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Knowledge of diversity of wild palms (Arecaceae) in the Republic of Benin: finding gaps in the national inventory by combining field and digital accessible knowledge


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Despite efforts by researchers worldwide to assess the biodiversity of plant groups, many locations on Earth remain poorly surveyed, resulting in inadequate or biased knowledge. Robust estimates of inventory completeness could help alleviate the problem. This study aimed to identify areas representing gaps in current knowledge of African palms, with a focus on Benin (West Africa). We assessed the completeness of knowledge of African palms, targeting geographic distance and climatic difference from well-known sites. Data derived from intensive fieldwork were combined with independent data available online. Inventory completeness indices were calculated and coupled with other criteria. Results showed a high overall value for inventory completeness, as well as an even distribution of well-known areas across the country. However, poorly-known areas were identified, which were in remote locations with low accessibility. This study illustrates how biodiversity survey and inventory efforts can be guided by existing knowledge. We strongly recommend the combination of digital accessible knowledge and fieldwork, coupled with expert knowledge, to obtain a better picture of inventory completeness in tropical ecosystems.
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Biodiversity Informatics, 10, 2015, 45-55
1Laboratory of Biomathematics and Forest Estimations, University of Abomey-Calavi, Benin.
2Laboratory of Applied Ecology, Faculty of Agronomic Sciences, University of Abomey-Calavi,
01 BP 526, Cotonou, Benin. 3Department of Environmental Biology, University of Navarra,
Pamplona, Spain. *corresponding author:
Abstract.—Despite efforts by researchers worldwide to assess the biodiversity of plant groups, many
locations on Earth remain poorly surveyed, resulting in inadequate or biased knowledge. Robust estimates of
inventory completeness could help alleviate the problem. This study aimed to identify areas representing
gaps in current knowledge of African palms, with a focus on Benin (West Africa). We assessed the
completeness of knowledge of African palms, targeting geographic distance and climatic difference from
well-known sites. Data derived from intensive fieldwork were combined with independent data available
online. Inventory completeness indices were calculated and coupled with other criteria. Results showed a
high overall value for inventory completeness, as well as an even distribution of well-known areas across the
country. However, poorly-known areas were identified, which were in remote locations with low
accessibility. This study illustrates how biodiversity survey and inventory efforts can be guided by existing
knowledge. We strongly recommend the combination of digital accessible knowledge and fieldwork, coupled
with expert knowledge, to obtain a better picture of inventory completeness in tropical ecosystems.
Key words.—Biological databases, GIS, inventory, sampling efficiency, spatial resolution.
One of the greatest challenges that tropical
biologists are facing now is how to conserve
biological diversity in the current context of
demographic pressure, increase of needs,
overexploitation, climate change, and economic
crisis (FAO 2010). Under these threats, without
effective protection, much of tropical biodiversity
is unlikely to survive, so strategies to promote its
conservation are needed (Bruner et al. 2001).
Measurements of biological diversity can provide
baseline information on distribution, richness, and
relative abundance of taxa that is required for
taking appropriate conservation decisions
(Humphries et al. 1995; May 1988; Magurran
1988; Raven and Wilson 1992).
The national flora of Benin is estimated at
2807 species (Akoègninou et al. 2006). Some of
those species are of high socioeconomic
importance and have been studied in depth.
However, others remain not well assessed, such as
wild palm species. Wild palms are amongst the
most diverse plant groups in the world (Tomlinson
1990) and are species with significant cultural,
social, economic, and ecological uses (Monteiro et
al. 2006). They serve as bio-indicators in many
Latin-American countries (Kjaeret al. 2004;
Vormisto et al. 2004), and their occurrences could
be used as climate trend proxies. In sub-Saharan
Africa, and especially in Benin, wild palms are not
well documented. The species diversity is not well
known, and ecological studies are rare. These data,
together with a complete richness inventory, are
nonetheless critical to planning informed conserva-
tion actions.
Many studies now exist on the use of primary
biodiversity data that are both digital and
accessible in standard formats (Graham et al. 2004;
Guralnick et al. 2007; Sousa-Baena et al. 2014)
providing access to more than 6 x108 data records.
The magnitude of digital accessible knowledge
(DAK) is large though perhaps not sufficient when
measured against global biodiversity (Sousa-Baena
et al. 2014). In contrast, cases of use of extensive
fieldwork data not obtained from online data
portals (i.e., requiring time-consuming, expensive
field surveys) are less frequent. In addition,
assessing sampling effort across geographic space
requires an understanding of how species
assemblages differ among different environments,
across biogeographic barriers, and as a result of
Biodiversity Informatics, 10, 2015, 45-55
dispersal limitation. Species accumulation curves,
species richness estimates, and diversity
accumulation curves have been used to determine
the level of survey completeness (Thompson et al.
2007; Ariño et al. 2008; de Thoisy et al. 2008;
Aranda et al. 2010; Lovell et al. 2010). The
measured level of completeness can be compared
to the desired level of completeness for the same
locality, and some authors have defined particular
targets that may be broadly appropriate (Cardoso et
al. 2009). Several statistics are available for
calculating species richness estimates, including
non-parametric methods and extrapolations of
species accumulation curves, that vary in their
accuracy under different conditions, often having
drawbacks that may prevent their use in common
circumstances (e.g. low species density). Other less
well-known methods have been proposed to try to
overcome some of these challenges, such as the
generalization by Ariño (2010) of the probability
theory developed by Seber (1982).These novel
methods may help determining the completeness of
the inventory and bring out gaps in sampled areas
for further documentation (Chao and Jost 2012).
