If a tree is “Protected”, is it? Using satellite-borne LiDAR to understand efficacy of protection status in West and Central African Protected Areas

If a tree is “Protected”, is it? Using satellite-borne LiDAR to understand efficacy of protection status in West and Central African Protected Areas | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article If a tree is “Protected”, is it? Using satellite-borne LiDAR to understand efficacy of protection status in West and Central African Protected Areas Abigail Barenblitt, Mengyu Liang, Atticus Stovall, Veronika Leitold, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8744926/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Globally, Protected Areas (PAs) have had positive impacts on habitat protection and carbon storage and are viewed as critical for preserving hotspots of biodiversity and supporting SDG 15: Life on Land. However, global studies indicate that Afrotropical PAs may be more degraded and less effective than PAs in other regions, despite comparable geographic coverage. Here, we match PAs to Non-Protected Counterfactuals (NPCs) to understand how PA presence in West and Central Africa affects forest structure and structural diversity. Using the Global Ecosystem Dynamics Investigation (GEDI), we demonstrate that PAs have an inconsistent effect on canopy height, structural diversity, and plant area index across the region, varying by both country and PA designation. We found PA impact on preserving ecological structure may have a positive relationship with background deforestation rates, where positive differences between PAs and NPCs were particularly prominent in countries experiencing high deforestation. This highlights the need to take country-wide deforestation rates and socioeconomic pressures into account when understanding PA efficacy, as the lack of difference between PAs and NPCs in low deforestation countries does not necessarily indicate inadequate conservation outcomes. Our results demonstrate that governance type and PA establishment goals affect ecological outcomes, with forest structure consistently higher in National Parks. Our study reveals that when deforestation and country-wide values of structural metrics are factored into PA assessments, PAs in Africa have a higher positive impact than previously identified. Therefore, when assessing PAs effectiveness in regions like West and Central Africa, consideration of country-wide and designation-specific dynamics go beyond global studies to describe PA impact and outcomes. Earth and environmental sciences/Ecology/Conservation biology Earth and environmental sciences/Ecology/Biodiversity Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. INTRODUCTION Protected Areas (PAs) currently cover > 17% of the Earth’s land surface as of 2024 (UNEP-WCMC 2024). This is just over halfway to meeting the UN Convention on Biological Diversity’s (UNCBD) 30x30 goal, under which nearly 200 committed countries aim to protect 30% of their terrestrial and aquatic ecosystems, to address Target 3 of the Kunming-Montreal Global Biodiversity Framework (GBF), the creation of equitable, connected, and ecologically representative protected areas (Convention on Biological Diversity 2022). PAs additionally support Sustainable Development Goal (SDG) 15, particularly 15.1 and 15.2, which aim to conserve terrestrial ecosystems and end deforestation (United Nations 2025). Globally, PAs have had positive impacts on habitat protection and carbon storage and are viewed as critical for preserving hotspots of biodiversity (Cazalis 2020, Neugarten 2020). PAs also have positive spillover effects as demonstrated by higher biodiversity found in surrounding buffer zones (Brodie 2023). In unprotected areas, global trends of increasing forest and biodiversity loss have been observed (Neugarten 2020). While PAs experience lower rates of loss, the temporal change of vegetation loss in PAs reflects that of global forest trends (Wade 2022). This is due to increased pressure from anthropogenic activity like agriculture in recent years - especially in Afrotropical regions - along with pressures from wildfires, pests, and storm events (Wade 2022, Geldmann 2019). In fact, a past study found higher agriculture-driven PA degradation in Afrotropical PAs than in matched unprotected counterfactuals, raising concerns over PAs’ effectiveness for safeguarding biodiversity and climate mitigation (Geldmann 2019). Previous work in other tropical regions like Central America demonstrates significant positive trends in vegetation indices like NDVI in PAs, with varying degrees of forest growth depending on centralization—or level of state government involvement—of PAs and PA management capacity (Muñoz 2018). Additionally, the Global Ecosystem Dynamics Investigation (GEDI), the first spaceborne lidar mission designed to study Earth’s forests in 3D, has been used to demonstrate the effectiveness of PAs for climate change mitigation on a global level, noting that PAs have contributed to significant carbon emission avoidance (Duncanson 2023). Results from a global study of biomass in PAs indicate that despite comparable geographic coverage of African PAs to other continents, biomass densities within African PAs were lower than on other continents (Duncanson 2023). Earlier research also suggests that PAs in Africa experience higher rates of disturbance (Geldmann 2019). However, more regional focused study is needed to further disentangle African PAs’ role in resisting conversion pressure from socioeconomic development and conserving the natural habitats. Quantitative and evidence-based assessment of the structural makeup of PAs and their effectiveness in preserving vegetation structural complexity are much needed for conservation planning and policy in data-scarce regions of West and Central African PAs. A past study found that PAs in Europe were more structurally diverse than counterfactuals (Ceccherini 2023). In the United States, a study has identified a positive correlation between structural diversity and carbon storage (Crockett 2023). Therefore, by gleaning information pertaining to structural diversity in PAs, more knowledge can be gained regarding carbon storage and climate mitigation in these regions. As countries expand the coverage of PAs to achieve 30x30 goals, identifying metrics for tracking PA efficacy is critical for PA planning. For example, Biodiversity Priority Areas (BPAs), which emphasize the presence of IUCN Red List species, are one category by which to assess the potential impact of establishing a new PA (Neugraten 2020). Increased understanding of structural diversity within pre-existing PAs would further improve current methods for determining the impact of PAs and prioritizing regions for PA designation optimization. Additionally, improving the performance of existing PAs through management may prove to be a more effective goal for maintaining biodiversity than PA expansion alone (Singh 2020, Li 2024). Previous work demonstrates the utility of GEDI for studying structural diversity to better understand biodiversity (Torresani 2020, Hakkenberg 2023). In this study, we leverage GEDI height and structural diversity metrics to compare PAs across West and Central Africa to non-protected counterfactuals to better understand the effect of PAs on metrics that are linked with biodiversity capacity. We test whether PAs exhibit higher structural diversity as measured by GEDI than matched Non-Protected Counterfactuals (NPCs), and whether PA impact varies systematically by country, designation, and national deforestation ratios. 2. RESULTS Of the PAs analyzed, 1773 had sufficient data in the baseline year to be matched with an environmentally and socioeconomically similar NPC. Notably, quality filtering for L4C reduced the number of available PAs to 1748 and the number of available GEDI due to the strict quality filtering inherent to the product, especially in tropical regions, resulting in differences in sample sizes from different product levels (de Conto 2024). Across the entire study area, average RH98, and PAI were lower in PAs than in NPCs (0.5m and 0.1, respectively), but differences fell within the standard deviation for each metric. All summary values and available number of GEDI shots can be found in Table 1 . Table 1 Summary statistics of GEDI metrics across all shots in PAs and NPCs Metric Status Mean 𝜎 10th Perc 90th Perc # Available Shots RH98 PA 20.6 11.9 2.8 31.1 23,249,462 NPC 21.1 11.8 2.7 29.5 20,250,331 PAI PA 2.8 7.7 0.0 4.6 23,249,462 NPC 2.9 6.6 0.0 4.5 20,250,331 FHD RAW PA 2.7 0.6 1.4 3.2 23,249,462 NPC 2.7 0.6 1.3 3.2 20,250,331 WSCI PA 10.2 0.8 8.6 10.8 13,638,810 NPC 10.2 0.7 8.6 10.8 4,687,480 2.1 Fourteen out of sixteen countries show consistent positive impact of PAs on structure Average RH98 and PAI were highest in GAB (28.8m in PAs and 28.7m in NPCs; 3.97 in PAs and 3.92 in NPCs). GAB and GNQ had the highest average FHD values (3.03 in PAs and NPCs). GNQ had the highest average WSCI values (28.5 in PAs and 29.3 in NPCs). A table of all summary values across all countries is available in S1. When PA and NPC values were averaged on a country-level, the percent difference between PAs and NPCs was highest in GHA for measures of RH98 (59.6%), PAI (63.5%), FHD (13.6%), and WSCI (4.6%). COG demonstrated a negative percent difference for RH98, PAI, and FHD (-1.3%, -3.8%, and − 0.5%), while GNQ demonstrated a negative percent difference for FHD, RH98, and WSCI (-0.2%, -2.1%, -2.6%) (Fig. 1 ). 