Effect of the conversion of mangroves into shrimp farms on carbon stock in the sediment along the southern Red Sea coast, Saudi Arabia

The conservation of coastal ecosystems and specially mangroves ‘‘blue carbon’’ is receiving more attention as consequence of their recognition as high ecosystem carbon stocks and for the fact that these areas are undergoing land conversion. The aim of the present study is to evaluate the impact of land use changes due to conversion of mangroves to shrimp farms on the bulk density (SBD), organic carbon (SOC) concentration, and SOC stock in the sediments along the southern Saudi Arabian Red Sea coast. Shrimp farms and mangrove locations showed significant (P < 0.001) differences in SBD with high mean values in the sediments of shrimp farms. Shrimp farms and mangrove locations showed significant (P < 0.001) SOC concentration differences with high mean values in the sediments of mangroves. Considering the whole depth of sediment interval (0-100 cm), the highest value of SOC stock was recorded at mangroves (29.2 kg C m−2) and the lowest was identified at the locations of shrimp farms (19.9 kg C m−2). The results show that SOC stock of mangroves is 147% higher than that of shrimp farms confirming the fact that anthropogenic factors contributed significantly to SOC stock decrease. The mean cumulative potential carbon dioxide (CO2) emission due to loss soil carbon stock from mangrove conversion to the shrimp ponds was 34.9 kg CO2 m−2. In conclusion, the conversion of mangroves into shrimp farms contributed to the loss of SOC stock, therefore, the preservation of mangrove areas has an important value especially in arid areas such as Saudi Arabia. © 2019 Elsevier Inc.The financial support from the Distinguished Scientist Fellowship Program (DSFP) from King Saud University, Saudi Arabia is gratefully acknowledged.Peer reviewe

sPlotOpen – An environmentally-balanced, open-access, global dataset of vegetation plots

Motivation Assessing biodiversity status and trends in plant communities is critical for understanding, quantifying and predicting the effects of global change on ecosystems. Vegetation plots record the occurrence or abundance of all plant species co-occurring within delimited local areas. This allows species absences to be inferred, information seldom provided by existing global plant datasets. Although many vegetation plots have been recorded, most are not available to the global research community. A recent initiative, called ‘sPlot’, compiled the first global vegetation plot database, and continues to grow and curate it. The sPlot database, however, is extremely unbalanced spatially and environmentally, and is not open-access. Here, we address both these issues by (a) resampling the vegetation plots using several environmental variables as sampling strata and (b) securing permission from data holders of 105 local-to-regional datasets to openly release data. We thus present sPlotOpen, the largest open-access dataset of vegetation plots ever released. sPlotOpen can be used to explore global diversity at the plant community level, as ground truth data in remote sensing applications, or as a baseline for biodiversity monitoring. Main types of variable contained Vegetation plots (n = 95,104) recording cover or abundance of naturally co-occurring vascular plant species within delimited areas. sPlotOpen contains three partially overlapping resampled datasets (c. 50,000 plots each), to be used as replicates in global analyses. Besides geographical location, date, plot size, biome, elevation, slope, aspect, vegetation type, naturalness, coverage of various vegetation layers, and source dataset, plot-level data also include community-weighted means and variances of 18 plant functional traits from the TRY Plant Trait Database. Spatial location and grain Global, 0.01–40,000 m². Time period and grain 1888–2015, recording dates. Major taxa and level of measurement 42,677 vascular plant taxa, plot-level records. Software format Three main matrices (.csv), relationally linked

sPlotOpen – An environmentally balanced, open-access, global dataset of vegetation plots

