Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=tgrs20 GIScience & Remote Sensing ISSN: (Print) (Online) Journal homepage: www.tandfonline.com/journals/tgrs20 Co-occurrence and abundance of pollinators and pests in horticultural systems in Africa using an integrated Earth observation-based approach Grace Rebecca Aduvukha, Elfatih M. Abdel-Rahman, Bester Tawona Mudereri, Arthur W. Sichangi, Godfrey Ouma Makokha, H. Michael G. Lattorff, Samira A. Mohamed, Tobias Landmann, Henri E. Z. Tonnang & Thomas Dubois To cite this article: Grace Rebecca Aduvukha, Elfatih M. Abdel-Rahman, Bester Tawona Mudereri, Arthur W. Sichangi, Godfrey Ouma Makokha, H. Michael G. Lattorff, Samira A. Mohamed, Tobias Landmann, Henri E. Z. Tonnang & Thomas Dubois (2024) Co-occurrence and abundance of pollinators and pests in horticultural systems in Africa using an integrated Earth observation-based approach, GIScience & Remote Sensing, 61:1, 2347068, DOI: 10.1080/15481603.2024.2347068 To link to this article: https://doi.org/10.1080/15481603.2024.2347068 © 2024 International Centre of Insect Physiology and Ecology (icipe). Published by Informa UK Limited, trading as Taylor & Francis Group. View supplementary material Published online: 09 May 2024. 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Abdel-Rahmana,c, Bester Tawona Mudereria,d,e, Arthur W. Sichangib, Godfrey Ouma Makokhab,f, H. Michael G. Lattorffa,g, Samira A. Mohameda, Tobias Landmanna, Henri E. Z. Tonnanga,c and Thomas Duboisa aInternational Centre of Insect Physiology and Ecology (icipe), Nairobi, Kenya; bInstitute of Geomatics, GIS & Remote Sensing, Dedan Kimathi University of Technology, Nyeri, Kenya; cSchool of Agricultural, Earth and Environment Sciences, University of KwaZulu-Natal, Pietermaritzburg, South Africa; dInternational Potato Center (CIP), Kigali, Rwanda; eSchool of Animal, Plant and Environmental Sciences, University of the Witwatersrand, Wits, South Africa; fSchool of Science and Informatics, Taita Taveta University, Voi, Kenya; gSchool of Life Sciences, University of KwaZulu-Natal, Durban, South Africa ABSTRACT Flower-visiting insects that are pollinators play a critical role in promoting biodiversity in agroecosystems and agricultural food production through their pollination ecosystem service. However, several factors affect the survival of these pollinators and flower visitors, including the heavy and indiscriminate applica- tion of agrochemicals to control crop insect pests, which is impacted by various cropping patterns in a landscape and by shifting environmental conditions. Thus, this study focused on investigating the influence of cropping patterns on the spatial distribution of pollinators (Apis mellifera, Hymenoptera other than A. mellifera, and Syrphidae), flower visitors (Calliphoridae), and pests, i.e. fruit fly (Bactrocera dorsalis) and false codling moth (Thaumatotibia leucotreta) of the avocado, a pollinator-dependent crop. Cropping patterns, earth observation data and relevant environmental variables were used as the predictor variables for modeling the potential distribution and abundance of avocado pollinators, flower visitors and pests in one of the leading regions in avocado production in Kandara, Maragua, and Gatanga sub-Counties in Murang’a County, Kenya. In specific, species distribution modeling (SDM) and species abundance model- ing (SAM) techniques, i.e. the maximum entropy (MaxEnt) model (presence-only data) and negative binomial (NB) distribution in a generalized linear model (GLM) (abundance data) were used, respectively. Additionally, the spatial distribution probability of the co-occurrence of the pollinators, flower visitors and pests was also analyzed. This study revealed that cropping patterns was the most consistent influential predictor variables for the distribution of avocado pollinators, flower visitors and pests. A large area of Kandara and some parts of Maragua and Gatanga sub-Counties showed a high spatial distribution probability of the studied avocado pollinators, flower visitors and pests. However, only the majority of Kandara sub-County had a high spatial distribution probability score of the potential co-occurrence of the avocado pollinators, flower visitors and pests. Further, A. mellifera was the most abundant flower-visiting pollinator compared with the other studied pollinators, while B. dorsalis was the most abundant avocado pest compared with T. leucotreta. In addition, GLM analysis indicated that no environmental variable was significant in explaining the abundance of the studied avocado pollinators, whereas precipitation and elevation derivatives of aspect and hillshade were statistically significant (p ≤ 0.05) in explaining the abundance of B. dorsalis. Solar radiation was significant in explaining only the abundance of T. leucotreta. Our study revealed that SDM and SAM modeling outputs can be used to inform decision- making for the implementation of sustainable management efforts regarding pollinators, flower visitors, and insect pests. ARTICLE HISTORY Received 1 September 2023 Accepted 21 April 2024 KEYWORDS Avocado; Kenya; flower visitors; species distribution modeling 1. Introduction Biotic and abiotic pollination is an essential ecosystem service that accounts for 9.5% of the value of all food produced globally (Potts et al. 2010). Recent studies on the economic value of pollination provided by biotic pollinators estimated its worldwide value to be USD 195 billion to ~USD 387 billion yearly (mod- ified for inflation in March 2020) (Porto et al. 2020). Moreover, of the 115 most important global food crops, 87 depend on insects and other animal CONTACT Grace Rebecca Aduvukha gaduvukha@icipe.org This article was originally published with errors, which have now been corrected in the online version. Please see Correction [10.1080/15481603.2024.2356930] Supplemental data for this article can be accessed online at https://doi.org/10.1080/15481603.2024.2347068 GISCIENCE & REMOTE SENSING 2024, VOL. 61, NO. 1, 2347068 https://doi.org/10.1080/15481603.2024.2347068 © 2024 International Centre of Insect Physiology and Ecology (icipe). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. https://doi.org/10.1080/15481603.2024.2347068 http://www.tandfonline.com https://crossmark.crossref.org/dialog/?doi=10.1080/15481603.2024.2347068&domain=pdf&date_stamp=2024-05-16 00400305 Highlight 00400305 Highlight pollinators for the production of fruits, vegetables or seeds (Klein et al. 2007). Some studies have referred to a possible decline of these pollinators in various geographical setups (Novais et al. 2016; Rhodes 2018), while other studies have shown that managed bees are on the increase in differ- ent set-ups, as estimated from Food and Agriculture Organization (FAO) of the United Nations datasets from 1961 to 2017 (Phiri, Fèvre, and Hidano 2022). However, these insect pollinators are still endangered by factors such as landscape simplification, increasingly influenced by monoculture cropping systems; intensive use of agrochemicals such as synthetic pesticides; cli- mate variability; and increased occurrence of pollinator pests and diseases (Moreaux et al. 2022). Flower-visiting insects have been used to serve as an indication of pollination services in several crops, including avocado (Persea americana) (Garibaldi et al. 2020). Various insects visit the avocado flower; how- ever, the insect flower visitors that have been known to be the most efficient pollinators for avocado are the Western honeybee Apis mellifera (Dymond et al. 2021). Consequently, distinguishing pollinators from other generic flower visitors is important in avocado production for the successful management of key pollinator species (Sagwe et al. 2022). Avocado, a highly pollinator-dependent crop, is a vital horticultural commodity in Kenya and is largely cultivated by small-scale farmers. Eighty percent of avocado produced in Kenya is consumed in the domestic market, while the rest is exported as fresh or processed fruits/oils (Kathula 2021). However, the presence of insect pests like the false codling moth (Thaumatotibia leucotreta) and the oriental fruit fly Bactrocera dorsalis has a severe impact on the produc- tion of avocado in Kenya (Toukem et al. 2020). Integrated pest management (IPM) has been imple- mented in avocado production systems to combat these pests while limiting the use of chemical pesti- cides (Onsomu 2019), but excluding pollinator man- agement strategies. Nevertheless, the inclusion of pollinator management strategies through integrated pest and pollinator management (IPPM) would improve yields of pollinator-dependent crops, while sustaining biodiversity (Biddinger and Rajotte 2015). Spatial characterization