Dark diversity reinforces the importance of climate, soil and disturbance for Caatinga plant diversity

doi:10.21203/rs.3.rs-3249132/v1

Download PDF

Research Article

Dark diversity reinforces the importance of climate, soil and disturbance for Caatinga plant diversity

https://doi.org/10.21203/rs.3.rs-3249132/v1

This work is licensed under a CC BY 4.0 License

Journal Publication

published 27 Jan, 2024

Read the published version in Biodiversity and Conservation →

You are reading this latest preprint version

Species richness is the most commonly used metric to quantify biodiversity. However, examining dark diversity, the group of absent species which can potentially inhabit a site, can provide insights about the factors influencing species assembly. In this study we aimed to understand how climate, soil, acute, and chronic disturbance factors affect not only observed diversity but also dark diversity, completeness (how large is the observed diversity compared to dark diversity) and species pool size (observed and dark diversity together) patterns of a Seasonally Dry Tropical Forest in Brazil. Along 144 0.4-ha plots for trees and 0.04-ha plots for shrubs, herbs, and lianas, we recorded 2148 stems from 232 native species. In general, rainfall was the major drive of plants diversity, whereas soil fertility, deforestation and chronic anthropogenic disturbance had a minor effect. Dark diversity, completeness, species pool and observed diversity respond differently to the different predictors, indicating the complex biodiversity relationship with environmental and disturbance factors. These results highlight the importance of using different metrics of species richness and considering different groups in plant communities’ studies.

Completeness

Rainfall

Richness

Seasonally Dry Tropical Forests

Species pool

Many factors can affect plant diversity, such as climate, soil, and anthropogenic disturbance (Leigh Jr. 2004; Liang et al. 2022). The diversity of plants tends to be higher in wetter areas and more fertile soils (Turner 2001; Silva et al. 2017), whereas some species are negatively affected by deforestation (Haila 2002; Laurance 2008; Fahrig 2013; Watling et al. 2020) and chronic disturbances (i.e., the slow removal of biomass, sensu Singh 1998). Therefore, several factors together have a complex relationship with plant diversity, mostly measured as species richness.

Plant richness tends to be higher in larger areas or with more intense sampling effort (Ricklefs et al. 2014). However, some species could occur locally, but are absent (Pärtel et al. 2011; Tang et al. 2019), meaning that each site has its own dark diversity, i.e., the species that are not recorded at a target site, but belong to its site-specific species pool (Pärtel et al. 2011). Dark diversity, as the observed diversity, varies among regions and habitat types due to differences in environmental conditions, and land use (Lenoir et al. 2010). Additionally, the difference between the observed and the dark diversity, i.e., community completeness index, can be informative to detect local biodiversity drivers (sensu Szava-Kovats et al. 2012). Completeness shows how much the local diversity is varying when we account species pool size differences.

Seasonally dry tropical forests (SDTF) are one of the richest floras in the tropical zone, with high endemism of woody plants (Banda-R et al. 2016). SDTF can be associated with low levels of rainfall and high temperatures, with strong seasonal drought (Maia et al. 2020) on fertile soils, and with a moderate to high soil pH (Murphy and Lugo 1986; Pennington et al. 2009; Le Bagousse-Pinguet et al. 2017). Nevertheless, SDTF are also under intense disturbance, mainly acute (i.e., deforestation; Malhi et al. 2014; Allen et al. 2017; Rito et al. 2017) and chronic anthropogenic disturbance (Singh 1998). Most SDTFs historically support low-income rural populations widely dependent on forest resources for proper livelihood, such as fodder, firewood and timber (Singh 1998; Davidar et al. 2010; Ribeiro et al. 2015), setting additional pressure on plant diversity.

Caatinga, with over 800,000 km², is considered one of the largest SDTF (Sampaio 1995; Miles et al. 2006). Nearly half of the vegetation cover was lost due to human occupation and the remaining habitat is under intense human pressure (Antongiovanni et al. 2018; Souza et al. 2019) by slash-and-burn agriculture, logging and fuelwood extraction, causing both forest loss and degradation (Melo 2017; Silva et al. 2017). The seasonality and rainfall distribution, associated with elevated temperatures and high variable edaphic conditions, drive a diverse spectrum of Caatinga formations (Moro et al. 2014, 2015) from open areas with shrub and herbaceous vegetation, to areas where tree species predominate in both species’ richness, such as arboreal Caatingas (sensu Graeff 2015). In general, plant richness in Caatinga tend to be higher in wetter and less disturbed areas (Rito et al. 2016). However, how the dark diversity of the different life-forms of plants vary along environmental and disturbance gradients is still unknown in SDTF.

Aiming to understand how the relative importance of natural and anthropogenic factors affect not only species richness but also dark diversity, completeness, and species pool patterns of different life-forms (i.e., trees, shrubs, herbs, and lianas), we ask: 1. do the environmental and disturbance variables influence the observed diversity of plants (species richness) and their different life-forms in the Caatinga? 2. whether dark diversity, completeness and species pool richness respond differently compared to observed diversity and bring new information to biodiversity patterns and processes?

We tested the hypothesis that plant diversity increases, and dark diversity decreases with larger and less isolated patches landscapes surrounded by more habitat, with lower chronic anthropogenic disturbance, wetter and hotter climate, and higher soil fertility.

2.1 Study area

The study was carried out in Seasonally Dry Tropical Forest, named locally as Caatinga. The Caatinga constitutes the largest and one of the richest SDTF floras in the Neotropics (Pennington et al. 2009). The state of Ceará, which is located in the Northeast region of Brazil, is almost entirely encompassed within the Caatinga domain (IBGE 2019). Approximately 70% of Ceará is situated on the Precambrian crystalline basement, which is an ancient formation that has undergone significant erosion over time (Maia et al. 2010). According to Moro et al. (2015), the vegetation above the crystalline basement in the Ceará is classified as "caatinga do cristalino" (henceforth crystalline caatinga; Fig. 1). Crystalline caatinga experiences an average annual temperature of approximately 25°C and receives an annual rainfall between 240 to 1500 mm, which is concentrated over four months of the year during the summer and autumn seasons (Sampaio 1995; Prado 2003). The soils are shallow, with superficial pebble stones and moderate to rich in nutrients (Sampaio 1995; De Queiroz et al. 2017), with low water retention (De Queiroz et al. 2017). The vegetation is deciduous and spiny shrubland or woodland, with annual herbs (Leal et al. 2005; Pennington et al. 2009; De Queiroz et al. 2017).

2.2 Sampling design

We used the IFN (“Inventário Florestal Nacional”) database provided by the Brazilian Forest Service (“Serviço Florestal Brasileiro”; SFB 2019), which comprises 376 grids of 20 km x 20 km (named conglomerate) distant at least 20 Km from each other (Fig. 1). We selected only 144 plots located in crystalline Caatinga (see Moro et al. 2016). Starting in October 2013 and finishing in August 2014, a conglomerate was installed with four sample subunits with 20 m x 50 m each, representing a total area of 4,000 m². In each subunit, all trees, cacti, and palms with diameter at breast height (DAP) greater than or equal to 10 cm were sampled. As most woody plants in Caatinga have DAP ≤ 10 cm (SFB 2019), a smaller plot (10 m x 10 m) was installed inside the subunit and shrubs with a DAP between 5 and 9.9 cm, herbs and lianas were sampled. Some species were recorded in more than one life-form. A total of 2,148 botanical samples of trees, shrubs, herbs, and lianas individuals were collected and sent to the “Herbário Prisco Bezerra” of the Federal University of Ceará (UFC) to identify the species with assistance from specialists following the Flora e Fungi do Brasil. Although the IFN data has exotic and native species, we considered only native species in the following analyses (Table S1). Results with native and exotic species together are in Table S2.

2.3 Predictor variables

Standardized climatic data - For all the sites we extracted the 19 bioclimatic variables from CHELSA (www.chelsa.org.br), a high resolution (30 arc seconds or ~ 1 km at equator) database for the 1979–2013 period (Karger et al. 2017). We retained the uncorrelated variables (r < 0.25 and p > 0.05 ): mean annual temperature (°C*10, MAT), mean annual precipitation (mm/year, MAP) and precipitation of driest quarter (mm/quarter), and the range of variables covered by the study sites was 23.7°C to 28.9°C, 506 mm/yr to 1,172 mm/yr, and 3 mm/yr to 30 mm/yr, respectively. Because the Caatinga is a semi-arid region, we considered climatic variables related to temperature and precipitation as potentially significant ecological drivers of species richness.

Soil Sampling - Soil data were collected by IFN in each plot within a 2 meters radius from the central point of each conglomerate, with samples at a depth of 0–20 cm, using Dutch auger or digger. The soil samples were sent to a specialized laboratory for analysis. From 15 chemical and physical variables, we considered only the uncorrelated: phosphorus (mg.kg^− 1), base saturation index (%) and clay content (g.kg^− 1). Higher values of these variables were positively related to soil fertility (Lu et al. 2002; Santos et al. 2012). For more details about soil sampling, see the National Forest Inventory Field Manual and forms, available at: SNIF - IFN - Dados abertos (www.florestal.gov.br).

Deforestation variables - The crystalline caatinga map (Moro et al. 2015) was used as a mask layer to cut the Mapbiomas land use and land cover data. Then, we used georeferenced digital files containing data on vegetation remnants, with the last updated map and accuracy = 81.8% (MapBiomas 2019) adjusting the date of the satellite image to the end of 2014, coinciding with the forest inventory in the state of Ceará (SFB 2019). The Mapbiomas dataset was reclassified to create habitat and non-habitat. The cell size of satellite images used to create forest cover maps was 30 m x 30 m (MapBiomas 2019). The habitat category included only Caatinga vegetation, whereas non-habitat category included several land use types, such as pasture (1,924,517.82 ha, 39.57%), agriculture (497,305.69 ha, 10.22%), agriculture and pasture mosaic (1,642,524.19, 33.77%), temporary crops (217,739.72 ha, 4.48%), urban infrastructure (81,540.90 ha, 1.68%), mining (268.80 ha, 0.005%), perennial crop (279,565.96 ha, 5.75%), soy beans crop (1.43 ha, 0.00003%), other temporary crops (217,738.30 ha, 4.48%) and other non-vegetated areas (2,853.32 ha, 0.06%) (MapBiomas 2019; Souza Jr et al. 2020).

Additionally, the shapefile composed of a layer paved (provided by DNIT – “Departamento Nacional de Infraestruturas de Transportes”; http://servicos.dnit.gov.br/vgeo) and unpaved (provided by OSM – OpenStreetMap; http://www.openstreetmap.org/) roads (IBGE 2010) were superimposed on the shapefile of the forest remnants to obtain a more accurate scenario of the habitat amount pattern of the Caatinga. An extension of 110 m and 60 m for each side of the paved and unpaved roads, respectively, was arbitrarily considered as a deforested area (i.e., non-habitat) and subtracted from the original forest area (based on Antongiovanni et al. 2018).

We considered the habitat amount, patch size (the patch in which a sample plot is located) (Watling et al. 2020) and isolation (based in distance metric) with distance-based on nearest-neighbour distance effects (Prugh 2009) based on Fahrig (2013, 2017) and calculated with the “class level metric” in landscape metrics package (Hesselbarth et al. 2019) in R (R Core Team 2019). We extracted the landscape parameters for buffers from 1km to 6km from the focal plot. For all groups (i.e., trees, shrubs, herbs, and lianas), 6km was retained (Jackson and Fahrig 2015). Due to its the greatest effect which produced the strongest species-landscape relationship. The patch size data was obtained by measuring the area of patch that contains the plot and isolation was calculated on the basis the nearest-neighbors mean distance of the focal patch to three other patches in the local landscape (Fahrig 2013; Watling et al. 2020).

Chronic disturbance index (CDI) - We used the CDI calculated by Antongiovanni et al. (2020) that was estimated using 14 variables, such as human population, infrastructure, grazing, logging, and fire. A correlation matrix among the 14 variables demonstrated that 96.8% of the 91 estimated correlations were lower than 0.4, supporting their relative independence. All data is available in the Dryad Digital Repository by Antongiovanni et al. (2020). For those 45 plots that had no data, we took the three nearest points/pixels to the CDI value for the Ceará state.

2.4 Estimation of dark diversity, community completeness and species pool

To obtain the best possible estimates of observed and dark diversity, we used hypergeometric probability index (Carmona and Pärtel 2020). This index can be used to estimate the probability of a species occurring in a particular region based on its co-occurrence within other regions and was calculated using the package “DarkDiv” (Carmona and Pärtel 2020) in R. For each plot, a species was included in the dark diversity when it was absent from a target plot, but its suitability was higher than 70%. The regional pool was calculated between the sum of observed diversity and dark diversity (Pärtel et al. 2011; Zobel 2016). This species pool is site-specific and varies according to the specific ecological conditions at the target sites. In addition, community completeness was calculated as the proportion of local diversity in relation to its species pool size affiliated with specific habitat condition (Zobel 1997; Eriksson 1993; Zobel et al. 2011; Pärtel et al. 2013; Bennett et al. 2016; Zobel 2016; Aning 2017), using logit transformation: ln (observed richness/dark diversity). High value means that observed diversity is relatively large compared to dark diversity. The community completeness determines how much of the species pool was realized within a local community (Pärtel et al. 2013).

2.5 Data analysis

We tested for spatial correlation with semivariograms considering the statistical model with species richness and the spatial distribution of the plots. As the spatial correlation was low, we used general linear models (GLMs) with the response variables: plant richness, dark diversity, completeness and species pool. For predictor variables we use annual mean temperature, annual mean precipitation and precipitation of driest quarter for climate, base saturation index, assimilable phosphorus and clay content for soil and acute (habitat amount, patch size, patch isolation) and CDI for disturbances. The predictor variables were z-transformed to put the them on the same scale. For explanatory variables in a set of generalized linear models (GLMs) we use Poisson distribution corrected by over dispersed data for plant richness, dark diversity and species pool and, Gaussian distribution for completeness. All analyses were performed in R version 4.4.1 (R Core Team, 2019).

We found 232 native species, in which 78 were trees, 76 were shrubs, 77 were herbs and 23 were lianas (Table 1). The mean ± standard deviation of dark diversity was up to three times higher than observed diversity per plot. The negative values of completeness confirmed the high dark diversity in the studied area (Table 1).

Table 1

Total number of species and the mean ± standard deviation of observed, dark diversity, species pool (observed + dark diversity) and completeness (ln (observed/dark diversity)) of species per plot.
	Number of species	Observed diversity	Dark diversity	Species pool	Completeness
All	232	3.33 ± 2.64	13.78 ± 9.54	17.11 ± 10.22	-1.22 ± 1.18
Trees	78	1.14 ± 1.69	3.09 ± 4.67	4.24 ± 5.75	-3.11 ± 3.00
Shrubs	76	0.99 ± 1.08	1.51 ± 1.47	2.51 ± 2.11	-2.25 ± 3.04
‍Herbs	77	0.97 ± 1.02	1.72 ± 2.15	2.69 ± 2.75	-2.30 ± 3.05
Lianas	23	0.25 ± 0.55	0.59 ± 0.39	0.84 ± 0.81	-4.89 ± 2.67

The observed number of species, dark diversity, completeness and species pool size metrics were highly correlated r>|0.7|. Despite of that, the statistical models with dark diversity were different with observed diversity (Fig. 2). For instance, when considering all species together, precipitation in the driest quarter and patch size were positively significant for observed diversity. On the other hand, patch isolation was negatively significant for observed diversity, whereas it was only marginally positively significant for precipitation for dark diversity (Fig. 2, Table S1). In general, soil variables are poor predictors of species diversity (Table S1), except for clay content affecting dark diversity and percentage of base saturation affecting species pool of trees (Table S1, Fig. 2). On the other hand, habitat amount and rainfall are more prone to be related to species diversity (Fig. 2). Areas with higher rainfall in the driest months tend to have higher observed diversity of plants (all life forms together), but low dark diversity of shrubs (Fig. 2). Areas with higher mean annual rainfall tend to have higher species pool of all life-forms, observed diversity and species pool of trees, shrubs and herbs. Areas with higher temperature tend to have higher species pool and lower completeness of shrubs (Table S1, Fig. 2).

Concerning acute disturbance, habitat amount was positively related to dark diversity of lianas, whereas patch isolation negatively to observed diversity of all life forms, and herbs. Patch size was positively related to observed diversity of all life forms, trees, species pool and completeness of trees (Table S1, Fig. 2). Chronic anthropogenic disturbance was only positively related to completeness of trees (Fig. 2).

Our study addresses the effect of local abiotic conditions in shaping communities of vascular plants in Caatinga and shows that, in general, climate variables, particularly rainfall, are the best predictor of diversity. Moreover, the richness and dark diversity of the different life-forms tend to respond differently to environmental and disturbance variables. These results highlight the importance to calculate different metrics of species richness and consider different life-forms in plant community studies.

In general, the different metrics of richness respond differently to the environment and disturbance. For instance, whereas observed diversity of plants does not respond, species pool has a positive effect in higher rainfall, indicating that rainfall increases the regional diversity, but has little effect on the local diversity of plants. This result is consequence to the fact that observed and dark diversity might vary independently and high observed diversity do not always mean low dark diversity and vice versa (Pärtel et al. 2011). Additionally, this result also highlights the importance to consider not only the observed richness, but the different facets of diversity in community ecology (Lewis et al. 2016; Ronk et al. 2016).

In general, wetter areas tend to have higher diversity of plants, particularly trees. Rainfall is strongly related to plant assemblages in the Brazilian Caatinga vegetation, particularly affecting tree and shrub species richness (Ribeiro et al. 2015; Rito et al. 2017). Although rainfall positively affect the diversity of trees, the species pool of herbs was negatively affected by rainfall. As woody plants are also favored in wetter areas, herbs could be excluded by competition with them, and, thus, the richness of herbs could be higher in drier areas (Vieira et al. 2022). In fact, herbs can have 79% of plant richness in some dry areas (Queiroz et al. 2015), contributing not only to the local richness, but also to the regional species pool.

Habitat amount, on the other hand, had little effect on plant diversity. Deforestation drastically alters the amount of habitat, causing profound impacts on biodiversity (Fahrig 2013; Thompson et al. 2017; Watling et al. 2020). Recently, it has been argued that the amount of habitat in the landscape is the main predictor of biodiversity, whereas the spatial structure of the fragmentation plays a secondary role (Fahrig 2013, 2017). Our results show that in Caatinga habitat amount does not have a stronger effect than patch size and patch isolation, as proposed by Fahrig (2013) and Watling et al. (2020). In fact, more isolated and smaller forest fragments tend to have lower diversity. Interestingly, a high dark diversity for lianas was found in larger patches, although some studies show that lianas are favored by disturbance and, thus, are more abundant in small patches and edge of forest fragments (Laurance et al. 2001; Campbell et al. 2018). On the other hand, lianas in the Caatinga have shallow roots and low resistance to cavitation (Carvalho et al. 2016) and could be less favored by disturbance than lianas from wetter forests. Therefore, dry season probably has a minor effect on the liana diversity, whereas more habitat could not only imply on more suitable area for seed germination, but also more host trees for lianas (Schnitzer 2018).

Similar to habitat amount, chronic anthropogenic disturbance has minor effect on plant diversity. Chronic disturbance decreases tree species diversity and structure in a dry area of Caatinga vegetation (Ribeiro et al. 2015, 2016; Rito et al. 2017), disrupting species interaction (Câmara et al. 2018) and changing functional diversity (Sfair et al. 2018). Similarly, to our results, chronic disturbance can also have little impact on ecosystem services, such as carbon storage (Souza et al. 2019). We only found a positive effect of chronic disturbance on the completeness of trees: in more disturbed areas, the proportion of dark diversity is lower. In this case, probably the proportion of local diversity is high due to the propagation of disturbed-adapted species in relation to the regional species pool.

We have shown the importance of environmental and disturbance factors in changing native species richness, dark diversity, completeness and species pool in Caatinga landscape. Therefore, it is important to consider the different plant life-forms for a better evaluation of the processes structuring communities in such areas, which can be mediated by climate, soil, and disturbances variables. In addition, species richness and dark diversity of plants life-forms may respond differently to the distinct intensities of anthropogenic factors. A wider recognition of the importance of the dark diversity can contribute to the development of public policies, including prioritizing conservation areas on dry forests with high observed richness and species pool, which may be one of the best solutions to help Caatinga resist human disturbances.

Author Contributions: Conceptualization, M.D.O.G.A., J.C.S., and C.E.C; data curation, M.D.O.G.A., J.C.S., C.E.C; P.A.C.L.P., D.P.F.T, and M.P.; formal analysis, M.D.O.G.A. and J.C.S.; investigation, M.D.O.G.A., J.C.S., and C.E.C; methodology, M.D.O.G.A., J.C.S., C.E.C; P.A.C.L.P., D.P.F.T, F.H., and M.P.; resources, F.S.A.; software, M.D.O.G.A., J.C.S., C.E.C; P.A.C.L.P., D.P.F.T, and M.P.; supervision, J.C.S and M.P; validation, M.D.O.G.A., J.C.S., C.E.C; P.A.C.L.P., D.P.F.T, F.H., F.S.A. and M.P.; visualization, M.D.O.G.A., J.C.S., P.A.C.L.P., D.P.F.T, and M.P.; writing—original draft, M.D.O.G.A. and J.C.S.; writing—review and editing, M.D.O.G.A. and J.C.S. All authors have read and agreed to the published version of the manuscript.

Funding: This study was supported by Cearense Foundation for Scientific and Technological Development (Funcap), Higher Education Personnel Improvement Coordination (Capes), Estonian Research Council (PRG609), and European Union (EU).

Data Availability Statement: The species data and the soil variables, phosphorus, base saturation index and clay content, were provided by National Forest Inventory (IFN) (http://snif.florestal.gov.br/pt-br/inventario-florestal-nacional-ifn/ifn-dados-abertos/ifn-resultados-ceara, accessed on 14 December 2020); the climatic variables, annual mean temperature, annual precipitation and precipitation of driest quarter data were obtained from the Climatologies at high resolution for the earth’s land surface areas (https://www.nature.com/articles/s41597-020-00587-y, accessed on 5 March 2020); the deforestation variables was obtained from acute and chronic (http://datadryad.org/stash/dataset/doi:10.5061/dryad.k0p2ngf57, 10 October 2022) disturbances.

Acknowledgments: The authors are thankful to the Graduate Program in Ecology and Natural Resources, Federal Univesity of Ceará, Tartu Botanical Gardens, and Macroecology workgroup, Oecologicum-Institute of Ecology and Earth Sciences, University of Tartu.

Conflicts of Interest: The authors declare no conflict of interest.

Allen K, Dupuy JM, Gei MG, Hulshof C, Medvigy D, Pizano C, Powers JS et al (2017) Will seasonally dry tropical forests be sensitive or resistant to future changes in rainfall regimes? Environmental Research Letters 12:023001. https://doi.org/10.1088/1748-9326/aa5968.
Aning JV (2017) Diversity and completeness of north american mammal assemblages. Dissertation, Utrecht University.
Antongiovanni M, Venticinque EM, Matsumoto M, Fonseca CR (2020) Chronic anthropogenic disturbance on Caatinga dry forest fragments. Journal of Applied Ecology 00:1–11. https://doi.org/10.1111/1365-2664.13686.
Antongiovanni M, Venticinque EM, Fonseca CR (2018) Fragmentation patterns of the Caatinga drylands. Landscape Ecology 33:1353–1367. https://doi.org/10.1007/s10980-018-0672-6.
Banda-R K, Delgado-Salinas A, Dexter KG, Linares-Palomino R, Oliveira-Filho A, Prado D, Pullan M, Quintana C, Riina R, Rodriguez MGM, Weintritt J, Acevedo-Rodriguez P, Adarve J, Alvarez E, Aranguren BA, Arteaga JC, Aymard G, Castano A, Pennington RT et al (2016) Plant diversity patterns in neotropical dry forests and their conservation implications. Science 353:1383–1387. https://doi.org/10.1126/science.aaf5080.
Bennett JA, Riibak K, Kook E, Reier Ü, Tamme R, Guillermo Bueno C, Pärtel M (2016) Species pools, community completeness and invasion: Disentangling diversity effects on the establishment of native and alien species. Ecology letters 19:1496-1505. https://doi.org/10.1111/ele.12702.
Câmara T, Leal IR, Blüthgen N, Oliveira FMP, Queiroz RTd, Arnan X (2018) Effects of chronic anthropogenic disturbance and rainfall on the specialization of ant-plant mutualistic networks in the Caatinga, a Brazilian dry forest. Journal of Animal Ecology 87:1022–1033. https://doi.org/10.1111/1365-2656.12820.
Campbell MJ, Edwards W, Magrach A, Alamgir M, Porolak G, Mohandass D, Laurance WF (2018) Edge disturbance drives liana abundance increase and alteration of liana-host tree interactions in tropical forest fragments. Ecology and Evolution 8:4237–4251. https://doi.org/10.1002/ece3.3959.
Carmona CP, Pärtel M (2020). Estimating probabilistic site‐specific species pools and dark diversity from co‐occurrence data. Global Ecology and Biogeography 30:316–326. https://doi.org/10.1111/geb.13203.
Carvalho ECD, Martins FR, Oliveira RS, Soares AA, Araújo FS (2016) Why is liana abundance low in semiarid climates? Austral Ecology 41:559–571. https://doi.org/10.1111/aec.12345.
Davidar P, Sahoo S, Mammen PC, Acharya P, Puyravaud J-P, Arjunan M, Roessingh K et al (2010) Assessing the extent and causes of forest degradation in India: Where do we stand? Biological Conservation 143:2937–2944. https://doi.org/10.1016/j.biocon.2010.04.032.
De Queiroz LP, Cardoso D, Fernandes MF, Moro MF (2017) Diversity and Evolution of Flowering Plants of the Caatinga Domain. Caatinga 23–63. https://doi.org/10.1007/978-3-319-68339-3_2.
Eriksson O (1993) The species-pool hypothesis and plant community diversity. Oikos 68:371–374. https://doi.org/10.2307/3544854.
Fahrig L (2013) Rethinking patch size and isolation effects: the habitat amount hypothesis. Journal of Biogeography 40:1649–1663. https://doi.org/10.1111/jbi.12130.
Fahrig L (2017) Ecological Responses to Habitat Fragmentation Per Se. Annual Review of Ecology, Evolution, and Systematics 48:1–23. https://doi.org/10.1146/annurev-ecolsys-110316-022612.
Fahrig L, Arroyo-Rodríguez V, Bennett JR, Boucher-Lalonde V, Cazetta E, Currie DJ, Watling JI et al (2019) Is habitat fragmentation bad for biodiversity? Biological Conservation 230:179–186. https://doi.org/10.1016/j.biocon.2018.12.026.
Fahrig L (2021) What the habitat amount hypothesis does and does not predict: A reply to Saura. Journal of Biogeography 48:1530–1535. https://doi.org/10.1111/jbi.14061.
Graeff O (2015) Fitogeografia do Brasil: Uma atualização de bases e conceito. NAU, Rio de Janeiro.
Haila Y (2002) A Conceptual Genealogy of Fragmentation Research: From Island Biogeography to Landscape Ecology. Ecological Applications 12:321. https://doi.org/10.2307/3060944.
Hesselbarth MHK, Sciaini M, With KA, Wiegand K, Nowosad J (2019) landscapemetrics: an open‐source R tool to calculate landscape metrics. Ecography 42:1648–1657. https://doi.org/10.1111/ecog.04617.
IBGE (2010) http://geoftp.ibge.gov.br/cartas_e_mapas/bases_ cartograficas_continuas/bcim/versao2010/bcim_v3.04_dados/ geodatabase/. Accessed 05 March 2023.
IBGE (2010) Retrieved from geoftp.ibge.gov.br/organizacao_do_terri torio/malhas_territoriais/malhas_de_setores_censitarios__divisoes_ intramunicipais/censo_2010/setores_censitarios_shp. Accessed 05 March 2023.
IBGE (2019) Biomas e Sistema Costeiro-Marinho do Brasil - 1:250 000. https://www.ibge.gov.br/geociencias/cartas-e-mapas/informacoes-ambientais/15842-biomas.html?=&t=acesso-ao-produto. Accessed 18 apr 2023.
Jackson HB, Fahrig L (2015) Are ecologists conducting research at the optimal scale? Global Ecology and Biogeography 24:52–63. https://doi.org/10.1111/geb.12233.
Karger DN, Conrad O, Böhner J, Kawohl T, Kreft H, Soria-Auza RW, Kessler M (2017) Climatologies at high resolution for the earth’s land surface areas. Scientific Data 4:170122. https://doi.org/10.1038/sdata.2017.122.
Laurance WF, Pérez-Salicrup D, Delamônica P, Fearnside PM, D'Angelo S, Jerozolinski A, Lovejoy TE (2001) Rain Forest fragmentation and the structure of Amazonian liana communities. Ecology 82:105-116. https://doi.org/10.2307/2680089.
Laurance WF (2008) Theory meets reality: How habitat fragmentation research has transcended island biogeographic theory. Biological Conservation 141:1731–1744. https://doi.org/10.1016/j.biocon.2008.05.011.
Leal IR, Silva JMC, Tabarelli M, Lacher TE (2005) Changing the Course of Biodiversity Conservation in the Caatinga of Northeastern Brazil. Conservation Biology 19:701-706. https://doi.org/10.1111/j.1523-1739.2005. 00703.x.
Leigh Jr EG, Davidar P, Dick CW, Puyravaud JP, Terborgh JW, Ter Steege H, Wright SJ (2004) Why Do Some Tropical Forests Have So Many Species of Trees? Biotropica 6:447–473. https://doi.org/10.1646/1607.
Le Bagousse-Pinguet Y, Gross N, Maestre FT, Maire V, de Bello F, Fonseca CR, Liancourt P (2017) Testing the environmental filtering concept in global drylands. Journal of Ecology 105:1058–1069. https://doi.org/10.1111/1365-2745.12735.
Lenoir J, Gegout J-C, Guisan A, Vittoz P, Wohlgemuth T, Zimmermann NE, Dullinger S, Pauli H, Willner W, Svenning J-C (2010) Going against the flow: potential mechanisms for unexpected downslope range shifts in a warming climate. Ecography 33:295–303. https://doi.org/10.1111/j.1600-0587.2010.06279.x.
Lewis RJ, Szava-Kovats R, Pärtel M (2016) Estimating dark diversity and species pools: an empirical assessment of two methods. Methods in Ecology and Evolution 7:104–113. https://doi.org/10.1111/2041-210X.12443.
Liang J, Gamarra JGP, Picard N, Zhou M, Pijanowski B, Jacobs DF, Reich PB, Crowther TW, Nabuurs G-J, de-Miguel S, Fang J, Woodall CW, Svenning J-C, Jucker T, Bastin J-F, Wiser SK, Slik F, Hérault B, Alberti G, Hui C et al (2022) Co-limitation towards lower latitudes shapes global forest diversity gradients. Nature Ecology and Evolution 6:1423–1437. https://doi.org/10.1038/s41559-022-01831-x.
Lu D, Moran E, Mausel P (2002) Linking Amazonian secondary succession forest growth to soil properties. Land Degradation and Development 13:331–343. https://doi.org/10.1002/ldr.516.
MacArthur RH, Wilson EO (1967) The Theory of Island Biogeography. Princeton University Press Princeton, New Jersey.
Maia RP, Bezerra FHR, Sales VC (2010) Geomorfologia Do Nordeste: Concepções Clássicas E Atuais Acerca Das Superfícies De Aplainamento Nordestinas. Revista de Geografia 27:6–19.
Maia VA, de Souza CR, de Aguiar-Campos N, Fagundes NCA, Santos ABM, de Paula GGP, dos Santos RM et al (2020) Interactions between climate and soil shape tree community assembly and above-ground woody biomass of tropical dry forests. Forest Ecology and Management 474:118348. https://doi.org/10.1016/j.foreco.2020.118348.
Malhi Y, Gardner TA, Goldsmith GR, Silman MR & Zelazowski P (2014) Tropical forests in the Anthropocene. Annual Review of Environment and Resources 39: 125–159. https://doi.org/10.1146/annurev-environ-030713-155141.
MapBiomas (2019) MapBiomas Brazil news. Retrieved from https:// mapbiomas.org/en. Accessed 10 July 2020.
Melo FPL (2017) The socio-ecology of the Caatinga: Understanding how natural resource use shapes an ecosystem. In: JM Cardoso da Silva, IR Leal, M Tabarelli (eds) Caatinga: The largest tropical dry forest region in South America, Cham: Springer, pp 369–382.
Miles L, Newton AC, DeFries RS, Ravilious C, May I, Blyth S, Gordon JE (2006) A global overview of the conservation status of tropical dry forests. Journal of Biogeography 33:491–505. https://doi.org/10.1111/j.1365-2699.2005. 01424.x.
MMA (2011) Monitoramento do Desmatamento nos Biomas Brasileiros por Satélite – Acordo de Cooperação Técnica MMA/IBAMA. Monitoramento do Bioma Caatinga 2008–2009. Brasília: Ministério do Meio Ambiente.
MMA (2016) Áreas prioritárias para conservação, uso sustentável e repartição dos benefícios da biodiversidade brasileira – Caatinga (Portaria MMA N° 223, de 21 de junho de 2016).
Moro MF, Lughadha EN, Filer DL, Araújo FS, Martins FR (2014) A catalogue of the vascular plants of the Caatinga Phytogeographical Domain, a synthesis of floristic and phytosociological surveys. Phytotaxa 160:001–118. https://doi.org/10.11646/phytotaxa.160.1.1.
Moro MF, Lughadha EN, Filer DL, Araújo FS, Martins FR (2016) A Phytogeographical Metaanalysis of the Semiarid Caatinga Domain in Brazil. The Botanical Review 82:91–148. https://doi.org/10.1007/s12229-016-9164-z.
Moro MF, Silva IA, Araújo FSd, Nic Lughadha E, Meagher TR, Martins FR (2015) The Role of Edaphic Environment and Climate in Structuring Phylogenetic Pattern in Seasonally Dry Tropical Plant Communities. PloS One 10:e0119166. https://doi.org/10.1371/journal.pone.0119166.
Murphy PG, Lugo AE (1986) Ecology of Tropical Dry Forest. Annual Review of Ecology and Systematics 17:67–88. https://doi.org/10.1146/annurev.es.17.110186.
Olson DM, Dinerstein E, Wikramanayake ED, Burgess ND, Powell GVN, Underwood EC, Kassem KR et al (2001) Terrestrial Ecoregions of the World: A New Map of Life on Earth. BioScience 51:933–938. https://doi.org/10.1641/0006-3568(2001)051 [0933: TEOTWA]2.0.CO;2.
Pärtel M, Szava-Kovats R, Zobel M (2011) Dark diversity: shedding light on absent species. Trends in Ecology and Evolution 26:124–128. https://doi.org/10.1016/j.tree.2010.12.004.
Pärtel M, Szava-Kovats R, Zobel M (2013) Community completeness: linking local and dark diversity within the species pool concept. Folia Geobotanica 48:307–317. https://doi.org/10.1007/s12224-013-9169-x.
Pärtel M, Carmona CP, Zobel M, Moora M, Riibak K, Tamme R (2019) DarkDivNet – a global research collaboration to explore the dark diversity of plant communities. Journal of Vegetation Science 30:1039–1043. https://doi.org/10.1111/jvs.12798.
Pennington RT, Lavin M, Oliveira-Filho A (2009) Woody Plant Diversity, Evolution, and Ecology in the Tropics: Perspectives from Seasonally Dry Tropical Forests. Annual Review of Ecology, Evolution, and Systematics 40:437–457. https://doi.org/10.1146/annurev.ecolsys.1103.
Prado DE (2003) As caatingas da América do Sul. In: IR Leal, M Tabarelli, JMC Silva (eds.) Ecologia e Conservação da Caatinga. Editora Universitaria da UFPE, Recife, Brazil, pp 3-73.
Prugh LR (2009) An evaluation of patch connectivity measures. Ecological Applications 19:1300–1310. https://doi.org/10.1890/08-1524.1.
Queiroz RT, Moro MF, Loiola MIB (2015) Evaluating the relative importance of woody versus non-woody plants for alpha-diversity in a semiarid ecosystem in Brazil. Plant Ecology and Evolution 148:361–376. https://doi.org/10.5091/plecevo.2015.1071.
R Core Team (2019) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria Available at: http://www.Rproject.org/.
Ribeiro EMS, Arroyo-Rodríguez V, Santos BA, Tabarelli M, Leal IR (2015) Chronic anthropogenic disturbance drives the biological impoverishment of the Brazilian Caatinga vegetation. Journal of Applied Ecology 52:611–620. https://doi.org/10.1111/1365-2664.12420.
Ribeiro EMS, Santos BA, Arroyo-Rodríguez V, Tabarelli M, Souza G, Leal IR (2016) Phylogenetic impoverishment of plant communities following chronic human disturbances in the Brazilian Caatinga. Ecology 97:1583–1592. https://doi.org/10.1890/15-1122.1.
Ricklefs RE, Relyea R, Richter C (2014). Ecology: the economy of nature. Vol. 7. New York: WH Freeman.
Rito KF, Arroyo-Rodríguez V, Queiroz RT, Leal IR, Tabarelli M (2017) Precipitation mediates the effect of human disturbance on the Brazilian Caatinga vegetation. Journal of Ecology 105:828–838. https://doi.org/10.1111/1365-2745.12712.
Ronk A, de Bello F, Fibich P, Pärtel M (2016) Large-scale dark diversity estimates: new perspectives with combined methods. Ecology and Evolution 6:6266–6281. https://doi.org/10.1002/ece3.2371.
Sampaio E (1995) Overview of the Brazilian Caatinga. Seasonally dry tropical forests. In: Bullock SM, Mooney HA, Medina E (eds). Cambridge University Press, Cambridge, pp 35–63.
Santos RM, Oliveira-Filho AT, Eisenlohr PV, Queiroz LP, Cardoso DBOS, Rodal MJN (2012) Identity and relationships of the Arboreal Caatinga among other floristic units of seasonally dry tropical forests (SDTFs) of north-eastern and Central Brazil. Ecology and Evolution 2:409–428. https://doi.org/10.1002/ece3.91.
Schnitzer SA (2018) Testing ecological theory with lianas. New Phytologist 220:366–380. https://doi.org/10.1111/nph.15431.
Schnitzer SA, Bongers F (2011) Increasing liana abundance and biomass in tropical forests: emerging patterns and putative mechanisms. Ecology Letters 14:397–406. https://doi.org/10.1111/j.1461-0248.2011. 01590.x.
Serviço Florestal Brasileiro (SFB) (2019) Sistema Nacional de Informações Florestais – SNIF. Brasília. Disponível em:< http://snif.florestal.gov.br/pt-br/>. Accessed 26 October 2020.
Sfair JC, de Bello F, de França TQ, Baldauf C, Tabarelli M (2018) Chronic human disturbance affects plant trait distribution in a seasonally dry tropical forest. Environmental Research Letters 13:025005. https://doi.org/10.1088/1748-9326/aa9f5e.
Silva JMC, Barbosa LCF, Leal IR, Tabarelli M (2017) The Caatinga: Understanding the challenges. In: Cardoso da Silva JM, Leal IR, Tabarelli M (eds) Caatinga: The largest tropical dry forest region in South America, Cham: Springer, pp 3–19.
Singh SP (1998) Chronic disturbance, a principal cause of environmental degradation in developing countries. Environmental Conservation 25:1–2. https://doi.org/10.1017/s0376892998000010.
Souza Jr CM et al (2020) Reconstructing Three Decades of Land Use and Land Cover Changes in Brazilian Biomes with Landsat Archive and Earth Engine. Remote Sensing 12:2735. https://doi.org/10.3390/rs12172735.
Souza DG, Sfair JC, de Paula AS, Barros MF, Rito KF, Tabarelli M (2019) Multiple drivers of aboveground biomass in a human-modified landscape of the Caatinga dry forest. Forest Ecology and Management 435:57–65. https://doi.org/10.1016/j. foreco.2018.12.042.
Szava-Kovats RC, Zobel M, Pärtel M (2011). The local-regional species richness relationship: new perspectives on the null-hypothesis. Oikos 121:321–326. https://doi.org/10.1111/j.1600-0706.2011.19603. x.
Tang L, Wang R, He KS, Shi C, Yang T, Huang Y, Zheng P, Shi F (2019) Throwing light on dark diversity of vascular plants in China: predicting the distribution of dark and threatened species under global climate change. PeerJ 7:e6731. https://doi.org/10.7717/peerj.6731.
Thompson PL, Rayfield B, Gonzalez A (2016) Loss of habitat and connectivity erodes species diversity, ecosystem functioning, and stability in metacommunity networks. Ecography 40:98–108. https://doi.org/10.1111/ecog.02558.
Turner IM (2001). The ecology of trees in the tropical rain forest. Cambridge University Press.
Vieira LAF, Tabarelli M, Souza G, Queiroz RT, Santos BA (2022) Divergent herb communities in drier and chronically disturbed areas of the Brazilian Caatinga. Perspectives in Ecology and Conservation 20:132–140. https://doi.org/10.1016/j.pecon.2021.12.002.
Watling JI, Arroyo-Rodríguez V, Pfeifer M, Baeten L, Banks-Leite C, Cisneros LM, Fahrig L (2020) Support for the habitat amount hypothesis from a global synthesis of species density studies. Ecology Letters 23:674–681. https://doi.org/10.1111/ele.13471.
Zobel M (1997) The relative role of species pools in determining plant species richness: an alternative explanation of species coexistence? Trends in Ecology and Evolution 12:266–269. https://doi.org/10.1016/S0169-5347(97)01096-3.
Zobel M, Otto R, Laanisto L, Naranjo-Cigala A, Pärtel M, Fernández-Palacios JM (2011) The formation of species pools: historical habitat abundance affects current local diversity. Global Ecology and Biogeography 20:251–259. https://doi.org/10.1111/j.1466-8238.2010. 00593.x.
Zobel M (2016) The species pool concept as a framework for studying patterns of plant diversity. Journal of Vegetation Science 27:8–18. https://doi.org/10.1111/jvs.12333.

No competing interests reported.

Download PDF

Journal Publication

published 27 Jan, 2024

Read the published version in Biodiversity and Conservation →

Editorial decision: Major revision
26 Oct, 2023
Reviews received at journal
23 Aug, 2023
Reviewers agreed at journal
21 Aug, 2023
Reviewers invited by journal
20 Aug, 2023
Editor assigned by journal
10 Aug, 2023
Submission checks completed at journal
10 Aug, 2023
First submitted to journal
09 Aug, 2023

You are reading this latest preprint version

Dark diversity reinforces the importance of climate, soil and disturbance for Caatinga plant diversity

Status:

Journal Publication

Version 1

Abstract

Figures

1. Introduction

2. Materials and methods

2.1 Study area

2.2 Sampling design

2.3 Predictor variables

2.4 Estimation of dark diversity, community completeness and species pool

2.5 Data analysis

3. Results

4. Discussion

5. Conclusion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Journal Publication

Version 1