We carried out this study on both available
DAK and extensive fieldwork inventory of wild
palms (i) to describe the national species richness
of this group, and (ii) to estimate the completeness
of the inventory within the group. We assessed
knowledge gaps across Benin through estimation
of geographic and environmental distances to well-
known localities.
Study Area
Benin is a West African country located
between 6°20’ and 12°25’N and 1° and 3°40’E.
Biogeographically, Benin is subdivided into three
contrasting phytochorological zones: the Guineo-
Congolean zone, the Sudano-Guinean transition
zone and the Sudanian zone (Akoègninou et al.
2006; White 1983). Rainfall is bimodal in the
Guineo-Congolean zone. North of this zone,
rainfall distribution becomes unimodal. Human
activities have resulted in a high level of
degradation of the vegetation (Figure 1).
Data Sources
Our analyses are based on data from both
extensive fieldwork carried out from May 2013 to
April 2014, during which a megatransect covering
the whole country was executed, comprised of
daily transects, and data downloaded from the
Global Biodiversity Information Facility1,!com-
prising data on 11 wild palm species (8 observed
during our fieldwork, and 3 additional species
appearing in the GBIF dataset). The GBIF search
was done in January 2015 through the use of key
fields such as palms, Arecaceae, African palms,
African native palms, Borassus, Eremo-spatha,
Hyphaene, Laccosperma, Phoenix, Raphia, rattan,
raffia, Wild palms, etc. The initial dataset
contained 1847 records from the two sources.
The dataset was then cleaned via a series of
inspections and visualizations designed to detect
and document inconsistency, as follows. (1) We
created lists of unique names in each dataset in
Microsoft Excel, and manually inspected them for
repeated versions of the same taxonomic concepts:
misspellings, name variants, different versions of
authority information, etc. Such repeated name
variants were flagged, checked via independent
sources, and corrected to produce unique scientific
names that we believed correctly referred to single
taxa. (2) We checked for geographic coordinates
that fell outside of the country, but which were
referred to Benin. (3) Within the country, we
checked for consistency between descriptions of
district and position of geographic coordinates. In
each case, where possible, we created a corrected
version of the data record; where no clear
correction was possible, we discarded data,
recording data losses at each step in the cleaning
process. In all, 1375 records were finally
considered (1154 fieldwork + 221 GBIF records;
Figure 2) which were constrained also to include
only those with consistent coordinates.
Data Analysis and Interpretation
We aggregated point-based occurrence data to
½° spatial resolution across the country, which
near the Equator corresponds to a square ~56 km
on a side (Figure 2). This spatial resolution was the
product of a detailed analysis of balancing the
benefits of aggregating data (i.e., larger sample
sizes), versus the loss of spatial resolution that
accompanies broader aggregation areas that can
make imperceptible important geographic features.
The procedure consists on examining the relative
change in area-adjusted variance of the data versus
Biodiversity Informatics, 10, 2015, 45-55
Figure 1. Geographic pattern of Benin’s biogeographic zones (Sudanian, Sudano-Guinean, and
Guineo-Congolean) and soil types.
Biodiversity Informatics, 10, 2015, 45-55
Figure 2. Elevation map of Benin, with the geographic locations of records of palms collected in
the field and downloaded through GBIF. Grid squares delimit the ½° cells used to calculate
Biodiversity Informatics, 10, 2015, 45-55
increasing plot size, and selecting the smallest plot
size at which the trend of the slope of the overall
variance vs. area curve changed most. The concept
is similar to selecting the largest sample size
beyond which no significant increase in diversity is
expected (Ariño et al. 2008), and the resulting
quadrat size was consistent with the spatial
resolution used by Sousa-Baena et al. (2014) in
their analysis of Brazilian plant diversity and
presentation of the idea of DAK.
We produced the ½° grid shapefiles in the
Vector Grid module of QGIS, version 2.62. Next,
we attributed each data record to the corresponding
grid cell, and used a set of criteria on the
aggregated number of records per cell, per taxon,
to consider whether each cell was well-sampled.
We calculated (1) the total number of records
available from each grid square (termed N); (2) the
number of distinct species recorded from each grid
square (Sobs) for species appearing exclusively
within field data, exclusively as GBIF records, and
species recorded both as field data and GBIF data
records; and (3) the number of species whose
occurrence was recorded exactly once (a) and
exactly twice (b) at each grid cell. With that
information we were able to use Chao’s (Chao et
al. 2000) formula to calculate the corresponding
expected number of species (Sexp in Chao’s work,
which we will denote Sc here) for all three cases:
𝑆!=𝑆!"# +
then defined inventory completeness (C) according
to Chao as CC = Sobs / Sc.
