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Development and survival of the cheese mites, Acarus farris and Tyrophagus neiswanderi (Acari: Acaridae), at constant temperatures and 90% relative humidity
ARTICLE IN PRESS
Journal of Stored Products Research 43 (2007) 64–72 www.elsevier.com/locate/jspr
Development and survival of the cheese mites, Acarus farris and Tyrophagus neiswanderi (Acari: Acaridae), at constant temperatures and 90% relative humidity Ismael Sa´nchez-Ramos, Fernando A´lvarez-Alfageme, Pedro Castan˜era CSIC, CIB, Departamento de Biologı´a de Plantas, Ramiro de Maeztu, 9, 28040 Madrid, Spain Accepted 14 October 2005
Abstract Two species of acarid mites, Acarus farris and Tyrophagus neiswanderi, have been identified infesting Cabrales cheese in an Asturian maturing cave, the former being the prevalent species. The developmental rate and survival of immature stages of these mites were examined at constant temperatures, ranging from 7 to 29.7 1C for A. farris, and 10 to 31 1C for T. neiswanderi, and a relative humidity (r.h.) of 9075%. The larval stage of A. farris was particularly susceptible to low and high temperatures with 81.7% and 95.2% mortality at 7 and 29.7 1C, respectively. Tyrophagus neiswanderi larvae also showed the greatest mortality at extreme temperatures among immature stages, though at a lower level than for A. farris (8.6% and 25.6% at 10 and 31 1C, respectively). The optimal temperature for development appeared to be 27–28 1C for both species and the developmental rates were higher for A. farris than T. neiswanderi within the range of the cooler temperatures prevalent in the cheese-maturing caves. The nonlinear Logan type-III model provided the best fit for the relationship between developmental rates and temperature (R2a40.99) for all immature stages of A. farris, whereas the development of T. neiswanderi was better described by the Lactin model (R2a40.97). The lower and upper developmental threshold temperatures predicted for each stage of A. farris were 3–4 1C lower than those predicted for T. neiswanderi. The differential temperature-development rate for each species might explain the greater abundance of A. farris compared to T. neiswanderi. Furthermore, manipulation of temperature based on modeling predictions may well be used to control mite populations during the cheese maturing process. r 2005 Elsevier Ltd. All rights reserved. Keywords: Acarus farris; Tyrophagus neiswanderi; Pest modeling; Mite control; Cheese
1. Introduction Mites are a key problem for stored food throughout the world. They can be found infesting a wide variety of stored products such as ham, cheese, herring meal (Hughes, 1976) or grain (Griffiths et al., 1976; Van Hage-Hamstem and Johansson, 1992). The majority of the stored-products mites belong to the order Astigmata (Zdarkova, 1991), with the family Acaridae containing the most important species infesting stored food. Cheese is one of the most susceptible foodstuffs. Mites feed and reproduce on cheese surfaces producing an accumulation of dead mites, faeces, exuvia, eggs and bits Corresponding author. Tel.: +34 1 8373112x4264; fax: +34 1 5360432.
E-mail address: [email protected] (P. Castan˜era). 0022-474X/$ - see front matter r 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.jspr.2005.10.002
of food, which appear as a light brown dust that can reach a depth of 2 cm or more in heavy infestations (McClymont Peace, 1983). Thereby, weight losses produced by mite activity on cheese can reach 25% when no control measures are taken (Wilkin, 1979). In Spain, mite pests are particularly devastating in the production of traditional Asturian Cabrales cheese. This is a typical regional blue cheese, which is very important for the local economy. Cabrales cheese is matured in natural caves for a variable period of time that can be up to 3 months. During this period, mites can reach heavy infestations by feeding on cheese. As a result, mites are one of the most serious pest problems for Cabrales cheese production due to high labor costs to clean cheeses and the resulting weight reduction. The two species of astigmatid mites recorded infesting Cabrales cheese in maturing caves are Acarus farris
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(Oudemans) and Tyrophagus neiswanderi Johnston and Bruce, though we have found that A. farris is the prevalent species. The former species has been reported as a pest of cheese (Hughes, 1976; Su¨ss et al., 1978; Wilkin, 1979), whereas the latter has not been recorded on stored food products. Tyrophagus neiswanderi has been traditionally found infesting a wide range of ornamental plants and horticultural crops (Johnston and Bruce, 1965; Hughes, 1976; Czajkowska et al., 1988). Moreover, Tyrophagus mites are mainly considered as saprophagous or fungivorous mites (Czajkowska et al., 1988; Evans, 1992), and T. neiswanderi has recently been reared on a fungal pathogen (Czajkowska, 2002). Accordingly, the presence of T. neiswanderi infesting Cabrales cheese seems to be associated with the great number of fungi present on the surface of the cheeses (Nu´n˜ez et al., 1981) and inside the maturing caves. Temperature plays an important role in determining mite survival and development and, therefore, its manipulation is one of the simplest physical methods of control. Nevertheless, the effect of this factor on the development of A. farris and T. neiswanderi has not previously been studied. Furthermore, no attempt has been made to use developmental models to describe this relationship. Accordingly, it is essential to establish the developmental rate of immature stages in a wide range of temperatures to develop a reliable model that closely simulates the occurrence of immature stages. This relationship is a fundamental component of population dynamics and its accurate description is essential for predicting the developmental thresholds to develop sound control strategies. The aim of the present work was to investigate the temperature-dependent developmental rate and survival of all immature stages of A. farris and T. neiswanderi and to establish the distribution of adult emergence over a wide range of constant temperatures using nonlinear regression models. Additionally, an attempt was made to clarify the influence of temperature on the prevalence of A. farris in Cabrales cheese.
2. Material and methods
65
2.2. Development of immature stages The development of immature stages was monitored at constant temperatures of 7, 10, 15, 20, 25, 27, 29 and 29.7 1C for A. farris, and 10, 15, 20, 25, 27, 29 and 31 1C for T. neiswanderi. For all temperatures, environmental chambers were set at 9075% r.h. without light. Mite subpopulations were acclimated for at least one generation at each temperature. Assays were conducted using rearing cells (Barker, 1967). These cells were concave slides (15–18 mm diameter concavity and 0.5–0.8 mm depth of well) covered with a cover glass (22 22 mm) and sealed with a drop of water. Eggs were obtained by allowing females acclimated at each temperature to lay eggs for o24 h in the rearing cell. Thereafter, the eggs required at each temperature were removed using a thin camel hair brush and transferred individually to inside each cell together with a brewer’s yeast flake. At intermediate temperatures 100–160 eggs were used for both species. At extreme temperatures, 500–600 eggs were used for A. farris and about 200 for T. neiswanderi as higher mortality was expected. Egg development was checked daily, and hatching date, duration of each stage and survival were recorded for both species. 2.3. Mathematical models The percentages of mortality at constant temperatures for the different stages of A. farris and T. neiswanderi were fitted to the following equation: 2
y ¼ eaþbTþcT , where y is the percentage mortality, T is temperature and a, b and c are fitted parameters. The relationship between temperature and developmental rate (r ¼ 1=d, where d is the mean developmental time in days) was determined by fitting the nonlinear models of Logan type III (Hilbert and Logan, 1983), Lactin et al. (1995) and Briere et al. (1999). These models were selected because, unlike other nonlinear models, they can predict the low-temperature development threshold. The models were fitted to the stages of egg, larva, protonymph, tritonymph and to the complete preimaginal period.
2.1. Mites Stock cultures of A. farris and T. neiswanderi were established from infested samples of Cabrales cheese obtained from the maturing cave of CAPSA in Carren˜a de Cabrales (Spain). The mites were maintained on brewer’s yeast, and held in cylindrical plastic cages (12 cm diameter and 5.5 cm high). The cages were covered with round plastic plates that had a 5 cm diameter hole in the center sealed with filter paper disks for ventilation. The rearing cages were kept in an environmental chamber at 2570.5 1C, 9075% relative humidity (r.h.) in dark conditions, standing in a water-filled plastic tray to prevent escape and to maintain a high humidity. Adults were sexed by observing their secondary sexual characteristics (Hughes, 1976).