2.2 Central and West African PAs safeguard structural diverse ecosystems The distribution of FHD and RH98 skewed towards higher values in PAs than in NPCs in GMB, GHA, SLE, and TGO, indicating higher frequencies of high FHD and tall vegetation. In the 12 other countries, little difference was initially detected in the distributions of these values in PAs and NPCs. WSCI values skewed higher in PAs than in NPCs in GHA, SLE, and TGO. Of note, WSCI skewed towards lower values in PAs than in NPCs in GMB. Countries where value distributions show visible differences are included in Fig. 2 , and all metric distributions across all countries are available in S2. In six countries where distributions of these values demonstrated little difference between PAs and NPCS (CMR, COD, COG, GNQ, GAB, LIB), the distribution of normalized FHD values peaked at 0.02 or higher, the distribution of RH98 values peaked at 25m or higher, and the distribution of WSCI values peaked at 11 or higher. In the remaining six countries (BEN, CIV, GIN, GMB, NGA, SEN), all RH98 values skewed lower, with distributions of values peaking below 5m in all but two countries (GIN & CIV), where values peaked below 10m. In BEN, CIV, GIN, and GMB, FHD values skewed higher (> 0.01), whereas in NGA and SEN, values skewed lower ( 10). Due to the large number of PAs included in this study, a Google Earth Engine App has been made available to examine histograms of FHD and RH98 values at individual PAs ( https://mangrovescience.earthengine.app/view/gediprotectedareas ). 2.3 National parks show more consistent positive outcomes The most frequently observed PA designation was Other PAs (1559 individual PAs, 12,118,048 shots in PAs and matching NPCs). However, the majority of GEDI shots were collected over National Parks (104 individual PAs, 13,020,888 shots in PAs and matching NPCs). Notably, the loss of GEDI shots in the L4C quality filtering reduced the number of overall shots or eliminated the sampling of NPC matches for certain PA designations in BEN, GAB, GHA, and GMB (e.g., UNESCO-MAB Biospheres in GHA). The filtered L4C data yielded 10,650,580 shots in Other PAs and matching NPCs and 3,906,976 shots in National Parks and matching NPCs. The highest mean percent difference between PAs and NPCs was found in UNESCO-MAB Biospheres in GHA (FHD: 51.8%, RH98: 336.4%, PAI: 2,724.6%), followed by Other PAs, also in GHA (FHD: 14.6%, RH98: 65.5%, PAI: 64.4%, WSCI: 5%). National Parks had higher FHD or RH98 in all but four countries (COD, COG, GMB, & GNQ), whereas Ramsar Sites, which were present in 13 countries, had a negative effect on FHD or RH98 in all but three countries (CIV, COD, TGO). WSCI values were higher in National Parks in all but four countries (GMB, GNQ, COD, and SEN) and lower in Ramsar Sites in all but five countries (GMB, CIV, COD, GNB, and TGO) (S4, S5, S6). In countries where FHD and RH98 skewed higher in PAs than in NPCs, value distribution varied by PA designation. FHD and RH98 were higher in PAs in each of these countries across each PA designation, except in Ramsar PAs in GHA, where mean values of all metrics were lower in PAs than NPCS (FHD:-11.1%, RH98:-16.6%, PAI:-6%, WSCI:-3.6%) (Fig. 3 , S6). In countries where few differences were detected in the distribution of FHD and RH98 values, these distributions also varied by PA designation. Of note, Hunting Focus, National Parks, UNESCO-MAB Biospheres, and World Heritage PAs demonstrated a positive effect on average values across several GEDI metrics in several countries despite the lack of a discernible difference on a country-wide scale (e.g., BEN, CIV) (S4, S5, S6). 2.4 Positive PA metrics and deforestation rates Global Foret Watch values of deforestation ratio between 2000–2024 was highest in SLE (39%) followed by GIN and BEN (28%) (Hansen 2013). It was lowest in GAB (2.2%). In GHA, where GEDI structural metrics consistently demonstrated the highest positive difference between PAs and NPCs, the deforestation ratio was 25%. Conversely, in COG where FHD, RH98, and PAI were lower in PAs than NPCs, the deforestation ratio was 4.4%. All deforestation ratios compared to mean percent differences of FHD, RH98, PAI, and WSCI are demonstrated in Fig. 4 . 3. DISCUSSION With Protected Areas predicted to become increasingly critical to biodiversity in the face of habitat disturbance and loss, improved understanding of PA impact on structural features that are predictive of biodiversity is vital for the creation of new PAs, management of existing areas, and meeting SDG 15 targets for forest conservation (Ranius 2023, United Nations 2025). At a regional level, our results identify little average difference between ecological structure metrics in PAs and NPCs. However, the picture is complicated by the variation of patterns observed in individual countries, designation types, and overall structural metric distribution in and outside of PAs. 3.1 PA positive impact informed by background deforestation rates While the overall comparison of mean values across the region reveals little distinct pattern, PAs in Ghana demonstrated a positive effect on all measures of forest structural diversity. When the distribution of structural metrics are examined, there is a clear increase in frequency of higher values in Ghanaian PAs, indicating that PAs are preserving structural diversity. Notably, Ghana has experienced deforestation of approximately one fourth of its forests from 2000–2024 (Hansen 2013). The positive impact of Ghanaian PAs on forest structure appears to be exacerbated by the high levels of deforestation experienced country-wide, while also indicating these PAs are preserving forest structural diversity that would be lost otherwise. Conversely, in several countries, including Equatorial Guinea, the Republic of the Congo, and Gabon, forest structure metrics varied little between PAs and NPCs. This reflects findings in a previous study of global biomass that the effect of PAs in the Afrotropics is lower than other continents. However, the distributions of values in PAs and NPCs denoted higher values of height and structural diversity nationally in each of these countries. Therefore, an absence of clear positive difference in mean values does not necessarily indicate an ineffective PA, moreso that intact forest has remained intact in these countries, even outside of PAs in many cases. This trend could point to effective forest management strategies at a national scale, not just within PAs, and is reflective of low deforestation (< 6% since 2000) in these countries (Hansen 2013). As deforestation trends change, these PAs would be served by continued monitoring, especially as more GEDI data becomes available, to ensure PAs continue achieving conservation goals regardless of national deforestation. These results point to avenues for a more detailed assessment of PA effectiveness whereby metrics of structural diversity, in-situ biodiversity data, and deforestation rates are considered concurrently. 3.2 Designation type impacts PA trends The level of difference between PAs and NPCs also varied between designation types. Of note, National Parks had at least a minimally positive effect on structural metrics in most of the countries examined here. This is consistent with previous literature demonstrating a positive effect of National Parks for biodiversity and reducing deforestation (Zhang 2023, Li 2024). UNESCO-MAP Biosphere sites, which were present in seven countries, and World Heritage Sites, which were present in four countries, also displayed a positive effect in almost all countries where these designations were present. This was true in several countries where differences in mean values alone revealed little or a negative impact on structural metrics (e.g., Democratic Republic of the Congo). While National Parks are managed and established by national entities within a given country, UNESCO-MAP Biosphere sites and World Heritage Sites are established by international entities (Hasseini 2021, Barraclough 2023, Aschenbrand 2021). Unlike National Parks, these sites also maintain a sociocultural focus in their management framework, though the focus varies by country (Hasseini 2021, Barraclough 2023, Thomsen 2025). Biospheres and Heritage Sites offer an opportunity to integrate cultural and spiritual values with biodiversity conservation (Barraclough 2023). While these designations were less common throughout our region of study, the positive impact of these sites on structural metrics indicates that multiple-use sites may be effective at increasing or maintaining biodiversity potential. While locally governed sites were underrepresented in this study, the positive effect of UNESCO sites still points to the potential co-benefits of prioritizing PAs with a mind towards ecological conservation that works hand-in-hand with sociocultural needs. Conversely, in several countries, structural metrics skewed lower in Ramsar sites than their matched counterfactuals, a pattern that was especially stark in Ghana and Senegal. Ramsar Sites, or Wetlands of International Importance, aim to reduce the loss of wetlands through designation, management, and “wise-use” (Rattan 2021). In Afrotropical regions, many of these Ramsar Sites are characterized by mangroves rather than forest, which are necessarily low in species diversity (Padonou 2025). A study of a Ramsar site in Benin noted that mangroves within the site displayed a consistent number of layers (Padonou 2025). Therefore, structural components related to overall habitat health in more specialized ecoregions such as mangroves may not be as well explained by metrics like FHD and require further, more specialized study to understand the effect of PAs on soils, water quality, and biodiversity in conjunction with GEDI metrics. This study also found saplings and seedlings greater than 50% of tree density in this Ramsar site (Padonou 2025). GEDI height measurements are often less accurate in short-structure vegetation and may blend canopy and ground signals (Zhu 2023). Therefore, areas of recent restoration of short vegetation, such as mangroves, may not be as well detected by this instrument. Additionally, changes in water level have been observed to impact GEDI heights, though this effect is minimized in microtidal areas with submerged vegetation (Thomas 2023). Uncertainty in GEDI metrics over short wetlands could further be mitigated by the use of water level gauge data to account for these fluctuations. As more GEDI data becomes available, a timeseries analysis of structural complexity may prove more useful at identifying such restoration as older mangrove stands should show higher complexity and reach heights better detected by GEDI (Lucas 2020). However, other studies have noted urban expansion, mining, and harvesting for fuelwood in mangroves in the Afrotropical region and have detected substantial mangrove loss in Ramsar Sites, such as the Keta Lagoon Complex in Ghana (Duku 2021, Ofori 2025). Additionally, some mangrove restoration has been driven by a need for fuelwood, resulting in increased occurrences of monospecific mangrove stands, which may explain a lack of structural diversity in PAs (Ofori 2025). Therefore, while a generalized analysis of GEDI metrics may miss dynamics specific to mangroves and wetlands, it is likely that the patterns observed in this study support other evidence of deforestation and anthropogenic pressure to mangroves in the region. A Ramsar Site specific study would benefit our understanding of the impact of these PAs and why overall trends often vary from other PA designations in West and Central Africa. 