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Keywords:

  • bioclimatic model;
  • climex;
  • habitat model;
  • invasive alien species;
  • pest risk assessment;
  • pheromone;
  • species range;
  • Uraba lugens

Summary

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  • 1
    When invasive species are first detected in a new environment there is often a demand for information about the potential for the organism to spread and create impacts. Uraba lugens (Lepidoptera: Nolidae) is an Australian native moth that has invaded New Zealand in what are presumed to be two separate episodes. After U. lugens was found in Auckland in 2001 a climex™ model was prepared to gauge the potential for the moth to spread and inflict damage in New Zealand. Inconsistencies in fitting model parameters to the then known native distribution, indicated that the known native distribution was probably incomplete, and that the unknown part of its range was critical for defining its likely range limits in New Zealand.
  • 2
    A synthetic sex pheromone trapping survey was used to ascertain the altitudinal and upper rainfall range limits of U. lugens in part of its native range in Australia. The survey extended the known range of U. lugens into higher-altitude and higher-rainfall zones of Tasmania. We used the expanded distribution information to refine a climex model, and to project the climate suitability for U. lugens with particular emphasis on New Zealand.
  • 3
    The projected climatic suitability of New Zealand indicates that the area likely to be at risk of invasion by U. lugens is considerably more extensive than was indicated from its historical distribution records in Australia. It now includes all the eucalypt forestry areas. Similarly, the global potential distribution covers the major eucalypt forestry regions of the world.
  • 4
    The minimum heat sum necessary to complete a generation for U. lugens that was associated with its range limits was considerably lower than that predicted by average development rates gained from constant temperature and daylength studies. Possible explanations for this anomaly are discussed.
  • 5
    Synthesis and applications. The lack of suitable data on the distribution of organisms is perhaps the single most common challenge for ecological climate modellers. Trapping along climatic gradients with a synthetic pheromone lure offers a cheap, rapid means of ascertaining the climatic distribution of suitable high profile insects.

Introduction

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Uraba lugens Walker (syn Roeselia lugens: Lepidoptera: Noctuidae, Nolinae) is an invasive Australian moth with an exceptionally wide host range centred on Australian Myrtaceae, and a broad geographical distribution (Fig. 1). In its native range, U. lugens has been identified and studied as a pest of Eucalyptus spp. since at least 1899, when notable outbreaks were recorded (Froggatt 1900; Campbell 1962; Farr, Swain & Metcalf 2004). In New Zealand, U. lugens was identified originally on Eucalyptus spp. at the Mount Manganui golf course in 1996, whereupon an intensive eradication campaign in this area resulted in its local eradication. According to the New Zealand Forest Health database, it appears that the population had first been collected at Mount Maunganui in 1992, but the specimen was misidentified originally (J. Bain, Ensis, Forest Health Database, unpublished data). Subsequently, U. lugens was discovered in nearby Auckland in 2001, but the delimitation survey revealed that the population there had dispersed beyond the point where eradication was feasible (Ross 2003). U. lugens is currently found in Auckland, covering more than 40 000 ha, and spreading steadily southward and northward away from the original infestation zone (http://gis.scionresearch.com/maful).

image

Figure 1. The known distribution of (a) the host plants for Uraba lugens and (b) Uraba lugens in Australia. The records for U. lugens include the pheromone survey sites at which it was trapped during the survey reported here. U. lugens host records were obtained from Australia's Virtual Herbarium, and location records for U. lugens were collated from the Australian National Insect Collection (unpublished data), Brimblecom (1962); Turner (1944), Jane Elek (personal communication), Forestry Tasmania (unpublished data), Charlma Phillips – Forestry South Australia (unpublished data), Chris Burwell – QLD Museum (unpublished data), Campbell (1962), Harris (1974), Andy Gibb HortResearch (unpublished data), Anthony Rice, University of Tasmania (unpublished data), John Ireson; Tasmanian Department of Primary Industries Water and Environment (unpublished data), Kerrie Bacon, State Forests of NSW (unpublished data), Ken Henry, SARDI – Primary Industries Research South Australia, Ross Wylie, Queensland Department of Primary Industries, Vin Patel, University of Tasmania, David de Little, private consultant. In some cases, the Geosciences Australia place names index was used to derive geographical coordinates for locality information.