2.3.1. Logan type-III model The mathematical expression of this model is a combination of two functions. The first function represents the ascending rate of development with increasing temperatures. This function is sigmoidal and is analogous to Holling’s type-III functional response curve (Holling, 1965). The second function, developed by Logan et al. (1976), represents the descending portion of developmental rate with increasing temperatures. The expression is h i rðTÞ ¼ CðT T b Þ2 =ððT T b Þ2 þ D2 Þ eððT m ðTT b ÞÞ=DTÞ where T is temperature, rðTÞ is the rate of development at temperature T, Tb is the base temperature (for temperatures
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below Tb the rate of development presumably equals 0), Tm is the lethal maximum temperature threshold ( 1C above Tb), DT is the width of the high-temperature boundary area, c and D are empirical constants. 2.3.2. Lactin model Lactin et al. (1995) modified the first Logan nonlinear model (Logan et al., 1976, Eq. (6)) by eliminating a redundant parameter and incorporating an intercept parameter. This allows estimation of a low-temperature developmental threshold. The Lactin expression is rðTÞ ¼ erT e½rT max ðT max TÞ=D þ l, where T is temperature, rðTÞ is the rate of development at temperature T, Tmax is the supraoptimal temperature at which rðTÞ ¼ l, D is the range of temperatures between Tmax and the temperature at which rðTÞ is maximum. r describes the acceleration of the function from the lowtemperature threshold to the optimal temperature, and parameter l allows the curve to intersect the abscissa at suboptimal temperatures, thus allowing estimation of a lower developmental threshold. 2.3.3. Briere model This is a simplified developmental model for describing the nonlinear relationship between the developmental rate and temperature. The 3-parameter model proposed by Briere et al. (1999) is 8 > < 0; TpT 0 ; pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi rðTÞ ¼ aTðT T 0 Þ T L T ; T 0 pTpT L ; > : 0; TXT ; L where T is temperature, rðTÞ is the rate of development at temperature T, T0 is the lower temperature developmental threshold, TL is the lethal temperature (upper threshold) and a is an empirical constant. For both species, the distribution of adult emergence was determined by calculating the cumulative proportion of mites that reached the adult stage at normalized time x ¼ time/mean time for each temperature. The relationship was described using the equation of Stinner et al. (1975): F ðzÞ ¼ ð1 zÞ
ðkzb Þ
,
where F ðzÞ is the cumulative proportion of mites that completed development, z ¼ ðxmax xÞ=ðxmax xmin Þ, x is normalized time (time/mean time), xmin is normalized time at which the first mite completed development, xmax is normalized time at which the last mite completed development, and k and b are empirical constants. 2.4. Analytical methods Models were fitted and parameter values estimated using the SAS NLIN procedure, based on Marquardt’s nonlinear least-squares method (SAS Institute, 1987) and using Tablecurve 2D (Jandel Scientific, 1994). Initial parameter
estimates for Logan type-III and Lactin models were obtained as indicated by Logan (1988). Developmental models were compared by using the adjusted coefficient of determination given by Kvalseth (1985). This coefficient allows the comparison of functions with different degrees of freedom and its expression is R2a ¼ 1 ð1 R2i Þðn 1=n k 1Þ, where n is the number of observations, k is the number of parameters in the ith function, R2a is the adjusted coefficient of determination, and Ri2 is computed as , n n 2 X X 2 Ri ¼ 1 yj y^ ji ðyj yÞ2 j¼1
j¼1
for yj the jth observed developmental rate and y^ ji the jth predicted developmental rate from the ith function. 3. Results 3.1. Survival Parameter estimates for the functions describing the relationship between temperature and mortality are shown in Table 1. For both mites, the coefficient of determination obtained was higher than 0.94 except at the tritonymph stage of A. farris, where it was around 0.31. The mortality values obtained for the immature stages and for the combined preimaginal period in both species fitted a U-shaped pattern, with the exception of the tritonymph stage of A. farris (Fig. 1). In general, the percentage mortality of immature stages was higher for A. farris than for T. neiswanderi (Fig. 1e). In both species, the larva was the most susceptible developmental stage at the extreme temperatures assayed for each species, the percentage mortality being 81.7% and 95.2% for A. farris at 7 and 29.7 1C, respectively, and 8.3% and 25.6% for T. neiswanderi at 10 and 31 1C, respectively. 3.2. Developmental time Developmental time in both species decreased sharply as temperature increased from the lowest temperatures assayed to 15 1C, and then declined slowly until reaching the lowest value at 27 1C (Tables 2 and 3). Thereafter, small increases in developmental time were observed for both species until the highest temperature was reached. In both species, the egg stage had the longest developmental time at all temperatures, accounting for around 36% of the preimaginal period, whereas the larval, protonymph and tritonymph stages accounted for around 24%, 18% and 22% of the total developmental time, respectively. 3.3. Modeling immature development rates The estimated parameters and fit statistics of the three models tested against both species are presented in
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Table 1 2
Parameter estimates and coefficients of determination (R2) for the function y ¼ eaþbTþcT describing the relationship between temperature (1C) and mortality of all immature stages for A. farris and T. neiswanderi Species
Life stage
R2
Parameter estimates a
b
c
A. farris
Eggs Larvae Protonymph Tritonymph Egg to adult
8.0259 9.7593 17.2902 0.0593 7.6190
0.7933 0.9481 2.4268 0.2277 0.5499
0.02262 0.02600 0.06607 0.00835 0.01511
0.9435 0.9917 0.9860 0.3121 0.9845
T. neiswanderi
Eggs Larvae Protonymph Tritonymph Egg to adult
6.7010 12.5823 52.9414 22.4467 11.4104
0.6881 1.3982 7.0354 2.7962 1.1441
0.01850 0.03538 0.17480 0.06980 0.02930
0.9792 0.9711 0.9919 0.9961 0.9746
A. farris
(A)
Mortality (%)
(B)
(C)
(D)
100 80 60 40 20 0
100 80 60 40 20 0
0
5
5
10 15 20 25 30 35
10 15 20 25 30 35
100 80 60 40 20 0
100 80 60 40 20 0
100 80 60 40 20 0
0
5
10 15 20 25 30 35
0
5
10 15 20 25 30 35
0
5
10 15 20 25 30 35
100 80 60 40 20 0 0
5
10 15 20 25 30 35 100 80 60 40 20 0
100 80 60 40 20 0 0 100 80 60 40 20 0
(E)
0
T. neiswanderi
0
5
5
10 15 20 25 30 35
10 15 20 25 30 35
100 80 60 40 20 0
was obtained by the Logan type-III model ðR2a 40:99Þ, whereas the development of T. neiswanderi was better described by the Lactin model ðR2a 40:97Þ. The lower and upper developmental threshold temperatures predicted by these functions for each developmental stage of A. farris were lower than those predicted for the stages of T. neiswanderi (Table 6). Plots of the best fitting development functions and data are presented in Fig. 2. The optimal temperature estimates predicted by the Logan type-III model for the development of eggs, larvae, protonymphs, tritonymphs and for the total preimaginal period of A. farris were, respectively, 28.7, 26.3, 26.8, 27.7 and 27.9 1C. At these temperatures, the predicted developmental rates were 0.27, 0.39, 0.63, 0.55 and 0.10 day1. Likewise, the optimal temperatures predicted by the Lactin model for the different immature stages and for the total preimaginal period of T. neiswanderi were 27.7, 27.9, 27.4, 27.1 and 27.6 1C, and the developmental rates at these temperatures were 0.23, 0.36, 0.48, 0.38 and 0.09 day1, respectively. 3.4. Distribution of adult emergence
0
5
10 15 20 25 30 35
0
5
10 15 20 25 30 35
Temperature (ºC)
Fig. 1. Percentage mortality of A. farris and T. neiswanderi at constant temperatures (1C). (A) Egg. (B) Larva. (C) Protonymph. (D) Tritonymph. (E) Egg to adult. (K) Observed percentages of mortality; () line of best 2 fit according to the function y ¼ eaþbTþcT .
