Yellow sticky traps for decision-making in whitefly management: What has been achieved?

Crop Protection 47 (2013) 74e84

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Review

Yellow sticky traps for decision-making in whitefly management: What has been achieved? Delia M. Pinto-Zevallos a, b, Irene Vänninen a, * a b

MTT Agrifood Research Finland, Plant Production Research, 31600 Jokioinen, Finland Laboratory of Semiochemicals, Department of Chemistry, Federal University of Paraná, P.O. Box 19081, CEP 81531-990, Curitiba, Paraná, Brazil

a r t i c l e i n f o

a b s t r a c t

Article history: Received 18 June 2012 Received in revised form 24 January 2013 Accepted 25 January 2013

Yellow sticky traps (YSTs) are a key component of IPM programmes for several greenhouse pests. The development of YST-based decision-making tools, e.g. sampling protocols and economic thresholds (ETs), however, has been limited. This review assesses to what extent YST-counts comply with the four criteria of effective sampling (reliability, representativeness, relevance and practicality) as described by Binns et al. (2000) in an attempt to understand the feasibility of designing YST-based decision-making tools for managing whiteflies in greenhouse crops, particularly tomato (Lycopersicon esculentum Mill) and cucumber (Cucumis sativus L.). Many factors are known to affect whitefly flight behaviour and thus, trap catches. The possibility of manipulating such factors to improve YST efficiency and reliability or of interpreting whitefly catches in YSTs with automated tools is discussed. A few studies have shown the correlation between trap and direct visual pest counts from plants. These studies are discussed in the context of whiteflies and trap densities to enhance the representativeness of sampling with YSTs. Relevance implies that the results of sampling reflect crop loss to a sufficient degree. Only few YSTsbased action thresholds have been suggested in the literature, particularly for use with chemical control. There are a number of approaches and technological innovations that can improve the practicality of YSTs by decreasing the effort and time associated with counting insects, a method to facilitate the identification of species in mixed populations on the trap, and sampling methods such as sequential sampling for calculating appropriate sample size, which have been already put in practice to develop YSTs-based sampling protocols. Knowledge gaps are identified and discussed, and a route map for further research to advance YSTs as a decision-making tool is outlined, with geostatistical methods as the recommended approach for further increasing the usefulness of YSTs-based decision-making. Ó 2013 Elsevier Ltd. All rights reserved.

Keywords: Whitefly Greenhouse crops Yellow sticky traps Sampling Decision-making

1. Introduction The fact that many insects show preference to particular light wavelengths has lead entomologists and researchers involved in plant protection to develop monitoring tools and control strategies against many insect pests exploiting this behaviour. One good example of such an approach is the use of coloured traps. Yellow sticky traps (YSTs) in particular have been a subject of research for many decades, and incorporated in management programmes of various pests such as whiteflies, thrips, leaf mining flies, shore flies and fungus gnats in a number of crops. In greenhouses, they have become a key component of IPM programmes of several greenhouse pests (Steiner et al., 1999; Kaas, 2005; Park et al., 2011a). YSTs

* Corresponding author. Tel.: þ358 295317992; fax: þ358 20 772 040. E-mail addresses: [email protected] (D.M. Pinto-Zevallos), Irene.Vanninen@mtt.fi (I. Vänninen). 0261-2194/$ e see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.cropro.2013.01.009

serve as a tool for early detection, identification of hotspots as well as for estimating relative abundance and monitoring dispersal activity of adult whiteflies, including those occurring in greenhouses, namely Trialeurodes vaporariorum (Westwood) and Bemisia tabaci (Gennadius) (Gillespie and Quiring, 1992; Heinz et al., 1992; Naranjo et al., 1995). In addition, YSTs have the potential of suppressing adult populations alone or in combination with other control strategies such as biological control (Yano, 1987a; Gu et al., 2008 and references therein) or trap crops (Moreau, 2010; Moreau and Isman, 2011). The goal of using traps for insect monitoring is to predict insect densities that cause crop damage or yield reduction or commodity losses so that timely control actions can be taken. Pest densities are monitored indirectly from the crop, using trap catches as an indication of pest density on the plant. Therefore, determining the relationship between trap catches of the pest with its numbers in the crop and related yield losses are critical to make correct control decisions. In spite of the fact that YSTs have been widely used with

D.M. Pinto-Zevallos, I. Vänninen / Crop Protection 47 (2013) 74e84

several advantages for growers such as low cost and low training demands, the development of decision-making tools based on YSTs (e.g. sampling protocols and economic thresholds (ET)) has been rather limited. Nevertheless, in some greenhouses, whitefly control decisions are commonly guided by adult densities on traps (Kaas, 2005). The evaluation of sampling techniques (e.g. precision and efficiency) should be one of the first steps towards the development of sustainable sampling plans (Buntin, 1994). In spite of this, only a limited number of studies have compared YSTs (or other colour sticky traps) with other sampling techniques in different pest/crop systems (Table 1). YSTs have been regarded by some as a poor and inconsistent tool for estimating pest densities on plants, and therefore, not suitable for decision-making purposes in field conditions (Palumbo et al., 1995; Naranjo et al., 1995, 2010). According to Ekbom and Rumei (1990), however, YSTs can be considered in greenhouses as one of the most efficient sampling techniques, and they should be very useful as decision-making tools, once their optimal placement sites are determined for a given species and crop. It is possible that in greenhouse environments, YSTs are a more powerful tool than in the field for decision-making purposes, either alone or in combination with other techniques. There are several encouraging studies, especially for whiteflies, suggesting the potential use of YSTs in greenhouse conditions (Gillespie and Quiring, 1987; Yano, 1987b; Kim et al., 2001; Park et al., 2011a), and further development of this method holds promise. This review aims to clarify the potential of YSTs as a monitoring tool, and the feasibility of designing YST-based sampling protocols

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for the purpose of decision-making for pest management actions. The review’s logic is guided by assessing to what extent YST insectcounts comply with the four criteria of effective sampling of reliability, representativeness, relevance and practicality as described by Binns et al. (2000). Based on these criteria, we identify gaps of knowledge in the effective use of YSTs as decision-making tools that deserve further investigation. The review focuses on whiteflies and greenhouse vegetables, particularly tomato (Lycopersicon esculentum Mill) and cucumber (Cucumis sativus L.) in both seasonal and year-round production systems practiced in Northern latitudes, as these are the target crops of our whitefly management studies in the greenhouse cluster of Finnish Ostrobothnia (Vänninen et al., 2011; Vänninen, 2012). However, examples from studies of other systems are also included whenever feasible. 2. Criteria of effective sampling applied to yellow sticky traps 2.1. Reliability of yellow sticky traps According to Binns et al. (2000), reliability assumes that the results are not influenced by the person collecting the data, or by exogenous, uncontrolled variables such as weather or possible diurnal behaviour of the pest. Because YSTs rely on the behavioural responses of whiteflies, many physiological characteristics, environmental conditions as well as intra- and interspecific biotic interactions occurring in the greenhouse can affect the number of whiteflies ending up on the traps. In addition, characteristics of the trap itself and its placement in the greenhouse, or misuse can