of pollinators, flower visi- tors and pests is an essential cornerstone of IPPM. Currently, up-to-date spatial prediction of fruit crop pollinators, flower visitors and pests is scarce in agri- culture-promising countries like Kenya. To the best of our knowledge, only a few studies have assessed the spatial pattern of fruit crop insect pollinators, flower visitors and pests at a localized scale. For instance, Makori et al. (2022) used multisource spatial data to understand the spatial distribution and change pat- terns of stingless bees in Kenya and revealed a higher probability of their decline than of their proliferation. In another study, Mandela et al. (2018) analyzed the diversity and abundance of camphor basil (Ocimum kilimandscharicum) flower visitors in Kakamega forest in Kenya and reported that species diversity of the flower visitors increased with closeness to the forest edge. Additionally, Zingore et al. (2020) predicted the potential expansion of the peach fruit fly Bactrocera zonata and found that, under changing climatic con- ditions, the pest could invade wider regions in Africa and South America. Furthermore, Mahmoud et al. (2020) determined the habitat suitability of two fruit fly species (i.e. B. zonata and B. dorsalis) and their suitable co-occurrence range in Sudan, and found that the two pests were spread across a wide area in the country. Furthermore, Stotter (2009) assessed the spatial-temporal distribution of the T. leucotreta in South Africa in the citrus crop and found that male T. leucotreta were mostly confined to citrus orchards, thus providing insights into the local distribution of the T. leucotreta across the agricultural landscape. Statistical models have also been employed to esti- mate B. dorsalis and T. leucotreta abundance in avo- cado orchards by using climatic variables (Odanga et al. 2018) and a landscape productivity indicator (i.e. normalized difference vegetation index: NDVI) (Toukem et al. 2020). Both studies demonstrated the importance of climatic and NDVI variables in estimat- ing B. dorsalis and T. leucotreta abundance, respec- tively. The spatial distribution of pollinators, flower visitors, and pests is also influenced by the landscape structure, which could be tailored in terms of land use land cover (LULC) or cropping patterns (Mudereri et al. 2020; Ochungo et al. 2019). Although various milestones have been achieved by earlier studies, most have not pre- dicted spatial distribution probability for the 2 G. R. ADUVUKHA ET AL. avocado pollinators, flower visitors, and pests in a localized avocado production system such as Murang’a County. Furthermore, few studies have assessed the use of geospatial modeling approaches for determining the suitable sites for the co-occurrence of two or more pollinators, flower visitors, or pest species (Mahmoud et al. 2020). Moreover, studies have not looked at the role or effect of remotely sensed cropping patterns and environmental variables in estimating pollina- tor, flower visitor and pest distribution, co- occurrence and/or abundance. Therefore, this study sought to illustrate the synergy provided by remotely sensed outputs, e.g. cropping patterns in applications such as insect studies. Specifically, the aim of this study was twofold: (1) to predict the spatial distribution probability of avocado pollinators, flower visitors, and pests using cropping patterns, and environ- mental and topographic variables, together with an ecological niche modeling approach (maximum entropy: MaxEnt), and to determine the suitable co-occurrence range of the avocado pollinators, flower visitors and pests; and (2) to estimate and analyze the abundance of avocado pollinators, flower visitors and pests using environmental vari- ables and generalized linear models (GLMs). 2. Material and methods 2.1. Study area The study was conducted in the County of Murang’a in Kenya (Figure 1). The County lies between lati- tudes 0° 34’ 00’’ S and 1° 07’ 00’’ S, and longitudes 36° 00’ 00’’ E and 37° 27’ 00’’E. There are two distinct rainfall patterns in the area: long rains from March to May and short rains from October to November every year. The annual temperature ranges from 12°C to 20°C, while the annual rainfall ranges from 800 to 2600 mm (Ovuka and Lindqvist 2000). Murang’a County has a complex heterogeneous landscape, translating into heterogeneous cultiva- tion of crops like avocado, common bean (Phaseolus vulgaris), sweet potato (Ipomoea batatas), mango (Mangifera indica), maize (Zea mays), Figure 1. Map of the study area comprising Gatanga, Kandara and Maragua sub-Counties in Murang’a County, Kenya, with overlaid sampled avocado farms, elevation and other surface features. GISCIENCE & REMOTE SENSING 3 macadamia (Macadamia integrifolia), arrowroot (Maranta arundinacea), pineapple (Ananas comosus), banana (Musa spp), coffee (Coffea arabica) and tea (Camellia sinensis). Horticultural crops, e.g. avocado, depend on pollinators such as Hymenoptera Apis mellifera. The peak avocado flowering season begins in August (Sagwe et al. 2022), while the fruiting period occurs in February (Toukem et al. 2020). Gatanga, Kandara and Maragua sub-Counties in Murang’a County were selected, since they are cri- tical for avocado farming in Kenya. Further information regarding the study area has been described in Aduvukha et al. (2021). 2.2 Methodology Figure 2 demonstrates the methodology used for species distribution modeling (SDM), co-occurrence analysis, and species abundance modeling (SAM). In summary: i) SDM involved using the MaxEnt model while integrating remotely sensed data of cropping patterns and non-croplands variables mapped in Cropping patterns and other non-cropland Environmental variables Occurrence field data of avocado pollinators, flower visitors and pests Abundance field data of avocado pollinators, flower visitors and pests Data preprocessing Correlation matrix and variable selection Species distribution modelling (MaxEnt) Relative abundance and species abundance modeling (Generalized linear modeling) Relative abundance (%) and correlation of environmental variables and abundance of avocado pollinators, flower visitors and pests Spatial probability distribution maps of avocado pollinators, flower visitors and pests Convert the intersecting polygons to raster Convert ASCII pollinators, flower visitors and pests’ files to raster Symbolize using unique values Reclass raster to high, medium and low probabilities Convert reclassed raster to polygons Intersect analysis of the reclassed polygons High, medium and low co-occurrence map of; i) avocado pollinators and flower visitor ii) avocado pests iii) avocado pollinators, flower visitors and pest O ut pu ts A na ly si s D at a pr ep ar at io n an d ex pl or at or y da ta a na ly si s In pu t d at a Figure 2. Flow diagram of the approach adopted for the species distribution modelling, co-occurrence analysis, and species abundance modelling of avocado pollinators, flower visitors and pests. ASCII = American Standard Code for Information Interchange; MaxEnt = maximum entropy. 4 G. R. ADUVUKHA ET AL. Aduvukha et al., (2021), avocado pollinators, flower visitors and pests occurrences sampled in NDVI informed avocado farms (Adan et al. 2021; Sagwe et al. 2021; Toukem et al. 2020) and environmental variables; ii) co-occurrence analysis involved intersec- tion analysis of the outputs from the MaxEnt model; and iii) SAM involved utilizing the GLM, i.e. negative binomial distribution, to analyze the effect of the environmental variables on the abundance of the avocado pollinators, flower visitors, and pests. A detailed description of the datasets and analysis is presented herein. 2.2.1. Earth observation remote-sensing data description and processing 2.2.1.1. Remote sensing datasets and field data collection. Remote sensing datasets of Sentinel-1 and Sentinel-2, as well as derived spectral indices and vegetation phenology and field data, were used in the mapping of cropping patterns and non- croplands, as described below (Aduvukha et al. 2021). 