In addition, the probability theory developed
by Seber (1982) originally applied to the problem
of recognizing how many tagged animals had lost
their marks in a recapture experiment, and later
generalized by Ariño (2010) for estimating the
number of missing data records from any number
of overlapping datasets, was applied here. As the
fieldwork data had not been already shared with
available data from GBIF, the total number of
species existing in the study area would be:
𝑆!=𝑆!"#$% +𝑆!"#$ +𝑆!"#$%!"#$ +𝑆!
where SFIELD*GBIF is the number of recorded species
shared in both datasets, SFIELD is the number of
species recorded in field data but not in GBIF,
SGBIF the number of species recorded in GBIF data
but not in field data, and S0 the unknown number
of species that weren’t recorded in either
collection. Sp cannot thus be known but it can be
estimated (Seber 1982) by probability theory on
the intersection of the corresponding independent
datasets (Ariño 2010). In our case, with two
datasets, the estimate is
!!!(𝑆!"#$% +𝑆!"#$ +𝑆!"#$%!"#$),
!!"#$!!!"#$%!"#$ (!!"#$%!!!"#$%!"#$ ).
Completeness could then be calculated as CP =
Sobs/INTEGER(Sp) for samples not so small as to
introduce large bias in k due to the estimates of the
multinomial function used to derive it (Seber and
Felton, 1981; INTEGER indicates the whole
number part of a real number).
We then explored plots of Cx versus N to assess
appropriate and adequate definitions of relatively
completely versus incompletely inventoried grid
squares. As many cells either were not amenable to
estimating Sp for want of at least one parameter
(commonalities or exclusivities), or would not
yield Sc for want of singletons (a) or doubletons
(b), we decided to combine both approaches to
derive completeness rather than rely solely on
either Chao’s or Ariño’s approaches whenever
possible. We decided to use a highly conservative
criterion by estimating completeness on the highest
available value for expected species (either Sc or
Sp), when both could be calculated. Thus, we
obtained a lower limit for our completeness
estimate as:
𝐶!=𝑆!"#/𝑀𝐴𝑋 𝑆!,𝑆!.
We then classified each of the squares according to
the completeness criteria. We deemed a square to
be well-sampled if any of these was true: (1) Cc >
0.5 and singletons/doubletons available, (2) Cp >
0.5 and k available, or (3) expert judgment, based
on the known sampling density.
Biodiversity Informatics, 10, 2015, 45-55
Next, in QGIS, we linked the table with the
grid square statistics (i.e., Sobs, Sc, Sp, CM, Cc, Cp) to
the aggregation grid, and saved this file as a
shapefile. The shapefile of well-sampled grid
squares was further converted to raster (geotiff)
format using custom scripts in R (R Development
Core Team 2013). This raster coverage was the
basis for our identification of gaps, as follows.
We used the Proximity (Raster Distance)
function in QGIS to summarize geographic dis-
tance to any well-sampled area. To create a parallel
view of environmental difference from well-
sampled areas, we plotted 5000 random points
across the country, and used the Point Sampling
Tool in QGIS to link each point to the geographic
distance raster, and to raster coverages (2.5’ reso-
lution) summarizing annual mean temperature and
annual precipitation drawn from the WorldClim
climate data archive (Hijmans et al. 2005).
We exported the attributes table associated
with the random points, and analyzed it further in
Excel. We standardized values of each environ-
menttal variable to the overall range of the variable
as (xi xmin) / (xmax - xmin), where xi is the particular
observed value in question. We then calculated the
environmental distance matrix by obtaining the
Euclidean distances for the climate variables
between points falling in well-sampled cells (by
definition, points having a geo-graphic distance of
zero) and the points falling in the remaining cells
(those points with non-zero geographic distances).
Hence, each random point in incomplete cells was
defined by its distance in environmental space to
the points in well-sampled cells. Finally, the
environmental distances were imported into QGIS,
and linked to the random points. The shapefile
containing the random points was thus given a z-
value that is the environmental distance associated
with that point. This vector file was then rasterized
to provide continuous coverage across the region.
The raw data show a greater concentration of
wild palms records in the northwestern and
southernmost part of Benin. However, data
covered the whole country and did not appear to be
particularly concentrated along points of access
such as roads or rivers.
Inspecting the relationship between Sobs and
various C values, we observed a variation of
outputs. For Cc, completeness greater than 0.8 was
observed for more than 10 expected species and a
number of individuals between 0-50; for other
completeness indices, more variation was observed
in C values (Figure 3). By definition, cells for
which Cm could not be calculated were declared as
under-sampled. Well-sampled areas according to
the criteria defined in the Methods could in turn be
segregated into complete, with all species observed
(either valid CM =1 or by expert judgment), and
incomplete (0.5<CM <1) (Table 1) sites.
Inventory Completeness
There were 49 ½° cells in the entire country, of
which 86% held data. Globally, Benin showed a
high value for inventory completeness (0.58<CM
1). Most ecosystems hosting palm species in the
country appear to be well sampled, except some
sections in the northern and western fringes, as
well as remote areas with potentially difficult
accessibility as seen in low road density (Figure 3),
which do not seem to lack records but show low
completeness. Based on the consensus for
inventory completeness, two-thirds of well-known
sites were recognized to be complete, whereas 18%
had CM > 0.8 and 21% had 0.5 < CM < 0.8 (Figure
3). One-third of all cells covering the country were
finally declared as under-sampled.
