* Changes in phenology are an inevitable result of climate change, and will have wide-reaching impacts on species, ecosystems, human society and even feedback onto climate. Accurate understanding of phenology is important to adapt to and mitigate such changes. However, analysis of phenology globally has been constrained by lack of data, dependence on geographically limited, non-circular indicators and lack of power in statistical analyses. \ud * To address these challenges, especially for the study of tropical phenology, we developed a flexible and robust analytical approach - using Fourier analysis with confidence intervals - to objectively and quantitatively describe long-term observational phenology data even when data may be noisy. We then tested the power of this approach to detect regular cycles under different scenarios of data noise and length using both simulated and field data. \ud * We use Fourier analysis to quantify flowering phenology from newly available data for 856 individual plants of 70 species observed monthly since 1986 at Lopé National Park, Gabon. After applying a confidence test, we find that 59% of the individuals have regular flowering cycles, and 88% species flower annually. We find time series length to be a significant predictor of the likelihood of confidently detecting a regular cycle from the data. Using simulated data we find that cycle regularity has a greater impact on detecting phenology than event detectability. Power analysis of the Lopé field data shows that at least six years of data are needed for confident detection of the least noisy species, but this varies and is often greater than 20 years for the most noisy species. \ud * There are now a number of large phenology datasets from the tropics, from which insights into current regional and global changes may be gained, if flexible and quantitative analytical approaches are used. However consistent long-term data collection is costly and requires much effort. We provide support for the importance of such research and give suggestions as to how to avoid erroneous interpretation of shorter length datasets and maximize returns from long-term observational studies

Significant gaps remain in understanding the response of plant reproduction to environmental change. This is partly because measuring reproduction in long-lived plants requires direct observation over many years and such datasets have rarely been made publicly available. Here we introduce MASTREE+, a data set that collates reproductive time-series data from across the globe and makes these data freely available to the community. MASTREE+ includes 73,828 georeferenced observations of annual reproduction (e.g. seed and fruit counts) in perennial plant populations worldwide.These observations consist of 5971 population-level time-series from 974 species in 66 countries. The mean and median time-series length is 12.4 and 10 years respectively, and the data set includes 1122 series that extend over at least two decades (≥20 years of observations). For a subset of well-studied species, MASTREE+ includes extensive replication of time-series across geographical and climatic gradients. Herewe describe the open-access data set, available as a.csv file, and we introduce an associated web-based app for data exploration. MASTREE+ will provide the basis for improved understanding of the response of long-lived plant reproduction to environmental change. Additionally, MASTREE+ will enable investigation of the ecology and evolution of reproductive strategies in perennial plants, and the role of plant reproduction as a driver of ecosystem dynamics.

The predicted relationship between home-range size and group mass in primates developed by Clutton-Brock and Harvey (1977) has proved extremely robust in describing the use of space by most primate species. However, mandrills (Mandrillus sphinx) are now known to have an extreme group mass in the wild, far larger than that of the species used originally to generate that relationship, and so it was unknown whether this relationship would be robust for this species. We investigated the home-range size and use of a wild horde of ca. 700 mandrills in Lopé National Park, Gabon, using radiotelemetry. The total area the horde used over a 6-yr period [100% minimum convex polygon (MCP)] was 182 km 2, including 89 km2 of suitable forest habitat. Mandrills used gallery forests and isolated forest fragments with high botanical diversity far more intensively that the continuous forest and completely avoided savanna and marsh. Peeled polygons and fixed kernel contours revealed multiple centres of use, with the horde spending more than half its time in <10% of the total documented range, typical of a frugivore using a patchy environment. Home-range size and internal structure varied considerably between years, but total home range fitted the predicted relationship between group mass and home range size, despite being an outlier to the dataset. We discuss the conservation implications of the species' space requirements, in light of current pressures on land use in their range

Phenology is a key component of ecosystem function and is increasingly included in assessments of ecological change. We consider how existing, and emerging, tropical phenology monitoring programs can be made most effective by investigating major sources of noise in data collection at a long‐term study site. Researchers at Lopé NP, Gabon, have recorded monthly crown observations of leaf, flower and fruit phenology for 88 plant species since 1984. For a subset of these data, we first identified dominant regular phenological cycles, using Fourier analysis, and then tested the impact of observation uncertainty on cycle detectability, using expert knowledge and generalized linear mixed modeling (827 individual plants of 61 species). We show that experienced field observers can provide important information on major sources of noise in data collection and that observation length, phenophase visibility and duration are all positive predictors of cycle detectability. We find that when a phenological event lasts >4 wk, an additional 10 yr of data increases cycle detectability by 114 percent and that cycle detectability is 92 percent higher for the most visible events compared to the least. We also find that cycle detectability is four times as high for flowers compared to ripe fruits after 10 yr. To maximize returns in the short‐term, resources for long‐term monitoring of phenology should be targeted toward highly visible phenophases and events that last longer than the observation interval. In addition, programs that monitor flowering phenology are likely to accurately detect regular cycles more quickly than those monitoring fruits, thus providing a baseline for future assessments of change.