The current database includes 61 sites along the Pacific coast under superhumid (total annual precipitation, P > 5,000 mm y^–1) and humid (P > 2,500 mm y^–1) climates, and 52 sites along the Caribbean coasts (including oceanic and continental areas) extending over semi-humid (in Antioquia, Southwestern Caribbean), semi-arid and arid (Cordoba-Magdalena), and desert (Guajira, Northeastern Caribbean) climates (P range: < 500–< 2,500 mm y^–1) (Supplementary Figure 3 and Supplementary Table 1). Country-wide, Rhizophora spp. exhibited the greatest IVI (mean: 165.7; range: 0–300) given the high relative density, dominance and frequency, in continental (CC) and oceanic (OC) locations in the Caribbean (means: 89.7 and 134.4, respectively), but most significantly along the Pacific (222.7) (Supplementary Figures 3,4). Avicennia germinans was the second-most important species (mean: 27.3; range: 0–195), with the higher contribution in the OC and CC (even forming monospecific fringes in basin settings, Supplementary Figure 1) than in the Pacific (means: 81.3, 36.1 and 11.7, respectively). The third species was Laguncularia racemosa (mean: 19.2; range: 0–205) with greatest values in the OC, followed by CC and the Pacific (means: 85.6, 22.9 and 4.7, respectively), seemly as a response to the natural and anthropogenic disturbances. Pelliciera rhizophorae is scant and of little importance country-wide, but forms monospecific stands in some areas along the Pacific coast. Conocarpus erectus was recorded in a few sites along the Caribbean and the Pacific coasts, mostly due to its habit of colonizing the inner-most areas with well-drained sediments.

We previously reported that IVI for Rhizophora and Avicennia was partially correlated with mean annual temperature, mean annual rainfall, and rainfall seasonality (collating data from WorldClim 2; Blanco-Libreros and Álvarez-León, 2019), therefore clear differences in species composition and metrics are expected among Colombia’s climatic zones. Using the current database, the correlation of IVI with latitude and longitude coordinates (Supplementary Figure 5) shows marked biogeographical patterns. For instance, Rhizophora displayed a negative correlation with both latitude and longitude, while A. germinans showed a positive correlation with latitude. L. racemosa and P. rhizophorae exhibited positive and negative correlations with latitude, respectively. Stronger patterns would be expected when crossing HELIO_SP.CO v.2 data with Caldas-Lang climate and total annual precipitation data (Figure 1), as well as other sources (e.g., Koppen-Geiger climate classification). The addition of OC data to the database provides an opportunity to explore the influence of dry and maritime climate (influenced by cyclonic activity) on mangrove attributes.

We recommend using structural variables such as density, mean tree diameter, and basal area for understanding the climatic and biotic drivers of macroecological patterns. For instance, by using crossed linear correlations between density for the three dominant species (Rhizophora spp. A. germinans and L. racemosa) and geographic coordinates, insights are gained about the overall role of climate on large-scale distributions and ecological interactions at plot or local scales (Figure 2). Density in Rhizophora spp. was negatively correlated with latitude and longitude country-wide, indicating the negative effect of reduced rainfall, particularly along the CC. Density in A. germinans showed no significant correlation but it increased significantly to the northeastern coast of the CC (toward arid and desert climates). As a consequence, both species showed an antagonistic pattern country-wide but more markedly along the Caribbean region. Along this coast in particular, R. mangle forms extensive monospecific stands toward the southwest and A. germinans does it toward the northeast. In the Central Caribbean where both species coexist, they are spatially segregated, with R. mangle forming monospecific seaward fringes and A. germinans forming monospecific basin stands. Such patterns demonstrate the differences in ecological niches described in the literature. Finally, L. racemosa, a species that has not been studied in depth in the literature, shows significant correlations with latitude and longitude at different spatial scales and with A. germinans. Such patterns suggest a complex interaction between climatic, biotic and disturbance regime drivers. For instance, while country-wide L. racemosa is positively correlated with A. germinans, it is negatively correlated along the CC. This might result from the out-competition by Rhizophora spp. along the Pacific coast, but the release of local competition with A. germinans in the Caribbean coast with increased disturbance due to logging or cyclonic activity (e.g., Blanco-Libreros and Estrada-Urrea, 2015). Spatial patterns for P. rhizophorae and C. erectus are weaker due to the scarcity of occurrences country-wide (Supplementary Figure 6). However, the use of HELIO_SP.CO v.2 data in combination with other sources of information may be helpful for studying such patterns [see Blanco-Libreros and Ramírez-Ruíz (2021)]. We recommend the exploration of interactions among climatic, geomorphic and biotic drivers by using multiple regression models (both linear and non-linear, geographically structured or not). Finally, we also recommend studying how the disturbance regime in OC seems to promote species coexistence among the three dominant species island-wide in San Andrés and how they are locally segregated [see Medina-Calderón et al. (2021)].

We propose four main potential uses of the present database: (1) species distribution modeling and species conservation status assessment, (2) above-ground blue carbon and ecosystem services estimation, (3) land cover and use assessments in the coastalscape, and (4) current and future responses to coastal climate change. Presence and absence data can be used in large-scale species distribution modeling (SDM) efforts using various mathematical approaches, and they have been recently applied to neotropical mangroves (Rodríguez-Medina et al., 2020). WorldClim, CHELSA, and other climate data sources are commonly used for SDM. Presence and absence data, in combination with records in the Global Biodiversity Information Facility (GBIF), can also be used as spatial references for designating areas of interest (buffers) to study threats to vulnerable species such as Pelliciera spp. (see Blanco-Libreros and Ramírez-Ruíz, 2021). For this species, we used landscape metrics to understand mangrove habitat fragmentation relative to urbanization, quantifying the magnitude of this specific threat, an approach that can be applied to other mangrove tree species.