Tables 4 and 5. A high coefficient of determination ðR2a 40:83Þ was obtained with the three models for all developmental stages. However, the best fit for A. farris
The distribution of adult emergence was well described for both species using the equation developed by Stinner et al. (1975), as evidenced by the high R2 (Table 7, Fig. 3). For A. farris, adult emergence began at the normalized age of 0.79 (79% of the mean time of development), and was completed at the normalized age of 1.49, (149% of the mean developmental time). In the case of T. neiswanderi, these values were very similar: 0.82 and 1.59, respectively. 4. Discussion The immature stages of A. farris and T. neiswanderi showed differential survival when developing at different constant temperatures. The differences in mortality observed between both species were particularly distinct at
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Table 2 Mean developmental time (days7SE) of immature stages of A. farris at constant temperatures Temperature (1C)
Initial no. of eggs
7
518
10
160
15
143
20
108
25
107
27
160
29
160
29.7
600
Life stage (n) Hatched eggs
Larvae
Protonymph
Tritonymph
Egg to adult
36.570.2 (334) 16.770.2 (142) 8.970.1 (134) 5.570.1 (98) 4.170.1 (99) 4.070.1 (112) 3.870.1 (89) 4.570.1 (63)
28.270.7 (61) 11.970.2 (122) 5.570.1 (127) 3.670.1 (90) 2.570.1 (89) 2.670.1 (82) 3.170.1 (37) 3.770.3 (3)
21.270.7 (40) 10.270.2 (118) 3.970.1 (124) 2.470.1 (90) 1.670.1 (87) 1.670.1 (80) 2.070.1 (33) 3.070.0 (2)
21.070.5 (40) 12.670.3 (107) 4.670.1 (120) 2.970.1 (88) 2.070.1 (84) 1.970.1 (78) 2.270.1 (33) 5.071.0 (2)
105.571.8 (40) 50.970.6 (107) 22.970.2 (120) 14.470.1 (88) 10.270.1 (84) 10.070.1 (78) 10.470.2 (33) 15.570.5 (2)
(n), Number of individuals at each developmental stage.
Table 3 Mean developmental time (days7SE) of immature stages of T. neiswanderi at constant temperatures Temperature (1C)
Initial no. of eggs
10
215
15
110
20
110
25
110
27
110
29
110
31
210
Life stage (n) Hatched eggs
Larvae
Protonymph
Tritonymph
Egg to adult
29.470.2 (204) 11.670.1 (107) 6.970.2 (109) 4.370.1 (106) 4.370.1 (103) 4.570.1 (101) 5.070.1 (160)
19.670.2 (187) 7.670.1 (103) 4.470.1 (107) 3.070.1 (105) 2.870.1 (101) 2.870.1 (96) 3.370.1 (119)
15.470.2 (185) 5.770.1 (102) 3.470.1 (107) 2.270.1 (104) 2.170.1 (101) 2.270.1 (96) 2.870.1 (99)
19.170.2 (177) 7.170.1 (101) 4.070.1 (107) 2.670.1 (104) 2.770.1 (101) 2.870.1 (95) 3.670.1 (82)
83.170.5 (177) 31.970.2 (101) 18.770.2 (107) 12.170.1 (104) 11.870.1 (101) 12.370.2 (95) 14.270.2 (82)
(n), Number of individuals at each developmental stage.
temperatures greater than 25 1C. At the egg stage, the lower mortality found in T. neiswanderi could be due to the fact that the exochorion present in Tyrophagus species acts to prevent desiccation, a well-established mortality-increasing factor in mites (Fields, 1992), whereas in A. farris the probable absence of an exochorion, as in Acarus siro (Witalin˜ski, 1993), a closely related species, would account for the high mortality observed. The desiccation effect of high temperatures in mobile immature stages seems to be especially decisive in astigmatid mites, since they have weakly sclerotized cuticles that permit the loss of fluids through the body surface (Wharton et al., 1979; Evans, 1992). Water loss occurs even at 90% r.h. because the
water activity of astigmatid mites is 0.99 (Wharton and Furumizo, 1977), and at temperatures near the upper developmental threshold this effect is much more marked (Wharton and Arlian, 1972). Furthermore, this effect appears to be greater in smaller stages because they have a greater surface/volume relationship (Sa´nchez-Ramos and Castan˜era, 2001), and would explain the differences of mortality observed at high temperatures among different stages of the same species, and, therefore, the fact that the larval stage was the most susceptible to high extreme temperatures. This is in agreement with results previously established in T. putrescentiae (Sa´nchez-Ramos and Castan˜era, 2001).
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Table 4 Parameter estimates for the three temperature-dependent models describing the relationships between temperature (1C) and developmental rates (r ¼ 1/d) of all immature stages for A. farris Model
Parameter
Life stage Eggs
Larvae
Protonymph
Tritonymph
Egg to adult
Logan type III
c D DT Tm Tb R2a
0.4779 24.2619 0.3201 29.3390 1.0588 0.9902
1.6716 43.1020 2.1925 33.5425 0.2418 0.9921
2.1131 36.4789 1.2708 29.9963 1.2708 0.9925
1.5671 34.7511 0.6427 29.1586 1.3799 0.9930
0.2224 27.2944 0.6256 29.1848 1.4433 0.9913
Lactin
r Tmax D l R2a
0.1263 35.1415 7.8418 0.0542 0.9751
0.1536 32.3653 6.4542 0.0524 0.9921
0.1916 31.1705 5.1961 0.0226 0.9759
0.0188 30.6317 0.6663 0.1100 0.9917
0.0045 31.7815 0.6787 1.0246 0.9904
Briere
a T0 TL R2a
0.00013910 2.0502 34.1320 0.9772
0.00029249 4.5013 31.3854 0.9791
0.00055216 6.6781 30.6906 0.9387
0.00050422 7.1936 30.3347 0.8315
0.00007794 5.2919 31.4656 0.9262
R2a is the adjusted coefficient of determination.
Table 5 Parameter estimates for the three temperature-dependent models describing the relationships between temperature (1C) and developmental rates (r ¼ 1/d) of all immature stages for T. neiswanderi Model
Parameter
Life stage Eggs
Larvae
Protonymph
Tritonymph
Egg to adult
Logan type III
c D DT Tm Tb R2a
10.1752 133.8118 4.5646 45.6734 2.1880 0.9480
2.4298 57.1383 2.6431 35.9912 1.4749 0.9973
23.2169 153.1676 3.2239 41.8224 1.8482 0.9778
13.8830 117.7182 4.1881 42.3828 2.7101 0.9602
2.8905 121.1698 4.0933 43.2970 2.1135 0.9792
Lactin
r Tmax D l R2a
0.1343 35.1609 7.3939 0.0573 0.9746
0.1457 34.7338 6.8139 0.0567 0.9977
0.1555 33.8303 6.3810 0.0659 0.9910
0.1407 34.1718 7.0348 0.0939 0.9808
0.1385 34.8518 7.2015 0.0192 0.9906
Briere
a T0 TL R2a
0.00016022 6.5660 33.7139 0.9737
0.00024397 6.6551 33.8296 0.9842
0.00036269 7.4125 32.9019 0.9706
0.00029544 7.2163 32.6789 0.9759
0.00005997 6.7882 33.4946 0.9830
R2a is the adjusted coefficient of determination.
At low temperatures, we have also observed differences in mortality among immature stages within each species, which is in agreement with reported findings for other astigmatid mites like A. siro (Cunnington, 1965) or T. putrescentiae (Sa´nchez-Ramos and Castan˜era, 2001). Several mechanisms have been proposed underlying the lethal effects of low temperatures on insects and mites, such as the accumulation of toxic products which otherwise would be eliminated at normal temperatures (Mullen and
Arbogast, 1984), alterations in the functionality of the cell membrane, imbalances in the rate of biochemical reactions due to reduced enzyme activity or modifications of the ionic activity of molecules (Fields, 1992). Nevertheless, it cannot be explained with our data why the early stages of these mites are more susceptible than the later ones. Interestingly, the immature stages of A. farris seem to be slightly better adapted to low and intermediate temperatures than those of T. neiswanderi, as evidenced by the
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Table 6 Thermal developmental thresholds (1C) estimated by the best-fitted functions for the immature stages of A. farris and T. neiswanderi A. farris
Life stage
Eggs Larvae Protonymph Tritonymph Egg to adult
T. neiswanderi
LT
UT
LT
UT
1.1 0.2 1.7 1.4 1.4
30.3 31.5 30.5 30.0 30.3
5.3 3.9 4.0 5.7 5.2
34.6 34.4 33.6 33.7 34.4
LT, predicted lower developmental threshold. UT, predicted upper developmental threshold.
