Reliable Biomass Estimates Research Articles

Allometric equations and carbon sequestration potential of mango (Mangifera indica) and avocado (Persea americana) in Kenya

Reliable biomass estimates are needed to quantify the climate benefits of fruit trees and include them in landscape restoration and international commitments such as the Nationally Determined Contributions under the Paris Agreements. Fruit trees are widespread in smallholder farming systems, where they provide income, nutrition benefits, and other products and ecosystem services with Mangifera indica (mango) and Persea americana (avocado) being the most popular in East Africa region. However, their contribution to climate change mitigation through carbon sequestration and reduction in greenhouse gas emission has not been explored because of a lack of allometric equations to quantify their biomass accurately. This study developed allometric equations from data collected on 51 mango trees and 40 avocado trees harvested in Makueni and Kiambu Counties in Kenya, respectively. Allometric equation using mean diameter of primary branches (µDPB) or diameter at breast height (DBH) as the predictor variable provided better predictions for mango and avocado, with a low mean absolute percentage error (MAPE) of 28.8 % and 18.4 %, respectively. From an inventory of 23 plots in Makueni (total area = 4.4 ha) and 36 plots in Kiambu (total area = 14.42 ha), aboveground biomass in mango trees was estimated to be 22.3 ± 6.3 Mg ha−1 and 20.6 ± 5.4 Mg ha−1, respectively. Much of the biomass was found in trees located in homesteads and orchards. Allometric equations developed in this study will support the quantification of carbon stocks for projects aimed at generating carbon credits by including fruit trees in the landscape.

Modelling aboveground biomass of a multistage managed forest through synergistic use of Landsat-OLI, ALOS-2 L-band SAR and GEDI metrics

To better understand forest carbon budgets and design forest-based climate change mitigation solutions, reliable biomass estimation is critical. Integrating multi-sensor remote sensing data can improve forest monitoring and evaluation. This study adopted a one-hectare plot size sampling design to improve the integration of GEDI footprints with in-situ, optical, and SAR data for the estimation of forest above-ground biomass (AGB). The study was carried out for a managed tropical forest in the Himalayan foothills of India. The space-borne GEDI retrieved the canopy height of the study area with an RMSE of 3.36 m and Adj. R2 of 0.70. Extrapolating GEDI footprint heights (RH95) with Landsat 8 indices using Random Forest (RF) yielded a spatial canopy height of the study area with an R2 of 0.97 and RMSE of 2.32 m. Using the GEDI canopy height, foliage density, and plant area index, the AGB at GEDI footprints level was estimated using RF, with an R2 of 0.91 and an RMSE of 20.10 Mg ha−1. The spatial AGB model that only considered ALOS-2 SAR variables had an R2 of 0.61 and an RMSE of 18.27 Mg ha−1, whereas the RF model that considered both SAR variables and canopy height had a superior R2 of 0.77 and a lower RMSE of 13.86 Mg ha−1. The addition of canopy height data reduced the RMSE of the AGB model by 4.41 Mg ha−1 and also predicted a higher range of AGB. The study demonstrates the effectiveness of combining GEDI data with other sensors (optical and SAR data) to provide precise AGB of multistage managed forests using a one-hectare plot size sampling design.

Comparison of artificial neural network with compatible biomass model for predicting aboveground biomass of individual tree

Forest biomass is an important index in forest development planning and forest resource monitoring. In order to provide a more efficient and low-biased method for estimating individual tree biomass, we introduced artificial neural network here. We used the data of aboveground biomass of 101 Larix olgensis trees harvested from the Dongzhelenghe Forest Farm in Heilongjiang Province to develop four aggregation model systems (AMS), based on different combination of the variables (diameter at breast height, tree height, crown width). The weighted functions were used to eliminate heteroscedasticity. Then, we trained artificial neural network (ANN) biomass model based on the optimal combination. The models were tested by the leave-one-out cross-validation method to compare the accuracy of the two biomass estimation methods. The results showed that biomass model based on only one variable, diameter at breast height, could accurately estimate the biomass of L. olgensis. Adding two indices, tree height and crown width, could improve the fitting performance of models, with AMS4 performing the best among the four addictive model systems. The biomass models developed by the two methods both could estimate biomass at tree level accurately, with the coefficient of determination (R2) of each component was higher than 0.87. Compared with the AMS4, R2 of leaf biomass model was about 0.05 higher, and that of other organs were also about 0.01 higher in artificial neural network model system. In addition, the root mean square error (RMSE) and other indicators were also significantly smaller. For example, the RMSE of tree stem and aboveground biomass were smaller by 2.135 kg and 3.908 kg, respectively. The model's validation statistics mean relative error (MRE) performed better. In general, ANN was a flexible and reliable biomass estimation method, which was worthy consideration when predicting tree component biomass or aboveground biomass.