Table 1. Decision table for levels of knowledge of palm species across Benin.
under-sampled: CM either <0.5 or cannot be calculated, and expert judgment not
complete (CM = 1)
complete (based on expert judgment)
incomplete (Sp or Sc >Sobs and CM valid)
Biodiversity Informatics, 10, 2015, 45-55
Figure 3. Completeness of inventories of palm trees in Benin at ½°spatial resolution and
environmental and geographic distances from well-sampled cells. Bar diagrams in each cell
represent the number of observed species (Sobs), and upper limit for expected species (Sexp),
classified by completeness criteria. The density of shades represent the combination of the
environmental distance (reds) and geographic distance (blues) between the shaded area and the
well-sampled cells.
Biodiversity Informatics, 10, 2015, 45-55
We analysed completeness values for the ½°
resolution which showed completeness evenly dis-
tributed across biogeographic zones in Benin based
on the geographic distance map. The environ-
mental distance was higher in lowlands beyond the
Atacora Mountains (northwestern part of the
country), in remote areas, and in border regions.
Comparison of soil types in the country
revealed variability across biogeographic zones
(Figure 1). Ferralitic soils are found mostly in the
southern part (and in the northeast corner) whereas
more ferruginous soils are found northwards. Well-
known areas covered much of these soil types,
suggesting completeness of inventory of palm
communities on soil types.
Homogenous, relatively stable climatic condi-
tions were encountered across most ecosystems in
the country. Annual mean temperature was
between 25-29oC and annual precipitation between
700-1300 mm. Regions with higher temperature
generally had lower precipi-tation and vice versa
(Figure 4). However, some areas were environ-
mentally different, especially above 10o N. We
calculated distances to well-known cells in climate
space, which turned out to be roughly comparable
to geographic distances to well-known sites
(Figure 3). Combining both distances, we produced
a view of areas that seem both poorly known and
are both geographically remote and environmen-
tally different from well-known sites (Figure 3).
This study represents a first attempt in charac-
terizing completeness of knowledge of palm com-
munity composition across Benin through the use
of data from our fieldwork and data available to
the broader scientific community. Inventory com-
pleteness was high across the country and most of
the country’s ecosystems hosting palm species are
thus well sampled. As such, the current state of the
inventory of wild palms across ecosystems in
Benin is considered reasonably complete. This
result comes from the concordance of the findings
from different estimates. Although palm records
were more concentrated in some areas, these
higher concentrations were not linked to accessi-
bility features (e.g., roads), as has often been
described in whole-region biodiversity studies
(e.g., Escala et al. 1997). However, the opposite
was not true: the few sections where low
completeness existed did not lack records, but
often coincided with remote areas having a low
density of roads or other access points, or being
otherwise harder to reach. Some of these in-
complete sectors had also high environmental
distances to well-sampled areas, and constituted
gaps in sampling and knowledge.
Figure 4. Scatterplot of
precipitation vs.
temperature at 5000
random points across
Benin, classified
according to the relative
geographic distance to
well-sampled cells.
Biodiversity Informatics, 10, 2015, 45-55
Gap areas are places that have not been well
sampled (Kier et al. 2005; Stehmann 2009). Gap
analyses mostly focus on a particular taxon and its
distribution and diversity across regions, eco-
regions, or biomes (Mora et al. 2008). Meanwhile,
it is important to know how complete areas of
inventories are, in order to apply appropriate levels
of confidence (Colwell and Coddington 1994). For
Benin, gaps resulting from wild palm inventory
assessment are located in remote areas, such as in
mountainous regions. These areas have not been
previously mentioned as hosting palm biodiversity
(Akoègninou et al. 2006). The dryness of the
climate could also explain the rarity of palm
species in these areas. Contrary to the findings of
Soria-Auza and Kessler (2008), palm diversity
assessment in Benin was not influenced by uneven
collecting effort. The current study was based on
intensive fieldwork through different seasons with
the help of knowledgeable local people in the field.
In addition, wild palms are recognizably distinct
species, with little room for identification error: the
species have long been described and few taxo-
nomic misidentifications have been reported. As
such, taxonomic bias is not likely to have affected
the inventory, contrary to situations for other taxa
(Soberón et al. 2000; Pyke and Ehrlich 2010).
The value of sharing data has been recognized
for some time (Nelson 2009). Earlier, data were
often safely and jealously kept by their owner (be
it an individual, laboratory, or museum) and could
only be accessed through remuneration of some
sort, e.g. authorship (Scoble 2000; Ponder et al.
2001; Wang et al. 2007). However, recent advan-
ces in information technology and an increased
willingness to share primary biodiversity data are
enabling unprecedented access (Soberón and
Peterson 2004), as in case of GBIF. This sea-
change makes the research more interesting and
easy; as more data are available, more predictions
and analyses can be developed. As the bioinfor-
matics community pointed out, only by looking at
vast databases that describe the whole of the
system will we be able to understand the big
picture, and find correlations and patterns (Hardis-
ty et al. 2013). However, more efforts should be
made by data providers to assure the quality of the
data that they are sharing, as most of these data
require thorough cleaning (Otegui et al. 2013).
This study revealed insightful information that
will potentially impact scientific knowledge and
conservation efforts. Even if exhaustive inventories
of African palms are somehow feasible objectives
for short-term fieldwork, our results demonstrate
that, with the addition of digital accessible
knowledge on top of existing survey data, a
relatively complete picture about the group of
interest could be obtained. This observation has
important implications for sampling, as combi-
nation of available data source reduces the time,
effort, and money required for new field surveys,
which are nevertheless necessary to gather new
data. Further, re-visitation of the already studied
areas would provide information to understand and
appreciate the level of changes in the landscape
where these palms are found.
