Temperature-driven Model Research Articles

The Response of Soil Respiration to Temperature and Humidity in the Thermokarst Depression Zone of the Headwater Wetlands of Qinghai Lake.

As the climate warms, the thickening of the active layer of permafrost has led to permafrost melting and surface collapse, forming thermokarst landforms. These changes significantly impact regional vegetation, soil physicochemical properties, and hydrological processes, thereby exacerbating regional carbon cycling. This study analyzed the relationship between soil respiration rate (Rs), soil temperature (T), and volumetric water content (VWC) in the thermokarst depression zone of the headwater wetlands of Qinghai Lake, revealing their influence on these soil parameters. Results showed a significant positive correlation between soil temperature and Rs (p < 0.001), and a significant negative correlation between VWC and Rs (p < 0.001). The inhibitory effect of VWC on Rs in the thermokarst depression zone was stronger than under natural conditions (p < 0.05). Single-factor models indicated that the temperature-driven model had higher explanatory power for Rs variation in both the thermokarst depression zone (R2 = 0.509) and under natural conditions (R2 = 0.414), while the humidity-driven model had lower explanatory power. Dual-factor models further improved explanatory power, slightly more so in the thermokarst depression zone. This indicates that temperature and humidity jointly drive Rs. Additionally, during the daytime, temperature had a more significant impact on Rs under natural conditions, while increased VWC inhibited Rs. At night, the positive correlation between Rs and temperature in the thermokarst depression zone increased significantly. The temperature sensitivity (Q10) values of Rs were 3.32 and 1.80 for the thermokarst depression zone and natural conditions, respectively, indicating higher sensitivity to temperature changes at night in the thermokarst depression zone. This study highlights the complexity of soil respiration responses to temperature and humidity in the thermokarst depression zone of Qinghai Lake's headwater wetlands, contributing to understanding carbon cycling in wetland ecosystems and predicting wetland carbon emissions under climate change.

A model for predicting the phenology of Philaenus spumarius.

The design and implementation of Philaenus spumarius control strategies can take advantage of properly calibrated models describing and predicting the phenology of vector populations in agroecosystems. We developed a temperature-driven physiological-based model based on the system of Kolmogorov partial differential equations to predict the phenological dynamics of P. spumarius. The model considers the initial physiological age distribution of eggs, the diapause termination process, and the development rate functions of post-diapausing eggs and nymphal stages, estimated from data collected in laboratory experiments and field surveys in Italy. The temperature threshold and cumulative degree days for egg diapause termination were estimated as 6.5°C and 120 DD, respectively. Preimaginal development rate functions exhibited lower thresholds ranging between 2.1 and 5.0°C, optimal temperatures between 26.6 and 28.3°C, and upper threshold between 33.0 and 35°C. The model correctly simulates the emergence of the 3rd, 4th, and 5th nymphal instars, key stages to target monitoring actions and control measures against P. spumarius. Precision in simulating the phenology of the 1st and 2nd nymphal stages was less satisfactory. The model is a useful rational decision tool to support scheduling monitoring and control actions against the late and most important nymphal stages of P. spumarius.

Improving Reliability in Reconstruction of Landsat EVI Seasonal Trajectory over Cloud-Prone, Fragmented, and Mosaic Agricultural Landscapes

Although the Landsat 30 m Enhanced Vegetation Index (EVI) products are important input variables in land surface models, recurring Landsat 5/7 EVI time series over cloud-prone, fragmented, and mosaic agricultural landscapes is still a great challenge. In this study, we put forward a simple, but effective “Light and Temperature-Driven Growth model and Double Logistic function fusion algorithm” (LTDG_DL). The empirical basis of the LTDG_DL algorithm was traced from the de Wit crop growth simulation model and the commonly observed nonlinear correlation between the EVI and the Leaf Area Index (LAI). It assimilates the ground daily solar radiation and air temperature to generate seasonal profiles of the empirical LAI and EVI and conducts the within-season calibration of the empirical EVI by adjusting crop growth using cloud-free Landsat EVI observations. The initial date of seedling emergence (DOYini) and the accumulated Growing Degree Days for completion of the vegetative and Flowering stage (FGDDs) were variables to which the algorithm’s accuracy was most sensitive. The variable-constrained optimization of the LTDG_DL algorithm was performed by loading the seedling emergence calendar of local prevailing crops and establishing an FGDD lookup table with an exhaustive sampling without replication method. Compared to temporal interpolation functions and Landsat–MODIS spatiotemporal fusion algorithms, the LTDG_DL algorithm had superior performance in the predictions of the EVI increment slope at the vegetative growth stage, the timing of the peak EVI, and the protection of key Landsat EVI observations over cloud-contaminated and complex landscape agricultural systems. Finally, the advantages and limitations of the LTDG_DL algorithm are discussed.