3.4 High deforestation and low PA impact in Guinea While positive PA impact appears higher in several countries with high deforestation rates, this was not universally true. In Guinea, which experienced 28% loss of forest cover between 2000–2024, little difference was detected between PAs and NPCs for all metrics of structure. This remained true when comparing value distributions, which revealed lower height values nationwide. This may indicate that PAs and NPCs in this country are similarly affected by deforestation pressures and supports previous work demonstrating agricultural expansion into PAs in the region (Meng 2023, Singh 2020). Despite the positive impact of PAs on structural metrics in a number of countries, previous studies have identified the presence of threats, including livestock farming, logging, fires, mining, and crops in PAs (Dulias 2022, Meng, 2023, Singh 2020). Therefore, in high-deforestation countries where PAs appear to have no positive effect on measures of structural diversity, PA management requires further investigation to understand if PAs are meeting other management goals not connected with structural metrics, or if other management strategies are needed. 3.5 PA biodiversity potential Our study builds on the empirical findings that higher values of canopy height and other metrics of structural diversity indicate higher biodiversity potential, using previous hypotheses, such as the Height Variation Hypothesis, to support this assumption (Torresani 2023). Countries where PAs are taller and more structurally diverse than NPCs will likely demonstrate higher biodiversity in PAs as well. Utilizing the Google Earth Engine app produced to support our work, individual hotspots may also be inspected for biodiversity potential. However, the assumption linking structural diversity to biodiversity is not universally applicable and should be held in consideration with other assessments of PA effectiveness, such as protection of rare or insubstantially protected ecosystems. For example, while Ghana PAs are overall more structurally than their non-protected counterparts, other studies have noted that a number of key ecosystems, like savannas, are underrepresented in the overall coverage of PAs (Singh 2020). Our work lends itself to further analysis of in-situ data in the region to identify the direct impact of PAs on biodiversity, and whether assumptions like the Height Variation Hypothesis hold true in this region. The impact of Protected Areas, while known to be critical to biodiversity conservation worldwide, demonstrates a complex pattern across the West and Central African region and a need for nuanced examination of PA dynamics. As GEDI continues collecting data, time series data would further improve analyses of PA trends by tracking where tall, structurally diverse forests persist, or where degraded forests regenerate independent of trends in unprotected forests in a given region. Finally, it is worth examining GEDI collected over Afrotropical wetlands and Ramsar sites in conjunction with in-situ data describing other features related to biodiversity, such as species occurrence data, soil sampling and salinity levels, to better understand PA impact on management goals in these ecosystems. Across the region, PA management would benefit from comparing high impact PAs to others where goals to structural diversity have yet to be met. Such comparison would benefit the selection of new PAs to meet the 30x30 goal. 4. METHODS 4.1 Study area The Afrotropical region consists of a large swathe of countries, each with their own governance and makeup of landcover types. The countries observed in this study included Benin (BEN), Cameroon (CMR), Democratic Republic of the Congo (COD), Republic of the Congo (COG), Côte d’Ivoire (CIV), Equatorial Guinea (GNQ), Guinea (GIN), Gabon (GAB), the Gambia (GMB), Ghana (GHA), Guinea Bissau (GNB), Liberia (LBR), Nigeria (NGA), Senegal (SEN), Sierra Leone (SLE), and Togo (TGO) (Fig. 5 ). West and Central Africa encompass several distinct ecoregions, including Desert, Sahel Shrubs, Grassland, Savanna, and Tropical Forest (Makinde et al 2024). Within these ecoregions, landcover types include cropland, forest, settlements, grasses, shrubs, woodland, savannah, mangroves, flooded forests, and wetlands, among other vegetation types (Barnieh 2020, Fasona 2009). Most of the region experiences one rainy season lasting from 1–6 months, with some countries like Liberia and Nigeria experiencing two (CILSS 2016). Landcover change and loss vary across the region with forest coverage ranging from 20–92% and deforestation ranging from 2–39% (Hansen 2013). Forest loss and change are typically driven by agriculture, cutting for fuel, logging, bush burning, oil exploration, salinization, and erosion (Fasona 2009, Barnieh 2022). 4.2 PA site matching We performed our analysis using the Multi-Mission Algorithm and Analysis Platform (MAAP), a cloud computing platform created as a collaborative effort between NASA and ESA to facilitate large-scale data processing for missions like GEDI (Albinet, 2019). A workflow of the methodology is available in Fig. 6 . The GEDI products utilized in this study included L2A (Elevation and Height Metrics Data Global Footprint Level), L2B (Canopy Cover and Vertical Profile Metrics Data Global Footprint Level), and L4C (Footprint Level Waveform Structural Complexity Index). The full collection of footprints from 2019–2023 was subset to each country of interest and only data meeting specified quality control flags (quality flag = 1 for L2A, l2a_quality_flag = 1, l2b_quality_flag = 1, sensitivity > 0.95 for L2B, and l2_quality_flag = 1 and sensitivity > 0.95 for L4C) were retrieved to standardize data quality controls and reduce data loads (Dubayah 2020). New GEDI footprints from the instrument’s reinstallation were not included due to lack of accessibility on the MAAP at the time this study was performed. To consolidate a list of protected areas, we utilized the World Database of Protected Areas (WDPA), a global dataset of marine and terrestrial protected areas compiled and maintained by the UN Environment Program (UNEP) and the International Union for the Conservation of Nature (IUCN). Through this dataset, Designation Type is also available. Protected areas for this study were subset to terrestrial ecosystems where GEDI shots were available. A total of 2080 protected areas were input into the analysis. The WDPA lists 35 unique PA designations in this region. To account for different naming conventions of similar PA types, we examined WDPA defined National Parks, World Heritage Sites, UNESCO-MAB Biosphere Reserves, and Ramsar Sites. Remaining PAs consisted of domestically designated sites and were aggregated into the categories of, Hunting Focus (Game Reserve, Game Sanctuary, Hunting Area, Hunting Reserve, Hunting Zone,), and Other (Aquatic Reserve, Area of Absolute Protection, Biosphere Reserve, Botanical Reserve, Classified Forest, Community Reserve, Flora Sanctuary, Forest Reserve, Integral Nature Reserve, Marine Park, Natural Park, Natural Reserve, Nature Reserve, Presidential Reserve, Resource Reserve, Scientific Reserve, Strict Nature Reserve, Wetland Reserve, Wildlife Reserve, Wildlife Sanctuary, Zone de Peche Reservee) (UNEP-WCMC 2019). PA allocations are non-random, and to account for site-selection bias and assess the causal effect of PA designation, we matched 1km sites in PAs to sites in NPCs using a statistical matching method (Liang et al., 2023). 1km samples were used to match PA to NPC pixels using baseline data from the year 2000. Datasets used to control for confounding factors on PA performance include a suite of biogeophysical and socioeconomic variables (Table 2 ). Control NPC pixels were considered viable if they occurred within the same country and landcover type, and are not necessarily geographically paired with treatment PA pixels. Full details of the matching algorithm are available in Duncanson et al. 2023 and Liang et al. 2023. Table 2 Datasets to be used for matching PAs to non-protected counterfactuals. Table adapted from Duncanson et al 2023. Usage Data Time Range Sources Primary Data for Analysis World Database on Protected Areas 2023 UNEP & IUSN ( www.protectedplanet.net ) GEDI 2019–2023 L2A (LPDAAC: 10.5067/GEDI/GEDI02_A.002) L2 B (LPDAAC: 10.5067/GEDI/GEDI02_B.002 ) L4C (de Conto, 2025) Exact Matching Landcover 2000 GLAD UMD (Hansen, 2013) Mangrove Cover 2001 Global Mangrove Watch (Bunting, 2022) Propensity Matching Population Density 2000 Gridded Population of the World Version 4 (Center For International Earth Science Information Network-CIESIN-Columbia University, 2018) Population Count Mean Temperature 1990–1999 World Clim V1 Bioclimatic Variables (Hijmans, 2005) Mean Precipitation Elevation 2000 SRTM (NASA JPL, 2013) Slope Distance to Cities Hewson, 2019 Travel Time to Cities 2000 Nelson, 2008 4.3 Structural comparisons Several metrics are available through the L2A and L2B products that allow for structural comparison between PAs and NPCs. We categorized vegetation structures into three categories following Hakkenberg et al 2023. These include: Canopy Dimensions, Structural Heterogeneity, and Spatial Configuration (Table 3 ). L2A relative height (RH) metrics at every 10th percentile, as well as the 98th percentile, were collected. From L2B, cover, plant area index (PAI), foliage height diversity (FHD), and plant area vegetation density (PAVD) at each 5m height bin were collected. After examining PAVD profiles, which showed little difference between PA and NPCs, this variable was discarded from the analysis. To account for the known correlation between FHD