Browsing by Subject "Capture-Mark-Recapture"

Species factories are defined as times and places in the fossil record where and when an exceptionally large number of new species occurs. While several tailored solutions for the mammalian record have been proposed, how to identify species factories computationally in a standardized way is still an open question. To quantify what is exceptional, we first need to quantify what is regular. One of the main challenges in this identification process is to account for sampling unevenness, which depends on several methodological decisions, including the scale of the analysis (aggrega- tion radius). In this thesis we used Capture-Mark-Recapture methods (CMR) with spatial aggregation guided by network modelling, to estimate the sampling probabilities for the species in the NOW database of mammalian fossil occurrences. Since the mammalian record is sparse and most localities include only a few species, we coupled CMR with tailored spatial aggregation approaches to estimate the sampling prob- abilities. We then used these sampling probabilities to quantify background speciation rates and assess what rates are abnormal. We represented aggregated fossil data as a bipartite network and used community detection to evaluate how the choice of an aggre- gation radius impacts the modular structure. After aggregating the data according to the radius chosen using networks analysis, we es- timated sampling probabilities using CMR. These probabilities allow the adjustment for sampling unevenness so that the difference in findings can be compared across locations and cannot be due to differences in sampling. We identified as species factories the locations with origination rate in the highest 5% after adjustment per time unit. Once the species factories had been identified, we looked for paleoecological patterns in these places that may be lacking elsewhere, finding that species factories present a lower number of findings and of different species among findings, but a higher ratio between the amount of different species and of total findings than the rest of the locations. This would indicate that, even if species factories might accommodate fewer species, they present a higher diversity. To make sure these results were not only due to chance, we performed the same analysis on 100 randomized experiments obtained using a modified version of the Curveball Algo- rithm and compared the values obtained from the original dataset and the ones obtained from the randomized ones. This comparison showed us that species factories tend to have more extreme values than the ones obtained through randomization, which would indicate that species factories present specific paleoecological patterns that are not present in other locations.