Table 1 Studies comparing yellow sticky traps (YSTs) with other sampling techniques in different pest/crop systems. Pest/crop system

Methods

R2 (coefficient of determination in regression)

Precisiona

Efficiencyb

Reference

Empoasca fabae/alfalfa

YST (48 h) Suction adults (S) Sweep (Sw) Water pan (WP) (48 h) Absolute (drop trap) technique, adults (AT) YST (various) Suction techniques (S) Black pan (BP) Whole plant (AC)

R2 R2 R2 R2

RV: RV: RV: RV: RV:

RNP: RNP: RNP: RNP: RNP:

De Gooyer et al. (1998)

Bemisia spp./cotton

B. tabaci/cotton

Frankliniella occidentalis/ nectarine Frankliniella occidentalis/ lettuce

Homalodisca vitripennis/citrus

Thrips/mango

Frankliniella occidentalis/ greenhouse rose a

YST (various) Black pan (BP) Leafturn adults (L) Buds counts in laboratory (BC) YST Direct counts (DC) Beat pan (BP) Sticky traps ST (yellow, Y; blue, B) Actual counts (AC) Pole-bucket (PB) D-Vac Beat-net (BN) YST YST Direct (CO2) method from panicles Flower tapping (FT) YST Absolute counts (AC)

¼ ¼ ¼ ¼

0.66 0.71 0.82 0.55

(YST vs. AT) (S vs. AT) (Sw vs. AT) (YST vs. AT)

R2 ¼ 0.102; 0.288; 0.252 cylinder, square and rectangle YSTs vs. AC respectively) R2 ¼ 0. 903; 0.787 (S vs. AC) R2 ¼ 0.912 (BP vs. AC) R2 ¼ 0.87 (cylinder YST vs. L) R2 ¼ 0.77 (BP vs. L) Not presented

R2 R2 R2 R2

¼ ¼ ¼ ¼

0.73 0.85 0.30 0.19

(DC vs. AC) (BP vs. AC) (YST vs. AC) (BST vs. AC)

13.83 11.96 18.77 23.39 20.21

42.53 33.44 31.35 25.15 14.99

Not assessed

Not assessed

Natwick et al. (1995)

Not assessed

BC and (cylinder) YST are 3.5 and 3.6 times more expensive than L BC: RNP ¼ 12.5e94.22 (in various sampling dates and orchards) YST: RNP ¼ 11.92e55.19 DC: RNP ¼ The highest at various crop stages Not assessed for ST

Naranjo et al. (1995)

BC: RV ¼ 13.6e100 (in various sampling dates and orchards) YST: RV ¼ 11.54e65.60 DC: RV ¼ 9.4e29.6 at various crop stages for adults BP: RV ¼ 10.6e18.7 at various crop stages for adults

Pearsall and Myers (2000)

Palumbo (2003)

R2 ¼ 0.828 (YST vs. PB) R2 ¼ 0.844 (YST vs. D-Vac) R2 ¼ 0.844 (YST vs. BN)

Not assessed

Not assessed

Castle and Naranjo (2008)

R2 R2 R2 R2 R2 R2

Not assessed

Not assessed

Aliakbarpour and Rawi (2011)

Not assessed

Not assessed but YST takes half of time than FT

Pizzol et al. (2010)

¼ ¼ ¼ ¼ ¼ ¼

0.955 (T. hawaiiensis) 0.824 (S. dorsalis) 0.881 (F. scultzei) 0.916 (M. usitatus) 0.85 (YST vs. FT) 0.84 (YST vs. AC)

Precision measured as RV¼ (SEM/mean) 100, where SEM ¼ standard error of the mean. The lower the value, the better the precision is (Buntin, 1994). Relative Net Precision (RNP) measured as RNP ¼ 100/[(RVm)(cu)], where RVm ¼ mean relative variation and cu ¼ cost in minutes, or in costs based on the number of samples to reach a precision of 0.25. The higher the value, the better the efficiency (Pedigo et al., 1972).

b

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influence trap catches (Webb et al., 1985; Gillespie and Quiring, 1992). Obviously, some of these factors can be manipulated for enhancing the capture efficiency of the traps. Others, however, are subjected to the ecology and management of the crop, and thus, they either cannot be manipulated, or only to a limited extent. In spite of this, the degree of influence of such factors on the pest’s tendency of ending up on the traps can be studied and taken into account when interpreting trap catches. Some factors must be taken as they are, without the possibility of manipulating them, as it is difficult to calculate how they affect pest behaviour at a given time, or their practical importance is small or masked by the influence of other, more important factors. 2.1.1. Factors increasing trap efficiency and amenable to manipulation Trap efficiency is defined as the percentage of insects caught with respect to those insects entering the effective radius of the trap; effective radius, in turn, is the maximum distance from which a trap attracts insects (Hartstack et al., 1971). In cucumber and tomato, traps should be placed at 50 cm or closer to the plant at about 15e30 cm below canopy top level (Webb et al., 1985; Quiring, 1986; Gillespie and Quiring, 1992; Shen and Ren, 2003; Qiu and Ren, 2006; Hou et al., 2006a) and vertically parallel to the plant rows (Gillespie and Quiring, 1992; Steiner, 1993; Hou et al., 2006a; Gu et al., 2008; Zhang and Yu, 2009) to maximize catches of both T. vaporariorum and B. tabaci. This may allow treatment of YSTs catches as samples of one plant (Gillespie and Quiring, 1992). Although there is some evidence that the physiological state of whiteflies such as age (Gillespie and Quiring, 1992; Blackmer et al., 1995), propensity for migration or settling on the plant (Blackmer et al., 1995; Hayashi, 2001), whitefly sex (El-Helaly et al., 1981; Liu et al., 1994; Malumphy et al., 2010), and female egg load (Blackmer and Byrne, 1993; Isaacs and Byrne, 1998) can influence their attraction to YSTs, any effect of the physiological condition on the whiteflies’ tendency to orientate to traps in practice may decrease when YSTs are 50 cm or closer to the plant, and YSTs then can capture adults of all physiological states (migration stage, settling stage) and of both of the sexes (Gillespie and Quiring, 1992). The characteristics of the trap such as size, shape and colour, can be in principle adjusted, but may be, in practice, restricted to those YSTs already available in the market. Park et al. (2011a) suggest that small-sized traps should be used whenever possible to reduce the time allocated to the evaluation (i.e. insect counts) as long as there is a good correlation between trap and direct counts. Moreover, there is a negative relationship between the size of the trap and the numbers of adults captured per unit area (Zhang and Yu, 2009; Kim and Lim, 2011). Nevertheless, the size of the trap should be taken into account when calculating the sample size in formally developed protocols, as it may affect the number of traps to be sampled (Parrella and Jones, 1985). Protocols based on larger traps (30.5  30.5 cm2) may result in lower numbers of traps compared to protocols based on smaller traps (11.4  14 cm2) as shown on a study to estimate densities of Liriomyza trifolii in Chrysanthemum greenhouses (Parrella and Jones, 1985). The cost-efficiency of increasing the size of the trap to reduce sample size merits assessment. The development of techniques and automated methods to reduce time allocated to evaluate the trap may improve the cost-efficiency of larger traps (Steiner et al., 1999; Solis-Sánchez et al., 2009). Although YSTs are attractive to whiteflies owing to their colour, the attractiveness is limited to the visual range of the pests. The visual range can be limited due to the properties of the crop such as height in tomato and cucumber where traps are placed below the top of the canopy and are, therefore, visible to whiteflies only from a certain distance. Within the unobstructed distance to traps, the