and RH98 (Schneider 2020), a normalized value for FHD was also generated using a min-max normalization with RH100. Hereafter, all references to FHD refer to the normalized value unless otherwise indicated by “RAW”. From L4C, the Waveform Structural Complexity Index (WSCI) was collected. WSCI is constructed through the matching of airborne lidar from ALS with GEDI shots to build a comprehensive index of structural complexity accounting for horizontal, vertical, and 3D structural complexity (de Conto 2024). Each of these metric values was extracted for each PA and NPC sample where GEDI shots were available. Table 3 Structural metrics used for evaluation of structural diversity Structure Category Metric Definition Units Canopy Dimensions RH98 Maximum canopy height at 98th percentile of relative heights m PAI Total Plant Area Index m2/m2 Structural Heterogeneity FHD Foliage Height Diversity unitless WSCI Waveform Structural Complexity Index unitless Spatial Configuration PAVD Plant Area Volume Density m2/m3 Declarations Competing interests: The authors declare that there is no conflict of interest regarding the publication of this article. Data availability: All code used to perform analyses have been made available in https://github.com/GEDI-PA/vl_GEDI-PA_2024. Visualization of distribution of RH98 and FHD values in all analyzed PAs is available at https://mangrovescience.earthengine.app/view/gediprotectedareas. Acknowledgements: This project was funded by the NASA Earth Science Applications grant number NNH22ZDA001N-ECON:A.40 Ecological Conservation within NASA's Earth Science Division. References Albinet, C., Whitehurst, A.S., Jewell, L.A., Bugbee, K., Laur, H., Murphy, K., Frommknecht, B., Scipal, K., Costa, G., Jai, B., Ramachandran, R., Lavalle, M., Duncanson, L. A Joint ESA-NASA Multi-mission Algorithm and Analysis Platform (MAAP) for Biomass, NISAR, and GEDI. Surv Geophys 40, 1017–1027. https://doi.org/10.1007/s10712-019-09541-z. (2019). Barnieh, B. A., Jia, L., Menenti, M., Zhou, J. & Zeng, Y. Mapping land use land cover transitions at different spatiotemporal scales in West Africa. Sustainability (Switzerland) 12, 1–52. (2020). Barraclough, A. D., Reed, M., Coetzer, K., Price, M., Schultz, L., Moreira- Muñoz, A., Måren, I. 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Torresani, M., Rocchini, D., Alberti, A., Moudrý, V., Heym, M., Thouverai, E., Kacic, P., & Tomelleri, E. LiDAR GEDI derived tree canopy height heterogeneity reveals patterns of biodiversity in forest ecosystems. Ecological Informatics 76. (2023). UNEP-WCMC. User Manual for the World Database on Protected Areas and world database on other effective area-based conservation measures: 1.6. UNEP-WCMC: Cambridge, UK. Available at: http://wcmc.io/WDPA_Manual. (2019). UNEP-WCMC. Protected Areas Map of the World, March 2024. www.protectedplanet.net. (2024). United Nations. The Sustainable Development Goals Report. (2025). Wade, C. M., Austin, K. G., Cajka, J., Lapidus, D., Everett, K. H., Galperin, D., Maynard, R., Sobel, A. What Is Threatening Forests in Protected Areas? A Global Assessment of Deforestation in Protected Areas, 2001–2018. https://doi.org/10.3390/f11050539. (2022). Zhang, Y., West, P., Thakholi, L., Suryawanshi, K., Supuma, M., Straub, D., Sithole, S. S., Sharma, R., Schleicher, J., Ruli, B., Rodríguez-Rodríguez, D., Rasmussen, M. B., Ramenzoni, V. C., Qin, S., Delgado Pugley, D., Palfrey, R., Oldekop, J., Nuesiri, E. O., Hai, V., Nguyen, T., Ndam, N., Mungai, C., Milne, S., Mabele, M., Lucitante, S., Lucistante, H., Liljeblad, J., Kiwango, W., Kik, A., Jones, N., Johnson, M., Jarrett, C., James, R., Holmes, G., Gisbon, L., Ghoddousi, A., Geldmann, J., Gebara, M., Edwards, T., Dressler, W., Douglas, L., Dimitrakopoulous, P., Davidov, V., Compaoré-Sawadogo, E., Collins, Y., Cepek, M., Burow, P., Brockington, D., Philippe, M., Balinga, B., Austin, B., Astuti, R., Ampumuza, C., Agyei, F. K. Annual Review of Environment and Resources Governance and Conservation Effectiveness in Protected Areas and Indigenous and Locally Managed Areas. 46, 55–55. (2025). Zhu, X., Nie, S., Zhu, Y., Chen, Y., Yang, B., & Li, W. Evaluation and Comparison of ICESat-2 and GEDI Data for Terrain and Canopy Height Retrievals in Short-Stature Vegetation. Remote Sensing 15. (2023). Additional Declarations There is NO Competing Interest. Supplementary Files Supplemental.docx Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8744926","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":588433435,"identity":"852015b5-2b72-4300-8efe-153ff0589e35","order_by":0,"name":"Abigail Barenblitt","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuElEQVRIiWNgGAWjYDACCTB5QIYfwmYmXguPZAPJWgwOEKuFf3bzsw8fd9zhMb6RnXiDocI6sYGgJXeOGc+ceeYZj9mN3M0WDGfSCWthuJFgzMzbdhikZZsEY9thwlrkb6R/Zv4L1GI8A6TlHxFaDG7kGDMDDecxkABpaSBCi+GdM8WMvW3PeCTOvN1skXAs3ZigFrnb7ZsZfrbdkeNvz91440ONtSxBLagggTTlo2AUjIJRMApwAQDJ5EI50vHa6QAAAABJRU5ErkJggg==","orcid":"","institution":"NASA/ ESSIC","correspondingAuthor":true,"prefix":"","firstName":"Abigail","middleName":"","lastName":"Barenblitt","suffix":""},{"id":588433436,"identity":"0a62cf1a-978a-4c6e-b2be-e68577b86773","order_by":1,"name":"Mengyu Liang","email":"","orcid":"","institution":"Stanford","correspondingAuthor":false,"prefix":"","firstName":"Mengyu","middleName":"","lastName":"Liang","suffix":""},{"id":588433437,"identity":"c4c04f43-7e1d-4774-9afa-c610f0ecd313","order_by":2,"name":"Atticus Stovall","email":"","orcid":"","institution":"University of Maryland","correspondingAuthor":false,"prefix":"","firstName":"Atticus","middleName":"","lastName":"Stovall","suffix":""},{"id":588433438,"identity":"4f1fce1f-1032-470a-b67b-8e1e60031fc7","order_by":3,"name":"Veronika Leitold","email":"","orcid":"","institution":"University of Maryland, College Park","correspondingAuthor":false,"prefix":"","firstName":"Veronika","middleName":"","lastName":"Leitold","suffix":""},{"id":588433439,"identity":"a78aa78d-090d-48ca-8093-afb581f4a34a","order_by":4,"name":"Alex Mandel","email":"","orcid":"","institution":"Development Seed","correspondingAuthor":false,"prefix":"","firstName":"Alex","middleName":"","lastName":"Mandel","suffix":""},{"id":588433440,"identity":"bef0116c-b05e-4f09-a0df-f5921d50984d","order_by":5,"name":"Temilola Fatoyinbo","email":"","orcid":"","institution":"NASA Goddard Space Flight Center","correspondingAuthor":false,"prefix":"","firstName":"Temilola","middleName":"","lastName":"Fatoyinbo","suffix":""},{"id":588433441,"identity":"0d7941c5-0559-4da7-9bff-19ba3d085308","order_by":6,"name":"Laura Duncanson","email":"","orcid":"https://orcid.org/0000-0003-4031-3493","institution":"University of Maryland","correspondingAuthor":false,"prefix":"","firstName":"Laura","middleName":"","lastName":"Duncanson","suffix":""}],"badges":[],"createdAt":"2026-01-30 21:21:00","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8744926/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8744926/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102290854,"identity":"bc23b1e8-bc25-426d-ae21-f0ab64834db7","added_by":"auto","created_at":"2026-02-10 09:12:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1290114,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u0026nbsp;\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8744926/v1/ed85d0bff31ddedfa16f20c1.png"},{"id":102290859,"identity":"b8729482-126a-4a00-895b-13385d96ab64","added_by":"auto","created_at":"2026-02-10 09:12:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2077681,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eRelative distribution of FHD and RH98 values in each country. Relative frequency is calculated using a kernel density estimation available in the R ggplot package. PA values are depicted in blue and NPC values are depicted in purple. Areas of green indicate where the area difference between plots is positive in PAs, and areas of gray indicate where the area difference between plots is negative in PAs.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8744926/v1/a7b960760e10cc02b0b941ac.png"},{"id":102290901,"identity":"31f22777-41e1-42ef-b042-51ded70df51f","added_by":"auto","created_at":"2026-02-10 09:12:30","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1341268,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eRelative distribution of FHD, RH98, PAI, and WSCI values in Ghana separated by PA governance designation. PA values are depicted in blue and NPC values are depicted in purple. Areas of green indicate where the area difference between plots is positive in PAs, and areas of gray indicate where the area difference between plots is negative in PAs. Due to L4C specific quality filtering, WSCI data was unavailable for UNESCO-MAB Biospheres.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8744926/v1/55b38e53ff5a442cb0c0f5a8.png"},{"id":102290908,"identity":"d18cac7e-8ef0-4ee0-b005-a8315f7add56","added_by":"auto","created_at":"2026-02-10 09:12:35","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":463200,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eComparison of mean percent difference of FHD, WSCI, RH98, and PAI with deforestation percentages in each country. Deforestation ratios between 2000-2024 are acquired from Global Forest Watch (Hansen 2013). Mean % difference for each variable is displayed on the y axis. Country values are distinguished by different colors for each.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8744926/v1/f5920b395d3c7383571c4531.png"},{"id":102290916,"identity":"ce6a3ec6-76e5-414f-9861-94f4845399a3","added_by":"auto","created_at":"2026-02-10 09:12:37","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2246978,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eStudy area comprising 16 countries in West and Central Africa. A) PAs are indicated in green. B) GEDI shots sampled over PAs and NPCs are indicated in blue and purple, respectively. Map subset demonstrates all available GEDI shots compared to those sampled in PAs and NPCs.