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U. lugens takes its common name, the gum leaf skeletonizer, from the feeding habits of the early larval stadia, which feed on the epidermis of the leaves of gum trees, leaving a skeletal network of veins. It is of concern to New Zealand biosecurity authorities from a number of perspectives. The larvae have hollow urticating hairs or setae that contain an envenomating substance, including a histamine, that can cause severe skin reactions that can last for up to 42 days (Southcott 1978). It also poses an immediate and severe threat to some amenity trees in the Auckland region, with Lophostemon confertus trees being severely defoliated (D. J. Kriticos, personal observation). To a minor extent, some native Myrtaceae that are in the immediate vicinity of suitable Australian Myrtaceae hosts are at risk of minor ‘spillover’ feeding damage when larvae from eucalypt hosts come into contact with the native plants (Kriticos et al. 2005). As the invasion spreads north and south from Auckland, U. lugens will encounter extensive commercial Eucalyptus spp. plantations, posing a significant economic threat (Potter & Stephens 2005; Potter et al. 2005).

The potential distribution and relative abundance of U. lugens in New Zealand is of interest to policymakers and other stakeholders, who wish to understand the potential spatial extent of the invasion so that the impacts can be gauged and appropriate ameliorative measures can be considered prior to the invasion reaching valuable plantation areas.

Climate-based distribution models have been used previously to project the potential distribution of taxa ranging from plants, invertebrates, pathogens and even vertebrates. Numerous systems have been developed for this purpose (Guisan & Zimmermann 2000; Kriticos & Randall 2001). Their strengths lie in being able to identify locations that may be suitable for a taxon. This information is of immense value to conservation managers wishing to identify the range of a rare and endangered species in order to protect its habitat. In the case of an invading organism this information can identify those natural or production assets at risk from the invasion. While each of the habitat models has its own strengths and weaknesses, there are some thematic issues. Models built using native-range only data are likely to be biased conservatively due to the effect of biotic release that distinguishes between the fundamental and realized niches (Davis et al. 1998a; Wharton & Kriticos 2004). Correlative regression-based models such as Bioclim (Busby 1991), grasp (Lehmann et al. 2002) and garp (Stockwell & Peters 1999) are likely to be unreliable when extrapolating into novel environments such as new continents or future climate scenarios. Models based solely on records of invasive species in a newly invaded region are likely to be conservatively biased as the invaded range is likely to be a subset of its suitable range. Finally, many of the models rely solely upon climate variables, ignoring other habitat factors such as disturbance regime and soils. Models such as Bioclim, climate (Pheloung 1996), climex (Sutherst & Maywald 1985; Sutherst et al. 2004) and stash (Sykes et al. 1996) model those areas that are climatically suitable, rather than habitat per se.

The known distribution of U. lugens in Australia was used to develop a climex model to project its potential distribution in New Zealand. climex was chosen for this task because of its popularity as a pest risk assessment tool and its ability to project distributions into novel environments and to provide some insight into the ecoclimatic mechanisms that are limiting a species distribution (Kriticos & Randall 2001; Sutherst 2003). In developing this model, Kriticos et al. (2005) raised some questions regarding the completeness of the known distribution of U. lugens in the Australian state of Tasmania. First, the distribution of U. lugens in Tasmania appeared to be restricted unreasonably to relatively dry areas. Fitting the model to the known distribution would have required applying a suspiciously large degree of wet stress to limit U. lugens to the drier zones, which strongly affected its projected potential distribution in New Zealand. Secondly, the projected potential distribution of U. lugens in New Zealand was also highly sensitive to its precise range limit near Arthurs Lake in the central highlands of Tasmania. The apparent range limit coincided with a steep elevation gradient, terminating on the central plateau, the coldest region in Tasmania. The geographical location of the single record for U. lugens near Arthurs Lake was recorded imprecisely, thereby casting a major uncertainty over the potential distribution of U. lugens in New Zealand. These two sources of uncertainty warranted further investigation because important (and expensive) decisions were being influenced by perceptions of the potential range of this species in New Zealand in relation to the eucalypt forest plantations (M. Ross, Biosecurity New Zealand, personal communicatrion).

Traps baited with pheromone lures have been used extensively to ascertain the presence of a variety of insects – usually in agricultural, horticultural or silvicultural settings where the organism is a known potential pest and the aim is to ascertain if the pest is present through time (Suckling & Karg 2000). Delta traps have been used in Auckland, New Zealand to trap U. lugens to estimate the spatial extent of the invasion through time and to confirm the predicted adult flight phenology (Withers et al. 2003; Suckling et al. 2005).

