背景:通过将重点从物种丰富度转移到基于功能性状的方法,可以增强对生物多样性和生态系统服务之间联系的理解,当与比较系统发育方法配对时,可以提供更深入的见解。我们调查了由膜翅目昆虫在新南威尔士州被“自然”或“受干扰”景观包围的果园中参观苹果花的授粉服务的功能生态学和系统发育多样性,澳大利亚。我们评估了形态和行为特征(毛羽,身体尺寸,glossa长度,花粉负荷纯度,和松散花粉的概率)表现出非随机系统发育模式。然后,探索蜜蜂是否,这个系统中的主要传粉者,填充的唯一或重叠功能实体(FE)。对于每个景观,我们计算了系统发育多样性,并使用FEs来评估功能丰富度,均匀度,和转移。
结果:使用基于超保守元件(UCE;距1,969个基因座的1,382,620bp)的系统基因组矩阵来推断48个膜翅目形态物种的完全分辨且支持良好的最大似然系统发育。景观类别之间的物种丰富度没有显着差异。自然位点的传粉者群落具有更高的系统发育复杂性(X=2.37)和功能差异(x炭黑=0.74±0.02s.e.),而受干扰的位点(X=1.65和x炭黑=0.6±0.01s.e.)。毛羽表现出显著的系统发育聚类(K=0.94),而身体大小,glossa长度,松散的花粉表现出较弱的非随机系统发育模式(K在0.3-0.5之间)。花粉负载纯度与系统发育无关。17种蜜蜂形态物种的组合包括9种FEs:8种FEs由本地蜜蜂组成,其中3种包含所有本地蜜蜂分类群的65%。引入的蜜蜂(Apismellifera)占据了独特的FE,可能是由于其不同的进化史。两种景观类型都支持六个FEs,每个FEs有三个重叠:两个本地蜜蜂FEs和蜜蜂FE。
结论:蜜蜂毛羽是唯一表现出可证明的系统发育信号的功能性状。尽管物种丰富度不同,以及果园景观类型之间的功能和系统发育多样性,两者都保持相等的蜜蜂FE数。虽然没有本地蜜蜂分类单元类似于蜜蜂FE,四个本地蜜蜂FEs与蜜蜂具有相同的毛羽水平。对澳大利亚蜜蜂种群的健康威胁可能会破坏苹果的授粉服务,和其他依赖授粉的粮食作物,鉴于所研究的传粉者组合中的功能冗余水平较低。
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.