2.2.1.1.1. Sentinel-1 radar data. A total of 30 Sentinel-1 images were obtained from the European Space Agency (ESA) Copernicus data hub (ESA 2019) for all four seasons, i.e. hot dry (season 1, n = 5), long rainy (season 2, n = 9), cool dry (season 3, n = 8), and short rainy (season 4, n = 8) (Aduvukha et al. 2021). Sentinel-1 is a synthetic aperture radar sensor, provid- ing images in the C-band frequencies continuously in all weather conditions, both day and night with a revisit period of 12 days (ESA 2019). The Sentinel-1 sensor acquires images in four modes, i.e.: stripmap (SM) (images small islands); interferometric wide swath (IW) (main acquisition over land); extra-wide swath (EW) (utilizes TOPSAR: Terrain Observation with Progressive Scans to acquire wider area data compared to IW); and wave (uses “leap frog” acquisition mode). Processing levels of the modes include Level-0, Level-1 (Single Look Complex-(SLC), ground range detected- (GRD)), and Level-2. In detail, Level-0 contains noise, orbit and altitude information, internal calibration and echo source packets; Level-1 SLC products are pro- cessed at natural pixel spacing and they preserve the phase information, while Level-1 GRD products are generated with less speckle and increased image qual- ity, as well as containing the detected amplitude; and Level-2 contains geolocated geophysical products derived from Level-1 (ESA 2019). This study utilized the IW and Level 1 GRD products. Vertical transmit and vertical receive (VV) and vertical transmit and hor- izontal receive (VH) modes of dual polarization were utilized in Sentinel-1 images. The sensor image pre- Figure 3. Map of cropping pattern and non-croplands in Kandara, Maragua and Gatanga sub-Counties, Murang’a County, Kenya (Aduvukha et al. 2021). GISCIENCE & REMOTE SENSING 5 processing was carried out using the Sentinel applica- tion platform (SNAP) toolbox and they included the application of the precise orbit file, thermal noise and image border, radiometric calibration, and speckle fil- tering (Filipponi 2019). In addition, the 90 m shuttle radar topography was bilinearly resampled to 10 m for terrain correction of the Sentinel-1 data. A subset composite image VV and VH image of each season was then obtained after stacking the individual processed images (Aduvukha et al. 2021). 2.2.1.1.2. Sentinel-2. Time-series (10 December 2017 to 15 December 2018) multi-sensor datasets from the freely available Sentinel-2 sensor and Sentinel-1 sen- sor were utilized in this study. Sentinel-2 comprises optical imagery of 13 multiple spectral bands, span- ning across the visible, near-infrared, and short-wave infrared part of the spectrum, with resolutions ran- ging between 10 m, 20 m, and 60 m. It covers a horizontal distance of 290 km as it captures the Earth’s surface images (ESA 2019). A total of 128 images across four seasons were used, i.e. hot dry (season 1, n = 42), long rainy (season 2, n = 24), cool dry (season 3, n = 22) and short rainy (season 4, n = 40) (Aduvukha et al. 2021). Atmospheric correction (redu- cing the atmosphere’s effects of scattering and absorption on the reflectance values of images cap- tured by satellite or aerial sensors) was carried out using the Sen2cor module in the SNAP toolbox (ESA 2019). Other preprocessing procedures performed in SNAP included cloud masking, resampling (20 m Sentinel-2 bands to 10 m, using the nearest- neighbor technique), layer stacking, mosaicking, and computation of the median pixel image for each sea- son. The Sentinel-2 spectral bands used were bands 2, 3, 4, 5, 6, 7, 8a, 11, and 12 (Aduvukha et al. 2021). 2.2.1.1.3. Vegetation indices. Vegetation indices are used to describe various aspects of vegetation, includ- ing vegetation cover, vegetation health, and vegeta- tion water content features, through using a combination of the spectral characteristics of more than one wavelength (Xue and Su 2017). Eight indices (Table 1) were derived from the composite seasonal images of Sentinel-2 imagery and used in this study (Aduvukha et al. 2021). 2.2.1.1.4. Phenological variable. Vegetation pheno- logical variables were incorporated in this study, since they target the growth cycle of the vegetative components of the landscape (Kimball 2014). These variables (Araya 2017) (Table 2) were simulated from the multi-season NDVI images of Sentinel-2 using TIMESAT software (Jönsson and Eklundh 2004). Local functions were fit to the data points in the time-series NDVI curve data to analyze the phenological signals, which were then combined into a global model. Consequently, a smooth model function was employed to extract phenological variables for each season. A thresholding method, with a relative thresh- old of 0.3, was used to define the timings of the phenological events (Table 2) (Landman et al., unpub- lished work). A composite image for the vegetation phenological variables (n = 15) from each of the four seasons was created and used in the cropping pattern classification analysis (Table 2). 2.2.1.1.5. Cropping pattern and non-croplands field data collection. Field data for cropping pattern and non-croplands were sampled from 12 December 2018 to 19 December 2018 (Aduvukha et al. 2021). The cropping patterns included monocrop maize, mixed crop maize, monocrop avocado, mixed crop avocado, monocrop coffee, monocrop tea and monocrop Table 1. Summary of the vegetation indices used in the mapping of cropping patterns and non-croplands, as adopted from Aduvukha et al. (2021). No. Index Formula Reference 1 Atmospherically resistant vegetation index-2 (ARVI2) � 0:18þ 1:17 � NIR� Red NIRþRed � � (Kaufman and Tanre 1992) 2 Enhanced vegetation index (EVI) 2:5 � NIR� Red NIRþ6�Red� 7:5�Blueþ1 (Ahamed et al. 2011) 3 Green normalized difference vegetation index (GNDVI) NIR� Green NIRþGreen (Gitelson, Kaufman, and Merzlyak 1996) 4 Modified soil adjusted vegetation index (MSAVI) NIR� Redð Þ 1þLð Þ NIRþRedþL (Qi et al. 1994) 5 Normalized difference vegetation index (NDVI) NIR� Red NIRþRed (Tucker et al. 1979) 6 Normalized difference water index (NDWI) NIR� SWIR3 NIRþSWIR3 (Gao 1996) 7 Soil adjusted vegetation index (SAVI) NIR� Red NIRþRedþ0:5 � � � 1þ 0:5ð Þ (Huete 1988) 8 Two-band enhanced vegetation index (EVI2) 2:5 � NIR� Red NIRþ2:4�Redþ1 (Jiang et al. 2007) Note: NIR= near-infrared band; L= 2*s*(NIR-Red) *(NIR-s* Red)/(NIR+Red), where s is the slope of the soil line from a plot of red versus near-infrared brightness values. 6 G. R. ADUVUKHA ET AL. pineapple, while the non-croplands included areas of water, forest, shrubland, and built-up areas (Aduvukha et al. 2021). A stratified random sampling method was used to collect the ground truth field data as points (i.e. pixels) by using a mobile-based global positioning system (GPS), and GPS Essentials (GPS Essentials 2020) with a maximum allowable error of ±3 m. Furthermore, the collected field data points were set at a sampling distance of ≥20 m each to avoid spatial autocorrelation instances with respect to the 10 m resolution of Sentinel-2 imagery bands used. On-screen digitizing on high-resolution imagery provided by Google Earth imagery (Google Earth 2020) was then employed to convert the pixels of reference data to homogenous units (i.e. polygons) to be used for classification. Studies have shown that polygon-based training areas perform better than pixel-based training areas do (King’ori et al. 2023). A summary of the number of points and their corre- sponding pixels within the polygons of each class is shown in Aduvukha et al. (2021). 2.2.1.2 Cropping pattern and non-croplands clas- sification. This included selecting the most impor- tant variables among the remote sensing dataset combinations (Table 3) and thereafter using a machine-learning algorithm for classification. Specifically, a guided regularized random forest (GRRF) algorithm was used to select important vari- ables in each of the eight remotely sensed data com- bination scenarios (Table 3) (Aduvukha et al. 2021). The GRRF for selecting most important variables has been found to perform better than other methods, such as regularized random forest (RRF) and random forest (RF) algorithms, do (Deng and Runger 2013). The limitation of RF in selecting the most important variables lies in its susceptibility to select highly cor- related variables, while RRF may select variables that are not robustly relevant (Deng and Runger 2013). On the other hand, the strength of GRRF in selecting the most robust variables lies in its facility to subject each feature to a penalty coefficient by altering the coeffi- cient of importance of gamma (γ) value of 0 to 1, while maintaining the base coefficient of lambda (λ) value of 1 (Deng and Runger 2013). The importance of the variables was assessed using the mean decrease accuracy ranking method (Han, Guo, and Yu 2016). Further explanation of GRRF, RF and RRF in variable selection can be found in Deng and Runger (2013). Thereafter, a RF classification algorithm was used to delineate the different cropping patterns and non- croplands (Aduvukha et al. 2021). The RF algorithm was preferred to other supervised classification meth- ods, such as maximum likelihood. This is because RF is non-parametric, i.e. it does not assume the data dis- tribution but it learns first from the “seen” data and predicts the pattern of the “unseen” data (Breiman Table 2. Vegetation phenological variables that were