ORCID Profile
0000-0003-4609-2660
Current Organisations
The University of Auckland
,
The New Zealand Institute for Plant & Food Research Limited
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Publications
Courtship behaviour and display-site sharing appears conditional on body size in a lekking bat
Publisher: Elsevier BV
Date: 02-2018
Females as mobile resources: communal roosts promote the adoption of lek breeding in a temperate bat
Publisher: Oxford University Press (OUP)
Date: 2015
Using agent-based models to predict pollen deposition in a dioecious crop
Publisher: Cold Spring Harbor Laboratory
Date: 08-2022
DOI: 10.1101/2022.07.28.501917
Abstract: Pollination involves complex interactions between plants and pollinators, and variation in plant or pollinator biology can lead to variability in pollination services that are difficult to predict. Models that effectively predict pollination services could enhance the ability to conserve plant-pollinator mutualisms in natural systems and increase crop yields in managed systems. However, while most pollination models have focused either on effects of plant or pollination biology, few models have integrated plant-pollinator interactions. Moreover, crop management causes variation in plant-pollinator interactions and pollination services, but management is rarely considered in pollination models. Here we used extensive datasets for kiwifruit ( Actinidia chinensis var. deliciosa ) to develop an agent-based model to track insect-provided pollination services with variation in crop cultivars, pollinator traits, and orchard layouts. This allowed us to predict pollination outcomes in a dioecious crop under a range of management scenarios. Our sensitivity analysis indicated that flower density and the proportion of female flowers are the most important factors in successful pollination, both of which growers control via cultivar selection and cultural management practices. Our analysis also indicated that economically viable pollination services and crop yields are attained with ∼60% female flowers and a peak foraging activity of 6 to 8 bees per 1,000 open flowers with diminishing returns for additional pollinators. The quality of pollination service varied across simulated orchard layouts, highlighting the potential use of this model as a framework to screen novel orchard configurations. More broadly, linking complex plant and pollinator interactions in pollination models can help identify factors that may improve crop yields and provide a framework for identifying factors important to pollination in natural ecosystems. - We develop a model using extensive empirical datasets to predict pollen deposition based on the interactions between flowers and pollinators in a dioecious crop system - We conducted a thorough sensitivity analysis, and analysis of the effect of stochastic variance between model runs, which can be used to inform future design of stochastic agent-based models - Our model effectively predicted the outcomes of varying management regimes of orchard layouts and pollinator introductions on pollination in a dioecious crop - Our model can be extended for other functionally dioecious crops or plant communities where managers want to understand how their decisions impact pollination
Possible mechanisms of pollination failure in hybrid carrot seed and implications for industry in a changing climate
Publisher: Public Library of Science (PLoS)
Date: 30-06-2017
Stigmatic pollen delivery by flies and bees: Methods comparing multiple species within a pollinator community
Publisher: Elsevier BV
Date: 03-2017
Hairiness: the missing link between pollinators and pollination
Publisher: PeerJ
Date: 21-12-2016
DOI: 10.7717/PEERJ.2779
Abstract: Functional traits are the primary biotic component driving organism influence on ecosystem functions in consequence, traits are widely used in ecological research. However, most animal trait-based studies use easy-to-measure characteristics of species that are at best only weakly associated with functions. Animal-mediated pollination is a key ecosystem function and is likely to be influenced by pollinator traits, but to date no one has identified functional traits that are simple to measure and have good predictive power. Here, we show that a simple, easy to measure trait (hairiness) can predict pollinator effectiveness with high accuracy. We used a novel image analysis method to calculate entropy values for insect body surfaces as a measure of hairiness. We evaluated the power of our method for predicting pollinator effectiveness by regressing pollinator hairiness (entropy) against single visit pollen deposition (SVD) and pollen loads on insects. We used linear models and AIC C model selection to determine which body regions were the best predictors of SVD and pollen load. We found that hairiness can be used as a robust proxy of SVD. The best models for predicting SVD for the flower species Brassica rapa and Actinidia deliciosa were hairiness on the face and thorax as predictors ( R 2 = 0.98 and 0.91 respectively). The best model for predicting pollen load for B . rapa was hairiness on the face ( R 2 = 0.81). We suggest that the match between pollinator body region hairiness and plant reproductive structure morphology is a powerful predictor of pollinator effectiveness. We show that pollinator hairiness is strongly linked to pollination—an important ecosystem function, and provide a rigorous and time-efficient method for measuring hairiness. Identifying and accurately measuring key traits that drive ecosystem processes is critical as global change increasingly alters ecological communities, and subsequently, ecosystem functions worldwide.
Pollination of macadamia: Review and opportunities for improving yields
Publisher: Elsevier BV
Date: 12-2015
Related Organisations
The New Zealand Institute For Plant & Food Research Limited
Location: New Zealand
Related Funding Activities
No related grants have been discovered for David Pattemore.