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EFFECTS OF TEMPERATURE ON CERTAIN POPULATION PARAMETERS OF LIRIOMYZA SATIVAE BLAN‐CHARD (DIPTERA: AGROMYZIDAE)
Author(s) -
Runjie ZHANG,
Daojian YU,
Changqing ZHOU
Publication year - 2000
Publication title -
insect science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.991
H-Index - 45
eISSN - 1744-7917
pISSN - 1672-9609
DOI - 10.1111/j.1744-7917.2000.tb00358.x
Subject(s) - agromyzidae , biology , fecundity , phaseolus , photoperiodism , population , population dynamics , reproduction , zoology , botany , generation time , pupa , horticulture , larva , ecology , demography , sociology
Effects of temperature on population parameters and the intrinsic rate of natural increase of the leafminer, Liriomyza sativae Blanchard, were studied at constant temperatures, 15, 20, 25, 30 and 3593 80% RH and a photoperiod of 12 :12 (L:D) in the laboratory with Phaseolus vulgaris as the host plant. Developmental time of the immature stage decreased from 38 d at 15C to lld at 35 9 3 Regression equations relating temperature ( t ) to development rates ( y ) for egg, larval and pupal stages, were y = 1.7862t ‐ 13.841, y = 1.162 t ‐ 4.946 and y = 0.634 t ‐ 5.146, respectively. Longevity of female adult decreased from 20 d at 15°C to 9 d with temperature up to 35C The most favorable temperature range for reproduction was 20°C ‐ 30 o C in which the fecundity ranged from 158 to 282 eggs per female. The lowest total mortality was 9% at 25 o C and the highest was 49% at 35 93 High intrinsic rate of natural increase ( r m ) was 0.27 and high net reproductive rate ( R 0 ) was 116.8 at temperature range between 25 t and 30 t indicating that this range was optimal for population growth and that population density might increase 117 times per generation under this temperature condition. Mean generation time (T) and time for population to double (t) decreased as temperature increased, showing a negative linear trend with temperature. The relationship between finite rate of increase (A) and temperature, however, was a positive linear regression.