used in mapping of cropping patterns and non- croplands, as adopted from Araya (2017) and Aduvukha et al. (2021). No. Phenological variable Definition of the NDVI curve and physiological description 1 Onset_value The NDVI value at the start of the growth (seedling growth stage) 2 Onset_time The time when the growth onset is achieved 3 Max_value The maximum NDVI value in the season 4 Max_time The time when the Max_value is attained (anthesis growth stage) 5 Offset_value The NDVI value at the end of the season 6 Offset_time The time when growth offset is attained (senescence growth stage) 7 LengthGS The length of the growing season 8 BeforeMaxT The length of time between onset and Max_value 9 AfterMaxT The length of time between Max_value and offset 10 GreenUpSlope The rate of increase in NDVI value between onset and offset 11 BrownDownSlope The rate of decrease in NDVI value between Max_value and offset 12 TINDVI The area under the NDVI curve between onset and offset 13 TINDVIBeforeMax The area under the NDVI curve between onset and Max_value 14 TINDVIAfterMax The area under the NDVI curve between Max_value and offset 15 TINDVIAsymmetry The difference between TINDVIBeforeMax and TINDVIAfterMax Table 3. The remote sensing datasets combination scenarios and number of variables for the classification of cropping pat- terns and non-croplands, as highlighted in Aduvukha et al. (2021). Variable combination Number of variables Sentinel-2 bands only 40 Sentinel-2 bands and Sentinel-1 48 Sentinel-2 bands and vegetation indices 48 Sentinel-2 bands and vegetation phenology 55 Sentinel-2 bands, vegetation indices and Sentinel-1 56 Sentinel-2, vegetation indices and vegetation phenology 63 Sentinel-2, vegetation phenology and Sentinel-1 63 Sentinel-2, vegetation indices, vegetation phenology and Sentinel-1 71 GISCIENCE & REMOTE SENSING 7 2001; Wiener and Liaw 2002), thus reducing chances of overfitting. On the other hand, maximum likelihood assumes a normal distribution of the training data, hence high chances of overfitting the predictions (Sisodia, Tiwari, and Kumar 2014). Data were parti- tioned as 70% for training and 30% for testing (Aduvukha et al. 2021). The variable selection and crop- ping patterns and non-croplands classification were implemented in R software using the caret package (R Core Team 2019). Consequently, the area under class method (Olofsson et al. 2013) was used to construct the con- fusion matrix for accuracy assessment to estimate the user’s accuracy (UA), producer’s accuracy (PA), overall accuracy (OA), and kappa coefficient (Aduvukha et al. 2021). 2.2.2. Pollinators, flower visitors, and pests field data collection 2.2.2.1. NDVI field characterization for sampling avocado farms. Time-series Sentinel-2 images of both dry and wet seasons accessed from the Google Earth Engine cloud computing platform were used (Gorelick et al. 2017). The seasons were determined from Climate Hazards Group InfraRed Precipitation with Station Data (CHIRPS) rainfall data (Adan et al. 2021). Ninety images for the dry season were obtained from 1 January 2018 to 28 February 2018, while 50 images for the wet season were obtained from 1 March 2018 to 31 March 2018. One hundred random points were generated within the study area, and a K-means clustering technique was used to cate- gorize the extracted NDVI values into three levels, i.e. high, medium, and low (Table 4) (Adan et al. 2021). The dry and wet season NDVI images were com- bined to provide a composite NDVI image (Adan et al. 2021). Using expert knowledge (observing texture, pattern, shade, tone, and color hue) (Li et al. 2020), the classification accuracy of the composite NDVI was assessed. An overall accuracy of 86.2% was attained, as detailed in Adan et al. (2021). 2.2.2.2. Sampling protocol for farms. The vegeta- tion intensity classes of low, medium, and high obtained from NDVI as described in Section 2.2.2.1 were used as sampling strata, in which pollinator, flower visitor and pest data were collected in the study area. The sampling was carried out during the avocado peak flowering (26 August 2019–4 September 2019) and peak fruiting (27 January 2020–13 February 2020) sea- sons, respectively, in farm sizes of approximately 0.2–0.4 ha (Sagwe et al. 2021). Thirty-five farms were selected across the low, medium, and high NDVI regions, using a multi-stage sampling proto- col, as detailed in Adan et al. (2021). In summary, the avocado farms selection protocol in each stra- tum included: (1) minimal number of avocado trees per farm at seven; (2) socio-economic data on farmers’ willingness-to-pay for IPPM technologies (IPM only where biological treatment of pests was introduced, pollinators only (P) where managed bees were introduced, IPPM where both managed bees and biological treatment of pests were intro- duced, and control where neither treatments of pests nor managed bees were introduced); and (3) setting specific distances among avocado farms with the different IPPM technologies. The specific distances between farms with the different technologies were as follows (i) IPPM and P were at least 1.5–3.0 km separate from each other, (ii) IPM and control were at least 0.5 km away from each other, and (iii) IPPM or P were at least 3.5 km away from either IPM or control sites (Adan et al. 2021). The implementation of the different IPPM technologies was assessed in a separate study by Toukem et al. (2022) to establish the effect of the inclusion of pollinators in pest management of crops such as avocado. 2.2.2.3. Avocado pollinator, flower visitor, and pest occurrence and abundance data. For purposes of this study, flower-visiting insects that are good at pollinating avocado crops were defined as “pollinators,” while those flower-visiting insects that are poor at polli- nating avocado crops were called “flower visitors” (Sagwe et al. 2022). Furthermore, in this study, “occur- rences” were defined as geolocated instances of the Table 4. Normalized difference vegetation index (NDVI) of dry and wet seasons description and range, as described in Adan et al. (2021). NDVI range NDVI intensity description Dry season Wet season High 0.537–0.853 0.559–0.865 Medium 0.318–0.537 0.345–0.559 Low <0.318 <0.345 8 G. R. ADUVUKHA ET AL. observed pollinator, flower visitor, or pest, while “abun- dance” was defined as the relative number (count) of a pollinator, flower visitor, or pest trapped per unit area of the farm size. Therefore, for avocado pollinators and flower visitors, three avocado trees, spaced 20 m apart within each of the selected farms, were randomly selected, and each of the three avocado trees was observed for 5 min from 0800 to 1700 h (Greenwich Mean Time: GMT + 3). The pollinators and/or flower visitors were captured using sweep nets (white in color) and were forthwith preserved in 70% ethanol (Sagwe et al. 2022). The sampled pollinators and flower visitors were identified by Robert Copeland, icipe, Nairobi, Kenya, and categorized into four groups: (1) A. mellifera (order Hymenoptera), (2) Hymenoptera excluding A. mellifera (order Hymenoptera), (3) Syrphidae (order Diptera) and (4) Calliphoridae (order Diptera). Other categories of pollinators or flower visitors were also sampled, but were of very low count, and thus were not included in this study. Regarding avocado pests, traps for the specific pests were set on two separate avocado trees at a distance of ≥20 m apart in each of the selected farms (Toukem et al. 2020). Lynfield traps (icipe, Nairobi, Kenya) with para-pheromone methyl euge- nol (River Bioscience, Addo, South Africa) were used to trap B. dorsalis, while T. leucotreta were trapped using white delta-shaped traps, lured with the rele- vant sex pheromone (Kenya Biologics, Nairobi, Kenya). Insects from the Lynfield traps were kept in 70% ethanol for preservation, while sheets with trapped insects from the white delta-shaped traps were enclosed in a polythene sheet before analysis in the laboratory, as detailed in Toukem et al. (2020). The pests were subsequently identified by Robert Copeland, icipe, Nairobi, Kenya. On the other hand, the abundance of the avocado pests was collected after 2 weeks within the sampling period only on 17 farms that were set as control treat- ments out of the total 35 selected farms, since the other 18 farms were put on other IPPM treatments before sampling the abundance of pests. A mobile-based GPS application, i.e. GPS Essentials (Schollmeyer Software Engineering, Munich, Germany), was used to geolocate the specific trees within each avocado farm where pollinators, flower visitors, and pests were sampled. 