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8744926/v1/ffb71f11dffc96d659a0f283.png"},{"id":102290907,"identity":"0eb32397-31f2-4788-bac3-b6a7459c0aac","added_by":"auto","created_at":"2026-02-10 09:12:34","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1474922,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eWorkflow of experiment design. Orange boxes indicate inputs, blue hexagons indicate processes, and green ovals indicate outputs. Site matching occurred for PAs and non-PAs. GEDI footprints were then subset to these regions. Comparison of Structural Complexity metrics was calculated and evaluated based on country and governance type.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8744926/v1/088dcddbf46b44f92e1ccaad.png"},{"id":102291031,"identity":"7bbd25fa-173c-4a25-96ca-20e286c18ed1","added_by":"auto","created_at":"2026-02-10 09:13:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":10956765,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8744926/v1/6a6e6f1b-6c62-4290-8304-e1d9f77896e4.pdf"},{"id":102290910,"identity":"0bc06485-cccb-44dd-ae5d-55b86eebe3c1","added_by":"auto","created_at":"2026-02-10 09:12:36","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":9723236,"visible":true,"origin":"","legend":"","description":"","filename":"Supplemental.docx","url":"https://assets-eu.researchsquare.com/files/rs-8744926/v1/8f31719f34ccce32bc8bd325.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"If a tree is “Protected”, is it? Using satellite-borne LiDAR to understand efficacy of protection status in West and Central African Protected Areas","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eProtected Areas (PAs) currently cover\u0026thinsp;\u0026gt;\u0026thinsp;17% of the Earth\u0026rsquo;s land surface as of 2024 (UNEP-WCMC 2024). This is just over halfway to meeting the UN Convention on Biological Diversity\u0026rsquo;s (UNCBD) 30x30 goal, under which nearly 200 committed countries aim to protect 30% of their terrestrial and aquatic ecosystems, to address Target 3 of the Kunming-Montreal Global Biodiversity Framework (GBF), the creation of equitable, connected, and ecologically representative protected areas (Convention on Biological Diversity 2022). PAs additionally support Sustainable Development Goal (SDG) 15, particularly 15.1 and 15.2, which aim to conserve terrestrial ecosystems and end deforestation (United Nations 2025). Globally, PAs have had positive impacts on habitat protection and carbon storage and are viewed as critical for preserving hotspots of biodiversity (Cazalis 2020, Neugarten 2020). PAs also have positive spillover effects as demonstrated by higher biodiversity found in surrounding buffer zones (Brodie 2023).\u003c/p\u003e \u003cp\u003eIn unprotected areas, global trends of increasing forest and biodiversity loss have been observed (Neugarten 2020). While PAs experience lower rates of loss, the temporal change of vegetation loss in PAs reflects that of global forest trends (Wade 2022). This is due to increased pressure from anthropogenic activity like agriculture in recent years - especially in Afrotropical regions - along with pressures from wildfires, pests, and storm events (Wade 2022, Geldmann 2019). In fact, a past study found higher agriculture-driven PA degradation in Afrotropical PAs than in matched unprotected counterfactuals, raising concerns over PAs\u0026rsquo; effectiveness for safeguarding biodiversity and climate mitigation (Geldmann 2019).\u003c/p\u003e \u003cp\u003ePrevious work in other tropical regions like Central America demonstrates significant positive trends in vegetation indices like NDVI in PAs, with varying degrees of forest growth depending on centralization\u0026mdash;or level of state government involvement\u0026mdash;of PAs and PA management capacity (Mu\u0026ntilde;oz 2018). Additionally, the Global Ecosystem Dynamics Investigation (GEDI), the first spaceborne lidar mission designed to study Earth\u0026rsquo;s forests in 3D, has been used to demonstrate the effectiveness of PAs for climate change mitigation on a global level, noting that PAs have contributed to significant carbon emission avoidance (Duncanson 2023). Results from a global study of biomass in PAs indicate that despite comparable geographic coverage of African PAs to other continents, biomass densities within African PAs were lower than on other continents (Duncanson 2023). Earlier research also suggests that PAs in Africa experience higher rates of disturbance (Geldmann 2019). However, more regional focused study is needed to further disentangle African PAs\u0026rsquo; role in resisting conversion pressure from socioeconomic development and conserving the natural habitats.\u003c/p\u003e \u003cp\u003eQuantitative and evidence-based assessment of the structural makeup of PAs and their effectiveness in preserving vegetation structural complexity are much needed for conservation planning and policy in data-scarce regions of West and Central African PAs. A past study found that PAs in Europe were more structurally diverse than counterfactuals (Ceccherini 2023). In the United States, a study has identified a positive correlation between structural diversity and carbon storage (Crockett 2023). Therefore, by gleaning information pertaining to structural diversity in PAs, more knowledge can be gained regarding carbon storage and climate mitigation in these regions.\u003c/p\u003e \u003cp\u003eAs countries expand the coverage of PAs to achieve 30x30 goals, identifying metrics for tracking PA efficacy is critical for PA planning. For example, Biodiversity Priority Areas (BPAs), which emphasize the presence of IUCN Red List species, are one category by which to assess the potential impact of establishing a new PA (Neugraten 2020). Increased understanding of structural diversity within pre-existing PAs would further improve current methods for determining the impact of PAs and prioritizing regions for PA designation optimization. Additionally, improving the performance of existing PAs through management may prove to be a more effective goal for maintaining biodiversity than PA expansion alone (Singh 2020, Li 2024). Previous work demonstrates the utility of GEDI for studying structural diversity to better understand biodiversity (Torresani 2020, Hakkenberg 2023).\u003c/p\u003e \u003cp\u003eIn this study, we leverage GEDI height and structural diversity metrics to compare PAs across West and Central Africa to non-protected counterfactuals to better understand the effect of PAs on metrics that are linked with biodiversity capacity. We test whether PAs exhibit higher structural diversity as measured by GEDI than matched Non-Protected Counterfactuals (NPCs), and whether PA impact varies systematically by country, designation, and national deforestation ratios.\u003c/p\u003e"},{"header":"2. RESULTS","content":"\u003cp\u003eOf the PAs analyzed, 1773 had sufficient data in the baseline year to be matched with an environmentally and socioeconomically similar NPC. Notably, quality filtering for L4C reduced the number of available PAs to 1748 and the number of available GEDI due to the strict quality filtering inherent to the product, especially in tropical regions, resulting in differences in sample sizes from different product levels (de Conto 2024). Across the entire study area, average RH98, and PAI were lower in PAs than in NPCs (0.5m and 0.1, respectively), but differences fell within the standard deviation for each metric. All summary values and available number of GEDI shots can be found in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary statistics of GEDI metrics across all shots in PAs and NPCs\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetric\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStatus\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026#120590;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10th Perc\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e90th Perc\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e# Available Shots\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRH98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e31.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e23,249,462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNPC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e29.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e20,250,331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e23,249,462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNPC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e20,250,331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFHD RAW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e23,249,462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNPC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e20,250,331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eWSCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e13,638,810\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNPC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4,687,480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Fourteen out of sixteen countries show consistent positive impact of PAs on structure\u003c/h2\u003e \u003cp\u003eAverage RH98 and PAI were highest in GAB (28.8m in PAs and 28.7m in NPCs; 3.97 in PAs and 3.92 in NPCs). GAB and GNQ had the highest average FHD values (3.03 in PAs and NPCs). GNQ had the highest average WSCI values (28.5 in PAs and 29.3 in NPCs). A table of all summary values across all countries is available in S1.\u003c/p\u003e \u003cp\u003eWhen PA and NPC values were averaged on a country-level, the percent difference between PAs and NPCs was highest in GHA for measures of RH98 (59.6%), PAI (63.5%), FHD (13.6%), and WSCI (4.6%). COG demonstrated a negative percent difference for RH98, PAI, and FHD (-1.3%, -3.8%, and \u0026minus;\u0026thinsp;0.5%), while GNQ demonstrated a negative percent difference for FHD, RH98, and WSCI (-0.2%, -2.1%, -2.6%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Central and West African PAs safeguard structural diverse ecosystems\u003c/h2\u003e \u003cp\u003eThe distribution of FHD and RH98 skewed towards higher values in PAs than in NPCs in GMB, GHA, SLE, and TGO, indicating higher frequencies of high FHD and tall vegetation. In the 12 other countries, little difference was initially detected in the distributions of these values in PAs and NPCs. WSCI values skewed higher in PAs than in NPCs in GHA, SLE, and TGO. Of note, WSCI skewed towards lower values in PAs than in NPCs in GMB. Countries where value distributions show visible differences are included in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, and all metric distributions across all countries are available in S2.