attractiveness of visual traps can be increased to some extent by changing their shape and size or modifying the colour components to enhance trap efficiency (Kim and Lim, 2011; Park et al., 2011a). Despite the different shapes (squares, rectangles, cylinders or ribbons) of YSTs that have been tested for their efficiency (Byrne et al., 1986; Quiring, 1986; Kim et al., 2001; Kim and Lim, 2011; Idris et al., 2012), rectangular traps are the most commonly adopted, presumably for a practicality issue. Studies in cotton (Gossypium hirsutum L.) have shown that cylinder-shaped traps are more efficient in catching B. tabaci adults compared to other trap designs (Byrne et al., 1986; Naranjo et al., 1995), and they are used for monitoring T. vaporariorum in commercial cherry tomato greenhouses in Korea (Kim et al., 2001). However, shaping traps into cylinders demands extra handling time that should be included in costefficiency analyses of the different trap shapes. Yellow sticky geometrical figures on black backgrounds increase capture efficiency at low densities of adult B. tabaci (Kim and Lim, 2011). In laboratory trials, triangle-shaped yellow sticky cards and circular yellow sticky cards, both contrasted against a black background, resulted in larger captures compared to other shapes (circles, diamonds and rectangles) and background colours (white, green and blue), respectively. These results suggest that B. tabaci perceives shapes, even though previous observations on the behavioural responses of T. vaporariorum lead to the conclusion that leaf shape and structure do not play a role in hosteplant selection for these generalist insects (van Lenteren and Noldus, 1990). In addition, shape and size of attractive area may interact as larger yellow circles (two circles, 18 cm diameter per trap on a black background) trapped 70% of B. tabaci taking off from the plant canopy, whereas only 49% and 32% were caught by a trap with two smaller yellow circles (13 cm diameter each on a black background) or a rectangular YST (24  39 cm), respectively (Kim and Lim, 2011). Very large yellow surfaces have been reported to repel whiteflies though (Antignus, 2010). Olfactory cues have been treated as not relevant in the selection of hosteplant species by whiteflies from a distance (van Lenteren and Noldus, 1990), but recent studies showed that volatile organic compounds can either attract (Górski, 2004) or repel whiteflies (Bleeker et al., 2009, 2011) or mask other odours the insects are responsive to (Togni et al., 2010). Adding odours to enhance trap catches deserves further research. Preliminary studies have explored the response of T. vaporariorum to odours of essential oils (Górski, 2004), and fatty acids derived from the whiteflies themselves to improve the efficiency of the trap (Campbell, 2010). Testing, however, has been done only in olfactometers in the laboratory and not in greenhouse conditions. The assessment of odour-enhanced traps needs to include the response of natural enemies such as Encarsia formosa since several predators and parasitoids strongly rely on odour cues. Background odours may interfere with the foraging activity of beneficial arthropods, or alternatively, odours released by traps may also attract beneficial insects and, in this way, may hamper biological control. Screening for compounds that do not conflict with biological control in greenhouses might be valuable. Odours aiming at maximizing catches may have better applicability in masstrapping technique (Shipp, 1995). Preferences for particular yellow hues or “shades” are known to occur in controlled conditions in the laboratory (Quiring, 1986). This however, may not be relevant in practice, because in contrast to laboratory experiments, whiteflies face a “no-choice” situation in the greenhouse as well as diverse backgrounds and continuous fluctuations in light conditions (Quiring, 1986; Kaas, 2005; Durmusoglu et al., 2009). In accordance with results showing that traps of different shades of green (520e540 nm) are also attractive to whiteflies (Chu et al., 2000), trapping efficiency can be enhanced

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by specific spectral sources such as light-emitting diodes (LEDs) emitting yellow green-light (to catch whiteflies in the settled state) or UV-wavelengths (to attract those in the migrational state) (Chen et al., 2004; Simmons et al., 2004; Mutwiwa and Tantau, 2005). The influence of cardinal orientation of traps on catches has been studied for B. tabaci (Hou et al., 2006b; Saleh et al., 2010), but whether particular directions may enhance captures is not clear. Young adults disperse towards plants in all directions from a release point (van Lenteren and Noldus, 1990) which suggests the orientation of a trap may not enhance trap captures. Nevertheless, it has been recommended that the traps should be placed in East/ West direction for maximizing catches of adult whiteflies in ornamental greenhouses (Steiner, 1993; Steiner et al., 1999). It is possible that any effect of cardinal direction on the catch size could be the result of an interaction with other factors, under particular circumstances. Further studies are required to dissect the role of the cardinal direction from other, possibly more relevant factors, and to assess its relevance on trap catches. 2.1.2. Factors affecting trap efficiency but not amenable to manipulation Among abiotic factors, temperature influences flight behaviour of whiteflies, including T. vaporariorum and B. tabaci (Weber, 1931; Bellows et al., 1988; Blackmer and Byrne, 1993; Liu et al., 1994; Isaacs and Byrne, 1998), and the efficiency of YSTs to the greatest degree (Biffi Urteaga, 2009). Although environmental conditions in the greenhouse are mostly constrained by the requirements of the crop, it is crucial to take the temperate effect into account when interpreting trap counts, particularly at low (<20  C) temperature ranges. Temperatures below 20  C restrict whitefly flight, with no flight activity below 16  C for T. vaporariorum and below 18  C for B. tabaci (Liu et al., 1994; Biffi Urteaga, 2009). The two species display also different upper temperature limits regarding increased flight activity in response to increasing temperature (Liu et al., 1994). The development of different ETs for different temperature ranges (e.g. Yano, 1987b) can overcome the challenge of interpreting the temperature factor. In addition, the development of automated decision-making tools may be a good way to incorporate the well-known effect of temperature on whitefly activity, whereupon subsequent dynamic interpretation of trap catches may become possible. Humidity can also influence trap catches of whiteflies (Ekbom and Rumei, 1990), but it may not be important enough in greenhouse conditions. Factors related to light conditions in the greenhouse have a great influence on whitefly activity and therefore, trap capture efficiency, but the importance and effect of these factors on trap catches in commercial greenhouses is less well known than for temperature. Increasing light intensity enhances the flight activity as well as the behavioural patterns of whiteflies related to certain wavelengths. With more light, the settling response tends to predominate (Coombe, 1981). This is reflected in a positive correlation between trap catches and light intensity (El-Helaly et al., 1981; Byrne and von Bretzel, 1987; Chu et al., 1998), with activity peaking at 0.73 kW/m2 (Riis and Nachman, 2006). There is hardly any flight activity below 2 lux or in darkness (Weber, 1931; Liu et al., 1994; Chu et al., 1998). The peak period of flight activity usually coincides with the morning hours and is related to eclosion and teneral periodicity, which, in turn, depends on the onset of the light period and is modulated by temperature (Liu et al., 1994; Hoffman and Byrne, 1986). The use of artificial light considerably influences the light intensity and spectrum as well as the length of the photophase in year-round production compared to seasonal cropping in the early spring (FebruaryeMarch) and late autumn (SeptembereOctober) conditions in northern countries. Moreover, lamps create