Data collection for this research was supported
by the University of Abomey-Calavi (Republic of
Benin) through WILD-PALM project. Analyses
were fully developed and implemented through
active collaboration with Town Peterson and
Lindsay Campbell. We thank Salako Valère,
Akpona Jean Didier, and Donou Marcel for their
contributions, and two anonymous referees whose
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... Cultivation potential of species such as Adansonia digitata L. (Cuni Sanchez et al. 2011) and Tamarindus indica L. (Fandohan et al. 2013) has been assessed in climatic dimensions; the relevant climatic variables are generally derived via spatial interpolation among weather station data (Hijmans et al. 2005). However, incorporation of local environmental features would give better and more relevant insights into suitability of areas (Idohou et al. 2015;Pearson and Dawson 2003), as climate-based models will frequently miss fine details owing to their broad spatial autocorrelation structure and the interpolation that is necessary for their derivation. Satellite imagery provides finer spatial resolution, and also the advantage of reflecting more dimensions of the environmental landscape, including dimensions of land use and land cover, soil characteristics, and topography. ...
... All socioeconomically important wild palm species occurring in Benin were examined, based on occurrence data from across their geographic distributions in the country (Idohou et al. 2015 Beauv. An intensive field survey during 2013-2014 served to collect occurrence data for the eight species at fine spatial resolutions (Fig. 1), as all records were documented with good spatial precision via GPS readings. ...
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Wild palms contribute significantly to food security and local economy in tropical areas, and particularly in sub-Saharan Africa. In light of this importance, eight palm species were explored [Borassus aethiopum (L.) Mart, Eremospatha macrocarpa (G. Mann et H. Wendl.) H. Wendl., Laccosperma opacum (G. Mann et H. Wendl.) Drude, Hyphaene thebaica (L.) Mart, Phoenix reclinata Jacq., Raphia hookeri G. Mann et H. Wendl., R. sudanica A. Chev., and R. vinifera P. Beauv.] as targets for conservation, domestication, and cultivation in Benin. Cultivation potential was evaluated in a coarse-resolution, first-pass effort using ecological niche models to relate known occurrences of each species to vegetation indices (VEG), gross primary productivity (GPP), and soil characteristics (SOIL), and model outputs were related to human distribution and land-use patterns. Results showed that wild palms responded differentially to different suites of environmental factors: some species showed best model performance with VEG + GPP + SOIL, others with GPP + SOIL or VEG + GPP, or with a single factor. Two species had broad potential distributions across the country; others had potential areas in the north (2 species) or the south (4 species). Raphia hookeri and R. vinifera showed greatest overlap in terms of ecology and distribution, whereas L. opacum and R. sudanica had the lowest similarity. These models constitute initial steps toward a sustainable scheme for planning exploration of the possibility of cultivation of these species.
... However, and according to Howell (1981), such a statement is not always verified as in some cases small seeds germinated faster than large seeds, e.g., Impatiens capensis Meerb. The germinative aptitude of seeds of B. aegyptiaca and R. heudelotii and the growth of their seedlings can be influenced by the morphology of the seeds (Padonou et al., 2013) or the fruit of which they come (Idohou et al., 2015). ...
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Balanites aegyptiaca (L.) Delile and Ricinodendron heudelotii (Bail.) Pierre are socioeconomically important species in sub-Saharan Africa. This study was conducted to assess the seed germinability and seedling growth of those species based on several treatments and to define proper conservation and domestication strategies in Benin. The seeds were randomly collected in their natural habitats. The experiment was conducted using a split-split plot design and the data was analyzed using the generalized linear mixed and survival models. The heaviest seeds (B. aegyptiaca seed mass ≥3 g and R. heudelotii ≥ 1.50 g) provided the highest germination rates (73.60 ± 5.19% and 62.50 ± 5.71%, respectively) when seeds were scarified with a hammer. For B. aegyptiaca seedlings, the seeds from the phytodistrict of North Borgou scarified with a hammer and the heaviest seeds showed the highest total height (36.43 ± 1.03 cm), basal diameter (2.84 ± 0.03 mm), the greatest number of leaves (32), and ramifications. The heaviest seeds of R. heudelotii had also the highest value for total height at the day-28 after sowing (26.73 ± 13.56 cm) until the day-105 (151.97 ± 6.37 cm). The heaviest seeds of R. heudelotii from the phytodistrict of Pobe showed the highest basal diameter (12.53 ± 1.47 mm) and the greatest number of leaves (14), with almost no ramification during the trial period. These findings constitute a step forward in upscaling the reproduction of these species for better contribution to economies while serving in restoration plans.
... Thus, the performance of seeds and the resulting seedlings may vary from one phytodistrict to another due to the variation in their pedoclimatic conditions. The germinative aptitude of seeds can be in uenced by the morphology of the seeds (Padonou et al. 2013) or of the fruit of which they come (Idohou et al. 2015). ...
