A comparison of forest structural methods of semiarid mangrove species using a field-based approach

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Francisco Flores-de-Santiago
https://orcid.org/0000-0001-8813-0093
Francisco Flores-Verdugo
https://orcid.org/0000-0002-9707-0819

Abstract

The data obtained from field-based forest inventories, mainly basal area and stem density, are relevant for the analysis of aboveground biomass and forest fragmentation. Due to its persistently flooded ground, fieldwork in mangrove forests is time-consuming and complicated. Since mangroves are sensitive to the effects of climate change, selecting a reliable field method is of utmost importance. To this end, we analyzed 4 mangrove classes: Rhizophora mangle (RM), Laguncularia racemosa (LR), Avicennia germinans (AG), and AG shrub. We georeferenced and counted all mangrove stems within four 0.04 ha (20 × 20 m square). We analyzed data from 3 circular area plots and the plotless point-centered quarter method (PCQM) based on the original square plots. Depending on the mangrove class, PCQM overestimated basal area by up to 34% and stem density by 21%. The 3 circular plot surveys underestimated basal area from –1% to –29% and stem density from –3 to –25%. Based on the results, we suggest using a circular plot of 0.04 ha (r = 11.28 m) in less dense forests (RM and AG) and a circular plot of 0.015 ha (r = 6.9 m) with forest densities greater than 3,500 stems/ha (LR and AG shrub). The advantages of using the circular plot approach over PCQM are that mangrove inventories can be quantified quickly and do not require a minimum number of sampling points.

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Flores-de-Santiago, F., & Flores-Verdugo, F. (2024). A comparison of forest structural methods of semiarid mangrove species using a field-based approach. Ciencias Marinas, 50(1A). https://doi.org/10.7773/cm.y2024.3432
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References

Araújo RJ, Shideler GS. 2019. Un paquete de R para el cálculo de parámetros estructurales de bosques de manglar utilizando métodos con y sin parcelas. 25(1):e2511696. http://dx.doi.org/10.21829/myb.2019.2511696

Ávila-Flores G, Juárez-Mancilla J, Hinojosa-Arango G, Cruz-Chávez P, López-Vivas JM, Arizpe-Covarrubias O. 2020. A practical index to estimate mangrove conservation status: The forests from La Paz, Mexico as a case study. Sustainability. 12(3):858. http://dx.doi.org/10.3390/su12030858

Cintrón G, Schaeffer-Novelli Y. 1984. Methods for studying mangrove structure. In: Snedaker SC, Snedaker JG (eds.), The mangrove ecosystem: research methods. Paris: Unesco. p. 91-113.

Cottam G, Curtis JT. 1956. The use of distance measures in phytosociological sampling. Ecol. 37(3):451-460. http://dx.doi.org/10.2307/1930167

Dookie S, Jaikishun S, Ansari AA. 2022. A comparative study of mangroves in degraded, natural, and restored ecosystems in Guyana. Biodivers. 23(2):40-48. https://doi.org/10.1080/14888386.2022.2107570

Ferreira AC, Morais Freire FA, Machado Rodrigues JV, Arruda Bezerra LE. 2022. Mangrove recovery in semiarid coast shows increase of ecological processes from biotic and abiotic drivers in response to hydrological restoration. Wetlands. 42:80. https://doi.org/10.1007/s13157-022-01603-0

Flores-de-Santiago F, Rodríguez-Sobreyra R, Álvarez-Sánchez LF, Valderrama-Landeros L, Amezcua F, Flores-Verdugo F. 2023. Understanding the natural expansion of white mangrove (Laguncularia racemosa) in an ephemeral inlet based on geomorphological analysis and remote sensing data. J Environ Manage. 338:117820. https://doi.org/10.1016/j.jenvman.2023.117820

Flores-de-Santiago F, Valderrama-Landeros L, Rodríguez-Sobreyra R, Flores-Verdugo F. 2020. Assessing the effect of flight altitude and overlap on orthoimage generation for UAV estimates of coastal wetlands. J Coastal Conserv 24:35. https://doi.org/10.1007/s11852-020-00753-9

Flores-de-Santiago F, Kovacs JM, Lafrance P. 2013. An object-oriented classification method for mapping mangroves in Guinea, West Africa, using multipolarized ALOS PALSAR L-band data. Int J Remote Sens. 34:563-586. http://dx.doi.org/10.1080/01431161.2012.715773

Flores-Verdugo F, González-Farías F, Ramírez-Flores O, Amezcua-Linares F, Yáñez-Arancibia A, Alvarez-Rubio M, Day JW. 1990. Mangrove ecology, aquatic primary productivity, and fish community dynamics in the Teacapán-Agua Brava lagoon-estuarine system (Mexican Pacific). Estuaries. 13:219-230. https://doi.org/10.2307/1351591

Flores-Verdugo F, González-Farias F, Zamorano DS, Ramirez-Garcia P. 1992. Mangrove ecosystems of the Pacific coast of Mexico: Distribution, structure, litterfall, and detritus dynamics. Physiol Ecol. 17:269-288. http://dx.doi.org/10.1016/B978-0-08-092567-7.50023-4