T. neiswanderi
A. farris
Developmental Rate (day-1)
(A)
(B)
(C)
(D)
0.3
0.3
0.2
0.2
0.1
0.1
0
5
10 15 20 25 30 35
0
0.4
0.4
0.3
0.3
0.2
0.2
0.1
0.1
0
0
5
10 15 20 25 30 35
0
0.8
0.8
0.6
0.6
0.4
0.4
0.2
0.2
0
0
5
10 15 20 25 30 35
0
0.6
0.6
0.4
0.4
0.2
0.2
0
0
5
10 15 20 25 30 35
0
0.12
0.12
0.08
0.08
0.04
0.04
0
(E)
0
0
5
10 15 20 25 30 35
0
0
5
10 15 20 25 30 35
0
5
10 15 20 25 30 35
0
5
10 15 20 25 30 35
0
5
10 15 20 25 30 35
0
5
10 15 20 25 30 35
Temperature (ºC)
Fig. 2. Predicted rate of development (day1) of A. farris by the Logan type-III model and T. neiswanderi by the Lactin model as a function of temperature (1C). (A) Egg. (B) Larva. (C) Protonymph. (D) Tritonymph. (E) Egg to adult. (K) Observed developmental rate; (–) line of best fit by nonlinear least square.
higher developmental rates of A. farris and the lower developmental thresholds. In contrast, the immature stages of T. neiswanderi are better adapted to higher temperatures as evidenced by their upper developmental thresholds. These data contribute to explaining the prevalence of A.
farris in the cool Cabrales maturing caves. Similarly, Robertson (1961) found that in places where three species of mites of the genus Tyrophagus coexisted, their relative abundance was mainly dependent on temperature. Nevertheless, other factors, such as food preference and quality for each mite species, as well as interspecific competition mechanisms should be considered to explain the relative abundance of A. farris and T. neiswanderi in the maturing caves. We have found the Logan type-III function to be the best of the developmental models selected to describe the relationship between developmental rate and temperature for A. farris, whereas the Lactin model provided the best results for T. neiswanderi. The application of these nonlinear models enables prediction of the thermal developmental thresholds of a species, a key tool for developing control strategies based on temperature modifications. In the case of Cabrales cheese, the application of high temperatures is not feasible because the physical and organoleptic characteristics of this product are greatly modified under these conditions. Therefore, temperature modification must be focused on low temperatures. Thus, Fields (1992) pointed out that to prevent mites from developing on damp grain, temperatures must be lowered to 2 1C. This is in accordance with the lower developmental thresholds here calculated for T. neiswanderi, and very close to the lower developmental thresholds obtained for A. farris. At this temperature, the predicted rate of development of this mite is so small, that almost no population increase and mite activity would be expected under these conditions. The results obtained here for A. farris and T. neiswanderi were well fitted to the Stinner function (Stinner et al., 1975) and revealed similar variability for these species as evidenced by similar steepness of both distributions. Therefore, with these functions and with the best developmental models obtained for each species it is possible to simulate the development times of A. farris and T. neiswanderi immature stages under fluctuating temperatures in the maturing caves. The simulation accumulates fractional development as a function of time by summing the rates provided by the best developmental models and these cumulative rates are then used as the independent variable in the Stinner equation to distribute the proportion of the population completing development over time (Wagner, 1995). The data presented here provide fundamental information to understand the effect of temperature on development and survival of A. farris and T. neiswanderi. Our findings indicate that application of lower temperatures during the maturing process of Cabrales cheese might be used to reduce mite populations below their economic threshold, since the microbial processes occurring during cheese-maturing are not significantly altered with detrimental effects on the organoleptic characteristics of the cheeses (P. Balbarie, pers. comm.). Nevertheless, further research is needed involving reproductive parameters and
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Table 7 Parameter estimates and coefficients of determination (R2) for the function describing the relationship between normalized time and the cumulative probability of adult emergence for A. farris and T. neiswanderi Species
A. farris T. neiswanderi a
Empirically obtained parametersa
Parameter estimatesb
R2
xmin
xmax
k
b
0.7882 0.8157
1.4880 1.5940
1.3424 1.9678
3.4829 7.0767
0.9679 0.9565
xmin, normalized time of first emergence, xmax, normalized time of last emergence. k and b, empirical constants.
b
References
A. farris 1
0.75
0.5
Cumulative proportion
0.25
0 0.70
0.85
1.00
1.15
1.30
1.45
1.60
1.30
1.45
1.60
T. neiswanderi 1
0.75
0.5
0.25
0 0.70
0.85
1.00
1.15
Normalized time (time / mean time) Fig. 3. Cumulative proportion of adult emergence for A. farris and T. neiswanderi as a function of normalized time (time/mean time). (K), observed cumulative proportion; (–), line of best fit according to the Stinner function.
evaluation of the intrinsic rate of increase of these mites under different constant temperatures. Acknowledgments The research reported in the present paper was funded by the Corporacio´n Agroalimentaria Pen˜asanta (CAPSA) and the MCYT (project n. PTR95.0612.OP). We are grateful to Dr. F. Ortego for valuable comments and discussion and to Philippe Balbarie (CAPSA) for assistance in mite sampling.
Barker, P.S., 1967. The effects of high humidity and different temperatures on the biology of Tyrophagus putrescentiae (Schrank) (Acarina: Tyroglyphidae). Canadian Journal of Zoology 45, 91–96. Briere, J.F., Pracros, P., Le Roux, A.Y., Pierre, J.S., 1999. A novel rate model of temperature-dependent development for arthropods. Environmental Entomology 28, 22–29. Cunnington, A.M., 1965. Physical limits for complete development of the grain mite, Acarus siro L. (Acarina, Acaridae), in relation to its world distribution. Journal of Applied Ecology 2, 295–306. Czajkowska, B., 2002. Development of acarid mites on Fusarium oxysporum—a pathogen of stored bulbs/corms of ornamental plants. Bulletin of the Polish Academy of Sciences: Biological Sciences 50, 37–48. Czajkowska, B., Van de Vrie, M., Kropczynska, D., 1988. Mites of the genus Tyrophagus as pests of ornamentals in greenhouses. Mededelingen van de Faculteit Landbouwwetenschappen Rijksuniversiteit Gent 53, 799–809. Evans, G.O., 1992. Principles of Acarology. CAB International, Wallingford. Fields, P.G., 1992. The control of stored-products insects and mites with extreme temperatures. Journal of Stored Products Research 28, 89–118. Griffiths, D.A., Wilkin, D.R., Southgate, B.J., Lynch, S.M., 1976. A survey of mites in bulk grain stored on farms in England and Wales. Annals of Applied Biology 82, 180–185. Hilbert, D.W., Logan, J.A., 1983. Empirical model of nymphal development for the migratory grasshopper, Melanoplus sanguinipes (Orthoptera: Acrididae). Environmental Entomology 12, 1–5. Holling, C.S., 1965. The functional response of predators to prey density and its role in mimicry and population regulation. Memoirs of the Entomological Society of Canada No. 45. Hughes, A.M., 1976. The mites of stored food and houses. Technical Bulletin No. 9, Ministry of Agriculture, Fisheries and Food. HMSO, London. Jandel Scientific, 1994. TableCurve User’s Manual. San Rafael, CA. Johnston, D.E., Bruce, W.A., 1965. Tyrophagus neiswanderi, a new acarid mite of agricultural importance (Acari-Acaridae). Research Bulletin Ohio Agricultural Research and Development Center 977, 1–17. Kvalseth, T.O., 1985. Cautionary note about R2. American Statistician 39, 279–285. Lactin, D.J., Holliday, N.J., Johnson, D.L., Craigen, R., 1995. Improved rate model of temperature-dependent development by arthropods. Environmental Entomology 24, 68–75. Logan, J.A., 1988. Toward an expert system for development of pest simulation models. Environmental Entomology 17, 359–376. Logan, J.A., Wolkind, D.J., Hoyt, S.C., Tanigoshi, L.K., 1976. An analytic model for description of temperature dependent rate phenomena in arthropods. Environmental Entomology 5, 1133–1140. McClymont Peace, D., 1983. Reproductive success of the mite Acarus siro L. on stored Cheddar cheese of different ages. Journal of Stored Products Research 19, 97–104.