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Balanites aegyptiaca (L.) Delile and Ricinodendron heudelotii (Bail.) Pierre are socioeconomically important but endemic species to sub-Saharan Africa. This study was conducted to assess the germination capacity of their seeds and seedling growth according to seed provenance, seed mass and pre-treatment techniques as a contribution to the development of strategies for their conservation and domestication in Benin. The seeds were randomly collected in the species occurrence phytodistricts. A split-split plot design with three replicates was used. The survival analysis and generalized linear mixed effects models were implemented on the data. Findings were that the heaviest seeds ( B. aegyptiaca seed mass ≥ 3 g and R. heudelotii ≥ 1.50 g) , provided the highest germination rates (73.60 ± 5.19% and 62.50 ± 5.71%) with seeds scarified with a hammer first emerging at day-8 and day-10 for B. aegyptiaca and R. heudelotii respectively. For B. aegyptiaca seedlings, the seeds from North Borgou phytodistrict scarified with a hammer and the heaviest seeds showed the highest total height (36.43 ± 1.03 cm), basal diameter (2.84 ± 0.03 mm), the greatest number of leaves (32) and ramifications (1). The heaviest seeds of R. heudelotii showed also the highest total height from the day-28 after sowing (26.73 ± 13.56 cm) until the day-105 (151.97 ± 6.37 cm) and those from Pobe phytodistrict showed the highest basal diameter (12.53 ± 1.47 mm) and the greatest number of leaves (14), with almost no ramification during the trial period. These findings constitute a step towards upscaling the reproducibility of these species for better contribution to economies while serving for restoration plans.
... However, such data are well-known to be massively influenced by biases related to sampling, in terms of the diverse logistic, practical, historical, and political factors that structure how biologists have been able to sample biodiversity on Earth, and report those data to the broader scientific community. These biases have been documented thoroughly in general (Yesson et al. 2007;Beck et al. 2013;Gaiji et al. 2013;Otegui et al. 2013a;Otegui et al. 2013b;Beck et al. 2014;Idohou et al. 2015;Anderson et al. 2016;Asase and Peterson 2016;Peterson and Soberón 2018), and specifically for Mexico (Bojórquez-Tapia et al. 1995;Peterson et al. 1998;Soberón et al. 2000;Soberón et al. 2007). ...
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We assess a body of work that has attempted to use co-occurrence networks to infer the existence and type of biotic interactions between species. Although we see considerable interest in the approach as an exploratory tool for understanding patterns of co-occurrence of species, we note and describe numerous problems in the step of inferring biotic interactions from the co-occurrence patterns. These problems are both theoretical and empirical in nature, and limit confidence in inferences about interactions rather severely. We examine a series of examples that demonstrates striking discords between interactions inferred from co-occurrence patterns and previous experimental results and known life-history details.
... However, palms are relatively abundant in some sites, such as SATE (2.8% of the total number of plants) and RICA (2%), and two of the five most abundant species overall in all nine sites are palms (Syagrus flexuosa and Syagrus comosa), which together accounted for 8% of the individuals recorded. This emphasizes the importance of including monocots in studies of savanna vegetation, as proposed by Lima et al. (2003), Lenza et al. (2011) andIdohou et al. (2015). Such relative abundance of palms appears to be a distinguishing trait of Cerrado relative to savannas in other parts of the world (Furley 2004), where palms are relatively unimportant. ...
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Aims Different plant functional groups display diverging responses to the same environmental gradients. Here, we assess the effects of environmental and spatial predictors on species turnover of three functional groups of Brazilian savannas (Cerrado) plants—trees, palms and lianas—across the transition zone between the Cerrado and Amazon biomes in central Brazil. Methods We used edaphic, climatic and plant composition data from nine one-hectare plots to assess the effects of the environment and space on species turnover using a Redundancy Analysis and Generalized Dissimilarity Modeling (GDM), associated with variance partitioning. Important Findings We recorded 167 tree species, 5 palms and 4 liana species. Environmental variation was most important in explaining species turnover, relative to geographic distance, but the best predictors differed between functional groups: geographic distance and silt for lianas; silt for palms; geographic distance, temperature and elevation for trees. Geographic distances alone exerted little influence over species turnover for the three functional groups. The pure environmental variation explained most of the liana and palm turnover, while tree turnover was largely explained by the shared spatial and environmental contribution. The effects of geographic distance upon species turnover leveled off at about 300 km for trees, and 200 km for lianas, whereas they were unimportant for palm species turnover. Our results indicate that environmental factors that determine floristic composition and species turnover differ substantially between plant functional groups in savannas. Therefore, we recommend that studies that aim to investigate the role of environmental conditions in determining plant species turnover should examine plant functional groups separately.
... However well, this landscape has been visited by and sampled by botanists, although that work was mostly in terms of surveys of species' occurrences; most of the data remain in nondigitized formats. This information thus remains inaccessible to scientists with interest on African Idohou et al. 2015, Kouao et al. 2015), but we were stymied by the small numbers of primary occurrence data that are available for the region. As a consequence, in our second effort, we used a literature review to detect and identify landmark studies that have documented Cameroon Mountain sites in good detail, but found little or no access to the primary data that underlay those publications and that document the individual specimens collected. ...