2.2.3. Predictor variables 2.2.3.1. Environmental predictor variables and preprocessing. The most influential environmental variables that affect the distribution and abundance of avocado pollinators, flower visitors, and pests were used as predictor variables in the modeling experi- ments (Kjøhl, Nielsen, and Stenseth 2011; Odanga et al. 2018). These were average temperature, precipita- tion, solar radiation, wind speed, morning relative humidity, afternoon relative humidity, and elevation (Table 5). Slope, hillshade, and aspect were derived from elevation. In addition, the variable “cropping pat- terns,” which were derived from Aduvukha et al. (2021), were also included as comprising a predictor variable. These cropping patterns were monocrop tea, mixed crop avocado, mixed crop maize, monocrop avocado, monocrop coffee, monocrop maize, and monocrop pineapple and non-croplands. For the environmental variables, long-term mean average values of July, August, September, and October, estimated from 1961 to 1990 and 1970 to 2000 (Kriticos et al. 2012; Fick and Hijmans 2017), were used in predicting polli- nator and flower visitor spatial distribution probability, as they coincide with the pre-peak, during and post- Table 5. Predictor variables used in spatial distribution and abundance analysis of avocado pollinators, flower visitors, and pests. Variable Unit Resolution Year Source Average temperature °C 1 km 1970–2000 (Fick and Hijmans 2017) Precipitation mm 1 km 1970–2000 (Fick and Hijmans 2017) Solar radiation kJ m−2 day−1 1 km 1970–2000 (Fick and Hijmans 2017) Wind speed m s−1 1 km 1970–2000 (Fick and Hijmans 2017) Relative humidity (morning) % 10-arc minutes 1961 - 1990 (Kriticos et al. 2012) Relative humidity (afternoon) % 10-arc minutes 1961-1990 (Kriticos et al. 2012) Elevation m 1 km (Fick and Hijmans 2017) Slope %rise 1 km n/a Derived from elevation Hillshade n/a 1 km n/a Derived from elevation Aspect degrees 1 km n/a Derived from elevation Cropping pattern n/a 10 m 2018 Aduvukha et al. (2021) n/a=not applicable. GISCIENCE & REMOTE SENSING 9 peak flowering seasons of avocado in the study area. This period also coincided with the pollinator and flower visitor field data collection. For pest spatial dis- tribution probability modeling, long-term mean aver- age values of the environmental variables, estimated from 1961 to 1990 and 1970 to 2000 for December, January, February, and March, were used. This matched with the avocado pre-peak, during and post-peak fruit- ing season in Kenya, during which the pests were sampled. The bilinear interpolation method was used to resample the environmental predictor variables to 10 m × 10 m pixel size and then clipped to the size of the study area to be harmonized with the cropping pattern predictor variable that was developed by Aduvukha et al. (2021). 2.2.3.2. Predictor variable selection. The variance inflation factor (VIF) was used to determine the most uncorrelated predictor variables to reduce multicollinearity (Robinson and Schumacker 2009). A VIF threshold of ≥10 was set as an indicator of multicollinearity and redundancy in the predictor variables (Pradhan 2016) (Table 6). The total num- ber of predictor variables subjected to VIF were n = 11 (Table 1) for the pollinators, flower visitors, and pests, with seven predictor variables being retained for pollinators and flower visitors, while eight pre- dictor variables were retained for the pests (Table 6). However, some of the key predictor vari- ables, such as temperature that is known to influ- ence the distribution of pollinators, flower visitors, and pests, were suggested for exclusion by the VIF test. However, some of these predictor variables were retained, for instance, average temperature, based on their biological relevance to avocado pol- linators and flower visitors (average temperature with VIF of 135.99) and pests (average temperature with VIF of 70.71) distribution and abundance (EFSA 2011; Pradhan 2016). 2.2.4. Species distribution modelling. 2.2.4.1. Model settings. Before applying the SDMs to predict the spatial distribution probability of avocado pollinators, flower visitors, and pests, the geolocations of avocado pollinators, flower visitors, and pest obser- vations were reprojected to the Universal Transverse Mercator (UTM) coordinate system, zone 37 south (Snyder 1987). This was done to ensure compatibility with the coordinate system of the predictor environ- mental variables. The MaxEnt model, Version 3.4.1 (Phillips and Dudík 2008), was used to predict potentially suita- ble areas of avocado pollinators, flower visitors, and pest distributions. The model settings of the MaxEnt were majorly influenced by the number of occurrence points for each of the avocado pollina- tors, flower visitors, and pests (Phillips and Dudík 2008). Consequently, this influenced the MaxEnt feature types in modeling the avocado pollinators, flower visitors, and pests (Table S1 in Supplementary). Outliers were eliminated using a “ten percentile” training presence criterion, which declares the 10% most extreme presence observation as absent (Cord et al. 2014). Additionally, a regularization multiplier of two was employed to ensure a less localized prediction (Radosavljevic, Anderson, and Araújo 2014). Moreover, to ascertain a robust MaxEnt model, the replicate runs were set to 10 (Makori et al. 2017). Cross-validation replication type was used for A. mellifera, Syrphidae, Calliphoridae, B. dorsalis and T. leucotreta because of its robust- ness (Kohavi 1995), while a bootstrap replication type for Hymenoptera excluding A. mellifera was used because of the small sample size (Merow, Smith, and Silander 2013). A sensitivity analysis of the variable contribution to the model was con- ducted using a jackknife test. The jackknife test assesses how each variable affects the performance of the model by determining changes in the accu- racy of the model as it systematically eliminates Table 6. Variables selected after performing multicollinearity analysis using the variance inflation factor (VIF) of a minimum of 10 for avocado pollinators, flower visitors, and pests. Variable VIF <10 Avocado pollinators/Flower visitors aspect 1.11 cropping pattern 1.01 hillshade 1.18 precipitation 7.53 morning relative humidity 5.52 slope 1.34 wind speed 3.43 Avocado pest aspect 1.10 cropping pattern 1.12 hillshade 1.16 precipitation 4.07 afternoon relative humidity 2.72 slope 1.24 solar radiation 8.69 wind speed 4.14 10 G. R. ADUVUKHA ET AL. one variable at a time. The critical variables for predictions are identified by comparing how the model performs, with and without each variable (Phillips and Dudík 2008). 2.2.4.2. Model performance assessment. Model performance of the prediction of spatial distribution probability for the occurrence of avocado pollinators, flower visitors, and pests was assessed using the recei- ver operating characteristic (ROC)‘s threshold- independent area under the curve (AUC) (Merow, Smith, and Silander 2013). The AUC informs the prob- ability of whether presence (sensitivity) in comparison to absence (specificity) was ordered correctly by the model. The values of AUC range from 0 (no possibility of occurrence) to 1 (highest possibility of occurrence), with values greater than 0.7 being regarded as accep- table for predicting spatial distribution probability for the species (Araújo et al. 2005). 2.2.4.3. Co-occurrence spatial distribution of avo- cado pollinators, flower visitors, and pests. Potential co-occurrence analysis of the sampled polli- nators, flower visitors, and pests was carried out using the spatial distribution probability outputs generated from MaxEnt. The MaxEnt outputs were in the American Standard Code for Information Interchange (ASCII) file format, which were first con- verted to the raster image file format (tiff) and assigned three unique values: low, medium, or high. The tiff files were reclassified to low (0.01–0.35), med- ium (0.36–0.69), and high (0.70–0.99) classes. These classes represented the cluster thresholds of co- occurrence spatial distribution probability of the pol- linators, flower visitors, and pests, from the co- occurrence analysis. Each of the tiff files was con- verted to a vector polygon file to perform an intersect analysis among the respective classes represented by the respective polygons. This was done to assess areas of similarity between the pollinators and flower visitors (only pollinators and flower visitors) and pests (only pests) and also to compare the co-occurrence with the occurrence of all pollinators, flower visitors, and pests. Intersecting polygons were converted to raster data, resulting in the delineation of co- occurrence spatial distribution probabilities of all the pollinators and flower visitors, all the pests, and all the combined pollinators, flower visitors, and pests. 