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Mullen, M.A., Arbogast, R.T., 1984. Low temperatures to control storedproduct insects. In: Baur, F.J. (Ed.), Insect Management for Food Storage and Processing. AACC, St. Paul, MN, pp. 257–264. Nu´n˜ez, M., Medina, M., Gaya, P., Dı´ as-Amado, C., 1981. Les levures et les moisissures dans le fromage bleu de Cabrales. Lait 61 (601/602), 62–79. Robertson, P.L., 1961. A morphological study of variation in Tyrophagus (Acarina), with particular reference to populations infesting cheese. Bulletin of Entomological Research 52, 501–529. Sa´nchez-Ramos, I., Castan˜era, P., 2001. Development and survival of Tyrophagus putrescentiae (Acari: Acaridae) at constant temperatures. Environmental Entomology 30, 1082–1089. SAS Institute, 1987. SAS/STAT Guide for Personal Computers, version 6. SAS Institute, Cary, NC. Stinner, R.E., Butler Jr., G.D., Bacheler, J.S., Tuttle, C., 1975. Simulation of temperature-dependent development in population dynamics models. Canadian Entomologist 107, 1167–1174. Su¨ss, L., Galli, A., Zambrini, A., 1978. Studio dell’associazione Acarimuffe su formaggio Provolone in vitro ed in vivo. L’Industria del Latte 14, 13–22.
Van Hage-Hamstem, M., Johansson, S.G.O., 1992. Storage mites. Experimental and Applied Acarology 16, 117–128. Wagner, T.L., 1995. Temperature dependent development, mortality, and adult size of sweetpotato whitefly biotype B (Homoptera: Aleyrodidae) on cotton. Environmental Entomology 24, 1179–1188. Wharton, G.W., Arlian, L.G., 1972. Utilization of water by terrestrial mites and insects. In: Rodrı´ guez, J.G. (Ed.), Insect and Mite Nutrition. North-Holland, Amsterdam, pp. 153–165. Wharton, G.W., Furumizo, R.T., 1977. Supracoxal gland secretions as a source of fresh water for Acaridi. Acarologia 19, 112–116. Wharton, G.W., Duke, K.M., Epstein, H.M., 1979. Water and the physiology of house dust mites. In: Rodrı´ guez, J.G. (Ed.), Recent Advances in Acarology. Academic Press, New York, pp. 325–335. Wilkin, D.R., 1979. The control of mites in cheese stores. In: Rodrı´ guez, J.G. (Ed.), Recent Advances in Acarology. Academic Press, New York, pp. 221–229. Witalin˜ski, W., 1993. Egg shells in mites: vitelline envelope and chorion in Acaridida (Acari). Experimental and Applied Acarology 17, 321–344. Zdarkova, E., 1991. Stored product acarology. In: Dusbabek, F., Bukva, V. (Eds.), Modern Acarology, vol. 1. Academia, Prague, pp. 211–218.
Journal of Stored Products Research 43 (2007) 64–72 www.elsevier.com/locate/jspr
Development and survival of the cheese mites, Acarus farris and Tyrophagus neiswanderi (Acari: Acaridae), at constant temperatures and 90% relative humidity Ismael Sa´nchez-Ramos, Fernando A´lvarez-Alfageme, Pedro Castan˜era CSIC, CIB, Departamento de Biologı´a de Plantas, Ramiro de Maeztu, 9, 28040 Madrid, Spain Accepted 14 October 2005
Abstract Two species of acarid mites, Acarus farris and Tyrophagus neiswanderi, have been identified infesting Cabrales cheese in an Asturian maturing cave, the former being the prevalent species. The developmental rate and survival of immature stages of these mites were examined at constant temperatures, ranging from 7 to 29.7 1C for A. farris, and 10 to 31 1C for T. neiswanderi, and a relative humidity (r.h.) of 9075%. The larval stage of A. farris was particularly susceptible to low and high temperatures with 81.7% and 95.2% mortality at 7 and 29.7 1C, respectively. Tyrophagus neiswanderi larvae also showed the greatest mortality at extreme temperatures among immature stages, though at a lower level than for A. farris (8.6% and 25.6% at 10 and 31 1C, respectively). The optimal temperature for development appeared to be 27–28 1C for both species and the developmental rates were higher for A. farris than T. neiswanderi within the range of the cooler temperatures prevalent in the cheese-maturing caves. The nonlinear Logan type-III model provided the best fit for the relationship between developmental rates and temperature (R2a40.99) for all immature stages of A. farris, whereas the development of T. neiswanderi was better described by the Lactin model (R2a40.97). The lower and upper developmental threshold temperatures predicted for each stage of A. farris were 3–4 1C lower than those predicted for T. neiswanderi. The differential temperature-development rate for each species might explain the greater abundance of A. farris compared to T. neiswanderi. Furthermore, manipulation of temperature based on modeling predictions may well be used to control mite populations during the cheese maturing process. r 2005 Elsevier Ltd. All rights reserved. Keywords: Acarus farris; Tyrophagus neiswanderi; Pest modeling; Mite control; Cheese
1. Introduction Mites are a key problem for stored food throughout the world. They can be found infesting a wide variety of stored products such as ham, cheese, herring meal (Hughes, 1976) or grain (Griffiths et al., 1976; Van Hage-Hamstem and Johansson, 1992). The majority of the stored-products mites belong to the order Astigmata (Zdarkova, 1991), with the family Acaridae containing the most important species infesting stored food. Cheese is one of the most susceptible foodstuffs. Mites feed and reproduce on cheese surfaces producing an accumulation of dead mites, faeces, exuvia, eggs and bits Corresponding author. Tel.: +34 1 8373112x4264; fax: +34 1 5360432.
E-mail address: [email protected] (P. Castan˜era). 0022-474X/$ - see front matter r 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.jspr.2005.10.002
of food, which appear as a light brown dust that can reach a depth of 2 cm or more in heavy infestations (McClymont Peace, 1983). Thereby, weight losses produced by mite activity on cheese can reach 25% when no control measures are taken (Wilkin, 1979). In Spain, mite pests are particularly devastating in the production of traditional Asturian Cabrales cheese. This is a typical regional blue cheese, which is very important for the local economy. Cabrales cheese is matured in natural caves for a variable period of time that can be up to 3 months. During this period, mites can reach heavy infestations by feeding on cheese. As a result, mites are one of the most serious pest problems for Cabrales cheese production due to high labor costs to clean cheeses and the resulting weight reduction. The two species of astigmatid mites recorded infesting Cabrales cheese in maturing caves are Acarus farris
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(Oudemans) and Tyrophagus neiswanderi Johnston and Bruce, though we have found that A. farris is the prevalent species. The former species has been reported as a pest of cheese (Hughes, 1976; Su¨ss et al., 1978; Wilkin, 1979), whereas the latter has not been recorded on stored food products. Tyrophagus neiswanderi has been traditionally found infesting a wide range of ornamental plants and horticultural crops (Johnston and Bruce, 1965; Hughes, 1976; Czajkowska et al., 1988). Moreover, Tyrophagus mites are mainly considered as saprophagous or fungivorous mites (Czajkowska et al., 1988; Evans, 1992), and T. neiswanderi has recently been reared on a fungal pathogen (Czajkowska, 2002). Accordingly, the presence of T. neiswanderi infesting Cabrales cheese seems to be associated with the great number of fungi present on the surface of the cheeses (Nu´n˜ez et al., 1981) and inside the maturing caves. Temperature plays an important role in determining mite survival and development and, therefore, its manipulation is one of the simplest physical methods of control. Nevertheless, the effect of this factor on the development of A. farris and T. neiswanderi has not previously been studied. Furthermore, no attempt has been made to use developmental models to describe this relationship. Accordingly, it is essential to establish the developmental rate of immature stages in a wide range of temperatures to develop a reliable model that closely simulates the occurrence of immature stages. This relationship is a fundamental component of population dynamics and its accurate description is essential for predicting the developmental thresholds to develop sound control strategies. The aim of the present work was to investigate the temperature-dependent developmental rate and survival of all immature stages of A. farris and T. neiswanderi and to establish the distribution of adult emergence over a wide range of constant temperatures using nonlinear regression models. Additionally, an attempt was made to clarify the influence of temperature on the prevalence of A. farris in Cabrales cheese.