Motivation Assessing biodiversity status and trends in plant communities is critical for understanding, quantifying and predicting the effects of global change on ecosystems. Vegetation plots record the occurrence or abundance of all plant species co-occurring within delimited local areas. This allows species absences to be inferred, information seldom provided by existing global plant datasets. Although many vegetation plots have been recorded, most are not available to the global research community. A recent initiative, called ‘sPlot’, compiled the first global vegetation plot database, and continues to grow and curate it. The sPlot database, however, is extremely unbalanced spatially and environmentally, and is not open-access. Here, we address both these issues by (a) resampling the vegetation plots using several environmental variables as sampling strata and (b) securing permission from data holders of 105 local-to-regional datasets to openly release data. We thus present sPlotOpen, the largest open-access dataset of vegetation plots ever released. sPlotOpen can be used to explore global diversity at the plant community level, as ground truth data in remote sensing applications, or as a baseline for biodiversity monitoring. Main types of variable contained Vegetation plots (n = 95,104) recording cover or abundance of naturally co-occurring vascular plant species within delimited areas. sPlotOpen contains three partially overlapping resampled datasets (c. 50,000 plots each), to be used as replicates in global analyses. Besides geographical location, date, plot size, biome, elevation, slope, aspect, vegetation type, naturalness, coverage of various vegetation layers, and source dataset, plot-level data also include community-weighted means and variances of 18 plant functional traits from the TRY Plant Trait Database. Spatial location and grain Global, 0.01–40,000 m². Time period and grain 1888–2015, recording dates. Major taxa and level of measurement 42,677 vascular plant taxa, plot-level records. Software format Three main matrices (.csv), relationally linked

sPlotOpen - An environmentally balanced, open-access, global dataset of vegetation plots

Motivation Assessing biodiversity status and trends in plant communities is critical for understanding, quantifying and predicting the effects of global change on ecosystems. Vegetation plots record the occurrence or abundance of all plant species co-occurring within delimited local areas. This allows species absences to be inferred, information seldom provided by existing global plant datasets. Although many vegetation plots have been recorded, most are not available to the global research community. A recent initiative, called 'sPlot', compiled the first global vegetation plot database, and continues to grow and curate it. The sPlot database, however, is extremely unbalanced spatially and environmentally, and is not open-access. Here, we address both these issues by (a) resampling the vegetation plots using several environmental variables as sampling strata and (b) securing permission from data holders of 105 local-to-regional datasets to openly release data. We thus present sPlotOpen, the largest open-access dataset of vegetation plots ever released. sPlotOpen can be used to explore global diversity at the plant community level, as ground truth data in remote sensing applications, or as a baseline for biodiversity monitoring. Main types of variable contained Vegetation plots (n = 95,104) recording cover or abundance of naturally co-occurring vascular plant species within delimited areas. sPlotOpen contains three partially overlapping resampled datasets (c. 50,000 plots each), to be used as replicates in global analyses. Besides geographical location, date, plot size, biome, elevation, slope, aspect, vegetation type, naturalness, coverage of various vegetation layers, and source dataset, plot-level data also include community-weighted means and variances of 18 plant functional traits from the TRY Plant Trait Database. Spatial location and grain Global, 0.01-40,000 m(2). Time period and grain 1888-2015, recording dates. Major taxa and level of measurement 42,677 vascular plant taxa, plot-level records. Software format Three main matrices (.csv), relationally linked