Flores-Verdugo F, Zebadua-Penagos F, Flores-de-Santiago F. 2015. Assessing the influence of artificially constructed channels in the growth of afforested black mangrove (Avicennia germinans) within an arid coastal region. J Environ Manage. 160:113-120. http://dx.doi.org/10.1016/j.jenvman.2015.06.024

Hijbeek R, Koedam N, Khan MNI, Kairo JG, Schoukens J, Dahdouh-Guebas F. 2013. An Evaluation of Plotless Sampling Using Vegetation Simulations and Field Data from a Mangrove Forest. PLoS ONE. 8:e67201. http://dx.doi.org/10.1371/journal.pone.0067201

Kovacs JM, Flores-de-Santiago F, Bastien J, Lafrance P. 2010. An assessment of mangroves in Guinea, West Africa, using a field and remote sensing based approach. Wetlands. 30:773-782. http://dx.doi.org/10.1007/s13157-010-0065-3

Kovacs JM, Jiao X, Flores-de-Santiago F, Zhang C, Flores-Verdugo F. 2013. Assessing relationships between Radarsat-2 C-band and structural parameters of a degraded mangrove forest. Int J Remote Sens. 34:7002-7019. http://dx.doi.org/10.1080/01431161.2013.813090

Salum RB, Souza-Filho PWM, Simard M, Silva CA, Fernandes MEB, Cougo MF, Junior WN, Rogers K. 2020. Improving mangrove aboveground biomass estimates using LiDAR. Estuarine Coastal Shelf Sci. 236:106585. https://doi.org/10.1016/j.ecss.2020.106585

Sokal RR, Rohlf FJ. 2012. Biometry, 4th Ed. New York (NY): WH Freeman and Company. 960 p.

Tran TV, Reef R, Zhu X. 2022. A review of spectral indices for mangrove remote sensing. Remote Sens. 14(19):4868. https://doi.org/10.3390/rs14194868

Valderrama-Landeros L, Flores-Verdugo F, Flores-de-Santiago F. 2022. Assessing the coastal vulnerability by combining field surveys and the analytical potential of CoastSat in a highly impacted tourist destination. Geographies 2:642-656. https://doi.org/10.3390/geographies2040039

Valderrama-Landeros L, Flores-Verdugo F, Rodríguez-Sobreyra R, Kovacs JM, Flores-de-Santiago F. 2021. Extrapolating canopy phenology information using Sentinel-2 data and the Google Earth Engine platform to identify the optimal dates for remotely sensed image acquisition of semiarid mangroves. J Environ Manage. 279:111617. https://doi.org/10.1016/j.jenvman.2020.111617

Valderrama-Landeros L, López-Portillo J, Velázquez-Salazar S, Alcántara-Maya JA, Troche-Souza C, Rodríguez-Zúñiga MT, Vázquez-Balderas B, Villeda-Chavez E, Cruz-López MI, Ressl R. 2020. Regional distribution and change dynamics of mangroves in Mexico between 1970/80 and 2015. Wetlands. 40:1295-1305. https://doi.org/10.1007/s13157-020-01299-0

Valderrama-Landeros L, Flores-de-Santiago F, Kovacs JM, Flores-Verdugo F. 2018. An assessment of commonly employed satellite-based remote sensors for mapping mangrove species in Mexico using an NDVI-based classification scheme. Environ Monit Assess. 190:23. https://doi.org/10.1007/s10661-017-6399-z

Villeda-Chávez E, Lara AL, González-Zamorano P, Rubio EA, Valderrama L, Ramírez-García P, García-Calva L, Argüello-Velázquez J, Cruz-López MI. 2018. Muestreo de variables estructurales. In: Rodríguez-Zúñiga MT, Villeda-Chávez E, Vázquez-Lule AD, Bejarano M, Cruz-López MI, Olguín M, Villela-Gaytán SA, Flores R (eds.), Métodos para la caracterización de los manglares mexicanos: un enfoque espacial multiescala. Ciudad de México (Mexico): Comisión Nacional para el Conocimiento y Uso de la Biodiversidad. p. 71-130.

Vizcaya-Martínez DA, Flores-de-Santiago F, Valderrama-Landeros L, Serrano D, Rodríguez-Sobreyra R, Álvarez-Sánchez LF, Flores-Verdugo F. 2022. Monitoring detailed mangrove hurricane damage and early recovery using multisource remote sensing data. J Environ Manage. 320:115830. https://doi.org/10.1016/j.jenvman.2022.115830

Wang M, Cao W, Guan Q, Wu G, Wang F. 2018. Assessing changes of mangrove forest in a coastal region of southeast China using multi-temporal satellite images. Estuarine Coastal Shelf Sci. 207:283-292. https://doi.org/10.1016/j.ecss.2018.04.021

Ximenes AC, Cavanaugh KC, Arvor D, Murdiyarso D, Thomas N, Arcoverde GFB, Bispo PC, Stocken TV. 2023. A comparison of global mangrove maps: Assessing spatial and bioclimatic discrepancies at poleward range limits. Sci Total Environ. 860:160380. http://dx.doi.org/10.1016/j.scitotenv.2022.160380

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