Egg to L3 development and L3 lifespan with variable temperatures

When using temperatures oscillating around a constant value in the DM the results showed an increase in the development rate and success with greater increments for all temperatures (Figure 4.4 a). For example, the time to reach 99.9% of maximum development at a constant temperature of 20°C was 3.8 weeks (17.6% development)

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(a)

(b)

(c)

Figure 4.3 a, b and c - Development model estimates of mean development success (±SEM, minimum and maximum dashed lines) for Cooperia oncophora eggs to infective stage larvae over a 30 day period using temperature data from January 2002 to December 2011 for Warkworth (a), Palmerston North (b) and Lincoln (c).

0 5 10 15 20 25 30

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

D e ve lo p m e n t to L 3 [ % ] Month Warkworth, NZ 0 5 10 15 20 25 30

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

De ve lo p m e n t to L 3 [ % ] Month Palmerston North, NZ 0 5 10 15 20 25 30

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

De ve lo p m e n t to L 3 [ % ] Month Lincoln, NZ

Modelling development and survival

compared to 3.3 weeks (20.3% development) with a mean temperature of 20°C but alternating between 14 and 26°C.

In contrast, the result from the AM using the same settings showed a rapid decrease of lifespan when the L3 were exposed to a higher range of temperatures (Figure 4.4 b). At a constant temperature of 20°C the median survival was between 23-24 weeks but was below 15 weeks when using a mean temperature of 20°C but alternating between 14 and 26°C.

Using the DM and AM in the combined model with the same temperature settings the maximum number of L3 occured with the largest temperature variation but the longest lifespan occurs at the set temperature. At a constant temperature of 20°C the number of L3 reached a maximum of 17.6% at 5.2 weeks with a medium survival at 22.9 weeks at 20°C (Figure 4.4 c). These compare to a maximum number of L3s of 20.3% at 4.2 weeks with a medium survival at 15 weeks when using alternating temperatures between 14 and 26° C.

4.5 Discussion

In order to gain a better understanding of the effect of temperature on the development and lifespan of C. oncophora L3 two models were generated. Both models were constructed to allow for hourly changes in temperature and new recruitment of eggs or L3.

A variety of models have been used to estimate development and the lifespan of free- living stages of strongylid nematodes. Some models are species specific but most apply for a mix of species. None have been described specifically for C. oncophora. However, as discussed by Smith (2011), it has to be emphasised that the models in the current study were constructed to estimate larvae development and lifespan under near ideal laboratory conditions, so they do not account for effects found in the field other than temperature. The aim was not to give a tool for field prediction but to better understand the effect temperature has on the development and lifespan of C. oncophora infective larvae.

Modelling development and survival 86 0 5 10 15 20 25 30 0 5 10 15 20 25 30 35 40 L3 [ % e gg s] Time [days] ±0 ±2 ±4 ±6 (a) 0 50 100 0 100 200 300 400 500 600 L3 [ % a li ve ] Time [days] ±0 ±2 ±4 ±6 (b) 0 5 10 15 20 25 30 0 100 200 300 400 L3 [ % e gg s] Time [days] ±0 ±2 ±4 ±6 (c)

Figure 4.4 a, b and c - Model estimates for the development (a), lifespan (b) and a combination of both (c) for Cooperia oncophora L3 with oscillating temperatures. The temperature was changed on a 12 hourly basis around a mean of 14 (dotted), 20 (solid) and 26 (dashed) °C with fixed increments of ±0, 2, 4 and 6 °C.

Modelling development and survival

The required parameters for dt and st in the DM were calculated from data previously

gathered during a development experiment under constant temperatures in the laboratory. Although each temperature was investigated using the same experimental procedure some variation in the results between temperatures was observed (Chapter 2) which resulted in a relatively low R2 of 57% for the calculated regression to estimate the st but not for dt (R2=93.2%). However the estimates as displayed in Figure 4.1 (a)

and (b) indicated that at temperatures above 32°C, the highest temperature experimental data was recorded (Chapter 2), the st may not be valid causing only a

minor decrease with rising temperatures compared to a substantial increase in dt. This

would result in the model indicating a high development success as the individuals would rapidly progress through the EBT without being subject to an adequate mortality rate. Therefore additional data for a better prediction of dt and st above 32°C

is desirable especially towards the higher lethal temperature limit.

To model the development and survival of C. oncophora L3 the models for the development from egg to L3 and the lifespan of L3 were combined. The L3 emerging from the development model were transferred as new recruits into the lifespan model. This combined model used the same hourly temperatures for the development and the survival component. The combined model was built on the assumption that the process of developing and ageing could be described by this simple approach but no data was available to explicitly verify this. Nevertheless, the combined model made it possible to estimate the number of live L3 especially at higher temperatures where developed larvae were then subject to rapid ageing. Interestingly this approach indicated that for high temperatures some of the early developed L3 were already dying before the slower developing pre-infective stages finalized their development to L3, meaning the combined model gave lower maximum numbers of L3 at any time compared to the DM under these conditions.