sPlot - A new tool for global vegetation analyses

Dengler, Jurgen/0000-0003-3221-660X; Chytry, Milan/0000-0002-8122-3075; de Gasper, Andre Luis/0000-0002-1940-9581; Marceno, Corrado/0000-0003-4361-5200; Swacha, Grzegorz/0000-0002-6380-2954; He, Tianhua/0000-0002-0924-3637; Haider, Sylvia/0000-0002-2966-0534; Kuhn, Ingolf/0000-0003-1691-8249; Svenning, Jens-Christian/0000-0002-3415-0862; Jansen, Florian/0000-0002-0331-5185; Casella, Laura/0000-0003-2550-3010; Schmidt, Marco/0000-0001-6087-6117; Chepinoga, Victor/0000-0003-3809-7453; Petrik, Petr/0000-0001-8518-6737; Willner, Wolfgang/0000-0003-1591-8386; Jansen, Steven/0000-0002-4476-5334; De Sanctis, Michele/0000-0002-7280-6199; Niinemets, Ulo/0000-0002-3078-2192; Pauchard, Anibal/0000-0003-1284-3163; Vibrans, Alexander C./0000-0002-8789-5833; Biurrun, Idoia/0000-0002-1454-0433; De Patta Pillar, Valerio/0000-0001-6408-2891; Phillips, Oliver L/0000-0002-8993-6168; Sibik, Jozef/0000-0002-5949-862X; Lenoir, Jonathan/0000-0003-0638-9582; Venanzoni, Roberto/0000-0002-7768-0468; Gutierrez, Alvaro G./0000-0001-8928-3198; Cayuela, Luis/0000-0003-3562-2662; Nobis, Marcin/0000-0002-1594-2418; Agrillo, Emiliano/0000-0003-2346-8346; Manning, Peter/0000-0002-7940-2023; Venanzoni, Roberto/0000-0002-7768-0468; Virtanen, Risto/0000-0002-8295-8217; Higuchi, Pedro/0000-0002-3855-555X; Sopotlieva, Desislava/0000-0002-9281-7039; Kuzemko, Anna/0000-0002-9425-2756; Hatim, Mohamed/0000-0002-0872-5108; Mencuccini, Maurizio/0000-0003-0840-1477; Enquist, Brian J./0000-0002-6124-7096; De Bie, Els/0000-0001-7679-743X; Samimi, Cyrus/0000-0001-7001-7893; Nowak, Arkadiusz/0000-0001-8638-0208; Jimenez-Alfaro, Borja/0000-0001-6601-9597; Font, Xavier/0000-0002-7253-8905; Levesley, Aurora/0000-0002-7999-5519; Acic, Svetlana/0000-0001-6553-3797; Kattge, Jens/0000-0002-1022-8469; Silc, Urban/0000-0002-3052-699X; Arnst, Elise/0000-0003-2388-7428; Moretti, Marco/0000-0002-5845-3198; Kozub, Lukasz/0000-0002-6591-8045; Kacki, Zygmunt/0000-0002-2241-1631; Fagundez, Jaime/0000-0001-6605-7278; Purschke, Oliver/0000-0003-0444-0882; Martynenko, Vasiliy/0000-0002-9071-3789; Jandt, Ute/0000-0002-3177-3669; Peyre, Gwendolyn/0000-0002-1977-7181; SABATINI, FRANCESCO MARIA/0000-0002-7202-7697; Bruelheide, Helge/0000-0003-3135-0356; Wohlgemuth, Thomas/0000-0002-4623-0894; Onyshchenko, Viktor/0000-0001-9079-7241; Kuzmic, Filip/0000-0002-3894-7115; Ejrnaes, Rasmus/0000-0003-2538-8606; Jirousek, Martin/0000-0002-4293-478X; Noroozi, Jalil/0000-0003-4124-2359; Curran, Michael/0000-0002-1858-5612; Baraloto, Christopher/0000-0001-7322-8581; Ozinga, Wim/0000-0002-6369-7859WOS: 000466421500001Aims Vegetation-plot records provide information on the presence and cover or abundance of plants co-occurring in the same community. Vegetation-plot data are spread across research groups, environmental agencies and biodiversity research centers and, thus, are rarely accessible at continental or global scales. Here we present the sPlot database, which collates vegetation plots worldwide to allow for the exploration of global patterns in taxonomic, functional and phylogenetic diversity at the plant community level. Results sPlot version 2.1 contains records from 1,121,244 vegetation plots, which comprise 23,586,216 records of plant species and their relative cover or abundance in plots collected worldwide between 1885 and 2015. We complemented the information for each plot by retrieving climate and soil conditions and the biogeographic context (e.g., biomes) from external sources, and by calculating community-weighted means and variances of traits using gap-filled data from the global plant trait database TRY. Moreover, we created a phylogenetic tree for 50,167 out of the 54,519 species identified in the plots. We present the first maps of global patterns of community richness and community-weighted means of key traits. Conclusions The availability of vegetation plot data in sPlot offers new avenues for vegetation analysis at the global scale.German Research FoundationGerman Research Foundation (DFG) [DFG FZT 118]; TRY initiative on plant traitsWe are grateful to thousands of vegetation scientists who sampled vegetation plots in the field or digitized them into regional, national or international databases. We also appreciate the support of the German Research Foundation for funding sPlot as one of the iDiv (DFG FZT 118) research platforms, and the organization of three workshops through the sDiv calls. We acknowledge this support with naming the database "sPlot", where the "s" refers to the sDiv synthesis workshops. The study was supported by the TRY initiative on plant traits (http://www.try-db.org). For all further acknowledgements see Appendix S10. We thank Meelis Partel for his very fast and constructive feedback on an earlier version of this manuscript

sPlotOpen:an environmentally balanced, open-access, global dataset of vegetation plots

Abstract Motivation: Assessing biodiversity status and trends in plant communities is critical for understanding, quantifying and predicting the effects of global change on ecosystems. Vegetation plots record the occurrence or abundance of all plant species co-occurring within delimited local areas. This allows species absences to be inferred, information seldom provided by existing global plant datasets. Although many vegetation plots have been recorded, most are not available to the global research community. A recent initiative, called ‘sPlot’, compiled the first global vegetation plot database, and continues to grow and curate it. The sPlot database, however, is extremely unbalanced spatially and environmentally, and is not open-access. Here, we address both these issues by (a) resampling the vegetation plots using several environmental variables as sampling strata and (b) securing permission from data holders of 105 local-to-regional datasets to openly release data. We thus present sPlotOpen, the largest open-access dataset of vegetation plots ever released. sPlotOpen can be used to explore global diversity at the plant community level, as ground truth data in remote sensing applications, or as a baseline for biodiversity monitoring. Main types of variable contained: Vegetation plots (n = 95,104) recording cover or abundance of naturally co-occurring vascular plant species within delimited areas. sPlotOpen contains three partially overlapping resampled datasets (c. 50,000 plots each), to be used as replicates in global analyses. Besides geographical location, date, plot size, biome, elevation, slope, aspect, vegetation type, naturalness, coverage of various vegetation layers, and source dataset, plot-level data also include community-weighted means and variances of 18 plant functional traits from the TRY Plant Trait Database. Spatial location and grain: Global, 0.01–40,000 m². Time period and grain: 1888–2015, recording dates. Major taxa and level of measurement: 42,677 vascular plant taxa, plot-level records. Software format: Three main matrices (.csv), relationally linked

sPlot:a new tool for global vegetation analyses

Abstract Aims: Vegetation‐plot records provide information on the presence and cover or abundance of plants co‐occurring in the same community. Vegetation‐plot data are spread across research groups, environmental agencies and biodiversity research centers and, thus, are rarely accessible at continental or global scales. Here we present the sPlot database, which collates vegetation plots worldwide to allow for the exploration of global patterns in taxonomic, functional and phylogenetic diversity at the plant community level. Results: sPlot version 2.1 contains records from 1,121,244 vegetation plots, which comprise 23,586,216 records of plant species and their relative cover or abundance in plots collected worldwide between 1885 and 2015. We complemented the information for each plot by retrieving climate and soil conditions and the biogeographic context (e.g., biomes) from external sources, and by calculating community‐weighted means and variances of traits using gap‐filled data from the global plant trait database TRY. Moreover, we created a phylogenetic tree for 50,167 out of the 54,519 species identified in the plots. We present the first maps of global patterns of community richness and community‐weighted means of key traits. Conclusions: The availability of vegetation plot data in sPlot offers new avenues for vegetation analysis at the global scale