2.2.5. Species abundance modelling A generalized linear model (GLM) was implemented in R software, Version 3.6.1 (R Core Team 2019) to infer the relationship between the abundance of the avocado pollinators, flower visitors, and pests, and the selected environmental predictor variables. Specifically, the negative binomial distribution in GLM, which accommo- dates overdispersion of integer counts data, was used (Lindén and Mäntyniemi 2011). The coefficient estimates of all the environmental predictor variables and their significance level (p ≤ 0.05) were analyzed to assess their relationships with the abundance of avocado pollina- tors, flower visitors, and pests. Hymenoptera species excluding A. mellifera pollinators were not included in this analysis because of their very low counts (n = 8). 3. Results 3.1. Cropping pattern and non-croplands The best-performing classification scenario (OA = 94.33% and kappa = 0.93) (Table S2 in Supplementary), i.e. Sentinel-2 bands, vegetation indices and Sentinel-1 combination, was used in the spatial modeling of the avocado pollinators, flower visitors and pests (Aduvukha et al. 2021). The mapped cropping patterns were mono- crop avocado, mixed crop avocado, monocrop maize, mixed crop maize, monocrop coffee, monocrop tea, and monocrop pineapple while the non-croplands were comprised of built-up area, grassland, forest, shrubland, and water (Figure 3). 3.2. Species distribution modelling 3.2.1. Maximum entropy (MaxEnt) model performance All the MaxEnt models used for predicting the spatial distribution probability of all the studied avocado polli- nators (A. mellifera, Hymenoptera excluding A. mellifera and Syrphidae), flower visitors (Calliphoridae) and pests (B. dorsalis and T. leucotreta) demonstrated a good pre- diction performance within an AUC of 0.70–0.83 (Figure 4). 3.2.2. Predictor variable contribution The first three ranked variables for the prediction of pollinator distribution were wind speed (38.10%), precipitation (28.60%) and cropping pat- terns (26.60%) for A. mellifera; cropping pattern GISCIENCE & REMOTE SENSING 11 (39.40%), aspect (25.00%), and wind speed (18.70%) for Hymenoptera excluding A. mellifera; cropping pattern (57.80%), precipitation (33.70%), and wind speed (8.00%) for Syrphidae; and preci- pitation (45.50%), wind speed (41.70%), and crop- ping pattern (9.5%) for Calliphoridae (Table 7). The first three ranked variables for avocado pest dis- tribution were cropping pattern (47.00%), average temperature (42.90%) and wind speed (2.60%) for B. dorsalis; and cropping pattern (44.30%), slope (28.50%) and average temperature (19.80%) for T. leucotreta (Table 8). Based on the jackknife Figure 4. Mean area under the curve (AUC) to two decimal places for predicting spatial distribution probability of (a) Apis mellifera, (b) Hymenoptera excluding A. mellifera, (c) Syrphidae (d) Calliphoridae (e) Bactrocera dorsalis, and (f) Thaumatotibia leucotreta. 12 G. R. ADUVUKHA ET AL. tests performed on the MaxEnt model, the relative variable importance of the cropping pattern and environmental variables showed varied contribu- tions on the ecological niche (EN) models (Figure S1 in Supplementary). 3.2.3. Spatial distribution probability of avocado pollinators, flower visitors, and pests The MaxEnt model for avocado pollinators (A. mellifera and Syrphidae) and avocado flower visi- tors (Calliphoridae) predicted a medium to a high spatial probability distribution in Kandara sub- County, and a low to a high spatial probability dis- tribution in Maragua and Gatanga sub-Counties (Figure 5a–c and d). The majority of Kandara and portions of Maragua and Gatanga sub-Counties experienced the highest spatial distribution probabil- ity score of >0.9 for the presence of A. mellifera and Syrphidae pollinators, and Calliphoridae flower visi- tors. The MaxEnt model for Hymenoptera excluding A. mellifera pollinators (Figure 5b) also showed a low to a high spatial probability distribution score in the three sub-Counties. The MaxEnt model predicted high avocado pest spa- tial probability distribution scores (≥0.9) in the central and western sides of Maragua and Kandara sub- Counties, and low to high scores in Gatanga sub- County. A low distribution score was observed in the eastern side of Maragua and Gatanga sub-Counties for B. dorsalis and T. leucotreta (Figure 5e, f respectively). 3.2.4. Co-occurrence spatial distribution probability of avocado pollinators, flower visitors, and pests The avocado pollinator and flower visitor co-occurrence analysis showed a low to high probability of co- occurrence in the three studied sub-Counties, with the majority of Kandara showing a medium to high prob- ability of pollinator and flower visitor co-occurrence (Figure 6a). Further, avocado pest co-occurrence analysis showed a medium to high probability of co-occurrence in Kandara, while a low to a high probability of co- occurrence was present in Gatanga and Maragua (Figure 6b). On the other hand, the combined co- occurrence analysis of avocado pollinators, flower visi- tors, and pests showed a medium to a high probability of co-occurrence in Kandara and a low to a high prob- ability of co-occurrence in Maragua and Gatanga. (Figure 6c). The accuracy of the co-occurrence analysis (Figure 6) is taken to be similar to those of Figure 5 (SDM) since the inputs used are derived from Figure 5. 3.3. Species abundance modelling 3.3.1. Avocado pollinator, flower visitor, and pest abundance The relative abundance of the avocado pollinators and flower visitors showed that A. mellifera was rela- tively more abundant (80.84%) than Calliphoridae (10.05%) and Syrphidae (9.11%). Among the avocado pests, the relative abundance of B. dorsalis (96.62%) was higher than that of T. leucotreta (3.38%) (Table 9). Distribution of the abundance avocado pollinators, flower visitors, and pests per their respective farms are summarized in Table S3 in Supplementary. Table 7. Contribution (%) of predictor variables to Apis mellifera, Hymenoptera excluding A. mellifera, Syrphidae, and Calliphoridae spatial distribution probability from maximum entropy (MaxEnt) models using the jackknife test. Variable Apis mellifera Hymenoptera excluding A. mellifera Syrphidae Calliphoridae Cropping pattern 26.60 39.40 57.80 9.50 Aspect 1.60 25.00 0.30 1.80 Hillshade 0.20 6.10 0.00 0.10 Precipitation 28.60 7.20 33.70 45.50 Morning relative humidity 4.40 0.10 0.10 0.30 Slope 0.50 1.40 0.00 1.10 Average temperature 0.00 2.00 0.00 0.00 Wind speed 38.10 18.70 8.00 41.70 Table 8. Contribution (%) of predictor variables to Bactrocera dorsalis and Thaumatotibia leucotreta spatial distribution prob- ability from maximum entropy (MaxEnt) models using the jack- knife test. Variable Bactrocera dorsalis Thaumatotibia leucotreta Cropping pattern 47.00 44.30 Aspect 1.50 0.70 Hillshade 0.00 1.10 Precipitation 1.90 3.80 Afternoon relative humidity 1.50 0.30 Slope 1.10 28.50 Solar radiation 1.60 0.40 Average temperature 42.90 19.80 Wind speed 2.60 1.00 GISCIENCE & REMOTE SENSING 13 3.3.2. Generalized linear model Abundance of A. mellifera had a positive relationship with precipitation, slope, and wind speed, but a negative relationship with aspect, hillshade, morn- ing relative humidity, and average temperature (Table 10). Furthermore, the results showed that the abundance of Syrphidae pollinators had a positive relationship with aspect, precipitation, morning relative humidity, slope, average tempera- ture, and wind speed, but a negative relationship with hillshade, while the abundance of Calliphoridae flower visitors had a positive Figure 5. Spatial distribution probability of avocado pollinators (a) Apis mellifera, (b) hymenoptera excluding Apis mellifera, and (c) Syrphidae; avocado flower visitors (d) Calliphoridae and avocado pests (e) Bactrocera dorsalis (f) Thaumatotibia leucotreta predicted using the maximum entropy (MaxEnt) model. The dark blue color indicates a low spatial distribution probability, while the red color represents a high spatial distribution probability. The resolution of the maps is 10 m in relation to spatial resolution of the cropping pattern variable. 14 G. R. ADUVUKHA ET AL. relationship with aspect, hillshade, and wind speed, but a negative relationship with precipitation, morn- ing relative humidity, slope, and average tempera- ture. No variable was significant (p ≤ 0.05) for pollinators and flower visitors. Regarding the avocado pests, the abundance of B. dorsalis had a positive relationship with hill- shade, slope, average temperature, and afternoon relative humidity, but a negative relationship with aspect, precipitation, wind speed, and solar radia- tion (Table 10). However, only the relationship between the abundance of B. dorsalis with aspect, precipitation, and hillshade was statistically significant (p ≤ 0.05). Thaumatotibia leucotreta abundance showed a positive relationship with aspect, afternoon relative humidity, average tem- perature and wind speed, but a negative relation- ship with precipitation, hillshade, slope and solar radiation, with only solar radiation being statisti- cally significant (p ≤ 0.05). 