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With the emergence of a new field, biodiversity informatics, an important task has been to evaluate completeness of biodiversity information that is existing and available for various countries and regions. This paper offers a first and very basic assessment of sampling gaps and inventory completeness across the Cameroon Mountains. Because digital accessible knowledge is severely limited for the region, we relied on qualitative evaluations of inventory completeness, supplemented by large amounts of data from the National Herbarium of Cameroon (YA) database. Detailed botanical inventories have been developed for Mt Cameroon, the Kupe-Mwanenguba Mountains, Mt Oku, and the Mambila Plateau, leaving substantial geographic and environmental coverage gaps corresponding to Rumpi Hills, Mt Nlonako, Kimbi Fungom National Park, Bali and Bafut Ngemba, Mt Bamboutos, Kagwene, and Tchabal Mbabo. This paper provides a roadmap for a comprehensive botanical survey for this region. Completing this survey plan, the resulting data will allow researchers to track changes in biodiversity and identify priority areas for conservation on the various mountain ranges that make up this important biodiversity hotspot.
... On the other hand, variance is often dependent on scale. When dealing with spatially-explicit data, areas may show gaps at a given scale, but show too much variance to be meaningful at smaller scales or loose information (through data homogenization) at larger ones (Idohou et al. 2015). Thus, understanding the scale effects may be critical to interpret whether gaps found are significant. ...
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This best practice guide elaborates on processes of conducting Data Gap Analyses (DGA) of biodiversity data, which can help prioritize data mobilization activities. The guide illustrates general practices by summarizing 16 actual DGA exercises, with six focused on GBIF-mediated data discussed in detail. This guide is intended to support biodiversity data stakeholders including: Biodiversity information systems/networks Biodiversity data publishers Biodiversity organizations Research groups Information managers National/regional/thematic Biodiversity Information Facilities (BIFs) National/regional funding agencies
... This spatial resolution was the product of a detailed analysis of balancing benefits of aggregating data (i.e., larger sample sizes), versus the loss of spatial resolution that accompanies broader aggregation areas that can make important geographic features imperceptible (i.e., 1° resolution is a square ~110 km on a side). Details of this procedure are provided by Sousa-Baena et al. (2014) and explored and analyzed in more detail for African examples in Idohou et al. (2015) and Koffi et al. (2015). ...
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Biodiversity inventory in Kenya has been ongoing for about a century and a half, coinciding with the arrival of naturalists from Europe, America, and elsewhere outside Africa. Since the first collections in the mid-to-late 1800s, there has been a steady increase of plant surveys, frequency of inventory, and discovery of new species that have considerably increased knowledge of faunal and floristic elements. However, as in all other countries, such historical biological collection activities are more often than not, ad hoc, resulting in gaps in knowledge of species and their habitats. While Kenya is relatively rich botanically, with a succulent flora of about 428 taxa, it is apparent that the list is understated owing to, among other factors, difficulty of preparing herbarium material and restricted access to some sites. This study investigated completeness of geographic knowledge of succulent plants in Kenya, with the aim of establishing species distribution patterns and identifying gaps that will guide and justify priority setting for future work on the group. Species data were filtered from the general BRAHMS database at the East African Herbarium and cleaned via an iterative series of inspections and visualizations designed to detect and document inconsistencies in taxonomic concepts, geographic coordinates, and dates of collection. Eight grid squares fulfilled criteria for completeness of inventory: one in the city of Mombasa, one in the Kulal–Nyiro complex, one in Garissa, one in Baringo, and four grid squares in the Nairobi–Nakuru–Laikipia area. Poorly-known areas, mostly in the west, north, and north-eastern regions of the country, were extremely isolated from well-known sites, both geographically and environmentally. These localities should be prioritised for future inventory as they are likely to yield species new to science, species new to the national flora, and/or contribute new knowledge on habitats. To avoid inconsistencies and data leakage, biodiversity inventory and documentation needs streamlining to generate standardised metadata that should be digitised to enhance access and synthesis.
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Palms constitute vital species for local people’s well-being, especially in West Africa. This analytic review aims at providing an overview of West African palms flora diversity, uses, ecology, and conservation. Scientific papers related to palms in West Africa were searched on electronic databases Google Scholar, Web of Science, and general web search on Google using the names of palms occurring in West Africa. From 108 scientific articles, we extracted relevant information after a critical reading. Papers were published between 1930 and 2019 and most of the studies focused on biochemistry, ethnobotany, and population structure. We identify in the literature 25 species belonging to 12 genera, 32% of them growing in dry areas. Five growth forms were identified among West African palms species. Erect and solitary stem forms were the most representative. Concerning leaf forms, most west African palms (84%) have pinnate leaves. Sexual systems of palms were represented by monoecy, dioecy, and hermaphrody, with the predominance of monoecy (44%). The pleonanthic species are the most represented reproductive feature (76%) and only Raphia palms are hapaxanthic. As far as uses are concerned, there is a link between used parts and uses categories. According to the relative importance index, the four first palm species in West Africa, namely Borassus aethiopum Mart., Elaeis guineensis Jacq., Borassus akeassii Bayton, Ouedr. & Guinko, and Hyphaene thebaica Mart. grow in dry areas. Rattans have a low relative index value due to their non-consumed organs. Critical analysis was presented in the focus of population structure, distribution, and propagation aspects. The review highlights a research gap in carbon sequestration, phenology, and called for more research effort in semi-arid and arid areas. Such investigations would help in planning better sustainable management and conservation of palm in West Africa.
