Data from: The effects of temporal resolution on species turnover and on testing metacommunity models

Patterns of low temporal turnover in species composition found within
long paleoecological time series contrast with the high turnover
predicted by dispersal-limited neutral metacommunity models and thus have been used to support non-neutral models. However, predictions assume temporal resolution on the scale of a season or year whereas
individual fossil assemblages are typically time-averaged to decadal
or centennial time scales. Here, we simulate the effects of time
averaging by building time-averaged assemblages from local
dispersal-limited non-averaged (living) assemblages and compare the
predicted species turnover with observed patterns in mollusk and
ostracod fossil records. Time averaging substantially reduces temporal
turnover such that neutral predictions converge with those of
trade-off and density-dependent models, and tends to decrease species
dominance and increase the proportion of rare species. Observed
turnover rates are comparable to an appropriately scaled neutral model: patterns of high community stability can be produced or
reinforced by time averaging alone. The community attributes of local
time-averaged assemblages approach those of the metacommunity.
Time-averaged assemblages are thus unlikely to capture attributes
arising from processes operating at small spatial scales, but should
do well at capturing the turnover and diversity parameters of
metacommunities, and thus will be a valuable basis for analyzing the
large-scale processes that determine metacommunity evolution.

长期古生态学时间序列中观测到的物种组成低时间周转模式，与扩散限制中性集合群落（metacommunity）模型预测的高周转形成鲜明对比，因此该模式常被用于支撑非中性模型。然而，此类模型的预测前提是时间分辨率达到季节或年际尺度，但单个化石组合通常经时间平均后，对应年代际至世纪尺度的时间跨度。本研究通过从扩散限制的本地非时间平均（活体）组合中构建时间平均组合，模拟时间平均效应，并将预测的物种周转模式与软体动物（mollusk）和介形类（ostracod）化石记录中的观测模式进行对比。时间平均可显著降低物种时间周转速率，使得中性模型的预测结果与权衡和密度依赖模型的预测结果趋于一致，同时往往会降低物种优势度并提升稀有种的占比。观测到的周转速率与经合理尺度调整的中性模型结果相当：仅通过时间平均效应，即可产生或强化群落高稳定性的模式。本地时间平均组合的群落属性趋近于集合群落的属性。因此，经时间平均的化石组合难以反映小空间尺度过程所产生的群落属性，但可较好地捕捉集合群落的周转与多样性参数，进而成为解析调控集合群落演化的大尺度过程的重要基础。