In order to reduce the uncertainty surrounding the potential distribution of U. lugens in New Zealand, a pheromone trap survey was undertaken to establish the climatic limits of U. lugens in Tasmania. We describe here the development of the climex model, and the application of the pheromone trap survey to the task of parameterizing the climex model.

Materials and methods

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

pheromone development

Traps baited with pheromone lures have been used extensively to ascertain the presence of a variety of insects, usually in agricultural, horticultural or silvicultural settings where the organism is a known potential pest and the aim is to ascertain if the pest is present through time (Suckling & Karg 2000). Delta traps had been used previously in Auckland, New Zealand to trap U. lugens to estimate the spatial extent of the invasion through time and to confirm the predicted adult flight phenology (Withers et al. 2003; Suckling et al. 2005).

Live female U. lugens moths were cultured from eggs collected in Tasmania in July 2004. Gland extracts from these females were analysed qualitatively and quantitatively by gas-chromatography (GC) and GC-mass spectrometry. Pheromone compounds were identified by comparing their retention times and mass spectra with those of known synthetic standards. The compounds from the Tasmanian females were found to be the same as those from Auckland females, and the blend proportions were found to be similar (A. R. Gibb, unpublished data). A separate experiment to test the relative attractiveness of each of these blends in Tasmania was conducted at the same time as the habitat survey. Unfortunately, few moths were caught: no control, six Tasmanian blend and three Auckland blend. Using exact tests, the only statistically significant result was that the Tasmanian blend caught more moths than the control with no pheromone (P < 0·05%, n = 10).

pheromone trapping survey

Fresh delta traps (HortResearch, Auckland, New Zealand) were deployed with pheromone lures along roadsides and walking tracks throughout central, southern and western Tasmania. Six primary elevational transects were deployed, running from known suitable climates at low elevation up to the tree line or the transition from Eucalyptus spp. to other canopy dominants (Fig. 2). Each of these transects consisted of between eight and 11 trap sites. We attempted to place traps at 100-m elevation intervals; however, the exact spacing between trap sites varied depending upon the terrain and availability of a suitable safe roadside location. The primary elevational transects were supplemented with additional trap sites located along rainfall gradients and opportunistically en route between the primary transects when suitable hosts were encountered.

image

Figure 2. Distribution of Uraba lugens in Tasmania: ▵: historical records, ○: positive catches in pheromone traps; ×: no catches in pheromone traps.

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At each trap site, three traps were deployed on every second host tree with a gap between traps of 7–20 m. Global positioning system (GPS) coordinates and odometer readings from suitable landmarks were used to locate the trap sites for subsequent retrieval. The coordinates of each trap site were loaded into an ArcPad application running on an IPAQ hand-held PC with a Navman GPS sleeve (Navman, http://www.navman.com). This system was used to navigate to each trap site.

All traps were attached to Eucalyptus spp. either with a nail, or by wrapping the wire around the branch (for saplings), or using a 25-mm staple (U-shaped nails). Of these methods, the staples and the wrapping technique proved quickest to deploy and retrieve the traps. The traps were placed approximately 2 m above ground level on either the trunk or a suitable branch of the eucalypt tree, because oviposition is commonly on the lower stratum of the canopy (Morgan & Cobbinah 1977).

The timing of the pheromone trap deployment (22–27 January 2005) and retrieval (8–12 April 2005) was designed to capture the majority of the adult moth flight period with deployment commencing at the start of the adult flight period at the low elevation sites, and trap retrieval occurring after the expected end of the flight period at the high-elevation sites. In deciding on these dates, we used a population development model for U. lugens (Withers et al. 2003), and broad-scale information on the phenology of U. lugens in Tasmania taken from a range of references and personal communications [M. Mamoru, D. DeLittle, J. Elek, G. Allen, V. Patel, A. Rice, all personal communications; Forestry Commission Tasmania 1987; Farrow (1996).

