an important factor limiting demography and distribution patterns in many organisms. For species with a broad geographical distribution, the mechanism by which climate influences demography is likely to vary dramatically from one end of the range to the other. In this paper we first assess, in a Scottish population of hen harriers Circus cyaneus , how temperature and rainfall influence adult behaviour and chick mortality patterns at the nest. We then test for associations between harrier productivity and weather across Scotland, towards the northern edge of the range, and Spain, towards the southern edge of the range. We show that during the nestling period, female brooding time increased in cold weather. Male provisioning rate was negatively related to temperature and rainfall. Chick mortality increased in cold temperatures and was most likely to occur at nests where male prey delivery rates were low relative to temperature. Annual values of harrier fledged brood size across Scotland were positively related to summer temperature suggesting that the patterns seen in one population held at a national scale. In Spain, however, the opposite patterns were observed with fledged brood size being negatively related to temperature. This shows that whilst the impact of weather on productivity may be equally strong at two ends of a geographical range, the mechanisms vary dramatically. Large‐scale predictive models need to take such patterns into account.