The antestia bug, Antestiopsis thunbergii (Gmelin 1790) is a major pest of Arabica coffee in Africa. The bug prefers coffee at the highest elevations, contrary to other major pests. The objectives of this study were to describe the relationship between A. thunbergii populations and elevation, to elucidate this relationship using our knowledge of the pest thermal biology and to predict the pest distribution under climate warming. Antestiopsis thunbergii population density was assessed in 24 coffee farms located along a transect delimited across an elevation gradient in the range 1000–1700 m asl, on Mt. Kilimanjaro, Tanzania. Density was assessed for three different climatic seasons, the cool dry season in June 2014 and 2015, the short rainy season in October 2014 and the warm dry season in January 2015. The pest distribution was predicted over the same transect using three risk indices: the establishment risk index (ERI), the generation index (GI) and the activity index (AI). These indices were computed using simulated life table parameters obtained from temperature-dependent development models and temperature data from 1) field records using data loggers deployed over the transect and 2) predictions for year 2055 extracted from AFRICLIM database. The observed population density was the highest during the cool dry season and increased significantly with increasing elevation. For current temperature, the ERI increased with an increase in elevation and was therefore distributed similarly to observed populations, contrary to the other indices. This result suggests that immature stage susceptibility to extreme temperatures was a key factor of population distribution as impacted by elevation. In the future, distribution of the risk indices globally indicated a decrease of the risk at low elevation and an increase of the risk at the highest elevations. Based on these results, we concluded with recommendations to mitigate the risk of A. thunbergii infestation.

Temperature critically affects the performance of entomopathogenic fungi (EPF). Mathematical models are critical tools used in predictive microbiology but are less adopted for EPF. We selected eight nonlinear models to describe thermal biology; minimum (T_min), optimal (T_opt) and maximum (T_max) thresholds; and maximal growth (P_max) of EPF. Conidial germination and mycelial growth of Metarhizium anisopliae (ICIPE 7, ICIPE 20, ICIPE 62, ICIPE 69, ICIPE 78) and Beauveria bassiana (ICIPE 284) isolates incubated at 12, 16, 20, 24, 28, 32 and 36°C were measured and fitted to the models. The models were compared using the Akaike information criterion (AIC) and adjusted R². The best–fitting models for germination of the isolates were the cardinal temperature model with inflection (CTMI), Ratkowsky 3 and the generalised β function, while the best–fitting models for growth were CTMI, Ratkowsky 3, Lactin 1 and generalised β function. Brière 1, Brière 2, Ratkowsky 2, and Van Der Heide least fitted most germination and growth datasets. T_min, T_opt, T_max and P_max ranged from 13.3–13.6°C, 26.3–28.1°C, 35.7–36.3°C and 95.4–100.0% for germination, and 3.7–13.7°C, 25.9–28.6°C, 35.4–37.2°C and 1.44–2.34 mm day^–1 for growth, respectively. T_opt were below temperatures of central bee brood areas and partly mirrored temperatures of the isolates’ regions of origin. The best–fitting models can be used to better match EPF with different regions’ temperatures for optimal performance against target pests.