Radiation estimation and crop growth trajectory reconstruction by novel algorithms improve MOD16 evapotranspiration predictability for global multi-site paddy rice ecosystems

Reliable estimations in evapotranspiration (ET) of paddy rice ecosystems by satellite products are critical because of their important roles in regional hydrological processes and climate change. However, the NASA MODIS ET products (MOD16A2) and its derivatives do not have good correlations with all global paddy rice ET observations. In this research, MOD16 model sensitivity analyses and parameter optimization strategies were conducted in order to solve the problem. Results suggested that underestimation of daily net radiation (Rn) in overcast conditions and less satisfactory reconstruction of field-scale leaf area index (LAI) growth trajectory from the start date of field flooding and transplanting (FFTD) to the end of growing seasons by MODIS coarse vegetation index were identified as two major causes. A Light and Temperature-Driven Growth model and a Phenology-based LAI temporal Smoothing method fusion algorithm (LTDG_PhenoS) and an improved Rn estimation method were introducted and evaluated in paddy rice fields in South Korea, Japan, China, Philippines, India, Spain, Italy, and the USA from 2002 to 2019. The LTDG_PhenoS algorithm considers Landsat and MODIS EVI observations and meteorological data as input variables and 30-m LAI daily time series as outcomes. Introducing the global cloudy index algorithm resulted in improved estimations of daily Rn under all-sky conditions, with a significant decrease of root mean square error (RMSE) from 1.87 to 1.11 MJ m−2 day−1. The LTDG_PhenoS algorithm well reconstructed crop LAI growth dynamics from the FFTD to the end of rice growing seasons, with a substantial decline of RMSE from 1.49 to 0.27 m2/m−2. The FFTD estimations by the LTDG_PhenoS algorithm had an R2 of 0.97 and a small RMSE of less than 12-days. Daily ET rates estimated by novel algorithms had a substantial decline in RMSE from 2.88 to 0.90 mm day−1.

The impact of paleoclimatic changes on body size evolution in marine fishes

Body size is an important species trait, correlating with life span, fecundity, and other ecological factors. Over Earth's geological history, climate shifts have occurred, potentially shaping body size evolution in many clades. General rules attempting to summarize body size evolution include Bergmann's rule, which states that species reach larger sizes in cooler environments and smaller sizes in warmer environments, and Cope's rule, which poses that lineages tend to increase in size over evolutionary time. Tetraodontiform fishes (including pufferfishes, boxfishes, and ocean sunfishes) provide an extraordinary clade to test these rules in ectotherms owing to their exemplary fossil record and the great disparity in body size observed among extant and fossil species. We examined Bergmann's and Cope's rules in this group by combining phylogenomic data (1,103 exon loci from 185 extant species) with 210 anatomical characters coded from both fossil and extant species. We aggregated data layers on paleoclimate and body size from the species examined, and inferred a set of time-calibrated phylogenies using tip-dating approaches for downstream comparative analyses of body size evolution by implementing models that incorporate paleoclimatic information. We found strong support for a temperature-driven model in which increasing body size over time is correlated with decreasing oceanic temperatures. On average, extant tetraodontiforms are two to three times larger than their fossil counterparts, which otherwise evolved during periods of warmer ocean temperatures. These results provide strong support for both Bergmann's and Cope's rules, trends that are less studied in marine fishes compared to terrestrial vertebrates and marine invertebrates.

