Origin and evolution of the genus Piper in Peninsular India.

The evolution of Peninsular Indian biodiversity has been a fascinating topic of research due to historical connections of this region to the ancient Gondwanaland. We investigated the phylogeny and historical biogeography of nearly all extant species of the genus Piper reported from the region to assess the biogeographical origins and test mechanisms of lineage diversification (dispersal, vicariance and in situ radiation) of this highly diverse genus of angiosperms commonly found in the understory of evergreen forests. The phylogeny of 21 species of Piper reported from Peninsular India was reconstructed for the first time, which included three new putative species from the Western Ghats. We used BEAST for the divergence time estimations (using three constraints), and ancestral range estimations were performed with the dated phylogenetic tree using BIOGEOBEARS. Divergence dating analysis revealed that the genus Piper originated during lower Cretaceous around 110 Ma [95% highest posterior density (HPD): 116-105 Ma] and colonized Peninsular India five times independently, from Southeast Asia starting from the Oligocene. The two major dispersals into India occurred during the periods of 27.3 Ma (95% HPD: 35.8-19.9.) and 15.5 Ma (95% HPD: 24.9-7.11). This was followed by rapid radiations in some lineages with subsequent back dispersals to Southeast Asia. Our study indicates that dispersals from Southeast Asia led to the arrival of Piper to Indian subcontinent following the Indo-Eurasian collision. Members of Piper have colonized and diversified within the climatically stable habitats of Peninsular India. Furthermore, the present study provides evidence for the Miocene overland dispersal of Piper species to Africa from South Asia.

Ability of matrix models to explain the past and predict the future of plant populations.

Uncertainty associated with ecological forecasts has long been recognized, but forecast accuracy is rarely quantified. We evaluated how well data on 82 populations of 20 species of plants spanning 3 continents explained and predicted plant population dynamics. We parameterized stage-based matrix models with demographic data from individually marked plants and determined how well these models forecast population sizes observed at least 5 years into the future. Simple demographic models forecasted population dynamics poorly; only 40% of observed population sizes fell within our forecasts' 95% confidence limits. However, these models explained population dynamics during the years in which data were collected; observed changes in population size during the data-collection period were strongly positively correlated with population growth rate. Thus, these models are at least a sound way to quantify population status. Poor forecasts were not associated with the number of individual plants or years of data. We tested whether vital rates were density dependent and found both positive and negative density dependence. However, density dependence was not associated with forecast error. Forecast error was significantly associated with environmental differences between the data collection and forecast periods. To forecast population fates, more detailed models, such as those that project how environments are likely to change and how these changes will affect population dynamics, may be needed. Such detailed models are not always feasible. Thus, it may be wiser to make risk-averse decisions than to expect precise forecasts from models.