\u003c/p\u003e \u003cp\u003eIn six countries where distributions of these values demonstrated little difference between PAs and NPCS (CMR, COD, COG, GNQ, GAB, LIB), the distribution of normalized FHD values peaked at 0.02 or higher, the distribution of RH98 values peaked at 25m or higher, and the distribution of WSCI values peaked at 11 or higher.\u003c/p\u003e \u003cp\u003eIn the remaining six countries (BEN, CIV, GIN, GMB, NGA, SEN), all RH98 values skewed lower, with distributions of values peaking below 5m in all but two countries (GIN \u0026amp; CIV), where values peaked below 10m. In BEN, CIV, GIN, and GMB, FHD values skewed higher (\u0026gt;\u0026thinsp;0.01), whereas in NGA and SEN, values skewed lower (\u0026lt;\u0026thinsp;0.01). WSCI values in BEN, GHA, NGA, and SLE skewed higher (\u0026gt;\u0026thinsp;10). Due to the large number of PAs included in this study, a Google Earth Engine App has been made available to examine histograms of FHD and RH98 values at individual PAs (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://mangrovescience.earthengine.app/view/gediprotectedareas\u003c/span\u003e\u003cspan address=\"https://mangrovescience.earthengine.app/view/gediprotectedareas\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 National parks show more consistent positive outcomes\u003c/h2\u003e \u003cp\u003eThe most frequently observed PA designation was Other PAs (1559 individual PAs, 12,118,048 shots in PAs and matching NPCs). However, the majority of GEDI shots were collected over National Parks (104 individual PAs, 13,020,888 shots in PAs and matching NPCs). Notably, the loss of GEDI shots in the L4C quality filtering reduced the number of overall shots or eliminated the sampling of NPC matches for certain PA designations in BEN, GAB, GHA, and GMB (e.g., UNESCO-MAB Biospheres in GHA). The filtered L4C data yielded 10,650,580 shots in Other PAs and matching NPCs and 3,906,976 shots in National Parks and matching NPCs. The highest mean percent difference between PAs and NPCs was found in UNESCO-MAB Biospheres in GHA (FHD: 51.8%, RH98: 336.4%, PAI: 2,724.6%), followed by Other PAs, also in GHA (FHD: 14.6%, RH98: 65.5%, PAI: 64.4%, WSCI: 5%). National Parks had higher FHD or RH98 in all but four countries (COD, COG, GMB, \u0026amp; GNQ), whereas Ramsar Sites, which were present in 13 countries, had a negative effect on FHD or RH98 in all but three countries (CIV, COD, TGO). WSCI values were higher in National Parks in all but four countries (GMB, GNQ, COD, and SEN) and lower in Ramsar Sites in all but five countries (GMB, CIV, COD, GNB, and TGO) (S4, S5, S6).\u003c/p\u003e \u003cp\u003eIn countries where FHD and RH98 skewed higher in PAs than in NPCs, value distribution varied by PA designation. FHD and RH98 were higher in PAs in each of these countries across each PA designation, except in Ramsar PAs in GHA, where mean values of all metrics were lower in PAs than NPCS (FHD:-11.1%, RH98:-16.6%, PAI:-6%, WSCI:-3.6%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, S6).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn countries where few differences were detected in the distribution of FHD and RH98 values, these distributions also varied by PA designation. Of note, Hunting Focus, National Parks, UNESCO-MAB Biospheres, and World Heritage PAs demonstrated a positive effect on average values across several GEDI metrics in several countries despite the lack of a discernible difference on a country-wide scale (e.g., BEN, CIV) (S4, S5, S6).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Positive PA metrics and deforestation rates\u003c/h2\u003e \u003cp\u003eGlobal Foret Watch values of deforestation ratio between 2000\u0026ndash;2024 was highest in SLE (39%) followed by GIN and BEN (28%) (Hansen 2013). It was lowest in GAB (2.2%). In GHA, where GEDI structural metrics consistently demonstrated the highest positive difference between PAs and NPCs, the deforestation ratio was 25%. Conversely, in COG where FHD, RH98, and PAI were lower in PAs than NPCs, the deforestation ratio was 4.4%. All deforestation ratios compared to mean percent differences of FHD, RH98, PAI, and WSCI are demonstrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. DISCUSSION","content":"\u003cp\u003eWith Protected Areas predicted to become increasingly critical to biodiversity in the face of habitat disturbance and loss, improved understanding of PA impact on structural features that are predictive of biodiversity is vital for the creation of new PAs, management of existing areas, and meeting SDG 15 targets for forest conservation (Ranius 2023, United Nations 2025). At a regional level, our results identify little average difference between ecological structure metrics in PAs and NPCs. However, the picture is complicated by the variation of patterns observed in individual countries, designation types, and overall structural metric distribution in and outside of PAs.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 PA positive impact informed by background deforestation rates\u003c/h2\u003e \u003cp\u003eWhile the overall comparison of mean values across the region reveals little distinct pattern, PAs in Ghana demonstrated a positive effect on all measures of forest structural diversity. When the distribution of structural metrics are examined, there is a clear increase in frequency of higher values in Ghanaian PAs, indicating that PAs are preserving structural diversity. Notably, Ghana has experienced deforestation of approximately one fourth of its forests from 2000\u0026ndash;2024 (Hansen 2013). The positive impact of Ghanaian PAs on forest structure appears to be exacerbated by the high levels of deforestation experienced country-wide, while also indicating these PAs are preserving forest structural diversity that would be lost otherwise.\u003c/p\u003e \u003cp\u003eConversely, in several countries, including Equatorial Guinea, the Republic of the Congo, and Gabon, forest structure metrics varied little between PAs and NPCs. This reflects findings in a previous study of global biomass that the effect of PAs in the Afrotropics is lower than other continents. However, the distributions of values in PAs and NPCs denoted higher values of height and structural diversity nationally in each of these countries. Therefore, an absence of clear positive difference in mean values does not necessarily indicate an ineffective PA, moreso that intact forest has remained intact in these countries, even outside of PAs in many cases. This trend could point to effective forest management strategies at a national scale, not just within PAs, and is reflective of low deforestation (\u0026lt;\u0026thinsp;6% since 2000) in these countries (Hansen 2013). As deforestation trends change, these PAs would be served by continued monitoring, especially as more GEDI data becomes available, to ensure PAs continue achieving conservation goals regardless of national deforestation. These results point to avenues for a more detailed assessment of PA effectiveness whereby metrics of structural diversity, in-situ biodiversity data, and deforestation rates are considered concurrently.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Designation type impacts PA trends\u003c/h2\u003e \u003cp\u003eThe level of difference between PAs and NPCs also varied between designation types. Of note, National Parks had at least a minimally positive effect on structural metrics in most of the countries examined here. This is consistent with previous literature demonstrating a positive effect of National Parks for biodiversity and reducing deforestation (Zhang 2023, Li 2024). UNESCO-MAP Biosphere sites, which were present in seven countries, and World Heritage Sites, which were present in four countries, also displayed a positive effect in almost all countries where these designations were present. This was true in several countries where differences in mean values alone revealed little or a negative impact on structural metrics (e.g., Democratic Republic of the Congo). While National Parks are managed and established by national entities within a given country, UNESCO-MAP Biosphere sites and World Heritage Sites are established by international entities (Hasseini 2021, Barraclough 2023, Aschenbrand 2021). Unlike National Parks, these sites also maintain a sociocultural focus in their management framework, though the focus varies by country (Hasseini 2021, Barraclough 2023, Thomsen 2025). Biospheres and Heritage Sites offer an opportunity to integrate cultural and spiritual values with biodiversity conservation (Barraclough 2023). While these designations were less common throughout our region of study, the positive impact of these sites on structural metrics indicates that multiple-use sites may be effective at increasing or maintaining biodiversity potential. While locally governed sites were underrepresented in this study, the positive effect of UNESCO sites still points to the potential co-benefits of prioritizing PAs with a mind towards ecological conservation that works hand-in-hand with sociocultural needs.