Metabarcoding data allow for reliable biomass estimates in the most abundant animals on earth

Microscopic organisms are the dominant and most diverse organisms on Earth. Nematodes, as part of this microscopic diversity, are by far the most abundant animals and their diversity is equally high. Molecular metabarcoding is often applied to study the diversity of microorganisms, but has yet to become the standard to determine nematode communities. As such, the information metabarcoding provides, such as in terms of species coverage, taxonomic resolution and especially if sequence reads can be linked to the abundance or biomass of nematodes in a sample, has yet to be determined. Here, we applied metabarcoding using three primer sets located within ribosomal rRNA gene regions to target assembled mock-communities consisting of 18 different nematode species that we established in 9 different compositions. We determined abundances and biomass of all species added to examine if relative sequence abundance or biomass can be linked to relative sequence reads. We found that nematode communities are not equally represented by the three different primer sets and we found that relative read abundances almost perfectly correlated positively with relative species biomass for two of the primer sets. This strong biomass-read number correlation suggests that metabarcoding reads can reveal biomass information even amongst more complex nematode communities as present in the environment and possibly can be transferred to better study other groups of organisms. This biomass-read link is of particular importance for more reliably assessing nutrient flow through food-webs, as well as adjusting biogeochemical models through user-friendly and easily obtainable metabarcoding data.

An in situ study of production from diel oxygen modelling, oxygen exchange, and electron transport rate in the kelp Ecklonia radiata

Macroalgal forests provide the foundation for most shallow reef ecosystems in temperate environments; hence tools for accurately measuring primary productivity are integral for ecosystem management. This study compares estimates of production/potential production in an Ecklonia radiata kelp forest in Tasmania, Australia, using diel oxygen gross primary production (GPP) models, benthic exchange chambers, and electron transport rate in photosystem II measured using PAM fluorometry. Two approaches to modelling GPP show good fit with environmental dissolved oxygen (DO), with gross oxygen production of the kelp bed ranging between ~0-20 for one model and ~0-8 µmol O2 m-2 s-1 for the other, with total daily GPP (±SE) of 464 ± 28 and 347 ± 7 mmol O2 m-2, respectively. The oxygen production rate of E. radiata in benthic chambers ranged between 0 and 9.6 µmol O2 m-2 s-1, with total daily production as 204 ± 13 mmol O2 m-2, half that estimated from modelling DO. The peak value for maximum relative electron transport rate was 49 µmol e- m-2 s-1 at PAR of 208 µmol m-2 s-1. Oxygen evolution from benthic chambers and electron transport rates from PAM fluorometry were well correlated; however, the latter may overestimate oxygen production. Water column DO can measure GPP of the benthic communities; however, additional measurements/more sophisticated models may be necessary. Benthic exchange chambers and PAM fluorometry can potentially estimate the contribution of E. radiata to total daily production provided that the measurements can be calibrated with other methods to obtain actual productivity. Additionally, upscaling requires reliable biomass estimates.

Identifying small pelagic Mediterranean fish schools from acoustic and environmental data using optimized artificial neural networks

The Common Fisheries Policy of the European Union aims to exploit fish stocks at a level of Maximum Sustainable Yield by 2020 at the latest. At the Mediterranean level, the General Fisheries Commission for the Mediterranean (GFCM) has highlighted the importance of reversing the observed declining trend of fish stocks. In this complex context, it is important to obtain reliable biomass estimates to support scientifically sound advice for sustainable management of marine resources. This paper presents a machine learning methodology for the classification of pelagic species schools from acoustic and environmental data. In particular, the methodology was tuned for the recognition of anchovy, sardine and horse mackerel. These species have a central role in the fishing industry of Mediterranean countries and they are also of considerable importance in the trophic web because they occupy the so-called middle trophic level. The proposed methodology consists of a classifier based on an optimized two layer feed-forward neural network. Morphological, bathymetric, energetic and positional features, extracted from acoustic data, are used as input, together with other environmental data features. The classifier uses an optimal number of neurons in the hidden layer, and a feature selection strategy based on a genetic algorithm. Working on a dataset of 2565 fish schools, the proposed methodology permitted us to identify the these three fish species with an accuracy of around 95%.

Variation in wood basic density within and between tree species and site conditions of exclosures in Tigray, northern Ethiopia

A first database with wood basic density values for tree species growing in exclosures in Tigray was developed. The values from the database are likely to give reliable biomass estimates also if they are applied outside the currently studied exclosures. Wood basic density (wbd) is a principal measure for characterizing wood. The main objective of this study was to obtain wbd values for tree species growing in exclosures in the Tigray region, northern Ethiopia. Stem discs for wbd determination were collected from 305 trees among the 50 most abundant tree species located in six exclosures covering wide ranges of growing conditions. The main achievement of this study was the development of a first database with wbd values for tree species growing in the exclosures of Tigray. The mean wbd value of the 50 tree species included in this database was 0.707 g cm− 3 with a range between 0.400 and 1.154 g cm− 3. Since wide ranges of growing conditions and the most abundant tree species in the region were included, this database constitutes a solid basis for biomass and carbon estimation, and is valuable for aiding forest management decisions. The wbd values may also potentially be included in the Global Wood Density database since only a few of the 50 tree species are currently available there. For the tree species with the largest within-species wbd ranges, the ranges between exclosures are much smaller than the within-species ranges. This implies that it is likely to arrive at reliable biomass estimates also if existing wbd values are applied outside the currently studied exclosures. Still, it is recommended that future studies should test biomass models under different assumptions for determination of wbd to evaluate the effects on biomass estimation and that the wbd database should be enriched by including additional sites to capture larger ranges in growing conditions and more tree species.

Evaluation of three methods for biomass estimation in small invertebrates, using three large disparate parasite species as model organisms

Invertebrate biomass is considered one of the main factors driving processes in ecosystems. It can be measured directly, primarily by weighing individuals, but more often indirect estimators are used. We developed two indirect and non-destructive approaches to estimate biomass of small invertebrates in a simple manner. The first one was based on clay modelling and the second one was based on image analysis implemented with open-source software. Furthermore, we tested the accuracy of the widely used geometric approximation method (third method). We applied these three different methods to three morphologically disparate model species, an acanthocephalan worm, a crustacean and a flatworm. To validate our indirect estimations and to test their accuracy, we weighed specimens of the three species and calculated their tissue densities. Additionally, we propose an uncomplicated technique to estimate thickness of individuals under a microscope, a required measurement for two of the three indirect methods tested. The indirect methods proposed in this paper provided the best approximation to direct measurements. Despite its wide use, the geometric approximation method showed the lowest accuracy. The approaches developed herein are timely because the recently increasing number of studies requiring reliable biomass estimates for small invertebrates to explain crucial processes in ecosystems.

Individual tree biomass equations and growth models sensitive to climate variables for Larix spp. in China

Climate change has increased the need of information on amount of forest biomass. The biomass and carbon storage for larch (Larix spp.) in large geographic regions in China were failed to be accurately estimated from current biomass equations, because they were usually based on a few sample trees on local sites, generally incompatible to volume estimation, and not additive between components and total biomass. China needs reliable biomass estimation of the important species in the whole country. This study was based on the mensuration data of above- and belowground biomass from 600 and 198 destructive sample trees of larch from four regions in China, respectively. The main purpose was to develop compatible individual tree equations on both national and regional levels for above- and belowground biomass, biomass conversion factor and root-to-shoot ratio, using the nonlinear error-in-variable simultaneous equation approach. In addition, diameter at breast height (D) and tree height (H) growth models were also developed, and effects of key climate variables on biomass variation and growth process were analyzed. The results showed that mean prediction errors (MPEs) of regional aboveground biomass models were from 3.86 to 7.52%, and total relative errors (TREs) are within ±3%; and for regional belowground biomass equations, the MPEs are from 9.91 to 28.85%, and the TREs are within ±4%. The above- and belowground biomass and D- and H-growth were significantly related to mean annual temperature and mean annual precipitation. The biomass equations and growth models developed in this paper will provide good basis for estimating and predicting biomass of larch forests in China.

Biomass estimation in Pichia pastoris cultures by combined single-wavelength fluorescence measurements.

In this work, the evolution of different biogenic fluorophores involved in the metabolism of Pichia pastoris was determined at four different single-wavelength pairs (excitation/emission) during batch culture in microwell plates and used for effective and reliable biomass estimation by means of chemometric tools. The chemometric tools for biomass estimation were multiple linear regression (MLR), partial least squares regression (PLSR), and principal component regression (PCR). Variable importance in the projection (VIP) scores were used to rate the importance of model input variables, indicating tryptophan as the most important variable for biomass estimation. A direct correlation between the single fluorescence signals of tryptophan and biomass was additionally set up. Results indicate a successful fitting of the MLR, PLSR, PCR, and direct tryptophan correlation models for the present case and confirm the relevance of biogenic fluorophores for bioprocess state variables monitoring. The root mean squared error of prediction (RMSEP) between the predicted and measured values for the validation batches was 0.017, 0.023, 0.025, and 0.049 g L(-1) dry cell weight for MLR, PLSR, PCR, and direct tryptophan correlation, respectively. The presented approach of indirectly measuring biomass based on combined single-wavelength fluorescence measurements can be used for the development of a low-cost alternative to 2D fluorescence spectroscopy. Biotechnol. Bioeng. 2016;113: 2394-2402. © 2016 Wiley Periodicals, Inc.

Biomass estimation during macro-scale solid-state fermentation of basidiomycetes using established and novel approaches.

Solid-state fermentation (SSF) has been utilised in food production for millennia and is well suited for the cultivation of basidiomycetes, due to the robustness of the process and the possibility of using lignocellulose as the substrate. Basidiomycetes produce diverse enzymes and various primary and secondary metabolites, many of which have biotechnological potential. The quantification of the fungal biomass present is essential for the characterisation of growth kinetics in processes such as SSF. In SSF, fungi grow into the substrate and use it as a nutrient source. Therefore, direct biomass determination is not possible and indirect methods have to be employed. In the presented study, we compared 11 methods for quantifying fungal biomass during SSF of the basidiomycete Trametes hirsuta in a newly developed laboratory reactor (working volume 10L). The methods were based on measuring the levels of six cell-specific components (ergosterol, glucosamine, nucleic acids, number of fungal nuclei, protein and genomic DNA) and estimations of biological activity (respiration, activities of lignolytic and cellulolytic enzymes, and the glucose and protein contents of the liquid). The methods were evaluated with regards to reproducibility and plausibility of the results, time and resource requirements, possible influential factors, and matrix effects. The most reliable biomass estimates were obtained from measurements of ergosterol content, number of nuclei, and respiration. Thus, these three methods were deemed most suitable for process control and modelling.

Species identification of small pelagic fish schools by means of hydroacoustics in the Eastern Mediterranean Sea

Reliable biomass estimates by means of hydroacoustics largely depend on the correct identification of acoustic targets. Data collected during five summer acoustic surveys (2004-2008) in the North Aegean Sea (Greece) were analyzed to explore effective discrimination of small pelagic fish schools according to the species they belong. Discriminant Function Analyses (DFA) using bathymetric, energetic and morphometric school descriptors as explanatory variables were applied per research cruise as well as to pooled data from all surveys. Results revealed that the schools can be successfully classified into the five species considered (anchovy Engraulis encrasicolus, sardine Sardina pilchardus, round sardinella Sardinella aurita, blue whiting Micromessistius poutassou, and Atlantic mackerel Scomber scombrus). The percentage of correct classifications in terms of number of schools was higher in the analyses of the annual cruises (75.6%-95.36%) than in the analysis of pooled data (≈72%). This is because of (i) the lower number of species, as well as (ii) the reduced intraspecific variability, occurring in each separate cruise. Significant differences were detected among school descriptors for the different species, revealing discrete aspects of schooling behaviour for each species. The benefit of the specific approach is that the classification functions of the DFAs can be used to classify a larger set of schools, which has not been possible to assign to specific species. Overall the approach constitutes an objective, more automated and less time consuming procedure for the analysis of acoustic data and can contribute to the improvement of biomass estimates in the area.

The trophic-level-based ecosystem modelling approach: theoretical overview and practical uses

AbstractGascuel, D., Guénette, S., and Pauly, D. 2011. The trophic-level-based ecosystem modelling approach: theoretical overview and practical uses. – ICES Journal of Marine Science, 68: 1403–1416. A trophic-level (TL)-based ecosystem modelling approach is presented, where ecosystem functioning is modelled as a flow of biomass up the foodweb through predation and ontogenetic processes. The approach, based on simple equations derived from fluid dynamics, provides insights into ecosystem functioning and the impact of fishing. A virtual ecosystem is simulated and the model shown to be capable of mimicking the effects of various exploitation patterns on ecosystem biomass expected from the theory. It provides the theoretical basis to explain complex patterns, such as cascading effects, maximum sustainable ecosystem yield, and fishing down the foodweb. The utility of the TL-based approach as a practical tool for determining fishing impacts in specific ecosystems is illustrated using the Guinean ecosystem as a case study, showing how current fishing effort levels led to full exploitation of higher TLs, confirming and generalizing previous single-species assessment results. Finally, catch trophic spectrum analysis is presented to show that it provides reliable biomass estimates when catches per TL and primary production are known.

Developing harvest strategies for low-value and data-poor fisheries: Case studies from three Australian fisheries

Australia has developed a Harvest Strategy Policy (HSP) for Commonwealth fisheries. A harvest strategy specifies the management actions necessary to achieve defined resource objectives in a given fishery, resulting in a formal and proactive management approach that is transparent to all stakeholders. The objectives of the HSP are defined in terms of target and limit biomass reference points, although proxies can be used. It is a significant challenge, therefore, to develop harvest strategies for most small fisheries, which are typically data-poor, have only basic or no formal stock assessments, and have low Gross Value of Production (GVP). For the small Commonwealth fisheries, even indirect biomass estimates are unavailable and, due to their low GVP, it is unlikely that relevant research will be undertaken to determine reliable biomass estimates in the foreseeable future. This paper provides insight into developing harvest strategy frameworks that reconcile the reality and limitations of small fisheries with the intent of the HSP: to cease or avoid overfishing, to rebuild overfished stocks, and to maintain stocks at levels corresponding to the maximum economic yield. The process of harvest strategy development is evaluated and general principles for the pragmatic development and implementation of harvest strategies for small, low-value, data-poor fisheries are considered. These general principles include (i) the development of sets of triggers with conservative response levels, with progressively higher data and analysis requirements at higher response levels, (ii) identifying data gathering protocols and subsequent simple analyses to better assess the fishery, (iii) archiving biological data for possible future analysis, and (iv) the use of spatial management, either as the main aspect of the harvest strategy or an augmentation with other measures. The value of an iterative approach involving stakeholders and managers is emphasized. The above principles are illustrated by three case studies from Australia's small Commonwealth fisheries: the Western Deepwater and North West Slope Trawl Fisheries, the Coral Sea Fishery, and the Bass Strait Central Zone Scallop Fishery.

Estimating stem volume by tree crown area and tree shadow area extracted from pan‐sharpened Quickbird imagery in open Crimean juniper forests

Although open forests represent approximately 30% of the world's forest resources, there is a clear lack of reliable inventory data to allow sustainable management of this valuable resource from semi‐arid areas. This paper demonstrates that the low ground cover of open forest offers a unique opportunity for deriving single tree attributes from high‐resolution satellite imagery, allowing reliable biomass estimation. More particularly, this study investigates the relationship between field‐measured stem volume and tree attributes, including tree crown area and tree shadow area, measured from pan‐sharpened Quickbird imagery with a 0.61 m resolution in a sparse Crimean juniper (Juniperus excelsa M.Bieb.) forest in south‐western Turkey. First tree shadows and crowns were identified and delineated as individual polygons. Both visual delineation and computer‐aided automatic classification methods were tested. After delineation, stem volume as a function of these image‐measured attributes was modelled using linear regression. The statistical analyses indicated that stem volume was correlated with both shadow area and crown area. The best model for stem volume using shadow area resulted in an adjusted R 2 = 0.67, with a root mean square error (RMSE) of 12.5%. The model for stem volume using crown area resulted in an adjusted R 2 = 0.51, with a RMSE of 15.2%. The results showed that pan‐sharpened Quickbird imagery is suitable for estimating stem volume and may be useful in reducing the time required for obtaining inventory data in open Crimean juniper forests and other similar open forests.

Biomass equations for birch in Finland

Biomass equations were compiled for the above- and below-ground tree components of birch (Betula pendula Roth and Betula pubescens Ehrh.). The equations were based on 127 sample trees in 24 birch stands located on mineral soil sites. The study material consisted of 20 temporary plots and ten plots from four thinning experiments with different thinning intensities (unthinned, moderately and heavily thinned plots). The equations were estimated for the following individual tree components: stem wood, stem bark, living and dead branches, foliage, stump, and roots. In the data analysis, a multivariate procedure was applied in order to take into account the statistical dependency among the equations. Three multivariate variance component models were constructed for the above-ground biomass components, and one for the below-ground biomass components. The multivariate model (1) was mainly based on tree diameter and height, and in the multivariate models (2) and (3) additional commonly measured tree variables were used. The equations provided logical biomass predictions for a number of tree components, and were comparable with other functions used in Finland and Sweden. The applied statistical method generated equations that gave more reliable biomass estimates than the equations presented earlier. Furthermore, the structure of the multivariate models enables more flexible application of the equations, especially for research purposes.

Comparison of linear and mixed-effect regression models and a k-nearest neighbour approach for estimation of single-tree biomass

Allometric biomass models for individual trees are typically specific to site conditions and species. They are often based on a low number of easily measured independent variables, such as diameter in breast height and tree height. A prevalence of small data sets and few study sites limit their application domain. One challenge in the context of the actual climate change discussion is to find more general approaches for reliable biomass estimation. Therefore, nonparametric approaches can be seen as an alternative to commonly used regression models. In this pilot study, we compare a nonparametric instance-based k-nearest neighbour (k-NN) approach to estimate single-tree biomass with predictions from linear mixed-effect regression models and subsidiary linear models using data sets of Norway spruce ( Picea abies (L.) Karst.) and Scots pine ( Pinus sylvestris L.) from the National Forest Inventory of Finland. For all trees, the predictor variables diameter at breast height and tree height are known. The data sets were split randomly into a modelling and a test subset for each species. The test subsets were not considered for the estimation of regression coefficients nor as training data for the k-NN imputation. The relative root mean square errors of linear mixed models and k-NN estimations are slightly lower than those of an ordinary least squares regression model. Relative prediction errors of the k-NN approach are 16.4% for spruce and 14.5% for pine. Errors of the linear mixed models are 17.4% for spruce and 15.0% for pine. Our results show that nonparametric methods are suitable in the context of single-tree biomass estimation.

Monitoring secondary tropical forests using space-borne data: Implications for Central America

Tropical secondary forests, which play an important role in carbon sequestration, may be monitored using space-borne sensors. Secondary forest biomass or age estimation from space-borne data may be used to quantify the carbon sink these forests represent. At current capabilities, roughly three successional stages up to 15 years of age may be identified from Landsat TM data. Using synthetic aperture radar, reliable biomass estimates may be made up to approximately 60 tons/ha. The potential for overcoming these limitations is reviewed, including the synergy of radar and optical imagery and the unprecedented spatial and spectral resolutions of new sensors. Most of the available literature to date is from the Amazon; in this paper, applicability to Central America is considered, which has a much more heterogeneous landscape and the dynamics of secondary growth have a special significance in the framework of conservation biology and carbon sequestration. We conclude that critical issues in this region will be topographical correction and stratification according to ecological and site quality variables.

Bias Associated with Diet Samples in Audouin's Gulls

We analyzed five different types of food samples from Audouin's Gull (Larus audouinii), collected during the breeding seasons of 1994 and 1995 at its two main breeding colonies, the Ebro Delta and the Chafarinas Islands. These food samples included spontaneous regurgitates, dry boli containing partially digested food, food remains, pellets, and prey identified during direct observations of chick provisioning. We compared estimates of biomass, levels of taxonomic determination allowed by each kind of food sample, and the associated potential biases to assess which sampling method provides the best estimate of diet in gulls. Regurgitates allowed identification of most prey to species level and reliable biomass estimates, but their collection was time-consuming and invasive. Dry boli provided almost the same information as regurgitates at order level and were easy to collect. However, both underestimated soft-bodied prey and prey with large, hard parts. Food remains provided an estimate of diet composition that was highly biased towards prey with large distinctive hard parts. However, food remains were a good complement to dry boli, enhancing biomass estimates for food items that had a good relationship of weight and linear measurements of prey..hard parts. Direct observation allowed identification of prey only to upper taxonomic categories, and is useful when only a broad categorization of prey types is required. Pellets showed important biases towards fish with robust otoliths, and inaccurate conversion to biomass, but they can be useful for monitoring variations in the consumption of certain prey items. Several factors such as time spent collecting, sample availability, disturbance to animals, and the status of the species studied need to be considered when deciding on a method of diet assessment sampling.

Soil Impact on Satellite Based Vegetation Monitoring in Sahelian Mali

Soil Impact on Satellite Based Vegetation Monitoring in Sahelian Mali