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humidity, temperature and light intensity gradients in the greenhouse, possibly resulting in different whitefly activity patterns compared to seasonal crops where similar gradients do not occur. Alternatively, lamps may compete with YSTs as a trapping sink and interfere with the number of whiteflies ending up on the traps since they readily fly towards high-pressure sodium lamps after being released (Nina Johansen, pers. comm.). This may influence the proportion of whiteflies that are caught by YSTs at different times of the year, as well as the performance of both pests and biological control agents, and the development of sooty moulds. The reduced UV-level of the light spectrum in year-round crops in winter months at northern latitudes may be another variation source for trap catches. The spectrum of high-pressure sodium lamps contains very little, if any, UV-light. In the absence of UVlight, a condition that increases the settling behaviour in whiteflies, as the UV-signal inducing migration is lacking, the insects may be caught more readily by nearby YSTs than in natural radiation (Antignus et al., 2001). Therefore, YSTs may catch whiteflies more efficiently in winter months, particularly as the amount of daylight in Nordic conditions is small and the lamps may prevent the insects from seeing the scant daylight (which, in winter months, is almost devoid of UV-radiation at high latitudes). The same may happen also when cladding materials absorbing UV-light are used, resulting in the decrease of the flight activity of adults (Antignus et al., 2001). A possible means of enhancing YSTs is to equip them with a UVlight source, and the development of UV-LEDs may offer a possibility to do this (Mutwiwa and Tantau, 2005). The question then remains whether it is possible to attract whiteflies in the migrational state to traps also, and if so, whether it improves the predictive power of YSTs for estimating population densities or categorizing the density below or above a critical density. Changes in the spectrum of light, in addition to the year-round fluctuation of crop prices, may necessitate the development of different ETs for different lighting conditions. New developments in crop lighting may influence how trap results must be interpreted. It has been shown that the vicinity of YSTs to blue and red LED-lights in a crop decreases trap catches compared to the spectrum created by highpressure sodium lamps (Johansen et al., 2011). Although wind can influence whitefly take-off and flight behaviour in outdoor conditions (Byrne & von Bretzel, 1987) and, therefore, influences trap catches (Riis and Nachman, 2006), it is of minor importance for catches inside greenhouses. Wind can, however, influence how whiteflies are carried in via the vents from other greenhouses or outdoor plants (Teitel et al., 2005). Influx of whiteflies due to wind, in turn, may produce irregularities to population trends monitored by YSTs. 2.2. Representativeness Representativeness relates to bias, which is defined as the size of the difference between the expectation of the sample estimate and the population parameter being estimated: biased estimates are either greater or smaller than they ‘should’ be. Accuracy of the sampling refers to the closeness of population estimates to the actual population size, and it includes both precision and bias. High precision (low variation around the sample mean) is, however, less important than avoiding biased samples when developing sampling protocols (Binns et al., 2000). Bias is caused by the incorrect selection or processing of samples by the sampler (Binns et al., 2000, p. 22). In using YSTs, such bias can be caused by incorrect sample size or errors in counting insects from the traps, since traditional methods of counting pests in the trap rely on human visual judgement. YST counts are subjected to error caused by inaccuracies or fatigue/tiredness, particularly when numbers of trapped insects are very high (Steiner et al., 1999).

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2.2.1. Correlation of YST catches with adult and immature numbers on plants In greenhouses, studies showing the correlation of trap counts with whitefly densities on tomato and cherry tomato plants (Gillespie and Quiring, 1987; Kim et al., 1999) support YSTs as a good tool for estimating adult population densities. Good correlations between trap catches and direct visual counts from the plants in cherry tomato were achieved with YST densities varying between 30 and 40 traps in greenhouses ranging from 1500 to 1800 m2 (one trap every 37.5e60 m2) (Kim et al., 1999). Kim et al. (1999) found significant correlations between trap counts of adults with direct samples of adults and immature stages of T. vaporariorum from the upper and middle canopy strata of cherry tomato plants either in the same (Pearson’s r ¼ 0.9060 for adults, upper leaf stratum; r ¼ 0.7824 for larvae (instar not specified), middle leaf stratum) or previous week (r ¼ 0.8757 for adults, upper leaf stratum; r ¼ 0.8286 for larvae, middle leaf stratum). A good correlation (R2 ¼ 0.71 for linear regression) between adults on traps and on leaves has also been shown in cucumber but at a very high trap density (Quiring, 1986). The occurrence of mixed populations of whiteflies (Steiner, 1993) and pesticide application (Palumbo et al., 1995; Naranjo et al., 1995) can, however, reduce the relationship between trap counts and visual counts on the plant or result in a time-lag in the observations. Some pesticide treatments decrease flight activity of B. tabaci adults in cantaloupe fields (Palumbo et al., 1995). Systemic imidacloprid treatments of Poinsettia plants enhance the dispersal of B. tabaci adults to untreated plants when these are available (Irene Vänninen, unpublished), suggesting that whiteflies may be more attracted to YSTs in crops treated with this pesticide. In fact, after pesticide treatments, direct counts are recommended instead (Martin and Dale, 1989). Low populations and inadequate sample size may result in low correlations between trap counts and direct counts with other methods of sampling (Byrne et al., 1986; Parrella et al., 1989). In greenhouse cucumber, at a trap density of one per plant in a 99 m2 greenhouse with 14 plants, 62% of adult T. vaporariorum emerging from pupae in the centre of the greenhouse over 5 days were captured by YSTs (Quiring, 1986). At a similar trap density and greenhouse area with an established whitefly population, a daily average of 6.3% of adults of those counted from the top 5 cucumber leaves were recorded in YSTs (counted every two days) over a 30 day period. The daily proportion of whiteflies trapped fell down from 20 to 12% during the first four days to an average of 5% during the rest of the month. The correlation between trap catches and direct counts from plants grew weaker with a reduced whitefly density, and disappeared once whitefly densities fell down to 7 or fewer adults per trap per day, which corresponds to about 50 or less per trap per week. Likewise, T. vaporariorum in YSTs correlates well with visual counts of adults from Poinsettia plants at high, but not at low densities. Accurate estimates at low densities are exactly the ones that are important for detecting whiteflies and making decisions on corrective biocontrol releases. Nevertheless, even if the correlation between trap counts and adult numbers on plants is lower at low than at high whitefly densities, tomato and cucumber growers have experienced that YSTs reveal the presence of whiteflies earlier than direct observations from the crop by greenhouse workers who work in the canopies daily (Vänninen, 2012). According to Gillespie and Quiring (1987), in commercial cucumber T. vaporariorum is expected to be caught on YSTs starting from the densities of 0.01 adults per plant (one adult per 100 plants). Finding one adult among 100 plants is probably less likely than observing them in YSTs that serve to concentrate whiteflies from plants to an attractive collecting point. A contributing reason can of course be that workers may not systematically concentrate on observing whiteflies while working in the canopies, but observations are

a spin-off of their activities while picking fruit or pruning the plants. 2.2.2. Density of traps needed for trustworthy sampling in terms of representativeness One way for bias entering the estimates of population densities can be caused by the spatial autocorrelation between YST samples. This means that random sample values, at pairs of locations a certain distance apart, are more (or less) similar than expected for randomly associated pairs of observations (Legendre, 1993). Spatial autocorrelation can inflate Type I errors in statistical analyses, even though this may not always be the case (Diniz-Filho et al., 2003). Furthermore, the spatial dependency of individual trap catches could change with the trap size, which means that trap density or spacing may have to be adjusted in relation to trap size to satisfy the assumption of random sampling (Park et al., 2011a). YST catches of whiteflies in cherry tomato have been shown to be spatially autocorrelated (Kim et al., 2001; Park et al., 2011a). In this crop, positive spatial autocorrelation ceases to exist when YSTs are placed at a minimum distance of 12.5 m apart from each other (Kim et al., 2001). At such density of traps, whitefly sampling covers all parts of the crop, while avoiding redundant information between traps (Park et al., 2011a). Sparser placement of traps may be possible, though, without excessively sacrificing sampling precision (see Petrovskaya et al., 2011 referred to in the next paragraphs). In the context of developing enumerative sequential sampling plans aiming at fixed precision level, the requirement of the minimum effective distance may come into contradiction with the number of traps required by the sampling design at low pest densities in small greenhouses. In other words, the applied sampling design may require more samples (YSTs) than can be accommodated in the whole greenhouse area if the minimum effective distance rule is followed. Therefore, the requirement of spatial independence of samples in situations where classical statistics are used to enumeratively determine population density of whiteflies poses restrictions to the minimum and maximum density of traps. The issue of trap density is essentially an issue of spatial coverage bias (Royle et al., 2007): not all individuals in the pest population are exposed to sampling when trap density is decreased. In practice, there is large variation between the recommended trap densities from researchers, suppliers and extension officers. Suppliers recommend that one trap every 200 m2 should be placed for monitoring purposes of different greenhouse pests, including whiteflies, regardless of the crop (Koppert, 2012), while advisors in Wisconsin recommend that the greenhouse growers should place traps 13e18 m apart, approximately one for every 93 m2 (Rice Mahr et al., 2007), and in New Zealand recommendations are to place one trap every 100 m2 (Smith, 2009). In some European tomato greenhouse areas, however, advisors complement direct counts with trap counts placing 15 traps/ha (approximately one per 666 m2) on average (Arnó et al., 2009). In the Finnish Ostrobothnia area, the advisors selling monitoring services adjust the trap density according to the size of the monitored greenhouse, using one trap per 500 m2 in a greenhouse <3000 m2, one per 750 m2 in greenhouses of 3000e5000 m2, and one per 1000 m2 in greenhouses >5000 m2. Such values differ tremendously from the proposed trap density of one (60  4 cm2) trap every 18 or 20 plants proposed by Gillespie and Quiring (1987) to accurately estimate the adult population densities of T. vaporariorum in greenhouse tomato at levels as low as 0.1 per plant (or one every 10 plants), which is the critical density when parasitoids should be released (Gillespie and Quiring, 1987; Martin and Dale, 1989; Smith, 2009). Quiring (1986) reported that at the trap densities of one trap per one, five or ten cucumber plants, T. vaporariorum could not be found on the upper three or five leaves until the average YST catches reached nine or

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more whiteflies per trap per week (which corresponds to about 1.3 adults per trap per day). Nevertheless, Yano (1987b) proposed that one (90  4 cm2) yellow sticky ribbon should be hung every 100 plants for deciding releasing time of E. formosa for controlling T. vaporariorum in greenhouse tomato. In Finland, different trap densities have been tried by the advisors and growers to reduce monitoring costs, and the results showed similar population trends, although with decreasing whitefly numbers per trap with decreasing trap densities (Jenny Forsström, ProAgria ÖSP, pers. comm.). This implies that ETs must be adjusted to the density of traps. Because whitefly distribution is highly aggregated (Noldus et al., 1986), too low trap densities in large greenhouses may preclude the identification of whitefly hotspots that can be linked to influx of adults from neighbouring greenhouse compartments or from outdoors. Moreover, it may mean losing the excellent detection ability of YSTs for whiteflies at very low pest densities when the goal is early detection for a successful timing of parasitoid releases. A solution for these dilemmas is to release parasitoids preventively but even so, whitefly densities must be kept at very low levels all the time for biocontrol to be successful. Simple calculations based on the standard error of the mean as a measure of precision for trap count data may be useful for determining a minimum number of samples to reach a desired precision level, at least to follow population trends (Parrella et al., 1989). In addition, the problem of large numbers of samples needed to obtain reliable enumerative data caused by aggregated spatial distribution may be overcome by a method that ‘automatically’ averages the density over a larger area (Östrand and Anderbrant, 2003). The use of pheromone traps (or other traps based on olfaction) is such a method but at the moment there are no results available on how odour-enhanced traps would attract whiteflies in tomato and cucumber (cf. Górski, 2004). Traps with highly attractive visual cues might, in principle, attract whiteflies from a distance in low, open canopies (e.g. low potted plants). In high crop stands, the visibility and therefore, the attraction range of such traps is limited by physical obstruction by the plants. Attraction range indicates the maximum distance from which an insect can show directed instant movements towards an odour or visual cue in a time-independent manner (Östrand and Anderbrant, 2003). Recently, methods of numerical integration were suggested for estimating population sizes to reduce trap densities needed for a desired precision level in ecological monitoring programmes (Petrovskaya et al., 2011). Numerical integration rules can provide a significantly more accurate estimate of the population size from sparse trap data than the standard statistical approach. Integration on a rectangular sampling grid of 5  5 traps was concluded to keep the error consistently within 25% on an area of up to 100 ha (1 km  1 km). Petrovskaya et al. (2011) concluded, however, that as few as nine traps would be sufficient, but on such ultra-coarse grids, the integration cannot provide an estimate of the population size with any prescribed accuracy; instead, the results of the integration can be treated probabilistically by considering the integration error as a random variable. 2.3. Relevance of YST-based sampling plans in whitefly monitoring Relevance assumes, among other things, that the results of sampling reflect crop loss to a sufficient degree (Binns et al., 2000). We were, however, unable to find scientific studies relating the number of whiteflies per trap with crop losses of tomato and cucumber. Either the results were not published, or such studies have not been made for whiteflies. Examples on predicting crop loss level from trap counts of pests can be found for other pest species in greenhouse crops or other systems (e.g. Samways,

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1986 for the South African citrus thrips Scirtothrips aurantii Fuare in citrus orchards, and Shipp et al., 1998, 2000 for the Western flower thrips Frankliniella occidentalis (Pergande) in greenhouse sweet pepper and cucumber). The development of pest densityyield reduction curves based on trap counts is rather limited compared to those relationships based on direct observations. This may be due to difficulties for establishing a consistent relationship between YST counts and crop damage (Parrella et al., 1989). The lack of YST-based pest density-yield reduction curves can also be due to the lack of results showing a good relationship between trap counts and direct pest counts from the plants. The same may apply also to the relationship between whiteflies trapped and the development of sooty moulds on plants. Sooty mould is a visual parameter for the assessment of damage (Johnson et al., 1992), whose development is affected, particularly, by temperature and humidity. Nevertheless, in some systems counts of whiteflies on YSTs have been shown to be closely associated with virus infection (Berlinger, 1980). Action thresholds based on a weekly catch of 1.5 Bemisia per trap per pest management unit of 700 m2 are reported for chemical management of whiteflies in roses and Chrysanthemum in Tanzania (Ndomba, 2007). No information is given how this action threshold was determined. In Poinsettia, Steiner (1993) recommends an action threshold of five B. tabaci per trap per week (one trap per 200 plants) for chemical control. The threshold is apparently not based on a formal sampling protocol, but was linked to a crop infestation level of 5% infested plants which is tolerated by growers. Rice Mahr et al. (2007) recommend that whitefly populations in greenhouse vegetables (including tomato and cucumber) should be lowered with a selective pesticide if more than 15 adults are found in a trap (presumably at the suggested average trap density of one per 93 m2). No information is given how this threshold was derived. The development of YST-based ETs for releasing biological control agents might be more difficult because of the low whitefly densities required for the successful establishment of parasitoids and hence, of biological control programmes (Foster and Kelly, 1978; Stenseth and Aase, 1983). Nevertheless, Yano (1987b) developed practical guidelines for releasing E. formosa in tomato greenhouses based on numbers of T. vaporariorum adults per yellow sticky ribbon (4  90 cm2) at different temperature ranges. Trap density and related action thresholds were based on previous experiments conducted in small greenhouses (Yano, 1987a). Yano (1987b) recommended that 100 parasitized pupae should be introduced for 100 plants, if 10e20 or 1e5 adults per ribbon are found above 25  C or below 20  C, respectively, which should be followed by two similar introductions at fortnight intervals. To our knowledge this is not a widespread approach, and probably requires further assessment for commercial purposes. Growers can develop their own “action thresholds” based on experience, which can help to reduce the use of pesticides. In Finnish year-round tomato and cucumber crops, the growers tend to revert to chemical control at YST counts of 170e250 adults per trap per week (24e37 adults per trap per day). It is not, however, currently known how such densities relate to crop losses. However, at densities of about 700 whiteflies per trap per week, tomatoes start getting sticky form honeydew excreted by the whiteflies. Subjective thresholds based on traps should be taken cautiously since several factors such as the way of using the trap and the occurrence of migratory adults coming into the greenhouse may affect these thresholds (Cloyd, 2009). 2.4. Practicality Practical, method-related issues influencing the willingness of growers and advisors to adopt YST as a monitoring tool relate to

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price of traps, their overall fit with the crop production procedures (e.g. they should not hinder workers from pruning and harvesting the crop), the amount of work required to place and check the traps which, in turn, increases the total cost of the sampling programme, and the available information on how YST use contributes to the success of pest management. Other potential reasons include the education level of growers on IPM, the prevalent pest management strategy of growers (biological or chemical), and the importance placed on resistance management. To our knowledge, a comparison between the times allocated to different sampling techniques for whiteflies in greenhouse crops has not been conducted, and deserves further assessment. 2.4.1. Reducing time allocation to YST counts Approaches to decrease the effort and time associated to counting trapped adults of T. vaporariorum have been proposed by Heinz et al. (1992) and Steiner et al. (1999). By obtaining cell frequencies on the trap with the aid of a transparent grid (5  5 cm2), and analysing the distribution of four different taxa on the trap, Heinz et al. (1992) found that T. vaporariorum tends to cluster along the vertical trap plane, but they are uniformly distributed along the horizontal trap plane. Based on this, counting a 2.3 cm wide vertical column (20% of the trap) could explain over 90% of the variation observed in the whole trap with an 80% reduction in the time allocated to counting the total area. This approach tends, however, to underestimate the number of individuals per species on the trap, which may lead to overlooking action thresholds. Moreover, patterns of distribution of insects on the trap may be altered by trap height above the plants (Steiner et al., 1999). Steiner et al. (1999) proposed another method for counting T. vaporariorum. By overlaying a transparent grid, they counted the number of individuals per cell (1.27 cm2), and the cells occupied by each pest. The relationship of the number of cells occupied with the number of insects was then modelled for future prediction of the number of insects. They found that between 10 and 50 cells occupied accurately predicted the number of thrips or whiteflies in the trap, regardless of the location. Gerling and Horowitz (1984) used a grid of 80 squares (2  2 cm2) to reduce the time allocated to trap counts when the number of B. tabaci exceeded one thousand. According to their preliminary calculations and checks, counting insects on every second square, every second row, and multiplying the result by 4 could give a good estimate with an error below 10%. 2.4.2. Identification of species in mixed populations Identification of species from YSTs poses an extra effort when the two whitefly species co-exist in the greenhouse. Spinner et al. (2011) developed a presence-absence sampling plan to determine the occurrence of B. tabaci in mixed populations of whitefly based on counts of the fourth nymphal stage from plants. However, adults of different genera including Bemisia and Trialeurodes can be accurately identified and screened under low power magnification based on morphological characteristics, particularly pigmentation of the thorax and abdomen (Malumphy et al., 2009, 2010). Using this approach, the overall accuracy of species prediction of adult whiteflies on YSTs can reach 98%. Accuracy is achieved by good observational skills and experience. Misidentification between B. tabaci and T. vaporariorum may occur, when pigmented mature eggs near the apex of the abdomen, or dark yellow mycetomes in the abdomen are present. These can be confused as a pigmented abdomen, which is used as a morphological trait for distinguishing B. tabaci and T. vaporariorum (Malumphy et al., 2010). It is currently unknown how much extra time is needed to separate the species when counting the whiteflies on the traps compared to a singlespecies situation.

2.4.3. Technological innovations Technological innovations in the field of computer vision as well as image processing techniques and artificial intelligence (Qiao et al., 2008; Solis-Sánchez et al., 2009, 2011; Kumar et al., 2010) have the potential to replace traditional counting and improve time optimization and accuracy for estimating insect densities either on YSTs or leaves. An image processor system for estimating densities and an insect classification system based on video recording of YSTs have been developed and tested with adult populations of T. vaporariorum and B. tabaci, respectively (Qiao et al., 2008; Kumar et al., 2010). In addition, Solis-Sánchez et al. (2009, 2011) have proposed algorithms for a system based on the application of machine vision techniques for identifying and counting B. tabaci, and discriminating pests on YSTs. These techniques are currently in a prototype stage, but hold great promise for monitoring and early detection of whiteflies in greenhouses in the future. One limitation may be the identification of genus in mixed populations, unless the colour difference of the abdominal parts described by Malumphy et al. (2009) is sufficient to discriminate the two species based on imaging methods. 2.4.4. Checking interval The checking interval needs to give a balance between sampling precision and sampling efficiency. Too long an interval may reduce the efficiency of trap catches due to the decrease of the efficacy of the adhesive due to aging, dust or higher numbers of trapped insects (Yano, 1987b). Additionally, long intervals can decrease the sampling efficiency of this method due to the large numbers of trapped insects (Palumbo et al., 1995) resulting in bias. Traps are typically checked on a weekly basis (for example Heinz et al., 1992; Steiner et al., 1999; Kim et al., 2001). 2.4.5. Development of sampling plans Trap-based sampling plans in tomato and cucumber may be most useful when they support decision-making for timely corrective chemical treatments so that crop losses are avoided and whitefly densities are quickly brought to levels where biocontrol agents can again control them. Overall, choices need to be made depending on the goal of monitoring, and which control methods the grower emphasizes; the latter may vary in importance depending on the crop species, grower’s own beliefs, season and temperature. For example, it is more difficult to get biocontrol working successfully in cucumber than in tomato due to higher temperatures and better suitability of cucumber to the pest (van Lenteren and Noldus, 1990). It is important to start biocontrol at a very early stage of whitefly infestation in cucumber, but it is also important to know how to respond quickly with corrective chemical treatments when pest numbers start increasing. The development of sequential sampling protocols may be useful for defining the number of samples to be taken for accurate estimates of adult densities on YSTs. At densities near the critical one (i.e. the action threshold density), however, the number of samples needed to reach a correct control decision increases compared to situations when pest densities are lower or higher than the critical density. This is a practicality issue that may be solved by always using a selected density of traps, but only checking the needed number of them, depending on how close to the critical density the sampling shows the population size to be. To date only a few studies dealing with YST-based sampling protocols can be found in the literature (Parrella and Jones, 1985; Kim et al., 2001; Hall and Hentz, 2010). To our knowledge, only one study has aimed at developing a sequential sampling plan for estimating densities of T. vaporariorum with YSTs in greenhouse conditions. Kim et al. (2001) developed a sampling plan by placing one trap 7 m apart across rows, and 10 m down rows. According to

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their plan, between 6 and 12 traps (9.6  16 cm cylinders, one every 70 m2 or 1/180 plants) placed at least 12.5 m apart should be sampled for estimating typical densities occurring in greenhouses ranging between 0.10 and 0.15 ha (Kim et al., 2001). In Park et al. (2011a), the effective distance ensuring spatial independence of YST samples was approximately 16 m apart, which corresponds to about one trap per 250 m2. The spatial dependence and structure of T. vaporariorum populations are, however, different when assessed using YST and visual counts of adults and immature stages, respectively. For direct counts of nymphs, the minimum distance between sampling plots is 9 m (Park et al., 2011a). To reach a cost-effective YST monitoring, rather than estimating actual population densities, YST counts should be classified as being below or above a threshold density that denotes the break-even point of control costs vs. benefits obtained from the control action in terms of pest mortality and yield increase. The use of such categorization requires the establishment of this break-even point, i.e. ET or the critical density (Binns et al., 2000). 3. Future research YSTs were introduced as a monitoring tool in the 1980s and are now used for several flying pest species in field and greenhouse crops. Sticky traps are available and sold commercially in all parts of the world, but despite that, no explicit data are available on the quantitative extent of their use. There are also qualitative differences in their use, i.e. in some firms systematic data are collected from monitoring, whereas in others the traps are checked irregularly without doing a systematic book-keeping. Nevertheless, the extent of their use in practice is not reflected in the number of scientific publications that deal with the use of YSTs as the method of formal sampling for monitoring whiteflies. YSTs are currently mostly used either to indicate hotspots of pests in a crop or/and as a supporting tool in decision-making for taking control measures without any specific or standardized thresholds. Nevertheless, a successful case of quantitative ETs developed for thrips in cut roses (Casey et al., 2007) supports promise and the feasibility for implementing properly quantified YST-based decision-making tools for managing certain pests in greenhouse conditions. Below we summarize the knowledge gaps that, in our opinion, need to be addressed to develop YSTs into a more effective decision-making tool for whitefly management. Decision-making for pest management rests on sampling plans and on critical densities or ETs at a given time. For greenhouse whiteflies, there have been isolated efforts to develop sampling plans or ETs, but significant research is needed to develop sound tools. The most important issue is the establishment of the relationship between the number of insects trapped and direct counts of different life stages, particularly sessile immature stages. The study of Kim et al. (1999) and Gillespie and Quiring (1987) showed promising results, and should be taken as pioneering work to follow in other greenhouse crops paving the way for developing sampling plans to achieve an accurate and unbiased estimation of population densities of adults by YSTs (e.g. Kim et al., 2001). Correlations between YST catches and direct counts can be improved by using appropriate traps to enhance trap efficiency. In this sense, comparative studies on the user-friendliness and efficacy of new improvements are still scant. Explicit evaluation of the sampling precision and efficiency of various traps and ways of increasing the capture efficiency of YSTs have not been done yet in greenhouse crops, whereas in cotton a more systematic effort has been taken (Gerling and Horowitz, 1984; Naranjo et al., 1995). The development of YST-based ETs for whiteflies in cucumber and tomato could be guided by the establishment of a damage function based on the relationship between whitefly counts in traps