Forecasting regional apple first flowering using the sequential model and gridded meteorological data with spatially optimized calibration

• Accurately forecast the first flowering period of two typical apple producing areas in China. • It fills in the research of forecasting the first flowering period of apple from point scale to regional scale. • The phenological survey method of replacing time with space is very effective for the calibration of sequential model. • The prediction of the apple first flowering on a regional scale has important application value for optimizing orchard management. China is one of the largest apple-producing countries in the world, with large orchards and diverse climates. Accurately forecasting the first-flowering time of apple trees can assist orchard managers in their deciding when to apply anti-freeze. The temperature-driven sequential model from previous studies can be used to forecast the flowering phenology of deciduous fruit trees. However, this model requires many years of observational data for calibration, so flowering forecasts based on traditional phenological models cannot be implemented in areas that lack such historical data. To overcome this problem, the present work combines a spatial rather than a temporal phenological survey method with 1-km-gridded temperature products to calibrate the chill and heat requirement parameters of the sequential model. We then use the model to forecast the first-flowering on a regional scale for Luochuan and Linyi, which are two main apple-producing areas of China. The results show that the proposed method accurately forecasts regional flowering. The root mean squared errors (RMSE) for Luochuan and Linyi were 4.7 and 4.4 days, respectively, and the normalized RMSEs were all less than 5.19%. We expect the proposed regional first-flowering forecast method to be an important aid to optimize orchard management.

A model for the development of the free-living stages of Ostertagia leptospicularis, used in conjunction with on-farm egg count data, to estimate sources of pasture contamination on New Zealand red deer (Cervus elaphus) farms

Farmed red deer face challenges from nematode parasites, primarily the pulmonary species Dictyocaulus eckerti and the complex of Ostertagiinae nematodes in the abomasum. Previous investigations on New Zealand deer farms identified limited seasonality in faecal egg and larval output in all stock classes, however, this does not indicate the key times of year those eggs develop into infective-stage larvae, and subsequently contribute to infection risk. A simple temperature-driven model was developed for the free-living stages of a representative deer-specific Ostertagiinae species; Ostertagia leptospicularis. The model was then validated using development data generated from seasonal plot trials. Using faecal egg counts and animal numbers from the previous monitoring study, and local weather data from each of the sampling farms, the relative contribution of each stock class to infective third-stage larvae population on pasture was estimated for each month of the year (2018–2019). The mixed-age adult females were the primary source of infective larvae on most of the sampled farms, principally due to their numerical dominance (i.e., venison breeding farms). The frequent anthelmintic use in the youngest stock class (< 12 months old) on some farms limited their output of eggs and hence their contribution to pasture infestation with infective larvae. However, the trends of larval development on pasture differed between the farms (due to local climate), and between the sampling years (2018–2019). Therefore, calendar-based application of anthelmintics to older stock is not recommended, as contribution to pasture contamination is multifactorial. The study provides a better understanding of deer parasite epidemiology on which to base improved management practices.

Design and Evaluation of Calibrated and Seamless Ensemble Weather Forecasts for Crop Protection Applications

AbstractAgriculture is a highly weather-dependent activity, and climatic conditions impact both directly crop growth and indirectly diseases and pest developments causing yield losses. Weather forecasts are now a major component of various decision-support systems that assist farmers to optimize the positioning of crop protection treatments. However, properly accounting for weather uncertainty in these systems still remains a challenge. In this paper, three global and regional ensemble prediction systems (EPSs), covering different spatiotemporal scales, are coupled to a temperature-driven developmental model for grapevine moths in order to provide probabilistic forecasts of treatment dates. It is first shown that a parametric postprocessing of the EPSs significantly improves the prediction of treatment dates. Anticipating the need for phytosanitary treatments also requires seamless weather forecasts from the next hour to subseasonal time scales. An approach is presented to design seamless ensemble forecasts from the combination of the three EPSs used. The proposed method is able to leverage the increased performance of high-resolution EPS at short ranges, while ensuring a smooth transition toward larger-scale EPSs for longer ranges. The added value of this seamless integration on agronomic predictions is, however, difficult to assess with the current experimental setup. Additional simulations over a larger number of locations and years may be required.

Satellite-Based Observations Reveal Effects of Weather Variation on Rice Phenology