4. Discussion This study predicted the spatial distribution of avocado pollinators, flower visitors, and pests in Murang’a County, Kenya using accurate remotely sensed cropping patterns (OA 94.33% and kappa 0.83), environmental factors as predictor variables, and the MaxEnt ecological niche modeling approach. In addition, the study revealed the co-occurrence spatial distribution probabil- ity of the studied avocado pollinators, flower visitors, and pests. Furthermore, the study also examined the relationship between the abundance of avocado polli- nators, flower visitors, and pests with environmental variables by using a GLM model. Table 9. The abundance (n) and relative abundance (%) of avocado pollinators, flower visitors, and pests. Avocado pollinators, flower visitors and pests Abundance (n) Relative abundance (%) Apis mellifera 503 80.48 Syrphidae 58 9.28 Calliphoridae 64 10.24 Total 625 – Bactrocera dorsalis 25,433 96.62 Thaumatotibia leucotreta 889 3.38 Total 26,322 – Figure 6. Spatial distribution probability of co-occurrence of (a) avocado pollinators and flower visitors (b) avocado pests and (c) avocado pollinators, flower visitors and pests. The light green color indicates a low probability of co-occurrence spatial distribution, while the yellow and the red colors represent a medium and a high probability of co-occurrence, respectively. The resolution of the maps is 10 m in relation to spatial resolution of the cropping pattern variable. GISCIENCE & REMOTE SENSING 15 4.1. Species distribution modelling The study identified highly suitable habitats for each of the avocado pollinators, flower visitors, and pests in the northern and central parts of Kandara, the north- western part of Gatanga, and the south-eastern part of Maragua. These regions are characterized by con- ducive climatic conditions for avocado farming and thus demonstrated the importance of the LULC vari- able in predicting pollinator, flower visitor, and pest habitats or invasive plant species (Sittaro, Hutengs, and Vohland 2023; Tonnang et al. 2017). In terms of the influence of environmental variables on the dis- tribution of the avocado pollinators and flower visi- tors, the present study suggests that the distribution of the pollinators and flower visitors had a negative correlation with high wind speed (>2.5 ms−1, Figure S2 in Supplementary). There is a likelihood of increased resistance of the flight of A. mellifera and Syrphidae pollinators with high wind speed, espe- cially in the opposite direction (not measured in this study). This may cause a reduced flower visitation rate, consequently reducing their distribution (Hennessy et al. 2020). Wind speed has also been observed to contribute to an increased rate of cooling of some Diptera pollinators (Inouye et al. 2015). The high precipitation (>40 mm, Figure S2 in Supplementary) reported in this study area may be unsuitable for the distribution of A. mellifera and Syrphidae pollinators, and Calliphoridae flower visi- tors. Previous studies have highlighted how high Table 10. Summary of the relationship between the environmental variables and the abundance of avocado pollinators, flower visitors, and pests depicting the regression coefficient estimates and p-value (p ≤ 0.05). Avocado pollinators, flower visitors and pests Environmental variables Estimate p-value Apis mellifera (Intercept) 8.53 0.69 aspect −0.00 0.34 hillshade −0.01 0.24 slope 0.03 0.19 precipitation 0.01 0.60 average temperature −0.23 0.61 morning relative humidity −0.14 0.58 wind speed 3.74 0.17 Syrphidae (Intercept) −32.40 0.33 aspect 0.00 0.27 hillshade −0.00 0.71 slope 0.02 0.49 precipitation 0.02 0.92 average temperature 0.48 0.49 morning relative humidity 0.22 0.56 wind speed 4.46 0.13 Calliphoridae (Intercept) 5.31 0.80 aspect 0.00 0.75 hillshade 0.01 0.18 slope −0.24 0.40 precipitation −0.01 0.76 average temperature −0.32 0.53 morning relative humidity −0.13 0.59 wind speed 3.66 0.31 Bactrocera dorsalis (Intercept) 73.56 0.025* aspect −0.00 0.00* precipitation −0.38 <0.001* hillshade 0.04 4.15e-08* slope 0.04 0.12 afternoon relative humidity 0.08 0.48 average temperature 1.16 0.19 wind speed −1.99 0.20 solar radiation −0.00 0.22 Thaumatotibia leucotreta (Intercept) 127 0.00** aspect 5.47e-04 0.62 precipitation −8.11e-02 0.43 hillshade −5.97e-04 0.95 slope −5.07e-02 0.13 afternoon relative humidity 3.48e-02 0.83 average temperature 1.29e+00 0.24 wind speed 1.74e+00 0.34 solar radiation −7.68e-03 0.03* *Significant variables (p ≤ 0.05). 16 G. R. ADUVUKHA ET AL. rainfall negatively affects the foraging and flight activ- ities of A. mellifera pollinators, which may have resulted in their lower occurrence (González et al. 2009). Average temperature can positively affect the sur- vival, development, and reproduction of crop insect pests, thus affecting their distribution (Zingore et al. 2020). Studies have indicated that T. leucotreta sur- vives and propagates successfully in a temperature range between 16°C and 30°C (Jager and Marthalise 2013). Likewise, Choi et al. (2020) reported that the same temperature range positively influenced the fertility of B. dorsalis in the laying of eggs. Moreover, slope (>0%, <10%, Figure S2 in Supplementary), which is a derivative of elevation, positively influ- enced the spatial distribution probability of both B. dorsalis and T. leucotreta. This result corroborates Odanga et al. (2018) who found that B. dorsalis dis- tribution increased as elevation decreased, since B. dorsalis is a lowland pest. On the other hand, this study demonstrated that wind speed negatively affected the distribution of B. dorsalis, which implies that regions with high wind speed (≥3.2 ms−1, Figure S2 in Supplementary) would, for instance, negatively affect the flight of B. dorsalis, hence affecting their distribution (Susanto et al. 2022). Co-occurrence analysis revealed that a large area of Kandara exhibited medium to high-probability co- occurrence of the studied pollinators, flower visitors, and pests individually and combined. Sharing of the ecological niches between the studied pollinators, flower visitors, and pests may cause the competition for resources, especially by the dominant pollinator and pest thus affecting the populations of other pol- linators, flower visitors, and pests (Mahmoud et al. 2020). Nonetheless, there is a possibility of “minor” pollinators, flower visitors and pests to adapt to new ecological niches for their survival (Hassani et al. 2022). 4.2. Species abundance and generalized linear modelling In this study, A. mellifera (order Hymenoptera) was more abundant, compared with Syrphidae (order Diptera) pollinators and Calliphoridae flower visitors (order Diptera). Earlier studies have shown that A. mellifera has been observed to be the most abun- dant pollinator in fruit crop systems including avo- cado (Kjøhl, Nielsen, and Stenseth 2011), although A. mellifera is often a managed species. The B. dorsalis avocado pest was more abundant than T. leucotreta, presumably owing to the presence of other suitable host plants, e.g. mangos, which were also in their fruiting season during the time of the field data collection. The relationship between average temperature and morning relative humidity was negative to the abundance of A. mellifera since these pollinators pre- fer to start most of their activity at lower temperatures (12°C to 13°C) and because moderate morning rela- tive humidity provides warm and cooler conditions that also increase their activity (Nikolova et al. 2016). On the other hand, wind speed showed a positive relationship with the abundance of A. mellifera in this present study, but Hennessy et al. (2020) have reported that an increase in wind speed increased the resistance of the flight of bees, thereby causing fewer flowers to be visited. The negative relationship of abundance of A. mellifera with the aspect and hill- shade, which are both derivatives of elevation, can be explained by the species-area relationship, whereby an increase in elevation may also affect the air temperature, which tends to be cooler than the minimum temperature for A. mellifera activity (Lefebvre et al. 2018). On the other hand, precipitation showed a positive relationship with the abundance of A. mellifera, although high precipitation may cause mechanical difficulty in the flight of A. mellifera, thereby affecting the visitation of these pollinators – hence lower count observations (Lawson and Rands 2019). Surprisingly, no environmental variable was significant to the abundance of A. mellifera suppo- sedly because of the near indistinctive differences in the abundance counts among the different observed farms. The positive relationship between the abundance of B. dorsalis and T. leucotreta and average tempera- ture and afternoon relative humidity can be explained by the importance of temperature and moisture in improving pest egg formation (Potting and van der Straten 2010; Rashmi et al. 2020). Interestingly in this study, the abundance of B. dorsalis is shown to be positively correlated with hillshade and aspect, which are derivatives of elevation. This is contrary to Odanga et al. (2018), who found that B. dorsalis populations GISCIENCE & REMOTE SENSING 17 are negatively correlated with an increase in eleva- tion. On the other hand, slope, and hillshade, which are derivatives of elevation, showed a negative rela- tionship with the abundance of T. leucotreta. Odanga et al. (2018) demonstrated the fact that these pests exhibited no distinguishing characteristics across dif- ferent altitudinal ranges owing to their wider toler- ance to temperature ranges, which are also affected by altitude. The negative relationship between the abun- dance of B. dorsalis and T. leucotreta and precipita- tion is presumably caused by the inhibition of survival of the larva-pupal stage of the pest, which is inhibited when soil moisture is beyond the field capacity, while T. leucotreta is more pre- sent in warmer humid areas (Montoya, Flores, and Toledo 2008; Potting and van der Straten 2010). Wind speed had a negative relationship with the abundance of B. dorsalis in the present study, in that an increase in the speed of wind, especially in the opposing direction, can cause flight resistance of the pest, thus affecting their flight to the tar- geted area (Susanto et al. 2022). On the contrary, Verghese et al. (2006) found that wind speed had a positive relationship with the abundance of B. dorsalis, as the wind can also be a medium of dispersing the metathyl food lure for B. dorsalis, hence attracting them, and subsequently increas- ing the abundance of B. dorsalis catches. An increase in solar radiation causes an increase in temperature, whereby extremely high tempera- tures negatively affect B. dorsalis (Shrestha, Thapa, and Gautam 2019). The relationship between the abundance of B. dorsalis and precipitation, aspect and hillshade was significant (p ≤ 0.05), supposedly because of the contribution of these variables to temperature variations. The positive relationship between wind speed and the abundance of T. leucotreta could have influenced more numbers of T. leucotreta to be trapped, since the wind may aid the swinging of the traps for an increased number of T. leucotreta captures (Moore 2019). Presumably, the significance (p ≤ 0.05) of the relationship of the abundance of T. leucotreta with solar radiation demonstrated in the results was caused by the direct temperature effect on the degree of moisture, i.e. relative humidity, thus directly affect- ing the abundance of T. leucotreta (Potting and van der Straten 2010). The relationships between the abundance of Syrphidae and Calliphoridae and the environmental variables observed in this study may be inconclu- sive. This is because their observed counts in the sampled farms did not exhibit population densities that varied significantly for proper interpretation. A longer sampling period could provide an oppor- tunity to sample increased species abundance (Mandela et al. 2018). This study summarized Hymenoptera excluding A. mellifera, Syrphidae and Calliphoridae into larger groups such as families to increase the sample size suitable for the spatial distribution modeling. This may result in broadening the ecological niches of species in the specific orders and families, and so fail to account for species-specific ecological niches. However, the findings can be useful in deducing the general behavior of the respective pollinators and flower visitors, in comparison with other pollinators, such as A. mellifera of the Apidae family. 5. Conclusions This study revealed the possibility of combining accu- rate remotely sensed variables of cropping patterns and environmental variables in investigating the spa- tial distribution and abundance of the avocado polli- nators, flower visitors, and pests (AUC > 0.70). Specifically, this study revealed that the studied polli- nators, flower visitors, and pests had a medium to high probability of occurrence in Kandara and some parts of Maragua. Gatanga had a varied probability of their occurrence, from low to high. This study also highlighted the dominance of A. mellifera avocado pollinators and B. dorsalis as avocado pests. Moreover, the co-occurrence analysis of the avocado pollinators, flower visitors and pests also demon- strated the potential regions of their high, medium, and low simultaneous occurrences. This is crucial in providing key information necessary for the decision- makers and farmers in the implementation of IPPM techniques at a landscape scale. Future studies could look into the spatial modeling of these insects, during off-peak and peak flowering and fruiting seasons, in regions of similar agroecological zone settings. This would help to capture the diversity of the avocado insects and compare the most influential variables in the abundance of these insects in the different sea- sons. Consequently, this would further aid in the 18 G. R. ADUVUKHA ET AL. development of recommender systems for intelligent implementation of sustainable development efforts, such as IPPM. Acknowledgments We thank Rose Sagwe and Toukem Nadia for their assistance in collecting the occurrence and abundance data of avocado pollinators, flower visitors, and pests. Much appreciation also goes to the avocado farmers in Murang’a County Kenya, for their cooperation and for providing us with the information needed for this project. Disclosure statement No potential conflict of interest was reported by the author(s). Funding This work received financial support from the German Federal Ministry for Economic Cooperation and Development (BMZ), commissioned and administered through the Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) Fund for International Agricultural Research (FIA), grant number 17.7860.4–001; the Norwegian Agency for Development Cooperation, the Section for Research, Innovation, and Higher Education, grant number RAF-3058 KEN-18/0005; the Swedish International Development Cooperation Agency (Sida); the Swiss Agency for Development and Cooperation (SDC); the Australian Centre for International Agricultural Research (ACIAR); the Norwegian Agency for Development Cooperation (Norad); the German Federal Ministry for Economic Cooperation and Development (BMZ); and the Government of the Republic of Kenya. The views expressed herein do not necessarily reflect the official opinion of the donors. 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Introduction 2. Material and methods 2.1. Study area 2.2 Methodology 2.2.1. Earth observation remote-sensing data description and processing 2.2.1.1. Remote sensing datasets and field data collection 2.2.1.1.1. Sentinel-1 radar data 2.2.1.1.2. Sentinel-2 2.2.1.1.3. Vegetation indices 2.2.1.1.4. Phenological variable 2.2.1.1.5. Cropping pattern and non-croplands field data collection 2.2.1.2 Cropping pattern and non-croplands classification 2.2.2. Pollinators, flower visitors, and pests field data collection 2.2.2.1. NDVI field characterization for sampling avocado farms 2.2.2.2. Sampling protocol for farms 2.2.2.3. Avocado pollinator, flower visitor, and pest occurrence and abundance data 2.2.3. Predictor variables 2.2.3.1. Environmental predictor variables and preprocessing 2.2.3.2. Predictor variable selection 2.2.4. Species distribution modelling 2.2.4.1. Model settings 2.2.4.2. Model performance assessment 2.2.4.3. Co-occurrence spatial distribution of avocado pollinators, flower visitors, and pests 2.2.5. Species abundance modelling 3. Results 3.1. Cropping pattern and non-croplands 3.2. Species distribution modelling 3.2.1. Maximum entropy (MaxEnt) model performance 3.2.2. Predictor variable contribution 3.2.3. Spatial distribution probability of avocado pollinators, flower visitors, and pests 3.2.4. Co-occurrence spatial distribution probability of avocado pollinators, flower visitors, and pests 3.3. Species abundance modelling 3.3.1. Avocado pollinator, flower visitor, and pest abundance 3.3.2. Generalized linear model 4. Discussion 4.1. Species distribution mode