为一种全球水果作物服务的本地和引进蜜蜂授粉者之间的功能性状不匹配。

IF 2.3 Q2 ECOLOGY
Olivia M Bernauer, Michael G Branstetter, James M Cook, Simon M Tierney
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引用次数: 0

摘要

背景:通过将重点从物种丰富度转移到基于功能特征的方法,可以加深对生物多样性与生态系统服务之间联系的理解。我们研究了澳大利亚新南威尔士州被 "自然 "或 "干扰 "景观包围的果园中膜翅目昆虫为苹果花授粉的功能生态学和系统发育多样性。我们评估了形态和行为特征(毛发、体型、光泽长度、花粉负载纯度和散粉概率)是否表现出非随机的系统发育模式。然后,探讨了蜜蜂作为该系统中的主要传粉者,其功能实体(FE)是独特的还是重叠的。对于每个景观,我们都计算了系统发育多样性,并使用功能实体来评估功能的丰富度、均匀度和分流度:基于超保守元素(UCEs;1969 个位点 1,382,620 bp)的系统发生矩阵被用来推断 48 个膜翅目昆虫形态种的完全解析和支持的最大似然系统发生。不同景观类别之间的物种丰富度没有明显差异。自然地点传粉昆虫群落的系统发生复杂性(X = 2.37)和功能分化(x̄ = 0.74 ± 0.02 s.e.)高于干扰地点(X = 1.65 和 x̄ = 0.6 ± 0.01 s.e.)。毛羽显示出明显的系统发育聚类(K = 0.94),而体型、舌长和松散花粉则显示出较弱的非随机系统发育模式(K 介于 0.3-0.5 之间)。花粉负载纯度与系统发育没有关联。由 17 个蜜蜂形态种组成的集合体包括 9 个 FE:8 个 FE 由本地蜜蜂组成,其中 3 个 FE 包含了所有本地蜜蜂类群的 65%。引进的蜜蜂(Apis mellifera)占据了一个独特的FE,这可能是由于其不同的进化历史造成的。两种地貌类型各支持六个FE,其中三个重叠:两个本地蜜蜂FE和蜜蜂FE:结论:蜜蜂的毛发是唯一表现出系统发育信号的功能性特征。尽管果园景观类型之间在物种丰富度、功能和系统发育多样性方面存在差异,但两者都保持了相同的蜜蜂FE数量。虽然没有一种本地蜜蜂类群与蜜蜂FE相似,但有四种本地蜜蜂FE与蜜蜂的毛羽水平相同。澳大利亚蜜蜂种群面临的健康威胁很可能会破坏为苹果和其他依赖授粉的粮食作物提供的授粉服务,因为所调查的授粉昆虫组合的功能冗余度较低。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Functional trait mismatch between native and introduced bee pollinators servicing a global fruit crop.

Functional trait mismatch between native and introduced bee pollinators servicing a global fruit crop.

Background: Understanding connections between biodiversity and ecosystem services can be enhanced by shifting focus from species richness to functional trait-based approaches, that when paired with comparative phylogenetic methods can provide even deeper insights. We investigated the functional ecology and phylogenetic diversity of pollination services provided by hymenopteran insects visiting apple flowers in orchards surrounded by either 'natural' or 'disturbed' landscapes in New South Wales, Australia. We assessed whether morphological and behavioural traits (hairiness, body size, glossa length, pollen load purity, and probability of loose pollen) exhibited non-random phylogenetic patterns. Then, explored whether bees, the primary pollinators in this system, filled unique or overlapping functional entities (FEs). For each landscape, we calculated phylogenetic diversity and used FEs to assess functional richness, evenness, and diversion.

Results: A phylogenomic matrix based on ultraconserved elements (UCEs; 1,382,620 bp from 1,969 loci) was used to infer a fully-resolved and well-supported maximum likelihood phylogeny for 48 hymenopteran morphospecies. There was no significant difference in species richness between landscape categories. Pollinator communities at natural sites had higher phylogenetic complexity (X = 2.37) and functional divergence (x̄ = 0.74 ± 0.02 s.e.) than disturbed sites (X = 1.65 and x̄ = 0.6 ± 0.01 s.e.). Hairiness showed significant phylogenetic clustering (K = 0.94), whereas body size, glossa length, and loose pollen showed weaker non-random phylogenetic patterns (K between 0.3-0.5). Pollen load purity showed no association with phylogeny. The assemblage of 17 bee morphospecies comprised nine FEs: eight FEs consisted of native bees with three containing 65% of all native bee taxa. The introduced honey bee (Apis mellifera) occupied a unique FE, likely due to its different evolutionary history. Both landscape types supported six FEs each with three overlapping: two native bee FEs and the honey bee FE.

Conclusions: Bee hairiness was the only functional trait to exhibit demonstrable phylogenetic signal. Despite differences in species richness, and functional and phylogenetic diversity between orchard landscape types, both maintained equal bee FE numbers. While no native bee taxon was analogous to the honey bee FE, four native bee FEs shared the same hairiness level as honey bees. Health threats to honey bee populations in Australia will likely disrupt pollination services to apple, and other pollination-dependent food crops, given the low level of functional redundancy within the investigated pollinator assemblages.

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