Predicting Spatial Distribution of Key Honeybee Pests in Kenya Using Remotely Sensed and Bioclimatic Variables: Key Honeybee Pests Distribution Models

Makori, David M.; Fombong, Ayuka T.; Abdel‐Rahman, Elfatih M.; Kiatoko, Nkoba; Ongus, Juliette R.; Irungu, Janet; Mosomtai, Gladys; Makau, Sospeter; Mutanga, Onisimo; Odindi, John; Raina, Satish; Landmann, Tobias

doi:10.3390/ijgi6030066

Cited by 41 publications

(29 citation statements)

References 63 publications

(87 reference statements)

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“…The Savitzky-Golay function reduces the effects of residual signals and smooths the time-series EVI dataset to a degree determined by the size of the smoothing window and reduces the noise caused primarily by cloud contamination and atmospheric variability [67]. The start and end of season threshold parameters for the smoothing function were set at 20%, as suggested by Jonsson and Eklundh [64], to optimize the error that could be caused by varying start and end of season dates in different locations across the study area [69]. Only variables for the first season were used in this study since data from the second season were not consistent throughout all the years across the study area [69].…”

Section: Predictor Variablesmentioning

confidence: 99%

“…The start and end of season threshold parameters for the smoothing function were set at 20%, as suggested by Jonsson and Eklundh [64], to optimize the error that could be caused by varying start and end of season dates in different locations across the study area [69]. Only variables for the first season were used in this study since data from the second season were not consistent throughout all the years across the study area [69]. Our study area cuts across different climatic zones in Kenya with a varying number of rainy seasons; hence, some of our sample sites commonly experience unimodal rainfall (one rainy season), while others have bi-modal rainfall (two rainy seasons) in a calendar year.…”

Section: Predictor Variablesmentioning

confidence: 99%

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Importance of Remotely-Sensed Vegetation Variables for Predicting the Spatial Distribution of African Citrus Triozid (Trioza erytreae) in Kenya

Richard

Abdel‐Rahman

Mohamed

et al. 2018

IJGI

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Citrus is considered one of the most important fruit crops globally due to its contribution to food and nutritional security. However, the production of citrus has recently been in decline due to many biological, environmental, and socio-economic constraints. Amongst the biological ones, pests and diseases play a major role in threatening citrus quantity and quality. The most damaging disease in Kenya, is the African citrus greening disease (ACGD) or Huanglongbing (HLB) which is transmitted by the African citrus triozid (ACT), Trioza erytreae. HLB in Kenya is reported to have had the greatest impact on citrus production in the highlands, causing yield losses of 25% to 100%. This study aimed at predicting the occurrence of ACT using an ecological habitat suitability modeling approach. Specifically, we tested the contribution of vegetation phenological variables derived from remotely-sensed (RS) data combined with bio-climatic and topographical variables (BCL) to accurately predict the distribution of ACT in citrus-growing areas in Kenya. A MaxEnt (maximum entropy) suitability modeling approach was used on ACT presence-only data. Forty-seven (47) ACT observations were collected while 23 BCL and 12 RS covariates were used as predictor variables in the MaxEnt modeling. The BCL variables were extracted from the WorldClim data set, while the RS variables were predicted from vegetation phenological time-series data (spanning the years 2014–2016) and annually-summed land surface temperature (LST) metrics (2014–2016). We developed two MaxEnt models; one including both the BCL and the RS variables (BCL-RS) and another with only the BCL variables. Further, we tested the relationship between ACT habitat suitability and the surrounding land use/land cover (LULC) proportions using a random forest regression model. The results showed that the combined BCL-RS model predicted the distribution and habitat suitability for ACT better than the BCL-only model. The overall accuracy for the BCL-RS model result was 92% (true skills statistic: TSS = 0.83), whereas the BCL-only model had an accuracy of 85% (TSS = 0.57). Also, the results revealed that the proportion of shrub cover surrounding citrus orchards positively influenced the suitability probability of the ACT. These results provide a resourceful tool for precise, timely, and site-specific implementation of ACGD control strategies.