2. Material and methods
65
2.2. Development of immature stages The development of immature stages was monitored at constant temperatures of 7, 10, 15, 20, 25, 27, 29 and 29.7 1C for A. farris, and 10, 15, 20, 25, 27, 29 and 31 1C for T. neiswanderi. For all temperatures, environmental chambers were set at 9075% r.h. without light. Mite subpopulations were acclimated for at least one generation at each temperature. Assays were conducted using rearing cells (Barker, 1967). These cells were concave slides (15–18 mm diameter concavity and 0.5–0.8 mm depth of well) covered with a cover glass (22 22 mm) and sealed with a drop of water. Eggs were obtained by allowing females acclimated at each temperature to lay eggs for o24 h in the rearing cell. Thereafter, the eggs required at each temperature were removed using a thin camel hair brush and transferred individually to inside each cell together with a brewer’s yeast flake. At intermediate temperatures 100–160 eggs were used for both species. At extreme temperatures, 500–600 eggs were used for A. farris and about 200 for T. neiswanderi as higher mortality was expected. Egg development was checked daily, and hatching date, duration of each stage and survival were recorded for both species. 2.3. Mathematical models The percentages of mortality at constant temperatures for the different stages of A. farris and T. neiswanderi were fitted to the following equation: 2
y ¼ eaþbTþcT , where y is the percentage mortality, T is temperature and a, b and c are fitted parameters. The relationship between temperature and developmental rate (r ¼ 1=d, where d is the mean developmental time in days) was determined by fitting the nonlinear models of Logan type III (Hilbert and Logan, 1983), Lactin et al. (1995) and Briere et al. (1999). These models were selected because, unlike other nonlinear models, they can predict the low-temperature development threshold. The models were fitted to the stages of egg, larva, protonymph, tritonymph and to the complete preimaginal period.
2.1. Mites Stock cultures of A. farris and T. neiswanderi were established from infested samples of Cabrales cheese obtained from the maturing cave of CAPSA in Carren˜a de Cabrales (Spain). The mites were maintained on brewer’s yeast, and held in cylindrical plastic cages (12 cm diameter and 5.5 cm high). The cages were covered with round plastic plates that had a 5 cm diameter hole in the center sealed with filter paper disks for ventilation. The rearing cages were kept in an environmental chamber at 2570.5 1C, 9075% relative humidity (r.h.) in dark conditions, standing in a water-filled plastic tray to prevent escape and to maintain a high humidity. Adults were sexed by observing their secondary sexual characteristics (Hughes, 1976).
2.3.1. Logan type-III model The mathematical expression of this model is a combination of two functions. The first function represents the ascending rate of development with increasing temperatures. This function is sigmoidal and is analogous to Holling’s type-III functional response curve (Holling, 1965). The second function, developed by Logan et al. (1976), represents the descending portion of developmental rate with increasing temperatures. The expression is h i rðTÞ ¼ CðT T b Þ2 =ððT T b Þ2 þ D2 Þ eððT m ðTT b ÞÞ=DTÞ where T is temperature, rðTÞ is the rate of development at temperature T, Tb is the base temperature (for temperatures
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below Tb the rate of development presumably equals 0), Tm is the lethal maximum temperature threshold ( 1C above Tb), DT is the width of the high-temperature boundary area, c and D are empirical constants. 2.3.2. Lactin model Lactin et al. (1995) modified the first Logan nonlinear model (Logan et al., 1976, Eq. (6)) by eliminating a redundant parameter and incorporating an intercept parameter. This allows estimation of a low-temperature developmental threshold. The Lactin expression is rðTÞ ¼ erT e½rT max ðT max TÞ=D þ l, where T is temperature, rðTÞ is the rate of development at temperature T, Tmax is the supraoptimal temperature at which rðTÞ ¼ l, D is the range of temperatures between Tmax and the temperature at which rðTÞ is maximum. r describes the acceleration of the function from the lowtemperature threshold to the optimal temperature, and parameter l allows the curve to intersect the abscissa at suboptimal temperatures, thus allowing estimation of a lower developmental threshold. 2.3.3. Briere model This is a simplified developmental model for describing the nonlinear relationship between the developmental rate and temperature. The 3-parameter model proposed by Briere et al. (1999) is 8 > < 0; TpT 0 ; pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi rðTÞ ¼ aTðT T 0 Þ T L T ; T 0 pTpT L ; > : 0; TXT ; L where T is temperature, rðTÞ is the rate of development at temperature T, T0 is the lower temperature developmental threshold, TL is the lethal temperature (upper threshold) and a is an empirical constant. For both species, the distribution of adult emergence was determined by calculating the cumulative proportion of mites that reached the adult stage at normalized time x ¼ time/mean time for each temperature. The relationship was described using the equation of Stinner et al. (1975): F ðzÞ ¼ ð1 zÞ
ðkzb Þ
,
where F ðzÞ is the cumulative proportion of mites that completed development, z ¼ ðxmax xÞ=ðxmax xmin Þ, x is normalized time (time/mean time), xmin is normalized time at which the first mite completed development, xmax is normalized time at which the last mite completed development, and k and b are empirical constants. 2.4. Analytical methods Models were fitted and parameter values estimated using the SAS NLIN procedure, based on Marquardt’s nonlinear least-squares method (SAS Institute, 1987) and using Tablecurve 2D (Jandel Scientific, 1994). Initial parameter
estimates for Logan type-III and Lactin models were obtained as indicated by Logan (1988). Developmental models were compared by using the adjusted coefficient of determination given by Kvalseth (1985). This coefficient allows the comparison of functions with different degrees of freedom and its expression is R2a ¼ 1 ð1 R2i Þðn 1=n k 1Þ, where n is the number of observations, k is the number of parameters in the ith function, R2a is the adjusted coefficient of determination, and Ri2 is computed as , n n 2 X X 2 Ri ¼ 1 yj y^ ji ðyj yÞ2 j¼1
j¼1
for yj the jth observed developmental rate and y^ ji the jth predicted developmental rate from the ith function. 3. Results 3.1. Survival Parameter estimates for the functions describing the relationship between temperature and mortality are shown in Table 1. For both mites, the coefficient of determination obtained was higher than 0.94 except at the tritonymph stage of A. farris, where it was around 0.31. The mortality values obtained for the immature stages and for the combined preimaginal period in both species fitted a U-shaped pattern, with the exception of the tritonymph stage of A. farris (Fig. 1). In general, the percentage mortality of immature stages was higher for A. farris than for T. neiswanderi (Fig. 1e). In both species, the larva was the most susceptible developmental stage at the extreme temperatures assayed for each species, the percentage mortality being 81.7% and 95.2% for A. farris at 7 and 29.7 1C, respectively, and 8.3% and 25.6% for T. neiswanderi at 10 and 31 1C, respectively. 3.2. Developmental time Developmental time in both species decreased sharply as temperature increased from the lowest temperatures assayed to 15 1C, and then declined slowly until reaching the lowest value at 27 1C (Tables 2 and 3). Thereafter, small increases in developmental time were observed for both species until the highest temperature was reached. In both species, the egg stage had the longest developmental time at all temperatures, accounting for around 36% of the preimaginal period, whereas the larval, protonymph and tritonymph stages accounted for around 24%, 18% and 22% of the total developmental time, respectively. 3.3. Modeling immature development rates The estimated parameters and fit statistics of the three models tested against both species are presented in