\u003c/p\u003e \u003cp\u003eConversely, in several countries, structural metrics skewed lower in Ramsar sites than their matched counterfactuals, a pattern that was especially stark in Ghana and Senegal. Ramsar Sites, or Wetlands of International Importance, aim to reduce the loss of wetlands through designation, management, and \u0026ldquo;wise-use\u0026rdquo; (Rattan 2021). In Afrotropical regions, many of these Ramsar Sites are characterized by mangroves rather than forest, which are necessarily low in species diversity (Padonou 2025). A study of a Ramsar site in Benin noted that mangroves within the site displayed a consistent number of layers (Padonou 2025). Therefore, structural components related to overall habitat health in more specialized ecoregions such as mangroves may not be as well explained by metrics like FHD and require further, more specialized study to understand the effect of PAs on soils, water quality, and biodiversity in conjunction with GEDI metrics. This study also found saplings and seedlings greater than 50% of tree density in this Ramsar site (Padonou 2025). GEDI height measurements are often less accurate in short-structure vegetation and may blend canopy and ground signals (Zhu 2023). Therefore, areas of recent restoration of short vegetation, such as mangroves, may not be as well detected by this instrument. Additionally, changes in water level have been observed to impact GEDI heights, though this effect is minimized in microtidal areas with submerged vegetation (Thomas 2023). Uncertainty in GEDI metrics over short wetlands could further be mitigated by the use of water level gauge data to account for these fluctuations. As more GEDI data becomes available, a timeseries analysis of structural complexity may prove more useful at identifying such restoration as older mangrove stands should show higher complexity and reach heights better detected by GEDI (Lucas 2020). However, other studies have noted urban expansion, mining, and harvesting for fuelwood in mangroves in the Afrotropical region and have detected substantial mangrove loss in Ramsar Sites, such as the Keta Lagoon Complex in Ghana (Duku 2021, Ofori 2025). Additionally, some mangrove restoration has been driven by a need for fuelwood, resulting in increased occurrences of monospecific mangrove stands, which may explain a lack of structural diversity in PAs (Ofori 2025). Therefore, while a generalized analysis of GEDI metrics may miss dynamics specific to mangroves and wetlands, it is likely that the patterns observed in this study support other evidence of deforestation and anthropogenic pressure to mangroves in the region. A Ramsar Site specific study would benefit our understanding of the impact of these PAs and why overall trends often vary from other PA designations in West and Central Africa.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.4 High deforestation and low PA impact in Guinea\u003c/h2\u003e \u003cp\u003eWhile positive PA impact appears higher in several countries with high deforestation rates, this was not universally true. In Guinea, which experienced 28% loss of forest cover between 2000\u0026ndash;2024, little difference was detected between PAs and NPCs for all metrics of structure. This remained true when comparing value distributions, which revealed lower height values nationwide. This may indicate that PAs and NPCs in this country are similarly affected by deforestation pressures and supports previous work demonstrating agricultural expansion into PAs in the region (Meng 2023, Singh 2020). Despite the positive impact of PAs on structural metrics in a number of countries, previous studies have identified the presence of threats, including livestock farming, logging, fires, mining, and crops in PAs (Dulias 2022, Meng, 2023, Singh 2020). Therefore, in high-deforestation countries where PAs appear to have no positive effect on measures of structural diversity, PA management requires further investigation to understand if PAs are meeting other management goals not connected with structural metrics, or if other management strategies are needed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.5 PA biodiversity potential\u003c/h2\u003e \u003cp\u003eOur study builds on the empirical findings that higher values of canopy height and other metrics of structural diversity indicate higher biodiversity potential, using previous hypotheses, such as the Height Variation Hypothesis, to support this assumption (Torresani 2023). Countries where PAs are taller and more structurally diverse than NPCs will likely demonstrate higher biodiversity in PAs as well. Utilizing the Google Earth Engine app produced to support our work, individual hotspots may also be inspected for biodiversity potential. However, the assumption linking structural diversity to biodiversity is not universally applicable and should be held in consideration with other assessments of PA effectiveness, such as protection of rare or insubstantially protected ecosystems. For example, while Ghana PAs are overall more structurally than their non-protected counterparts, other studies have noted that a number of key ecosystems, like savannas, are underrepresented in the overall coverage of PAs (Singh 2020). Our work lends itself to further analysis of in-situ data in the region to identify the direct impact of PAs on biodiversity, and whether assumptions like the Height Variation Hypothesis hold true in this region.\u003c/p\u003e \u003cp\u003eThe impact of Protected Areas, while known to be critical to biodiversity conservation worldwide, demonstrates a complex pattern across the West and Central African region and a need for nuanced examination of PA dynamics. As GEDI continues collecting data, time series data would further improve analyses of PA trends by tracking where tall, structurally diverse forests persist, or where degraded forests regenerate independent of trends in unprotected forests in a given region. Finally, it is worth examining GEDI collected over Afrotropical wetlands and Ramsar sites in conjunction with in-situ data describing other features related to biodiversity, such as species occurrence data, soil sampling and salinity levels, to better understand PA impact on management goals in these ecosystems. Across the region, PA management would benefit from comparing high impact PAs to others where goals to structural diversity have yet to be met. Such comparison would benefit the selection of new PAs to meet the 30x30 goal.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. METHODS","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Study area\u003c/h2\u003e \u003cp\u003eThe Afrotropical region consists of a large swathe of countries, each with their own governance and makeup of landcover types. The countries observed in this study included Benin (BEN), Cameroon (CMR), Democratic Republic of the Congo (COD), Republic of the Congo (COG), C\u0026ocirc;te d\u0026rsquo;Ivoire (CIV), Equatorial Guinea (GNQ), Guinea (GIN), Gabon (GAB), the Gambia (GMB), Ghana (GHA), Guinea Bissau (GNB), Liberia (LBR), Nigeria (NGA), Senegal (SEN), Sierra Leone (SLE), and Togo (TGO) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWest and Central Africa encompass several distinct ecoregions, including Desert, Sahel Shrubs, Grassland, Savanna, and Tropical Forest (Makinde et al 2024). Within these ecoregions, landcover types include cropland, forest, settlements, grasses, shrubs, woodland, savannah, mangroves, flooded forests, and wetlands, among other vegetation types (Barnieh 2020, Fasona 2009). Most of the region experiences one rainy season lasting from 1\u0026ndash;6 months, with some countries like Liberia and Nigeria experiencing two (CILSS 2016). Landcover change and loss vary across the region with forest coverage ranging from 20\u0026ndash;92% and deforestation ranging from 2\u0026ndash;39% (Hansen 2013). Forest loss and change are typically driven by agriculture, cutting for fuel, logging, bush burning, oil exploration, salinization, and erosion (Fasona 2009, Barnieh 2022).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.2 PA site matching\u003c/h2\u003e \u003cp\u003eWe performed our analysis using the Multi-Mission Algorithm and Analysis Platform (MAAP), a cloud computing platform created as a collaborative effort between NASA and ESA to facilitate large-scale data processing for missions like GEDI (Albinet, 2019). A workflow of the methodology is available in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe GEDI products utilized in this study included L2A (Elevation and Height Metrics Data Global Footprint Level), L2B (Canopy Cover and Vertical Profile Metrics Data Global Footprint Level), and L4C (Footprint Level Waveform Structural Complexity Index). The full collection of footprints from 2019\u0026ndash;2023 was subset to each country of interest and only data meeting specified quality control flags (quality flag\u0026thinsp;=\u0026thinsp;1 for L2A, l2a_quality_flag\u0026thinsp;=\u0026thinsp;1, l2b_quality_flag\u0026thinsp;=\u0026thinsp;1, sensitivity\u0026thinsp;\u0026gt;\u0026thinsp;0.95 for L2B, and l2_quality_flag\u0026thinsp;=\u0026thinsp;1 and sensitivity\u0026thinsp;\u0026gt;\u0026thinsp;0.95 for L4C) were retrieved to standardize data quality controls and reduce data loads (Dubayah 2020). New GEDI footprints from the instrument\u0026rsquo;s reinstallation were not included due to lack of accessibility on the MAAP at the time this study was performed.\u003c/p\u003e \u003cp\u003eTo consolidate a list of protected areas, we utilized the World Database of Protected Areas (WDPA), a global dataset of marine and terrestrial protected areas compiled and maintained by the UN Environment Program (UNEP) and the International Union for the Conservation of Nature (IUCN). Through this dataset, Designation Type is also available. Protected areas for this study were subset to terrestrial ecosystems where GEDI shots were available. A total of 2080 protected areas were input into the analysis. The WDPA lists 35 unique PA designations in this region. To account for different naming conventions of similar PA types, we examined WDPA defined National Parks, World Heritage Sites, UNESCO-MAB Biosphere Reserves, and Ramsar Sites. Remaining PAs consisted of domestically designated sites and were aggregated into the categories of, Hunting Focus (Game Reserve, Game Sanctuary, Hunting Area, Hunting Reserve, Hunting Zone,), and Other (Aquatic Reserve, Area of Absolute Protection, Biosphere Reserve, Botanical Reserve, Classified Forest, Community Reserve, Flora Sanctuary, Forest Reserve, Integral Nature Reserve, Marine Park, Natural Park, Natural Reserve, Nature Reserve, Presidential Reserve, Resource Reserve, Scientific Reserve, Strict Nature Reserve, Wetland Reserve, Wildlife Reserve, Wildlife Sanctuary, Zone de Peche Reservee) (UNEP-WCMC 2019).\u003c/p\u003e \u003cp\u003ePA allocations are non-random, and to account for site-selection bias and assess the causal effect of PA designation, we matched 1km sites in PAs to sites in NPCs using a statistical matching method (Liang et al., 2023). 