Obtaining detailed data on the spatio-temporal variation in crop phenology is critical to increasing our understanding of agro-ecosystem function, such as their response to weather variation and climate change. It is challenging to collect such data over large areas through field observations. The use of satellite remote sensing data has made phenology data collection easier, although the quality and the utility of such data to understand agro-ecosystem function have not been widely studied. Here, we evaluated satellite data-based estimates of rice phenological stages in California, USA by comparing them with survey data and with predictions by a temperature-driven phenology model. We then used the satellite data-based estimates to quantify the crop phenological response to changes in weather. We used time-series of MODIS satellite data and PhenoRice, a rule-based rice phenology detection algorithm, to determine annual planting, heading and harvest dates of paddy rice in California between 2002 and 2017. At the state level, our satellite-based estimates of rice phenology were very similar to the official survey data, particularly for planting and harvest dates (RMSE = 3.8–4.0 days). Satellite based observations were also similar to predictions by the DD10 temperature-driven phenology model. We analyzed how the timing of these phenological stages varied with concurrent temperature and precipitation over this 16-year time period. We found that planting was earlier in warm springs (−1.4 days °C−1 for mean temperature between mid-April and mid-May) and later in wet years (5.3 days 100 mm-1 for total precipitation from March to April). Higher mean temperature during the pre-heading period of the growing season advanced heading by 2.9 days °C−1 and shortened duration from planting to heading by 1.9 days °C−1. The entire growing season was reduced by 3.2 days °C−1 because of the increased temperature during the rice season. Our findings confirm that satellite data can be an effective way to estimate variations in rice phenology and can provide critical information that can be used to improve understanding of agricultural responses to weather variation.

Effect of Rising Temperature on Lyme Disease: Ixodes scapularis Population Dynamics and Borrelia burgdorferi Transmission and Prevalence.

Warmer temperatures are expected to increase the incidence of Lyme disease through enhanced tick maturation rates and a longer season of transmission. In addition, there could be an increased risk of disease export because of infected mobile hosts, usually birds. A temperature-driven seasonal model of Borrelia burgdorferi (Lyme disease) transmission among four host types is constructed as a system of nonlinear ordinary differential equations. The model is developed and parametrized based on a collection of lab and field studies. The model is shown to produce biologically reasonable results for both the tick vector (Ixodes scapularis) and the hosts when compared to a different set of studies. The model is used to predict the response of Lyme disease risk to a mean annual temperature increase, based on current temperature cycles in Hanover, NH. Many of the risk measures suggested by the literature are shown to change with increased mean annual temperature. The most straightforward measure of disease risk is the abundance of infected questing ticks, averaged over a year. Compared to this measure, which is difficult and resource-intensive to track in the field, all other risk measures considered underestimate the rise of risk with rise in mean annual temperature. The measure coming closest was “degree days above zero.” Disease prevalence in ticks and hosts showed less increase with rising temperature. Single field measurements at the height of transmission season did not show much change at all with rising temperature.

The annual, temporal and spatial pattern of Setaria tundra outbreaks in Finnish reindeer: a mechanistic transmission model approach

BackgroundIn northern Finland (Lapland), reindeer are reared as semi-domesticated animals. The region has a short summer season of 2–3 months, yet reindeer are infected with the mosquito-borne filarioid parasite Setaria tundra. The infection causes peritonitis and perihepatitis, which cause significant economic losses due to reduced body weight of infected animals. The objective of this study was to: (i) describe the spatial and temporal pattern of outbreaks in three different areas across Finnish Lapland; and (ii) construct a temperature-driven mechanistic transmission model to quantify the potential role of temperature on intensity of S. tundra transmission in reindeer.MethodsWe developed a temperature-driven transmission model able to predict the number of S. tundra potentially transmitted from an infectious reindeer. We applied the model to the years 2004–2015, and compared the predictions to the proportion of reindeer whose livers were condemned due to S. tundra infection at the time of slaughter.ResultsThe mean proportion of liver condemnation increased in reindeer slaughtered in late autumn/winter compared to earlier dates. The outbreaks were geographically clustered each year but there were no fixed foci where outbreaks occurred. Larger outbreaks were recorded in the southern regions of reindeer-herding areas compared to the central or northern parts of Lapland. Our model showed that temperatures never allowed for transmission of more than a single generation of S. tundra each season. In southern (Kuusamo) and central (Sodankylä) Lapland, our model predicted an increasing trend from 1979 to 2015 for both the duration of the effective transmission period of S. tundra (P < 0.001) and for the potential number of L3 S. tundra larvae being transmitted from an infectious reindeer (P < 0.001).ConclusionsThe effective transmission period for S. tundra in reindeer is very short in Lapland, but it increased over the period studied. Only one generation of S. tundra can be transmitted in one season among reindeer in Lapland. Increasing temperatures may facilitate a range expansion and increasing duration of effective transmission period for S. tundra.

