Carbon Stocks in Aboveground and Belowground Biomass of Sub-Humid Tropical Forest in Southwestern Nigeria

Cephalobaenidae constitutes one of the main pentastomid groups infecting the respiratory tract of snakes. Six specimens of Liophis lineatus, a colubrid, were collected by active capture and pitfalls installed on the banks of the Batateiras river, close to its source, in the ‘APA - Área de Proteção Ambiental’ (a protected environmental area of the ‘IBAMA - Brazilian Institute for the Environment and Natural Resources’), and in a remnant of the humid tropical forest ‘FLONA - Floresta Nacional do Araripe’, both in the municipality of Crato, state of Ceará, Northeast Brazil. Out of the six specimens of L. lineatus examined, only one had its lung infected by the pentastomid Cephalobaena tetrapoda. This represents the first record of C. tetrapoda as a parasite of a snake in Northeast Brazil, as well as the first record of a colubrid, L. lineatus, as a new host for the pentastomid in Brazil.

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Spatial and temporal variation of carbon stocks in a lowland tropical forest in West Africa

Forest Ecology and Management ◽

10.1016/j.foreco.2012.09.045 ◽

2013 ◽

Vol 289 ◽

pp. 10-17 ◽

Cited By ~ 17

Author(s):

Jeremy A. Lindsell ◽

Erik Klop

Keyword(s):

West Africa ◽

Tropical Forest ◽

Temporal Variation ◽

Carbon Stocks ◽

Spatial And Temporal Variation ◽

Lowland Tropical Forest

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Closing a gap in tropical forest biomass estimation: taking crown mass variation into account in pantropical allometries

Biogeosciences ◽

10.5194/bg-13-1571-2016 ◽

2016 ◽

Vol 13 (5) ◽

pp. 1571-1585 ◽

Cited By ~ 36

Author(s):

Pierre Ploton ◽

Nicolas Barbier ◽

Stéphane Takoudjou Momo ◽

Maxime Réjou-Méchain ◽

Faustin Boyemba Bosela ◽

...

Keyword(s):

Tropical Forest ◽

Reference Model ◽

Original Data ◽

Carbon Stocks ◽

Forest Carbon ◽

Biomass Estimation ◽

Systematic Bias ◽

Data Set ◽

Allometric Models ◽

Large Trees

Abstract. Accurately monitoring tropical forest carbon stocks is a challenge that remains outstanding. Allometric models that consider tree diameter, height and wood density as predictors are currently used in most tropical forest carbon studies. In particular, a pantropical biomass model has been widely used for approximately a decade, and its most recent version will certainly constitute a reference model in the coming years. However, this reference model shows a systematic bias towards the largest trees. Because large trees are key drivers of forest carbon stocks and dynamics, understanding the origin and the consequences of this bias is of utmost concern. In this study, we compiled a unique tree mass data set of 673 trees destructively sampled in five tropical countries (101 trees > 100 cm in diameter) and an original data set of 130 forest plots (1 ha) from central Africa to quantify the prediction error of biomass allometric models at the individual and plot levels when explicitly taking crown mass variations into account or not doing so. We first showed that the proportion of crown to total tree aboveground biomass is highly variable among trees, ranging from 3 to 88 %. This proportion was constant on average for trees < 10 Mg (mean of 34 %) but, above this threshold, increased sharply with tree mass and exceeded 50 % on average for trees ≥  45 Mg. This increase coincided with a progressive deviation between the pantropical biomass model estimations and actual tree mass. Taking a crown mass proxy into account in a newly developed model consistently removed the bias observed for large trees (> 1 Mg) and reduced the range of plot-level error (in %) from [−23; 16] to [0; 10]. The disproportionally higher allocation of large trees to crown mass may thus explain the bias observed recently in the reference pantropical model. This bias leads to far-from-negligible, but often overlooked, systematic errors at the plot level and may be easily corrected by taking a crown mass proxy for the largest trees in a stand into account, thus suggesting that the accuracy of forest carbon estimates can be significantly improved at a minimal cost.

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