Search for: All records

Understanding the distribution and extent of suitable habitats is critical for the conservation of endangered and endemic taxa. Such knowledge is limited for many Central African species, including the rare and globally threatened Grey-necked Picathartes Picathartes oreas, one of only two species in the family Picathartidae endemic to the forests of Central Africa. Despite growing concerns about land-use change resulting in fragmentation and loss of forest cover in the region, neither the extent of suitable habitat nor the potential species’ distribution is well known. We combine 339 (new and historical) occurrence records of Grey-necked Picathartes with environmental variables to model the potential global distribution. We used a Maximum Entropy modelling approach that accounted for sampling bias. Our model suggests that Grey-necked Picathartes distribution is strongly associated with steeper slopes and high levels of forest cover, while bioclimatic, vegetation health, and habitat condition variables were all excluded from the final model. We predicted 17,327 km2 of suitable habitat for the species, of which only 2,490 km2 (14.4%) are within protected areas where conservation designations are strictly enforced. These findings show a smaller global distribution of predicted suitable habitat for the Grey-necked Picathartes than previously thought. This work provides evidence to inform a revision of the International Union for Conservation of Nature (IUCN) Red List status, and may warrant upgrading the status of the species from “Near Threatened” to “Vulnerable”. 

Mapping tree species richness across the tropics is of great interest for effective conservation and biodiversity management. In this study, we evaluated the potential of full‐waveform lidar data for mapping tree species richness across the tropics by relating measurements of vertical canopy structure, as a proxy for the occupation of vertical niche space, to tree species richness.

Tropics.

Present.

Trees.

First, we evaluated the characteristics of vertical canopy structure across 15 study sites using (simulated) large‐footprint full‐waveform lidar data (22 m diameter) and related these findings to in‐situ tree species information. Then, we developed structure–richness models at the local (within 25–50 ha plots), regional (biogeographical regions) and pan‐tropical scale at three spatial resolutions (1.0, 0.25 and 0.0625 ha) using Poisson regression.

The results showed a weak structure–richness relationship at the local scale. At the regional scale (within a biogeographical region) a stronger relationship between canopy structure and tree species richness across different tropical forest types was found, for example across Central Africa and in South America [R²ranging from .44–.56, root mean squared difference as a percentage of the mean (RMSD%) ranging between 23–61%]. Modelling the relationship pan‐tropically, across four continents, 39% of the variation in tree species richness could be explained with canopy structure alone (R² = .39 and RMSD% = 43%, 0.25‐ha resolution).

Our results may serve as a basis for the future development of a set of structure–richness models to map high resolution tree species richness using vertical canopy structure information from the Global Ecosystem Dynamics Investigation (GEDI). The value of this effort would be enhanced by access to a larger set of field reference data for all tropical regions. Future research could also support the use of GEDI data in frameworks using environmental and spectral information for modelling tree species richness across the tropics.

 

Fine roots constitute a significant component of the net primary productivity (NPP) of forest ecosystems but are much less studied than aboveground NPP. Comparisons across sites and regions are also hampered by inconsistent methodologies, especially in tropical areas. Here, we present a novel dataset of fine root biomass, productivity, residence time, and allocation in tropical old‐growth rainforest sites worldwide, measured using consistent methods, and examine how these variables are related to consistently determined soil and climatic characteristics. Our pantropical dataset spans intensive monitoring plots in lowland (wet, semi‐deciduous, and deciduous) and montane tropical forests in South America, Africa, and Southeast Asia (n = 47). Large spatial variation in fine root dynamics was observed across montane and lowland forest types. In lowland forests, we found a strong positive linear relationship between fine root productivity and sand content, this relationship was even stronger when we considered the fractional allocation of total NPP to fine roots, demonstrating that understanding allocation adds explanatory power to understanding fine root productivity and total NPP. Fine root residence time was a function of multiple factors: soil sand content, soil pH, and maximum water deficit, with longest residence times in acidic, sandy, and water‐stressed soils. In tropical montane forests, on the other hand, a different set of relationships prevailed, highlighting the very different nature of montane and lowland forest biomes. Root productivity was a strong positive linear function of mean annual temperature, root residence time was a strong positive function of soil nitrogen content in montane forests, and lastly decreasing soil P content increased allocation of productivity to fine roots. In contrast to the lowlands, environmental conditions were a better predictor for fine root productivity than for fractional allocation of total NPP to fine roots, suggesting that root productivity is a particularly strong driver of NPP allocation in tropical mountain regions.

 

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. Here we 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.