West Nile virus transmission and human infection risk in Veneto (Italy): a modelling analysis

An intensified and continuous West Nile virus (WNV) spread across northern Italy has been observed since 2008, which caused more than one hundred reported human infections until 2016. Veneto is one of the Italian regions where WNV is considered endemic, and the greatest intensity of circulation was observed during 2013 and 2016. By using entomological data collected across the region in those years, we calibrated a temperature-driven mathematical model through a Bayesian approach that simulates the WNV infection in an avian population with seasonal demography. We considered two alternative routes of life cycle re-activation of the virus at the beginning of each vector breeding season: in the first one the virus is maintained by infected birds, in the other by diapausing mosquitoes previously infected. Afterwards, we computed seasonal risk curves for human infection and quantified how they translate into reported symptomatic cases. According to our results, WNV is more likely to be re-activated each year via previously infected mosquitoes. The highest probability of human infection is expected to occur in August, consistently with observations. Our epidemiological estimates can be of particular interest for public health authorities, to support decisions in term of designing efficient surveillance plans and preventive measures.

Temperature-driven population abundance model for Culex pipiens and Culex restuans (Diptera: Culicidae)

We develop a temperature-driven abundance model for West Nile virus (WNV) vector species, Culex pipiens and Culex restuans. Temperature-dependent response functions for mosquito development, mortality, and diapause were formulated based on results from available laboratory and field studies. Numerical results compared to observed mosquito trap counts from 2004–2016 demonstrate the ability of our model to predict the observed trend of the mosquito population over a single season in the Peel Region, Ontario. The model has potential to be used as a real-time mosquito abundance forecasting tool with applications in mosquito control programs.

Soil CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; efflux from two mountain forests in the eastern Himalayas, Bhutan: components and controls

Abstract. The biogeochemistry of mountain forests in the Hindu Kush Himalaya range is poorly studied, although climate change is expected to disproportionally affect the region. We measured the soil CO2 efflux (Rs) at a high-elevation (3260 m) mixed forest and a lower-elevation (2460 m) broadleaf forest in Bhutan, eastern Himalayas, during 2015. Trenching was applied to estimate the contribution of autotrophic (Ra) and heterotrophic (Rh) soil respiration. The temperature (Q10) and the moisture sensitivities of Rh were determined under controlled laboratory conditions and were used to model Rh in the field. The higher-elevation mixed forest had a higher standing tree stock, reflected in higher soil C stocks and basal soil respiration. Annual Rs was similar between the two forest sites (14.5 ± 1.2 t C ha−1 for broadleaf; 12.8 ± 1.0 t C ha−1 for mixed). Modelled annual contribution of Rh was ∼ 65 % of Rs at both sites with a higher heterotrophic contribution during winter and lower contribution during the monsoon season. Rh, estimated from trenching, was in the range of modelled Rh but showed higher temporal variability. The measured temperature sensitivity of Rh was similar at the mixed and broadleaf forest sites (Q10 2.2–2.3) under intermediate soil moisture but decreased (Q10 1.5 at both sites) in dry soil. Rs closely followed the annual course of field soil temperature at both sites. Covariation between soil temperature and moisture (cold dry winters and warm wet summers) was likely the main cause for this close relationship. Under the prevailing weather conditions, a simple temperature-driven model was able to explain more than 90 % of the temporal variation in Rs. A longer time series and/or experimental climate manipulations are required to understand the effects of eventually occurring climate extremes such as monsoon failures.

Combining process-based and correlative models improves predictions of climate change effects on Schistosoma mansoni transmission in eastern Africa.

Currently, two broad types of approach for predicting the impact of climate change on vector-borne diseases can be distinguished: i) empirical-statistical (correlative) approaches that use statistical models of relationships between vector and/or pathogen presence and environmental factors; and ii) process-based (mechanistic) approaches that seek to simulate detailed biological or epidemiological processes that explicitly describe system behavior. Both have advantages and disadvantages, but it is generally acknowledged that both approaches have value in assessing the response of species in general to climate change. Here, we combine a previously developed dynamic, agentbased model of the temperature-sensitive stages of the Schistosoma mansoni and intermediate host snail lifecycles, with a statistical model of snail habitat suitability for eastern Africa. Baseline model output compared to empirical prevalence data suggest that the combined model performs better than a temperature-driven model alone, and highlights the importance of including snail habitat suitability when modeling schistosomiasis risk. There was general agreement among models in predicting changes in risk, with 24-36% of the eastern Africa region predicted to experience an increase in risk of up-to 20% as a result of increasing temperatures over the next 50 years. Vice versa the models predicted a general decrease in risk in 30-37% of the study area. The snail habitat suitability models also suggest that anthropogenically altered habitat play a vital role for the current distribution of the intermediate snail host, and hence we stress the importance of accounting for land use changes in models of future changes in schistosomiasis risk.