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The advent of online data aggregator infrastructures has facilitated the accumulation of Digital Accessible Knowledge (DAK) about biodiversity. Despite the vast amount of freely available data records, their usefulness for research depends on completeness of each body of data regarding their spatial, temporal and taxonomic coverage. In this paper, we assess the completeness of DAK about terrestrial mammals distributed across the Iberian Peninsula. We compiled a dataset with all records about mammals occurring in the Iberian Peninsula available in the Global Biodiversity Information Facility and in the national atlases from Portugal and Spain. After cleaning the dataset of errors as well as records lacking collection dates or not determined to species level, we assigned all occurrences to a 10-km grid. We assessed inventory completeness by calculating the ratio between observed and expected richness (based on the Chao2 richness index) in each grid cell and classified cells as well-sampled or under-sampled. We evaluated survey coverage of well-sampled cells along four environmental gradients and temporal coverage. Out of 796,283 retrieved records, quality issues led us to remove 616,141 records unfit for this use. The main reason for discarding records was missing collection dates. Only 25.95% cells contained enough records to robustly estimate completeness. The DAK about terrestrial mammals from the Iberian Peninsula was low, and spatially and temporally biased. Out of 5,874 cells holding data, only 620 (9.95%) were classified as well-sampled. Moreover, well-sampled cells were geographically aggregated and reached inventory completeness over the same temporal range. Despite the increasing availability of DAK, its usefulness is still compromised by quality issues and gaps in data. Future work should therefore focus on increasing data quality, in addition to mobilizing unpublished data.
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Information from natural history collections (NHCs) about the diversity, taxonomy and historical distributions of species worldwide is becoming increasingly available over the Internet. In light of this relatively new and rapidly increasing resource, we critically review its utility and limitations for addressing a diverse array of applications. When integrated with spatial environmental data, NHC data can be used to study a broad range of topics, from aspects of ecological and evolutionary theory, to applications in conservation, agriculture and human health. There are challenges inherent to using NHC data, such as taxonomic inaccuracies and biases in the spatial coverage of data, which require consideration. Promising research frontiers include the integration of NHC data with information from comparative genomics and phylogenetics, and stronger connections between the environmental analysis of NHC data and experimental and field-based tests of hypotheses.
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Soil ecosystems are inherently complex: space, time and biological diversity interact giving way to emergence of dynamic, complex features such as distribution patterns, abundance profiles, nutrient paths, etc. Specifically, sampling for soil diversity is fraught with problems that arise form the very different spatial scales that involve biological populations’ aggregates and subpopulations. Typical sampling techniques tend either to be ineffective for complexity assessment (i.e. too small to capture a representative subset of most populations and their distributions) or overshot their target with very large samples that can be cost-ineffective. Optimized sampling techniques that use the species-area curves may be inadequate for the purpose of measuring diversity, as they typically focus on the species accumulation rather than on the measurement of structure. Also, species-area curves are sensitive to the accumulation mechanism: the order in which subsamples accumulate matters. We propose an algorithmic method that tries to capture enough data for a cost-effective diversity (complexity) assessment while statistically ensuring consistency. Tests have been done with actual, species-level soil mesofauna fauna data. A C program implements the algorithm.
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The knowledge of species occurrence within an area is crucial to develop proper conservation strategies to protect species diversity. Biosphere Reserves (BRs), established to preserve biodiversity and sustainably use their resources, should therefore have precise information of its biodiversity. We compared and evaluated information on threatened and non-threatened vertebrate species available for Spanish BRs from three sources: management documents (MDs), the Global Biodiversity Information Facility index (GBIF), and atlases and red books. Our results suggest that information from any one source was rather partial, to a degree that depended on which vertebrate group was considered. Management documents did list a high percentage of threatened species found in BRs, reaching up to the total number of species of birds and mammals. Species lists overlap between all three sources ranged from 59 % for fish to 84 % for mammals. In addition, there is an inconsistency between national and international threatened species categories and it should thus call for revisions. Even though the information of non-threatened and threatened species occurrence in MDs of Spanish BRs is good, it is necessary to pay attention to amphibian and fish species which are less recorded.
A quick dip into the literature on diversity reveals a bewildering range of indices. Each of these indices seeks to characterize the diversity of a sample or community by a single number. To add yet more confusion an index may be known by more than one name and written in a variety of notations using a range of log bases. This diversity of diversity indices has arisen because, for a number of years, it was standard practice for an author to review existing indices, denounce them as useless, and promptly invent a new index. Southwood (1978) notes an interesting parallel in the proliferation of new designs of light traps and new permutations of diversity measures.
Practical approaches to measuring biodiversity are reviewed in relation to the present debate on systematic approaches to conservation, to fulfil the goal of representativeness: to identify and include the broadest possible sample of components that make up the biota of a given region. Rather than adapting earlier measures that had been developed for other purposes, the most recent measures result from a fresh look at what exactly is of value to conservationists. Although debate will continue as to where precisely these values lie, more of the discussion has been devoted to ways of estimating values in the absence of ideal information. We discuss the current principles by assuming that the currency of biodiversity is characters, that models of character distribution among organisms are required for comparisons of character diversity, and that character diversity measures can be calculated using taxonomic and environmental surrogates. Full text at: