## 5 Conclusions  
Results generated in this work support the fact that delineating pest management zones (MZ) based on nested field partitions of environmental features represents an effective means to overcome many of the limitations associated with zoning methods previously described in eSSPM, such as presence zones insensitive to differentiated levels of infestation and nested presences/absence. In general, more than one MC algorithm is necessary to delineate accurate MZ in eSSPM. Hierarchical MC algorithms tended to generate better outputs during binary and absence-only partitioning essays, whereas more sophisticated approaches (i.e., model-based, machine learning) tended to outperform the rest during presence-only complementary partitions. The validation of partitioning essays based on biologically meaningful CVI (i.e., BHI, BSI) are of great utility during the selection of best-performing algorithms and optimum numbers of MZ to be delineated. Still, due to inaccuracies observed in specific circumstances, we recommend complementing the evaluation of field partitioning essays with \(\text{S}^{2}_{\text{T}}\). We also conclude that the visual aids used during the classification of MZ (e.g., dendograms, bubble charts, maps) are important resources that facilitate relevant agronomic decisions such as what zones should be considered individual subfield units and what zones should be fused together. Similarly, SDM background tests displayed as ecological networks represent an efficient and flexible way to corroborate the environmental uniqueness of MZ and corroborate cases of fusion/separation of zones. A novel workflow to the delineation of MZ within the context of eSSPM was described. It is based on the following sequential steps: (a) selection and mapping of zoning factors, (b) binary partition of the managed crop field, (c) complementary partitions (i.e., presence-only, absence-only) of the managed crop field, (d) classification of zones included in preliminary partition models, and (e) ecological validation of corrected (i.e., final) zones. Conceptualization, L.J.M.-V.; writing--original draft preparation, L.J.M.-V. and A.L.-N.; writing--review and editing, L.J.M.-V., A.L.-N., R.L.-C. and S.C.-E.; visualization, L.J.M.-V., A.L.-N., R.L.-C., S.C.-E.; supervision, A.L.-N.; funding acquisition, A.L.-N. and [PERSON]. All authors have read and agreed to the published version of the manuscript. This research received funding from Fideicomiso o Fondo institutional de Fomento Regional para el Desarrollo Cientifico, Tecnologico y de Innovacion (FORDECyT)-Consejo Nacional de Ciencia y Tecnologia (CONACyT) project \"Generacion de estregias cientifico-tecnologicas con un enfequa multidisciplinario e interinstitucional para afrontar la amenza que representan los complejos ambrosiales en los sectores agricola y forestal de Mexico\" [292399, 2018]. Not applicable. We thank [PERSON] for facilitating unlimited access to his commercial orchard during the development of this research, as well as to the [PERSON] family for hosting our work team indefinite time during the development of this investigation. We also thank [PERSON] (R.I.P.) and [PERSON] for their unvaluable help during fieldwork and laboratory analyses. [PERSON] received scholarship support from the Mexican Consejo Nacional de Ciencia y Tecnologia (CONACyT) to develop this project as part of his graduate studies. Four types of MC algorithms were used during the development of this work: hierarchical, partitioning, machine learning (based), and model based. Hierarchical clustering algorithms are based on agglomerative methods that yield a dendrogram which can be cut at a chosen height to produce the desired number of clusters [50]. Each observation is initially placed in its own cluster and the clusters are successively joined together in order of their \"closeness\". The closeness of any two clusters is determined by a dissimilarity matrix and can be based on a variety of agglomeration methods, which in the case on this work were:  
1.