A comparison of fisheries biological reference points estimated from temperature-specific multi-species and single-species climate-enhanced stock assessment models

Multi-species statistical catch at age models (MSCAA) can quantify interacting effects of climate and fisheries harvest on species populations, and evaluate management trade-offs for fisheries that target several species in a food web. We modified an existing MSCAA model to include temperature-specific growth and predation rates and applied the modified model to three fish species, walleye pollock (Gadus chalcogrammus), Pacific cod (Gadus macrocephalus) and arrowtooth flounder (Atheresthes stomias), from the eastern Bering Sea (USA). We fit the model to data from 1979 through 2012, with and without trophic interactions and temperature effects, and use projections to derive single- and multi-species biological reference points (BRP and MBRP, respectively) for fisheries management. The multi-species model achieved a higher over-all goodness of fit to the data (i.e. lower negative log-likelihood) for pollock and Pacific cod. Variability from water temperature typically resulted in 5–15% changes in spawning, survey, and total biomasses, but did not strongly impact recruitment estimates or mortality. Despite this, inclusion of temperature in projections did have a strong effect on BRPs, including recommended yield, which were higher in single-species models for Pacific cod and arrowtooth flounder that included temperature compared to the same models without temperature effects. While the temperature-driven multi-species model resulted in higher yield MBPRs for arrowtooth flounder than the same model without temperature, we did not observe the same patterns in multi-species models for pollock and Pacific cod, where variability between harvest scenarios and predation greatly exceeded temperature-driven variability in yield MBRPs. Annual predation on juvenile pollock (primarily cannibalism) in the multi-species model was 2–5 times the annual harvest of adult fish in the system, thus predation represents a strong control on population dynamics that exceeds temperature-driven changes to growth and is attenuated through harvest-driven reductions in predator populations. Additionally, although we observed differences in spawning biomasses at the accepted biological catch (ABC) proxy between harvest scenarios and single- and multi-species models, discrepancies in spawning stock biomass estimates did not translate to large differences in yield. We found that multi-species models produced higher estimates of combined yield for aggregate maximum sustainable yield (MSY) targets than single species models, but were more conservative than single-species models when individual MSY targets were used, with the exception of scenarios where minimum biomass thresholds were imposed. Collectively our results suggest that climate and trophic drivers can interact to affect MBRPs, but for prey species with high predation rates, trophic- and management-driven changes may exceed direct effects of temperature on growth and predation. Additionally, MBRPs are not inherently more conservative than single-species BRPs. This framework provides a basis for the application of MSCAA models for tactical ecosystem-based fisheries management decisions under changing climate conditions.

Evaluation of olive flowering at low latitude sites in Argentina using a chilling requirement model

Olive production has expanded significantly from the Mediterranean Basin into the New World over the last two decades. In some cases, cultivars of European origin have been introduced at a large commercial scale with little previous evaluation of potential productivity. The objective of this study was to evaluate whether a temperature-driven simulation model developed in the Mediterranean Basin to predict normal flowering occurrence and flowering date using cultivar-specific thermal requirements was suitable for the low latitude areas of Northwest Argentina. The model was validated at eight sites over several years and a wide elevation range (350–1200 m above mean sea level) for three cultivars (‘Arbequina’, ‘Frantoio’, ‘Leccino’) with potentially different chilling requirements. In ‘Arbequina’, normal flowering was observed at almost all sites and in all years, while normal flowering events in ‘Frantoio’ and ‘Leccino’ were uncommon. The model successfully predicted if flowering would be normal in 92% and 83% of the cases in ‘Arbequina’ and ‘Frantoio’, respectively, but was somewhat less successful in ‘Leccino’ (61%). When flowering occurred, the predicted flowering date was within ± 7 days of the observed date in 71% of the cases. Overall, the model results indicate that cultivar-specific simulation models may be used as an approximate tool to predict whether individual cultivars will be successful in new growing areas. In Northwest Argentina, the model could be used to identify cultivars to replace ‘Frantoio’ and ‘Leccino’ and to simulate global warming scenarios.

Predicting codling moth (Cydia pomonella) phenology in North Carolina on the basis of temperature and improved generation turnover estimates.