Different temperature settings were used when comparing the estimates from the DM and AM to the experimental data. The use of hourly temperature values resulted in a comparable development rate for the DM with that observed in the laboratory experiment (Figure 4.2). However, the development success was lower compared to the experimental data when using hourly temperature values and in this case the use

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of the weekly maximum temperature provided a better estimate. Similarly, in another study with C. curticei the use of the mean maximum temperature resulted in better estimates than the average mean temperature (Ahluwalia and Charleston, 1974). In the present study, as the DM calculates development rate on an hourly basis the use of unmodified temperature data should provide better estimates and this differential indicates that more experimental data is required to fit more realistic curves. Alternatively, variation in the experimental data may be caused by the source of the larvae. The individual host has been shown to have an direct effect on the development success of the L3 (Jørgensen, 2000). The relationship between the estimates from the AM model and the experimental data reveal a similar behaviour where the use of the weekly maximum mean temperature resulted in improved estimates for the L3 lifespan compared to use of hourly temperatures. For both the DM and AM the data is based on a single experiment using a temperature range between 8-32 °C and 8-37 °C respectively. The difficulty in consistently fitting these models suggests that more data was required to more effectively calculate the parameters.

Recovery of L3 in the field experiment was very low compared to laboratory conditions, which was likely to be due to a heavy loss of larvae into the soil. Nevertheless, in general the DM should give an indication of the development rate as the appearance and increase in L3 numbers in the field samples should show a similar pattern. The only factor the model takes into account is the temperature compared to the huge variety of factors the larvae are exposed to under natural conditions.

The results of the DM using 10 years of temperature data showed a comparable yearly trend for development over a 30 day period (Figure 4.3). The DM model indicates the highest development success of 17-22% for February in Warkworth whereas in winter the model indicates a low development success. The DM also predicted a lower development success with decreasing temperatures when geographically moving south with the other two stations that were compared. This seasonal pattern of high development during the summer and low during the winter is consistent with previous results from field experiments with a variety of different trichostrongylid nematodes in New Zealand (Vlassoff, 1973; Waghorn et al., 2011) and Argentina (Fiel et al., 2012).

Modelling development and survival

The model calculations were also generally more variable during the spring where the variation in temperature was normally higher compared to winter, especially for Warkworth. However, the general humidity or rainfall is not accounted for, which is typically higher during winter and lower during summer. This has to be acknowledged as the faecal moisture content has a proven effect on the development of the infective stage larvae (Rossanigo and Gruner, 1995). The results from the AM are contrary to those of the DM as in summer the lifespan of the L3 is normally reduced by the higher temperatures and during the cooler winter period the model indicates a longer lifespan. The average estimated proportion of living larvae was > 98% for all 3 climate stations. However, the period for calculation was only 30 days and if extended is likely to show a difference reflecting climatic differences. As for development of larvae other environmental factors such as the influence of humidity, will have an effect on the ageing and survival of larvae.

Fluctuations of temperatures around a mean temperature will potentially affect development and ageing of larvae. In the present study, increasing fluctuations around the same mean temperature generally resulted in an increased L3 development rate and success in the DM but also a reduced lifespan of the L3 in the AM. The increase in the L3 development rate and success with increasing fluctuations implies that the increase in the period of higher temperature outweighs the decrease during the similar period of cooler temperature. In the case of the AM the effect of the fluctuations was the same, but the higher temperatures in the larger oscillating range increases the ageing rate, in other words the larvae deplete their stored nutrients faster by an increase in metabolic rate. When the models were combined the two effects accumulate and resulted in increased development rate and success as well as faster decline of the developed L3 by ageing with larger oscillations. In the present study the temperatures used were neither very high temperatures where L3 could rapidly die or very low temperatures where little development would occur. Nevertheless, this supports the observation from 3 different climate stations over a 10 year period where the Warkworth climate station had larger temperature fluctuations over summer than the other two stations resulting in development success and rate being greater during summer, but where there was also a faster decline of developed L3 compared to the

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other locations. These results are in contrast to the findings from Leathwick (2013a) who refined these assumptions and indicated that fluctuating temperatures at high temperatures were detrimental to development and ageing. The difference with the present study is the range of temperatures considered, as such high temperatures were not included here.

4.6 Conclusion

The DM and AM generally provide a good estimate for the development and lifespan of C. oncophora L3 and provide a useful tool to better understand the relationship between the ambient temperature and these life traits. However, a larger dataset for the calculation of the model parameters, especially for lower and higher temperatures, would better account for adverse temperature ranges and would have been preferable. The EBT modelling technique proved to be relatively straightforward and useful for this purpose. This approach allows individual components to be added (or removed) compared to some other modelling approaches based on field data where influences of separate factors are usually confounded. Additional components that could be added to the EBT include humidity, effects of freezing temperatures, infectivity of larvae as they age, herbage type and possibly soil type. Future work with this model should be to progressively include these additional components.

4.7 Acknowledgements

This study was funded by the Foundation for Research Science and Technology under contract C10X0714.

Ivermectin efficacy

Chapter 5

Efficacy of ivermectin on two isolates of Cooperia oncophora