Second, plot-level mean density and mean tree diameter are useful for estimating carbon in the above-ground biomass (AGC) using allometric equations (e.g., Yepes et al., 2016; Rovai et al., 2021b). Given the scarcity of field studies in Colombia, the present database may help to reduce spatial uncertainty in national and sub-national level modeling efforts (Bolívar et al., 2018). We have estimated that plot-level AGC may range between < 10 and 225 Mg ha^–1 in the mainland (G. F. Pérez-Vega unpublished monograph, Supplementary Figure 7 and Supplementary References). In addition to the climate mitigation ecosystem service provided by AGC, the basal area is correlated with wave dissipation capacity, functioning as coastal protection (Sánchez-Nuñez et al., 2020). Basal area (particularly in Rhizophora mangle) in this database may help to assess such service in OC and CC areas seasonally impacted by storm surges.

Third, land cover and land use change within mangroves and in its surroundings is a major driver of deforestation and fragmentation worldwide (Bryan-Brown et al., 2020). Species occurrences and forest attributes can be used as response variables to past drivers or as baseline information to assess future change. A recent assessment of the coastalscape configuration around Pelliciera spp. occurrences in response to urbanization may serve as an example (Blanco-Libreros and Ramírez-Ruíz, 2021). We encourage the use of the recently published official land cover data² to assess the current state of the coastalscape and to use urban expansion data³ to understand threats to urban mangroves in the largest coastal cities in Colombia (e.g., Cartagena, Buenaventura). Finally, global layers such as the nighttime lights,⁴ and national layers of national parks, african-descendant territories and coastal watersheds,⁵ and demographic variables for departments and municipalities⁶ can be also useful to understand the spatial coastalscape context and socio-economic dynamics affecting variables in mangroves in Colombia (Supplementary Figure 8). A few studies of this type have been conducted in mangroves and terrestrial forests in the Chocó-Darién Ecoregion (e.g., López-Angarita et al., 2018; Fagua et al., 2019). Land cover and land use change have been anecdotally suggested as major drivers of changes in mangrove area and species composition along the Caribbean coast of Colombia but a few quantitative studies exist (e.g., Blanco-Libreros and Estrada-Urrea, 2015; Mira et al., 2019; Bolívar-Anillo et al., 2020; Villate-Daza et al., 2020). Therefore, we strongly encourage colleagues to use the present database to advance in this field.

Climate change and climate variability have been described mostly along the Caribbean coast of Colombia.⁷ Future reductions in annual rainfall are predicted for the mid and northern Caribbean. Increased water stress on coastal watersheds is predicted for some areas due to land use changes, particularly urbanization. In addition, although the El Niño-Southern Oscillation is a strong driver of interannual variability on rainfall and runoff in Colombia its influence on Colombian mangroves has been little studied (i.e., Galeano et al., 2017; Riascos et al., 2018; Riascos and Blanco-libreros, 2019). However, strong El Niño and La Niña events are seemly responsible for species composition transitions in the mid-Caribbean coast (Bolívar-Anillo et al., 2020; Villate-Daza et al., 2020). In the specific case of San Andrés and Providence islands, as oceanic territories, an increased rate of cyclone activity is expected. Hurricanes Eta and Iota hit both islands on November 2020 affecting mangroves (Garcés-Ordoñez et al., 2021). The prevalence of such climatic and meteorologic drivers urges for the need for baseline data and for setting local, regional, and national monitoring programs. Nowadays, a long-term monitoring program only exists for Ciénaga Grande de Santa Marta (see text footnote 1). Additional permanent plots have been established in some departments of Colombia but maintenance and repeated measurements are contingent on budget constraints producing many gaps in the records or even abandonment of the monitoring programs. Long-term studies and datasets are also needed for understanding the role of oceanic drivers such as coastal erosion and sea level rise.

Finally, we are certain that HELIO_SP.CO v.2 data are also useful for validation of mangrove maps. The Ministry of Environment and Sustainable Development issued in 2018 the Decree 1,263 ushering the environmental departmental authorities to update mangrove maps by 2021. The validation of such maps requires field campaigns over extensive areas of difficult access. In order to contribute to the advance in this task, we successfully used version 1 coordinates as validation points for a 2019–2020 map built using Sentinel 2 imagery and cloud computing in Google Earth Engine [Supplementary Figure 8; see data in Valencia-Palacios and Blanco-Libreros (2021)]. Thus, we encourage mangrove cartographers to use the present database as an alternative validation way given the growing use of cloud-based mapping in Colombia, particularly in areas of remote access making large-scale ground-truthing either logistically difficult or prohibitively costly (e.g., Perea-Ardila et al., 2021). We also foresee applications for estimating anthropogenic pressures relative to distance from large populated centers, as well for estimating the benefits perceived from mangrove-based fisheries (Supplementary Figures 9,10). Despite departmental-level forest inventories have been conducted since 2000, official data are not easily disclosed to scientists (Supplementary Figure 11), and scientific forest structure studies published since 2000 still have a very limited geographical coverage [discussed by Bolívar et al. (2018) and Castellanos-Galindo et al. (2021a)]. We conclude that HELIO_SP.CO v.2 can be useful as a baseline for the XXI century to assess future change and drivers in Colombian mangroves.