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and yield losses. There is information available on the effect of whitefly immature densities on plants on yield losses in both tomato (Gusmão et al., 2006) and cucumber (Jeon et al., 2009). These studies can serve as starting points for establishing ETs for immature whiteflies and adults in greenhouse cucumber and tomato, and linking the densities of whiteflies on plants to those in YSTs. A crucial question is whether such correlation exists at trap densities that satisfy two demands simultaneously: they are both costeffective and produce sufficiently precise information on the population size of whiteflies in the crop for decision-making purposes. Furthermore, it is not known whether it is possible to satisfy all goals at the same time in terms of cost-effectiveness, monitoring the relative densities of whiteflies for the purpose of optimizing biological control (early detection and preventive and curative treatments by using parasitoids), detection of hotspots and initiating chemical control when whitefly densities increase above a critical level that predicts yield loss. Geostatistics focussing on spatial aspects of pest distribution is currently providing new insights into practical pest monitoring and the development of decision-making tools for pest management. Concentrating on the spatial aspects of pest occurrence requires that the objective of the sampling is redefined as mapping the population rather than estimating or categorizing the mean (Fleischer et al., 1999). The question is how the spatial information can be effectively used to reduce resources needed for monitoring the pest. Models that simultaneously address the two fundamental considerations of sampling animal populations, i.e. the bias related to both abundance (spatial coverage bias) and occurrence (imperfect detection of individuals that are exposed to sampling), might prove useful also to improve formal YST-based sampling plans of pest insects in greenhouse crops. To account for the spatial coverage bias, methods that unify spatial statistical models with models accommodating non-detection may be necessary to resolve important spatial inference problems based on pest survey data (e.g. Royle et al., 2007). Recent developments reported by Park et al. (2011a, 2012) of utilizing data for spatial component analysis for the purpose of predicting pest abundance in greenhouse crops while taking the spatial coverage bias into account pave the way for this approach. The application of georeferenced categorical sampling with associated probability maps serving as decision tools for pest management actions (Fleischer et al., 1999), and numerical integration methods (Petrovskaya et al., 2011) applied to trap counts of whiteflies for estimating population densities might help in developing more economically efficient trap-based ETs. In contrast to classical statistics, such advance may necessitate a shift from a fixed and prescribed accuracy to probabilistic sampling accuracy (Petrovskaya et al., 2011). This approach would satisfy both the demands of economically efficient low trap density and robust sampling precision, but the application of this approach to whitefly monitoring needs testing. Despite the goal being the use of as few traps as possible per greenhouse, it is desirable to initially conduct studies on the correlation between whitefly densities on plants and in traps on different, nested spatial scales. This is because it is necessary to understand the relationships between spatial patterns and ecological phenomena in detail before applying integrative methods based on very coarse-grid sampling (Bellehumeur and Legendre, 1998). By using autocorrelation structure and variograms (a traditional geostatistical tool for analysing spatial dependence), Park et al. (2011a, 2012) established the minimum spatial scale for YST placement to avoid redundant information from sampling. A variogram, however, assumes that local means and variances are stable across the mapped region, which is seldom true for insect populations (Fleischer et al., 1999). Nevertheless, an individual plant,

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the spatial scale defined by Park et al. (2011a, 2012) to identify whitefly patches for non-redundant adult sampling, and the ultracoarse grid scale of 3  3 traps suggested by Petrovskaya et al. (2011) could be used as three different scales for initiating studies on the correlation between whitefly counts in YSTs and yield losses in greenhouse tomato and cucumber. To complement the analysis of whitefly distribution at nested spatial scales, further information and data, so far analysed by classical statistics, are available from earlier studies (e.g. Yano, 1983; Noldus et al., 1985; Tsueda and Tsuchida, 1998; Arnó et al., 2006). The approach of numerical integration for economically effective sampling programmes should be combined with studies on further assessment of the effect of environmental factors on trap efficiency to better account for errors caused by the trapping method itself (Petrovskaya et al., 2011). Such studies would benefit from the very information that is currently not available in detail: the trapping efficiency of regular YSTs in comparison to those enhanced by light emitting diodes or different trap shapes on a contrasting background, and the use of such information for developing ETs, as well as the traps’ effective distances and sampling ranges for whiteflies in respect to environmental variables such as temperature, light spectrum and crop architecture. To account for the imperfect detection of individuals that are exposed by sampling and to reduce counting time, algorithms that enable the identification of whiteflies by imaging methods in traps are already available and await practical application. We are not, however, aware of algorithms to aid in identification of whitefly species in mixed species populations. Automated counting tools would be most useful when combined with prediction algorithms that take into account the effect of temperature on whitefly behaviour during the trapping period. The effect of temperature on YST counts of whiteflies was incorporated into the two temperature-differentiated ETs developed by Yano (1987b). Computer modelling could be used for developing algorithms that take temperature spatio-dynamically into account when producing estimates of whitefly density. To understand the developmental and behavioural aspects of temperature on whiteflies, the scale of one individual plant is likely to be insufficient. The scale of individual leaves must be incorporated into the models, as indicated by the results obtained by Park et al. (2011b) who showed that the population dynamics of the greenhouse whitefly is affected greatly by the leaf rather than air temperatures. For whitefly flight behaviour, though, air temperature is likely to be equally important. In year-round cultivation, there are specific issues of ETs that need to be studied to understand what role they play in developing YSTs into a more accurate decision-making tool. YST-based economic and action thresholds may be different in seasonal and yearround crops grown in the winter months. This is because plant physiology in winter months differs from that of seasonal crops due to different light conditions and their effects on plant metabolism, but also because pests may perform differently in winter months either due to the plants’ physiological condition or because the abiotic conditions influence the pest’s performance differently compared to summer crops (Vänninen et al., 2010). Different types of temperature gradients, as an example, form within artificially lighted canopies depending on whether top lights are used alone or in combination with interlights, and the type of interlights (cool light emitting diodes, or hot high-pressure sodium lamps). The question of economical efficiency of YST-based sampling has not been addressed at all so far in terms of showing the value of information obtained from YSTs placed at different densities. In other words, what is the density of YSTs that produces information the value of which is measurable as a long-term reduction of total management costs and improved control success in comparison to not using YSTs? The goal is to find the balance between the density

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