On retrieval, traps were scored for the presence of U. lugens moths. For analysis, the trap catches were combined and each trap site was scored only for presence or absence of U. lugens. Where the identity of moths was uncertain and where sufficient evidence remained on the trap a local expert, Peter McQuillan, University of Tasmania, confirmed that these moths were not U. lugens. Where the identity of a moth was indeterminate, the data were treated as missing for that trap.

climate surfaces

Several climate surfaces were used for this project. Initial parameter-fitting was conducted using the standard meteorological data set that is provided with climex version 2·0 (Sutherst et al. 2004). While this data set provided insufficient coverage for fine-scale parameter-fitting in Tasmania, it was adequate for initial fitting purposes.

anuclim (Houlder 2004) was used in conjunction with a fine-scale digital elevation model to develop a set of climate surfaces for Tasmania. The digital elevation model required to estimate climate variables was obtained from the Shuttle Radar Topography Mission (SRTM, http://srtm.usgs.gov/) at a horizontal resolution of 90 m, and then resampled to a resolution of 0·025 arc degrees using ArcGIS (Esri, Redlands, CA, USA). The esoclim module of anuclim produces monthly estimates for long-term averages for 16 climate variables including maximum temperature, minimum temperature and rainfall. climex also requires estimates of relative humidity at 09.00 h and 15.00 h in order to estimate pan evaporation and subsequently the soil moisture model. The relevant dry bulb temperature and dew point temperature variables from esoclim were used to generate the relative humidity estimates based on the Tetens equation as follows:

  • image(eqn 1)

where Tdew is the dew point temperature and Tdb is the dry bulb temperature at time t (either 0900 or 1500). This equation was derived from Allen et al. (1998), Jensen, Burman & Allen (1990) and Rosenberg, Blad & Verma (1990).

Finally, the 30 arc minute gridded climate data set from the University of East Anglia Climate Research Unit (CRU) (New, Hulme & Jones 1999) was used for the global prediction. For the global data set, a different approach was used to estimate relative humidity. The CRU climate data set produces estimates of vapour pressure. The Magnus equation was used to estimate the saturated vapour pressure at 09.00 h and 15.00 h. Two forms of the equation were used in order to provide estimates of relative humidity over water and over ice:

  • image(eqn 2)
  • image(eqn 3)

where Es is the saturated vapour pressure, Tw(h) is the wet bulb temperature at time h. Tw(h) is estimated as follows:

  • Tw(h) = [(3 × Td) + (2 × Tmean)]/5,(eqn 4)

where Td is the dew point temperature and is set to equal Tmin (under the assumptions of New, Hulme & Jones 2000). In this case, the relative humidity is derived as:

  • image(eqn 5)

where E is the vapour pressure provided by the CRU.

Given the inevitable inconsistencies between different climate data sets, it is generally inadvisable to mix and match different data sets, fitting parameters with one data set and then projecting distributions using another data set. In the latter case, the required data were not available to use esoclim for the entire world. In addition, the errors inherent in using the 30′ CRU gridded data in this manner are likely to be minimal at the global scale.

climex model

While ecophysiological data can be used to parameterize climex models, geographical distribution data remain the most useful and reliable input data (Kriticos et al. 2003a; Kriticos et al. 2003b; Sutherst 2003). The known distribution of U. lugens in Australia is presented in Figs 1 and 2. The geographical range covers the Mediterranean, temperate and subtropical regions of Australia. The recommended method of manually iteratively fitting parameters was used to define the climex model (Table 1). Two forms of U. lugens have been recognized, a univoltine form that lays egg batches that have a slightly clumped appearance, and a multivoltine form that lays eggs in straight rows (Campbell 1962). The univoltine form is found in cooler sites and has been reported to develop through 13 instars (Campbell 1962). The bivoltine form has been noted to develop through 11 instars (Campbell 1962), although Allen (1990) also notes that the number of instars in bivoltine South Australian populations can vary from eight to 13. Farr (2002) describes populations in southern forests in Western Australia as univoltine, having 11–13 instars and laying eggs in parallel rows. In this analysis we treat the two forms as a single taxon, and model their combined distribution.

Table 1. climex parameter values used for modelling the distribution of Uraba lugens based on the native (Australian) distribution. The role and meaning of parameters are described in Sutherst et al. (1999)
IndexParameterValuesUnitsa
  • a

    Where units are absent, values are dimensionless indices.

TemperatureDV0 = lower threshold8°C
DV1 = lower optimum temperature20°C
DV2 = upper optimum temperature27°C
DV3 = upper threshold29°C
MoistureSM0 = lower soil moisture threshold0·2 
SM1 = lower optimum soil moisture0·3 
SM2 = upper optimum soil moisture1·2 
SM3 = upper soil moisture threshold1·4 
Cold stressDTCS = degree-day threshold temperature18°C days
DHCS = stress accumulation rate–0·00055Week−1
Heat stressTTHS = temperature threshold29°C
THHS = stress accumulation rate0·0011Week−1
Dry stressSMDS = soil moisture dry stress threshold0·2 
HDS = stress accumulation rate–0·015Week−1
Wet stressSMWS = soil moisture wet stress threshold1·4 
HWS = stress accumulation rate0·009Week−1
PDDNumber of degree-days above DV0 necessary to complete one generation610°C days
Cold stress

A degree-day model was adjusted to the distribution limits of U. lugens in the Australian Alps and Tasmania. A temperature threshold model gave broadly similar results; however, while increasing cold stress accumulation provided a closer fit in northern New South Wales, it created unacceptable errors further south. Following the pheromone trap survey of Tasmania, the cold stress function was adjusted downwards to allow persistence at Liawenee in the Central Plateau.

