Investigating Biotic Interactions in Deep Time

Challenging the widespread perspective that long-term diversity patterns are shaped primarily by climate is not possible without using fossil record data to understand role of biotic interactions.Important recent development and application models utilize both living extinct species have enabled analyses move beyond simply excluding potential abiotic drivers explicitly modeling for first time.Analyses paleontological show interactions shape temporal trajectories rates origination extinction numerous taxa.Extinction keystone has disproportionate impacts on among surviving species.Recovery from events can be sped up or slowed down species.Historically, humans (Homo sapiens) acted as large, generalist predators, disrupting interaction networks non-human species. Recent renewed interest in how evolution life challenging widely held assumption changes primary biodiversity change. New approaches go traditional richness co-occurrence studies model modern biodiversity. Important developments three areas research include analysis (i) macroevolutionary rates, (ii) recovery events, (iii) affected We present multiple lines evidence an important measurable shaping communities lineages long timescales. Biotic occur when organisms same community directly indirectly influence one another. within species, positive negative, cover a wide range including predation, commensalism, mutualism, resource competition, parasitism [1.Pringle E.G. Orienting compass: availability major driver context dependence.PLoS Biol. 2016; 14e2000891Crossref PubMed Scopus (4) Google Scholar]. play structuring (e.g., [2.Rabosky D.L. Hurlbert A.H. Species at continental scales dominated ecological limits.Am. Nat. 2015; 185: 572-583Crossref (151) Scholar]). Understanding their importance past therefore shed light ancient patterns. 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Richness There Equilibrial Diversity Ecosystem Energy Zero-Sum Game comprehensive occurrence-based sets extant (Table [30.Faurby S. Svenning J.-C. species-level phylogeny all quaternary novel heuristic-hierarchical Bayesian approach.Mol. Phylogenet. 84: 14-26Crossref (84) begun close gap enabling testing hypotheses relating 1). Models processes increased sophistication allow explicit inclusion [31.Drury J.P. al.An assessment tools analyzing interplay phenotypic evolution.Syst. 67: 413-427Crossref 32.Manceau al.A unifying comparative framework traits coevolving interacting lineages.Syst. 66: 551-568PubMed 33.Silvestro al.Bayesian estimation data.Evol. Res. 41-59Google concurrent [34.Tóth A.B. al.Reorganization after end-pleistocene megafaunal 365: 1305-1308Crossref (19) Scholar]; 1).Table 1Approaches Recently Used Quantify Model Effects Using Occurrence Trait Data (Some Are Novel, While Others Extensions Previously Applied Methods)aRelevant methods papers included ‘Example applications’, even contain method propose. Where there application, focal taxa parentheses. Papers test (rather inferring eliminating factors) bolded starred.MethodApproachHow it detect evidenceAdvantagesLimitationsExample applicationsBirth–death process occurrencesA birth–death-sampling (BDS) describes tree occurrences, parameterized character origination, extinction, sampling. Markov Chain Monte Carlo algorithms estimate probability density phylogenies data.Interclade intraclade contribution. If two leads probable phylogenies, then inferred important.The contributing modeled, eliminates ambiguity inherent resulting pattern process) common approaches, generated different processes.Models often highly complex combination paucity difficult fit reliably. behavior yet characterized breadth scenarios biases. Some BDS assume complete does inherently consider whether specific could reasonably expected another.[46.Silvestro North American canids.Proc. 8684-8689Crossref (93) Scholar]* (canids), [24.Pires Scholar] (carnivor