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Table 1 2
Parameter estimates and coefficients of determination (R2) for the function y ¼ eaþbTþcT describing the relationship between temperature (1C) and mortality of all immature stages for A. farris and T. neiswanderi Species
Life stage
R2
Parameter estimates a
b
c
A. farris
Eggs Larvae Protonymph Tritonymph Egg to adult
8.0259 9.7593 17.2902 0.0593 7.6190
0.7933 0.9481 2.4268 0.2277 0.5499
0.02262 0.02600 0.06607 0.00835 0.01511
0.9435 0.9917 0.9860 0.3121 0.9845
T. neiswanderi
Eggs Larvae Protonymph Tritonymph Egg to adult
6.7010 12.5823 52.9414 22.4467 11.4104
0.6881 1.3982 7.0354 2.7962 1.1441
0.01850 0.03538 0.17480 0.06980 0.02930
0.9792 0.9711 0.9919 0.9961 0.9746
A. farris
(A)
Mortality (%)
(B)
(C)
(D)
100 80 60 40 20 0
100 80 60 40 20 0
0
5
5
10 15 20 25 30 35
10 15 20 25 30 35
100 80 60 40 20 0
100 80 60 40 20 0
100 80 60 40 20 0
0
5
10 15 20 25 30 35
0
5
10 15 20 25 30 35
0
5
10 15 20 25 30 35
100 80 60 40 20 0 0
5
10 15 20 25 30 35 100 80 60 40 20 0
100 80 60 40 20 0 0 100 80 60 40 20 0
(E)
0
T. neiswanderi
0
5
5
10 15 20 25 30 35
10 15 20 25 30 35
100 80 60 40 20 0
was obtained by the Logan type-III model ðR2a 40:99Þ, whereas the development of T. neiswanderi was better described by the Lactin model ðR2a 40:97Þ. The lower and upper developmental threshold temperatures predicted by these functions for each developmental stage of A. farris were lower than those predicted for the stages of T. neiswanderi (Table 6). Plots of the best fitting development functions and data are presented in Fig. 2. The optimal temperature estimates predicted by the Logan type-III model for the development of eggs, larvae, protonymphs, tritonymphs and for the total preimaginal period of A. farris were, respectively, 28.7, 26.3, 26.8, 27.7 and 27.9 1C. At these temperatures, the predicted developmental rates were 0.27, 0.39, 0.63, 0.55 and 0.10 day1. Likewise, the optimal temperatures predicted by the Lactin model for the different immature stages and for the total preimaginal period of T. neiswanderi were 27.7, 27.9, 27.4, 27.1 and 27.6 1C, and the developmental rates at these temperatures were 0.23, 0.36, 0.48, 0.38 and 0.09 day1, respectively. 3.4. Distribution of adult emergence
0
5
10 15 20 25 30 35
0
5
10 15 20 25 30 35
Temperature (ºC)
Fig. 1. Percentage mortality of A. farris and T. neiswanderi at constant temperatures (1C). (A) Egg. (B) Larva. (C) Protonymph. (D) Tritonymph. (E) Egg to adult. (K) Observed percentages of mortality; () line of best 2 fit according to the function y ¼ eaþbTþcT .
Tables 4 and 5. A high coefficient of determination ðR2a 40:83Þ was obtained with the three models for all developmental stages. However, the best fit for A. farris
The distribution of adult emergence was well described for both species using the equation developed by Stinner et al. (1975), as evidenced by the high R2 (Table 7, Fig. 3). For A. farris, adult emergence began at the normalized age of 0.79 (79% of the mean time of development), and was completed at the normalized age of 1.49, (149% of the mean developmental time). In the case of T. neiswanderi, these values were very similar: 0.82 and 1.59, respectively. 4. Discussion The immature stages of A. farris and T. neiswanderi showed differential survival when developing at different constant temperatures. The differences in mortality observed between both species were particularly distinct at
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Table 2 Mean developmental time (days7SE) of immature stages of A. farris at constant temperatures Temperature (1C)
Initial no. of eggs
7
518
10
160
15
143
20
108
25
107
27
160
29
160
29.7
600
Life stage (n) Hatched eggs
Larvae
Protonymph
Tritonymph
Egg to adult
36.570.2 (334) 16.770.2 (142) 8.970.1 (134) 5.570.1 (98) 4.170.1 (99) 4.070.1 (112) 3.870.1 (89) 4.570.1 (63)
28.270.7 (61) 11.970.2 (122) 5.570.1 (127) 3.670.1 (90) 2.570.1 (89) 2.670.1 (82) 3.170.1 (37) 3.770.3 (3)
21.270.7 (40) 10.270.2 (118) 3.970.1 (124) 2.470.1 (90) 1.670.1 (87) 1.670.1 (80) 2.070.1 (33) 3.070.0 (2)
21.070.5 (40) 12.670.3 (107) 4.670.1 (120) 2.970.1 (88) 2.070.1 (84) 1.970.1 (78) 2.270.1 (33) 5.071.0 (2)
105.571.8 (40) 50.970.6 (107) 22.970.2 (120) 14.470.1 (88) 10.270.1 (84) 10.070.1 (78) 10.470.2 (33) 15.570.5 (2)
(n), Number of individuals at each developmental stage.
Table 3 Mean developmental time (days7SE) of immature stages of T. neiswanderi at constant temperatures Temperature (1C)
Initial no. of eggs
10
215
15
110
20
110
25
110
27
110
29
110
31
210
Life stage (n) Hatched eggs
Larvae
Protonymph
Tritonymph
Egg to adult
29.470.2 (204) 11.670.1 (107) 6.970.2 (109) 4.370.1 (106) 4.370.1 (103) 4.570.1 (101) 5.070.1 (160)
19.670.2 (187) 7.670.1 (103) 4.470.1 (107) 3.070.1 (105) 2.870.1 (101) 2.870.1 (96) 3.370.1 (119)
15.470.2 (185) 5.770.1 (102) 3.470.1 (107) 2.270.1 (104) 2.170.1 (101) 2.270.1 (96) 2.870.1 (99)
19.170.2 (177) 7.170.1 (101) 4.070.1 (107) 2.670.1 (104) 2.770.1 (101) 2.870.1 (95) 3.670.1 (82)
83.170.5 (177) 31.970.2 (101) 18.770.2 (107) 12.170.1 (104) 11.870.1 (101) 12.370.2 (95) 14.270.2 (82)
(n), Number of individuals at each developmental stage.
temperatures greater than 25 1C. At the egg stage, the lower mortality found in T. neiswanderi could be due to the fact that the exochorion present in Tyrophagus species acts to prevent desiccation, a well-established mortality-increasing factor in mites (Fields, 1992), whereas in A. farris the probable absence of an exochorion, as in Acarus siro (Witalin˜ski, 1993), a closely related species, would account for the high mortality observed. The desiccation effect of high temperatures in mobile immature stages seems to be especially decisive in astigmatid mites, since they have weakly sclerotized cuticles that permit the loss of fluids through the body surface (Wharton et al., 1979; Evans, 1992). Water loss occurs even at 90% r.h. because the
water activity of astigmatid mites is 0.99 (Wharton and Furumizo, 1977), and at temperatures near the upper developmental threshold this effect is much more marked (Wharton and Arlian, 1972). Furthermore, this effect appears to be greater in smaller stages because they have a greater surface/volume relationship (Sa´nchez-Ramos and Castan˜era, 2001), and would explain the differences of mortality observed at high temperatures among different stages of the same species, and, therefore, the fact that the larval stage was the most susceptible to high extreme temperatures. This is in agreement with results previously established in T. putrescentiae (Sa´nchez-Ramos and Castan˜era, 2001).
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Table 4 Parameter estimates for the three temperature-dependent models describing the relationships between temperature (1C) and developmental rates (r ¼ 1/d) of all immature stages for A. farris Model
Parameter
Life stage Eggs
Larvae
Protonymph
Tritonymph
Egg to adult
Logan type III
c D DT Tm Tb R2a
0.4779 24.2619 0.3201 29.3390 1.0588 0.9902
1.6716 43.1020 2.1925 33.5425 0.2418 0.9921
2.1131 36.4789 1.2708 29.9963 1.2708 0.9925
1.5671 34.7511 0.6427 29.1586 1.3799 0.9930
0.2224 27.2944 0.6256 29.1848 1.4433 0.9913
Lactin
r Tmax D l R2a
0.1263 35.1415 7.8418 0.0542 0.9751
0.1536 32.3653 6.4542 0.0524 0.9921
0.1916 31.1705 5.1961 0.0226 0.9759
0.0188 30.6317 0.6663 0.1100 0.9917
0.0045 31.7815 0.6787 1.0246 0.9904
Briere
a T0 TL R2a
0.00013910 2.0502 34.1320 0.9772
0.00029249 4.5013 31.3854 0.9791
0.00055216 6.6781 30.6906 0.9387
0.00050422 7.1936 30.3347 0.8315
0.00007794 5.2919 31.4656 0.9262
R2a is the adjusted coefficient of determination.