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Section: Predictor Variablesmentioning

confidence: 99%

Section: Predictor Variablesmentioning

confidence: 99%

Importance of Remotely-Sensed Vegetation Variables for Predicting the Spatial Distribution of African Citrus Triozid (Trioza erytreae) in Kenya

Richard

Abdel‐Rahman

Mohamed

et al. 2018

IJGI

Self Cite

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“…Studies have shown that honeybee pests (for example, Varroa destructor Anderson et Trueman, 2000) can survive only in certain optimal bioclimatic conditions. For instance, the optimal temperature, humidity, precipitation, altitude and biomass/net primary productivity ranges for diff erent honeybee pests can vary signifi cantly (Makori et al, 2017); the quoted authors concluded that honeybee pests could be modelled using coarse scale bioclimatic data which were most relevant in all model results. Nonetheless, we have to accept the notion that microclimates are supposed to modulate the responses to the macroclimate, but currently the extent of such modulation in a coarse scale modelling exercise is diffi cult to verify.…”

Section: Introductionmentioning

confidence: 99%

Modelling the Bioclimatic Niche of a Cohort of Selected Mite Species (Acari, Acariformes) Associated with the Infestation of Stored Products

Tytar

Oksentyuk

2019

Vestnik Zoologii

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In this study an attempt is made to highlight important variables shaping the current bioclimatic niche of a number of mite species associated with the infestation of stored products by employing a species distribution modeling (SDM) approach. Using the ENVIREM dataset of bioclimatic variables, performance of the most robust models was mostly influenced by: 1) indices based on potential evapotranspiration, which characterize ambient energy and are mostly correlated with temperature variables, moisture regimes, and 2) strong fluctuations in temperature reflecting the severity of climate and/or extreme weather events. Although the considered mite species occupy man-made ecosystems, they remain more or less affected by the surrounding bioclimatic environment and therefore could be subjected to contemporary climate change. In this respect investigations are needed to see how this will affect future management targets concerning the safety of food storages.

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“…Many of these datasets are based on the climatic variables conceived by Nix [3], the most popular being the global climatic dataset known as WorldClim [10] [11]. This dataset is free for download on the Internet (http://www.worldclim.org/), and the variables represented in the WorldClim dataset, known as bioclimatic variables, have been used in various ecological studies [12] [13] [14]. The availability of datasets such as WorldClim is invaluable to biological studies in areas where climatic records are sparse or non-existent.…”

Section: Introductionmentioning

confidence: 99%

Assessing the Suitability of the WorldClim Dataset for Ecological Studies in Southern Kenya

Wango¹,

Musiega²,

Mundia³

2018

JGIS

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There have been numerous efforts to generate freely available climatic datasets for use in species distribution models, the most popular being the global climatic dataset known as WorldClim. The availability of such datasets is invaluable to scientists as many studies are performed in remote areas where no weather stations are found. However, many users do not critically assess the suitability of these datasets for their applications, and errors associated with global datasets are often assumed to be negligible. Understanding what a climate dataset can or cannot deliver requires the user to have a working knowledge of what the basic spatial climate-forcing factors are at the scale of his/her study, and to have a good understanding of the uncertainty in the dataset. In geographic studies, uncertainty is often described by the degree of error (uncertainty), or degree of accuracy (certainty) in data, and thematic uncertainty refers to the uncertainty in measures made for each variable, whereas temporal uncertainty refers to the uncertainty in time period represented by each variable. Here, we used climatic data from weather stations to investigate the climate-forcing factors in southern Kenya, and then used this weather station data to investigate the uncertainty in the WorldClim dataset. Results indicated that the nineteen core Worldclim variables, known as bioclimatic variables, accurately depicted the local variations in climate in the study area. However, whereas the monthly and seasonal temperature variables represented the same time period in different locations, the same was not true for the monthly and seasonal precipitation variables. The onset of rains is a key biological indicator, and scientists studying phenomena tied to the onset of rains need to keep in mind the temporal variations represented in the WorldClim dataset.

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Predicting Spatial Distribution of Key Honeybee Pests in Kenya Using Remotely Sensed and Bioclimatic Variables: Key Honeybee Pests Distribution Models

Cited by 41 publications

References 63 publications

Importance of Remotely-Sensed Vegetation Variables for Predicting the Spatial Distribution of African Citrus Triozid (Trioza erytreae) in Kenya

Importance of Remotely-Sensed Vegetation Variables for Predicting the Spatial Distribution of African Citrus Triozid (Trioza erytreae) in Kenya

Modelling the Bioclimatic Niche of a Cohort of Selected Mite Species (Acari, Acariformes) Associated with the Infestation of Stored Products

Assessing the Suitability of the WorldClim Dataset for Ecological Studies in Southern Kenya

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