Most mechanistic predator-prey modelling has involved either parameterization from process rate data or inverse modelling. Here, we take a median road: we aim at identifying the potential benefits of combining datasets, when both population growth and predation processes are viewed as stochastic. We fit a discrete-time, stochastic predator-prey model of the Leslie type to simulated time series of densities and kill rate data. Our model has both environmental stochasticity in the growth rates and interaction stochasticity, i.e., a stochastic functional response. We examine what the kill rate data brings to the quality of the estimates, and whether estimation is possible (for various time series lengths) solely with time series of population counts or biomass data. Both Bayesian and frequentist estimation are performed, providing multiple ways to check model identifiability. The Fisher Information Matrix suggests that models with and without kill rate data are all identifiable, although correlations remain between parameters that belong to the same functional form. However, our results show that if the attractor is a fixed point in the absence of stochasticity, identifying parameters in practice requires kill rate data as a complement to the time series of population densities, due to the relatively flat likelihood. Only noisy limit cycle attractors can be identified directly from population count data (as in inverse modelling), although even in this case, adding kill rate data - including in small amounts - can make the estimates much more precise. Overall, we show that under process stochasticity in interaction rates, interaction data might be essential to obtain identifiable dynamical models for multiple species. These results may extend to other biotic interactions than predation, for which similar models combining interaction rates and population counts could be developed.
翻译:多数机械捕食者- 捕食者- 捕食者- 捕食者- 捕食者- 捕食者- 捕食者- 捕食者- 捕食者- 捕食者- 捕食者- 模拟时间序列模拟密度和死亡率数据。 我们的模型在生长率和交互相近性( 即: 随机性功能反应) 中, 都有环境随机性。 我们用一个中位路 : 我们的目标是确定合并数据集的潜在好处, 当人口增长和预发性过程被视为随机时, 我们的目标是找出合并数据集( 不同的时间序列长度), 并找出( ) 仅与人口数量数或生物量数据的时间序列相匹配。 我们用多种方法来检查模型的可识别性。 渔业信息矩阵显示, 使用和不使用杀人率数据的模型的模型都是可以识别的, 但属于同一功能形式的参数之间仍有关联性。 然而, 我们的结果显示, 如果 捕捉者( ) 在没有精确的相互作用数量中, 只能使用固定点( ) 快速数据周期数据序列中, 将数据序列中, 也只能使用精确的数据序列中 。