Table 5 Parameter estimates for the three temperature-dependent models describing the relationships between temperature (1C) and developmental rates (r ¼ 1/d) of all immature stages for T. neiswanderi Model
Parameter
Life stage Eggs
Larvae
Protonymph
Tritonymph
Egg to adult
Logan type III
c D DT Tm Tb R2a
10.1752 133.8118 4.5646 45.6734 2.1880 0.9480
2.4298 57.1383 2.6431 35.9912 1.4749 0.9973
23.2169 153.1676 3.2239 41.8224 1.8482 0.9778
13.8830 117.7182 4.1881 42.3828 2.7101 0.9602
2.8905 121.1698 4.0933 43.2970 2.1135 0.9792
Lactin
r Tmax D l R2a
0.1343 35.1609 7.3939 0.0573 0.9746
0.1457 34.7338 6.8139 0.0567 0.9977
0.1555 33.8303 6.3810 0.0659 0.9910
0.1407 34.1718 7.0348 0.0939 0.9808
0.1385 34.8518 7.2015 0.0192 0.9906
Briere
a T0 TL R2a
0.00016022 6.5660 33.7139 0.9737
0.00024397 6.6551 33.8296 0.9842
0.00036269 7.4125 32.9019 0.9706
0.00029544 7.2163 32.6789 0.9759
0.00005997 6.7882 33.4946 0.9830
R2a is the adjusted coefficient of determination.
At low temperatures, we have also observed differences in mortality among immature stages within each species, which is in agreement with reported findings for other astigmatid mites like A. siro (Cunnington, 1965) or T. putrescentiae (Sa´nchez-Ramos and Castan˜era, 2001). Several mechanisms have been proposed underlying the lethal effects of low temperatures on insects and mites, such as the accumulation of toxic products which otherwise would be eliminated at normal temperatures (Mullen and
Arbogast, 1984), alterations in the functionality of the cell membrane, imbalances in the rate of biochemical reactions due to reduced enzyme activity or modifications of the ionic activity of molecules (Fields, 1992). Nevertheless, it cannot be explained with our data why the early stages of these mites are more susceptible than the later ones. Interestingly, the immature stages of A. farris seem to be slightly better adapted to low and intermediate temperatures than those of T. neiswanderi, as evidenced by the
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Table 6 Thermal developmental thresholds (1C) estimated by the best-fitted functions for the immature stages of A. farris and T. neiswanderi A. farris
Life stage
Eggs Larvae Protonymph Tritonymph Egg to adult
T. neiswanderi
LT
UT
LT
UT
1.1 0.2 1.7 1.4 1.4
30.3 31.5 30.5 30.0 30.3
5.3 3.9 4.0 5.7 5.2
34.6 34.4 33.6 33.7 34.4
LT, predicted lower developmental threshold. UT, predicted upper developmental threshold.
T. neiswanderi
A. farris
Developmental Rate (day-1)
(A)
(B)
(C)
(D)
0.3
0.3
0.2
0.2
0.1
0.1
0
5
10 15 20 25 30 35
0
0.4
0.4
0.3
0.3
0.2
0.2
0.1
0.1
0
0
5
10 15 20 25 30 35
0
0.8
0.8
0.6
0.6
0.4
0.4
0.2
0.2
0
0
5
10 15 20 25 30 35
0
0.6
0.6
0.4
0.4
0.2
0.2
0
0
5
10 15 20 25 30 35
0
0.12
0.12
0.08
0.08
0.04
0.04
0
(E)
0
0
5
10 15 20 25 30 35
0
0
5
10 15 20 25 30 35
0
5
10 15 20 25 30 35
0
5
10 15 20 25 30 35
0
5
10 15 20 25 30 35
0
5
10 15 20 25 30 35
Temperature (ºC)
Fig. 2. Predicted rate of development (day1) of A. farris by the Logan type-III model and T. neiswanderi by the Lactin model as a function of temperature (1C). (A) Egg. (B) Larva. (C) Protonymph. (D) Tritonymph. (E) Egg to adult. (K) Observed developmental rate; (–) line of best fit by nonlinear least square.
higher developmental rates of A. farris and the lower developmental thresholds. In contrast, the immature stages of T. neiswanderi are better adapted to higher temperatures as evidenced by their upper developmental thresholds. These data contribute to explaining the prevalence of A.
farris in the cool Cabrales maturing caves. Similarly, Robertson (1961) found that in places where three species of mites of the genus Tyrophagus coexisted, their relative abundance was mainly dependent on temperature. Nevertheless, other factors, such as food preference and quality for each mite species, as well as interspecific competition mechanisms should be considered to explain the relative abundance of A. farris and T. neiswanderi in the maturing caves. We have found the Logan type-III function to be the best of the developmental models selected to describe the relationship between developmental rate and temperature for A. farris, whereas the Lactin model provided the best results for T. neiswanderi. The application of these nonlinear models enables prediction of the thermal developmental thresholds of a species, a key tool for developing control strategies based on temperature modifications. In the case of Cabrales cheese, the application of high temperatures is not feasible because the physical and organoleptic characteristics of this product are greatly modified under these conditions. Therefore, temperature modification must be focused on low temperatures. Thus, Fields (1992) pointed out that to prevent mites from developing on damp grain, temperatures must be lowered to 2 1C. This is in accordance with the lower developmental thresholds here calculated for T. neiswanderi, and very close to the lower developmental thresholds obtained for A. farris. At this temperature, the predicted rate of development of this mite is so small, that almost no population increase and mite activity would be expected under these conditions. The results obtained here for A. farris and T. neiswanderi were well fitted to the Stinner function (Stinner et al., 1975) and revealed similar variability for these species as evidenced by similar steepness of both distributions. Therefore, with these functions and with the best developmental models obtained for each species it is possible to simulate the development times of A. farris and T. neiswanderi immature stages under fluctuating temperatures in the maturing caves. The simulation accumulates fractional development as a function of time by summing the rates provided by the best developmental models and these cumulative rates are then used as the independent variable in the Stinner equation to distribute the proportion of the population completing development over time (Wagner, 1995). The data presented here provide fundamental information to understand the effect of temperature on development and survival of A. farris and T. neiswanderi. Our findings indicate that application of lower temperatures during the maturing process of Cabrales cheese might be used to reduce mite populations below their economic threshold, since the microbial processes occurring during cheese-maturing are not significantly altered with detrimental effects on the organoleptic characteristics of the cheeses (P. Balbarie, pers. comm.). Nevertheless, further research is needed involving reproductive parameters and
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Table 7 Parameter estimates and coefficients of determination (R2) for the function describing the relationship between normalized time and the cumulative probability of adult emergence for A. farris and T. neiswanderi Species
A. farris T. neiswanderi a
Empirically obtained parametersa
Parameter estimatesb
R2
xmin
xmax
k
b
0.7882 0.8157
1.4880 1.5940
1.3424 1.9678
3.4829 7.0767
0.9679 0.9565
xmin, normalized time of first emergence, xmax, normalized time of last emergence. k and b, empirical constants.
b
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A. farris 1
0.75
0.5
Cumulative proportion
0.25
0 0.70
0.85
1.00
1.15
1.30
1.45
1.60
1.30
1.45
1.60
T. neiswanderi 1
0.75
0.5
0.25
0 0.70
0.85
1.00
1.15
Normalized time (time / mean time) Fig. 3. Cumulative proportion of adult emergence for A. farris and T. neiswanderi as a function of normalized time (time/mean time). (K), observed cumulative proportion; (–), line of best fit according to the Stinner function.
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