Heat stress

The heat stress threshold was set to 29 °C and the accumulation rate adjusted to fit the warmest locations known to be suitable for U. lugens, Geraldton in Western Australia and Lakefield near Cooktown in North Queensland.

Dry stress

The dry stress mechanism was adjusted to barely allow persistence of U. lugens in Geraldton, Western Australia.

Temperature index

The minimum temperature for development (DV0) was set to 8 °C, noting that the base temperature for development adopted by Withers et al. (2003) varied from 4·7 °C for eggs to 9·3 °C for larvae and 12·2 °C for pupae. In a controlled temperature development experiment, larvae of U. lugens barely developed, and failed to reach pupation at a constant 8 °C (D. J. Kriticos, unpublished data). The lower optimum temperature (DV1) was set to 20 °C and the upper optimum temperature (DV2) to 27 °C. The maximum temperature for development (DV3) was set to 29 °C after it was found that no larvae survived constant 32 °C conditions (D. J. Kriticos, unpublished data).

Minimum development heat sum to complete a generation

climex calculates the daily amount of heat available for development of the modelled organism by integrating the daily temperature cycle and subtracting that portion below the development threshold (DV0) (Sutherst et al. 2004). The daily heat sum values are then summed to provide an annual heat sum total expressed in degree days. At any location, across the course of a year, if a poikilotherm does not experience a threshold minimum development heat sum it may be unable to persist at that location. This range limiting condition is usually associated with the minimum developmental heat sum to complete one generation within a year. A threshold annual minimum development heat sum (PDD) was used to limit U. lugens in central Tasmania and at Mount Pinnabar on the New South Wales–Victorian border. In Tasmania, the trap survey showed that U. lugens was found throughout the Central Plateau around Liawenee (Fig. 2). However, the heat sum for Liawenee is only 610 degree-days above 8 °C, considerably less than the average heat sum for completion of a generation (http://www.bom.gov.au/climate/averages/tables/cw_096065.shtml). Accordingly, the PDD threshold was set to 610 degree days.

Wet stress

Wet stress was used to preclude U. lugens from the majority of south-western Tasmania. Parameters were adjusted to barely allow persistence at suitable locations along the highway between the Steppes and Strahan (Fig. 2).

Moisture index

The moisture requirements for U. lugens are mediated through their myrtaceous host plants. The parameters therefore reflect the requirements of trees. The lower soil moisture limit for development (SM0) is set to 0.1 to represent the permanent wilting point.

Results

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

pheromone survey

The known distribution of U. lugens in Tasmania was greatly extended by this study, both in terms of the westward (higher rainfall) extent and the high elevation limits (Fig. 2). In total, the survey consisted of 55 trap sites, of which 44 were included as part of the six primary elevation transects and the remainder were miscellaneous trap sites. All transects sampled up to a minimum of 713 m above sea level (Mt Reid) with traps at the Mt Field, Arthurs Lake, and Western Tiers transects located up to 1000 m. At Mt Reid, Hartz Mountains and Mt Field, the highest trap site was limited by the presence of Eucalyptus hosts. At the other transects, Eucalyptus hosts were available throughout the altitudinal range.

Of the 165 traps deployed on the elevation transects, 162 were retrieved and three traps were found to be missing, presumed blown apart by the wind. Two of the retrieved traps were found on the ground adjacent to the host tree in which they were deployed. At any one trap site no more than one trap was found missing or otherwise damaged; 43·2% of traps caught one or more U. lugens moths, and 52·7% of sites caught at least one confirmed U. lugens moth. The trap catch rate was right-skewed, with a mean catch of 1·16 (SD 2·2, range 0–30).