The codling moth, Cydia pomonella (L.) (Lepidoptera: Tortricidae), is a major worldwide pest of apples, pears and walnuts. A temperature-driven phenological model of codling moth, developed in Michigan, has been utilized in North Carolina and other states for decades. Systematic inaccuracy of this model in predicting moth emergence in North Carolina suggests that the relationship between emergence and temperature differs between the American midwest and southeast, or that additional factors may influence the system. A method was developed to optimize the estimation of generation turnover intervals. Emergence was modeled as a function of heat unit accumulation. Significant differences between emergence predictions based on the resultant model and the existing model developed in Michigan were found. A new model of codling moth emergence, incorporating improved estimates for generation turnover for North Carolina, offers predictive improvement with practical importance to management. Differences between the emergence of susceptible and resistant moth populations were also investigated, leading to the suggestion that resistance to insecticides should be considered in future studies of emergence phenology.

Modeling the potential impacts of climate change on Pacific salmon culture programs: an example at Winthrop National Fish Hatchery.

Hatcheries have long been used in an attempt to mitigate for declines in wild stocks of Pacific salmon (Oncorhynchus spp.), though the conservation benefit of hatcheries is a topic of ongoing debate. Irrespective of conservation benefits, a fundamental question is whether hatcheries will be able to function as they have in the past given anticipated future climate conditions. To begin to answer this question, we developed a deterministic modeling framework to evaluate how climate change may affect hatcheries that rear Pacific salmon. The framework considers the physiological tolerances for each species, incorporates a temperature-driven growth model, and uses two metrics commonly monitored by hatchery managers to determine the impacts of changes in water temperature and availability on hatchery rearing conditions. As a case study, we applied the model to the US Fish and Wildlife Service's Winthrop National Fish Hatchery. We projected that hatchery environmental conditions remained within the general physiological tolerances for Chinook salmon in the 2040s (assuming A1B greenhouse gas emissions scenario), but that warmer water temperatures in summer accelerated juvenile salmon growth. Increased growth during summer coincided with periods when water availability should also be lower, thus increasing the likelihood of physiological stress in juvenile salmon. The identification of these climate sensitivities led to a consideration of potential mitigation strategies such as chilling water, altering rations, or modifying rearing cycles. The framework can be refined with new information, but in its present form, it provides a consistent, repeatable method to assess the vulnerability of hatcheries to predicted climate change.

Predicting the impact of climate change on regional and seasonal abundance of the mealybug Phenacoccus solenopsis Tinsley (Hemiptera: Pseudococcidae) using temperature-driven phenology model linked to GIS

The mealybug Phenacoccus solenopsis Tinsley (Hemiptera: Pseudococcidae) is a highly invasive and polyphagous pest of global incidence. The fundamental hypothesis of the present study was that the temperature variations due to global climate change may affect seriously the future distribution and abundance of P. solenopsis, which might further aggravate the crop yield losses. We employed a temperature-based phenology model of P. solenopsis in a geographic information system for mapping population growth potentials of P. solenopsis. The three risk indices viz., establishment risk index, generation index and activity index were computed using interpolated temperature data from worldclim database for current (2000) and future (2050) climatic conditions. The daily minimum and maximum temperature data from four selected weather stations in India were used for analysing within-year variation of pest population. A linear relationship was established between the activity indices and yield losses at various locations reported in literatures for predicting the future trend of yield loss due to climate change. The results revealed that, under current temperature conditions P. solenopsis can complete >4.0 generations per year on ∼80% of the global cotton production areas. Economic losses are likely to occur in areas where at least 8.0 generations can develop in a year; under current climate ∼40% areas fall under this category. The increased geographical suitability at higher latitudes in cotton production areas, additional 2.0 generations per year, and 4.0 fold increase of population abundance of P. solenopsis are expected in tropical and sub-tropical cotton areas of Brazil, South Africa, Pakistan and India due to predicted climate change. Analysis of within year population increase at various selected locations in India revealed that, P. solenopsis attained maximum potential population increase during the major cotton growing season (May–June to October–November). On the other hand, the innate ability of P. solenopsis population to increase reduced considerably during off season and cooler winter months. The increased pest activity of P. solenopsis due to climate change may intensify the losses in cotton yield, with forecasted losses in India to increase from existing losses of million US$ 1217.10 to future losses of million US$ 1764.85 by the year 2050. Here, we illustrate the possible impact of climate change on future P. solenopsis exacerbation based on temperature-driven population studies, which will help in undertaking agro-ecoregion specific management strategies.