In five of the six primary elevation transects, U. lugens was found in the highest trap site. Using a generalized linear model with a logit transformation, the probability of a trap site containing one or more U. lugens moths was significantly negatively related to both elevation (P < 0·01) and latitude (P < 0·05), n = 55 sites.

climex model

Native distribution

The projected distribution of U. lugens in Australia conforms well to its known distribution (compare Figs 1 and 2 with Figs 3 and 4). There is a slight discrepancy in central Tasmania where the known distribution exceeds the modelled range.

image

Figure 3. Projected climate suitability for Uraba lugens in Tasmania, Australia using the climex ecoclimatic index (EI). The climex model was fitted to the combined historical and pheromone trap survey data.

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image

Figure 4. Projected climate suitability for Uraba lugens in Australia using the climex ecoclimatic index (EI). The climex model was fitted to the combined historical and pheromone trap survey data.

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Potential distribution

The potential distribution of U. lugens in New Zealand extends throughout most of the North and South Islands including all the Eucalyptus spp. forestry areas (Fig. 5). On the North Island, climate appears to be limiting in the elevated areas of the Hawkes Bay region, while wet stress reduces the climate suitability of some areas of the Coromandel Peninsula. On the South Island, the central Alps are unsuitable due to insufficient thermal accumulation, and the Otago Plains are climatically marginal to unsuitable due to a combination of cold stress and dry stress. The potential distribution of U. lugens modelled using the new trap data extends into considerably cooler and wetter climate zones in New Zealand than the previous model that relied solely upon the historical distribution (Fig. 5). Thus, the survey has extended the known distribution into cooler and wetter regions.

image

Figure 5. Projected climate suitability for Uraba lugens in New Zealand using the climex ecoclimatic index (EI). (a) Model fitted to historical data; (b) model fitted to the combined historical and pheromone trap survey data; and (c) the difference between (a) and (b), indicating the impact of the survey on the risk assessment.

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The global potential distribution of U. lugens covers all the Eucalyptus spp. forestry areas in subtropical, temperate and Mediterranean climate regions of the world (Fig. 6).

image

Figure 6. Projected global climate suitability for Uraba lugens using the climex ecoclimatic index (EI). The climex model was fitted to the combined historical and pheromone trap survey data.

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Discussion

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

In five of the six primary elevation transects, U. lugens was found in the highest trap site on the transect, demonstrating the suitability of the more extreme climate (and presumably also the more moderate climatic conditions found at lower elevation trap sites that failed to trap moths).

This study indicates that U. lugens is a hardy insect, able to tolerate a wide range of conditions from the warm and dry climate of mainland Australia during summer to the cool, wet subalpine conditions found at high elevation in central and western Tasmania. In so doing, it displays a large degree of plasticity in voltinism.

The apparent discrepancy in central Tasmania between the trapped moth occurrence and the potential distribution (Fig. 3) is due possibly to an inconsistency between the meteorological data set for Tasmania derived using esoclim (Houlder 2004) and the records from the Bureau of Meteorology (http://www.bom.gov.au/). Using values from the Bureau of Meteorology climate average dataset for Liawenee (station 096065) climex indicates that on average there will be 610 °C days above 8 °C available for development of U. lugens, whereas using the esoclim data set, climex indicates that there are only 445 °C days above 8 °C at the same location. The inconsistency is due probably to the paucity of meteorological data from central Tasmania with which to use as input into esoclim. In this study we elected to use the recorded data set as the basis for setting the PDD and cold stress parameters, rather than fit them to an individual derived climatic data set. One implication of this choice is to underline the need for caution in interpreting range limits (in particular the cold range limit) when using other gridded data sets such as that used for New Zealand. That is, the limits should be viewed as indicative, rather than prescriptive.

minimum development heat sum to complete a generation

climex uses the PDD heat sum threshold and the minimum temperature for development (DV0) to estimate the number of generations of the modelled taxa that can be supported at each location. The annual heat sum at Liawenee on the central plateau is only 610 °C days above a base temperature of 8 °C. This is considerably less than the minimum generational heat sum of 1060 °C days predicted by development studies under constant temperature and daylength conditions for a single generation of the bivoltine form (Allen & Keller 1991; D. J. Kriticos, Ensis unpublished data). None the less, moths were caught at each of the trap sites on the Steppes and the Central Plateau where temperatures appear to be insufficient to support adequate development of U. lugens. There are at least five possible explanations for this apparent anomaly. First, the moths could have developed elsewhere under warmer conditions and flown to these high-elevation sites. Given the consistency of trapping success and the geographical spread of sites across the central plateau over which U. lugens was trapped, this seems unlikely. In addition, these moths are known to be poor fliers (Harris 1975; Morgan & Cobbinah 1977). Secondly, the year prior to trapping could have been extremely warm in relation to the long-term average conditions on the central plateau. Thirdly, the univoltine form of U. lugens could have lower thermal requirements for development than the bivoltine form, or there could be a cold-adapted race of U. lugens, although sharing the same pheromone. Fourthly, the usual analytical technique for estimating generation times provides an estimate for the average moth in the population, not the most rapidly developing quantile. At the cool range margins, only the fastest developing moths may survive. To estimate the minimum heat accumulation required to enable any moths to complete their development, it may be more appropriate to sum the thermal development time for each lifestage less two standard deviations. Finally, Allen (1990) notes that in the field, larvae may pupate at the 8th to 13th instars. This suggests that pupation may occur at any point after a developmental threshold has been reached (8th instar) in response to some other external stimuli such as changing daylength or deteriorating food quality (Cobbinah 1985). Thus, the heat requirements to complete a generation may be shortened in the field compared to laboratory studies, where light : dark cycles are held constant and food supply quality and abundance is well maintained.

If the appropriate value for PDD is the minimum heat sum for the most rapidly developing individuals, then there are important implications for interpretation of the Number of Generations variable in climex. It is unclear whether in more moderate climates the average development period dictates the number of generations because of the requirement for synchronized mating within a generation and the consequent effect that the law of central tendency has on selective pressure. Thus, at the cool range margin, the selection trade-off may be skewed toward the minimum development time required for successful reproduction, at the expense of larger adult size and fecundity. At warmer locations the fitness trade-off may swing towards more extended development with larger adults and fecundity, rather than increased voltinism. Without more detailed studies of this problem, predictions of multivoltinism in climex based on multiples of PDD should be interpreted as indicating that the minimum heat sum requirements for multiple generations have been met, but not that the number of generations will be likely to occur. In addition to the possibility that there may be plasticity in the heat sum taken to complete a generation, other factors such as rainfall, day length, soil moisture availability and the coincidence of these factors in the range necessary for population growth may also act to constrain the realized pattern of voltinism in any given location.

These difficulties in defining the minimum heat sum required to complete a generation reinforce the cautions regarding the primacy of geographical distribution data in bioclimatic modelling, and the extreme caution that needs to be applied when comparing biological parameters derived from instantaneous measurements in a laboratory with climatic data. Such comparisons involve a scale change, where the dominant processes may change unpredictably between the two settings.

potential distribution of u. lugens

U. lugens adults are generally thought to fly a maximum of 1 km from their pupation site, though some long-distance dispersal (>12 km) could possibly occur over water (Suckling et al. 2005). This means that the adult male distribution is likely to be indicative of the distribution of the species generally, including the damaging larval stage. This assumption could be unreliable in very windy locations, where the adults could be transported intact for long distances from their natal sites. Outlying data should therefore be treated with caution, and efforts should be made to include data redundancy in survey effort.

A key assumption in developing climate-based distribution models is that the known distribution is limited by climate, and not by factors such as the distribution of suitable hosts. The distribution of U. lugens hosts in Australia is clearly not limiting its distribution throughout most of its range (Fig. 1). It was possible that hosts may have been limiting its range in some cold areas of Tasmania, such as on the Steppes, near Liawenee and at Hartz Mountains, where U. lugens were found at the tree line. However, at Mount Field, despite suitable hosts growing up to 1200 m, U. lugens was not found present above 600 m. This finding is consistent with the climex model that indicates that the annual heat sum would be insufficient for U. lugens above this elevation (Fig. 2), lending further support for the choice of value for the PDD parameter.

The pheromone trap survey extended the known distribution of U. lugens well beyond the known historical distribution limits into much cooler and wetter climates. In New Zealand, the potential distribution has been extended to include the majority of the cool, wet central North Island and a great deal more of the South Island up to the foothills of the Alps (Fig. 5). Central Otago remains climatically marginal to unsuitable for U. lugens due to a combination of dry stress and cold stress (Fig. 5).

The global potential distribution of U. lugens highlights a phytosanitary risk from U. lugens due to exports from Australia or New Zealand to overseas eucalypt growing regions particularly during the pupation period(s). In addition to forestry resources, amenity plantings in places such as California will also be under threat of invasion and attack by U. lugens, with its attendant economic and public health impacts.

The projected potential distribution may be at least partially conservative as it is based upon the realized niche, which includes constraints due to biotic factors such as competition, predation and parasitism (Hutchinson 1957; Davis et al. 1998b). As noted by Brown, Stevens & Kaufman (1996), where biotic factors are constraining ranges, they are most likely to manifest themselves in those regions of the potential range that are relatively warm and wet, and where environmental resources such as heat and moisture are not limiting. Hence, as the warm and wet range limits for U. lugens are not encountered in New Zealand, the model can be considered to be reasonably reliable, and potential climatic range expansion due to biotic release is unlikely. This form of underestimate in potential distribution is likely to occur in subtropical and tropical regions.

pheromone trapping for range delimitation

The pheromone trap system utilized for this study worked well. Despite the traps being set for 10 weeks, the trapped specimens were generally in good condition at the time the traps were retrieved. However, there were a number of traps that contained Lepidopteran carcasses that could have been U. lugens, but were too badly decomposed to be certain of the identity. It is also likely that the invasive wasp, Vespula germanica, and other wasps (many of which were found in traps), had attacked a number of these unidentifiable carcasses. Ten weeks was chosen as the trap duration in order to minimize the survey costs and still cover the expected adult moth flight duration at all surveyed elevations (January–March). The success of the trap system suggests that it is a useful, cost-effective technique that could also be applied to climate change studies to ascertain how insect species distributions change through time.

The lack of suitable data on the distribution of organisms is perhaps the single most common hindrance for bioclimatic modellers who generally have to rely upon museum or herbarium specimen data (Kriticos & Randall 2001; Sutherst 2003). There have been calls for systematically improving the quality and quantity of distribution data for organisms (Sutherst 2003). However, it is unclear how such a system would be paid for, and what factors would motivate relevant local authorities to collect and maintain data on their native or introduced pests when the optimal qualities of the data required for habitat modelling does not coincide with that required for pest or conservation management. In the absence of resolution of these issues and the availability of data in such databases that can support these modelling efforts, models will continue to include potentially large uncertainties.

Species range boundaries can be quite dynamic (Brown, Stevens & Kaufman 1996), while climate data sets are static. While this represents a potential scale mismatch, apart from the case of migratory species, the ramifications of basing a climex model on transect data are generally likely to be trivial. If the transect data extend the range boundary beyond the dynamic equilibrium boundary, the model will project this extra range as being at risk of invasion, albeit usually with a marginal indicated climate suitability. This bias is likely to be preferred by incursion managers.

The extension of the known range of U. lugens using a trapping survey may be an example of a widespread bias in museum and herbaria data sets toward range cores. For cryptic species in particular, it may be more difficult to detect them toward their range margins if their densities decline with habitat suitability. Consequently, many existing projections of climate suitability based on known distribution data may be conservatively biased.

The use of purpose-designed surveys such as that described in this paper may offer one cost-effective means of generating useful, unbiased data of a suitable quality for making more reliable projections. For poorly known species, for relatively little cost, four to five transects running from the edge of known suitable habitat into regions for which the distribution boundaries are unknown should be most informative. A preliminary climex model can be used to inform the choice of location of these transects to directly address the climatic gradients in different parts of the species’ range. If the spatial distribution of hosts or other non-climatic habitat factors are available prior to undertaking the survey, then GIS techniques can be used to further refine the trap system prior to deployment.

Acknowledgements

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

We are indebted to the following people and organizations that helped us to make this project a success. Dr Marianne Horak provided access to the Australian National Insect Collection. Mr Bevis Jordon, of Gunns Limited, helped in selecting sites and Mr Chris Barnes gave permission to deploy traps in Gunns forest coups. The Tasmanian Department of Primary Industries, Water and Environment, for permission to collect U. lugens throughout Tasmania, and Dr Jane Elek, Forestry Tasmania, for permission to trap in forest coups, and assistance with survey site selection. Ms Anne Drake of Zinifex Rosebery Mine kindly provided vehicle access to Mt Reid for the trapping. Dr John Ireson provided access to laboratory facilities and Dr Peter McQuillan, University of Tasmania, provided expert assistance with insect identifications. Mr Shaun Kolomeitz, University of Queensland, provided valuable help with accessing climate data. Biosecurity New Zealand and the Foundation for Research, Science and Technology (C04X0302) provided financial support for the development of the pheromone and for undertaking the survey. Dr Agathe Leriche helped with the production of some of the figures. Dr Janet Farr (Western Australian Department of Conservation and Land Management), the journal editors and an anonymous referee provided useful suggestions to improve the manuscript.

References

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
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