1km samples were used to match PA to NPC pixels using baseline data from the year 2000. Datasets used to control for confounding factors on PA performance include a suite of biogeophysical and socioeconomic variables (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Control NPC pixels were considered viable if they occurred within the same country and landcover type, and are not necessarily geographically paired with treatment PA pixels. Full details of the matching algorithm are available in Duncanson et al. 2023 and Liang et al. 2023.\u003c/p\u003e \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDatasets to be used for matching PAs to non-protected counterfactuals. Table adapted from Duncanson et al 2023.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUsage\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eData\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTime Range\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSources\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ePrimary Data for Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWorld Database on Protected Areas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUNEP \u0026amp; IUSN (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.protectedplanet.net\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGEDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2019\u0026ndash;2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eL2A (LPDAAC:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5067/GEDI/GEDI02_A.002)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cdiv class=\"ExternalRefDOI\"\u003eL2\u003c/div\u003eB (LPDAAC: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5067/GEDI/GEDI02_B.002\u003c/span\u003e\u003c/span\u003e)\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eL4C (de Conto, 2025)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eExact Matching\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLandcover\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGLAD UMD (Hansen, 2013)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMangrove Cover\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlobal Mangrove Watch (Bunting, 2022)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"8\"\u003e\n \u003cp\u003ePropensity Matching\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePopulation Density\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e2000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eGridded Population of the World Version 4 (Center For International Earth Science Information Network-CIESIN-Columbia University, 2018)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePopulation Count\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean Temperature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e1990\u0026ndash;1999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eWorld Clim V1 Bioclimatic Variables (Hijmans, 2005)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean Precipitation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eElevation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e2000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eSRTM (NASA JPL, 2013)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSlope\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDistance to Cities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHewson, 2019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTravel Time to Cities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNelson, 2008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e4.3 Structural comparisons\u003c/h2\u003e\n \u003cp\u003eSeveral metrics are available through the L2A and L2B products that allow for structural comparison between PAs and NPCs. We categorized vegetation structures into three categories following Hakkenberg et al 2023. These include: Canopy Dimensions, Structural Heterogeneity, and Spatial Configuration (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). L2A relative height (RH) metrics at every 10th percentile, as well as the 98th percentile, were collected. From L2B, cover, plant area index (PAI), foliage height diversity (FHD), and plant area vegetation density (PAVD) at each 5m height bin were collected. After examining PAVD profiles, which showed little difference between PA and NPCs, this variable was discarded from the analysis. To account for the known correlation between FHD and RH98 (Schneider 2020), a normalized value for FHD was also generated using a min-max normalization with RH100. Hereafter, all references to FHD refer to the normalized value unless otherwise indicated by \u0026ldquo;RAW\u0026rdquo;. From L4C, the Waveform Structural Complexity Index (WSCI) was collected. WSCI is constructed through the matching of airborne lidar from ALS with GEDI shots to build a comprehensive index of structural complexity accounting for horizontal, vertical, and 3D structural complexity (de Conto 2024). Each of these metric values was extracted for each PA and NPC sample where GEDI shots were available.\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eStructural metrics used for evaluation of structural diversity\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStructure Category\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMetric\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDefinition\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUnits\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCanopy Dimensions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRH98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMaximum canopy height at 98th percentile of relative heights\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003em\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePAI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal Plant Area Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003em2/m2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eStructural Heterogeneity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFHD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFoliage Height Diversity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eunitless\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWSCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWaveform Structural Complexity Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eunitless\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpatial Configuration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePAVD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlant Area Volume Density\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003em2/m3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \n\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompeting interests: \u003c/strong\u003eThe authors declare that there is no conflict of interest regarding the publication of this article. \u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eData availability: \u003c/strong\u003eAll code used to perform analyses have been made available in https://github.com/GEDI-PA/vl_GEDI-PA_2024. Visualization of distribution of RH98 and FHD values in all analyzed PAs is available at https://mangrovescience.earthengine.app/view/gediprotectedareas. \u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAcknowledgements: \u003c/strong\u003eThis project was funded by the NASA Earth Science Applications grant number NNH22ZDA001N-ECON:A.40 Ecological Conservation within NASA\u0026apos;s Earth Science Division.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlbinet, C., Whitehurst, A.S., Jewell, L.A., Bugbee, K., Laur, H., Murphy, K., Frommknecht, B., Scipal, K., Costa, G., Jai, B., Ramachandran, R., Lavalle, M., Duncanson, L. A Joint ESA-NASA Multi-mission Algorithm and Analysis Platform (MAAP) for Biomass, NISAR, and GEDI. Surv Geophys 40, 1017\u0026ndash;1027. https://doi.org/10.1007/s10712-019-09541-z. (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarnieh, B. A., Jia, L., Menenti, M., Zhou, J. \u0026amp; Zeng, Y. 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The Sustainable Development Goals Report. (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWade, C. M., Austin, K. G., Cajka, J., Lapidus, D., Everett, K. H., Galperin, D., Maynard, R., Sobel, A. What Is Threatening Forests in Protected Areas? A Global Assessment of Deforestation in Protected Areas, 2001\u0026ndash;2018. https://doi.org/10.3390/f11050539. (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, Y., West, P., Thakholi, L., Suryawanshi, K., Supuma, M., Straub, D., Sithole, S. S., Sharma, R., Schleicher, J., Ruli, B., Rodr\u0026iacute;guez-Rodr\u0026iacute;guez, D., Rasmussen, M. B., Ramenzoni, V. C., Qin, S., Delgado Pugley, D., Palfrey, R., Oldekop, J., Nuesiri, E. O., Hai, V., Nguyen, T., Ndam, N., Mungai, C., Milne, S., Mabele, M., Lucitante, S., Lucistante, H., Liljeblad, J., Kiwango, W., Kik, A., Jones, N., Johnson, M., Jarrett, C., James, R., Holmes, G., Gisbon, L., Ghoddousi, A., Geldmann, J., Gebara, M., Edwards, T., Dressler, W., Douglas, L., Dimitrakopoulous, P., Davidov, V., Compaor\u0026eacute;-Sawadogo, E., Collins, Y., Cepek, M., Burow, P., Brockington, D., Philippe, M., Balinga, B., Austin, B., Astuti, R., Ampumuza, C., Agyei, F. K. Annual Review of Environment and Resources Governance and Conservation Effectiveness in Protected Areas and Indigenous and Locally Managed Areas. 46, 55\u0026ndash;55. (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu, X., Nie, S., Zhu, Y., Chen, Y., Yang, B., \u0026amp; Li, W. Evaluation and Comparison of ICESat-2 and GEDI Data for Terrain and Canopy Height Retrievals in Short-Stature Vegetation. Remote Sensing 15. (2023).\u003c/span\u003e\u003c/li\u003e \u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8744926/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8744926/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGlobally, Protected Areas (PAs) have had positive impacts on habitat protection and carbon storage and are viewed as critical for preserving hotspots of biodiversity and supporting SDG 15: Life on Land. However, global studies indicate that Afrotropical PAs may be more degraded and less effective than PAs in other regions, despite comparable geographic coverage. Here, we match PAs to Non-Protected Counterfactuals (NPCs) to understand how PA presence in West and Central Africa affects forest structure and structural diversity. Using the Global Ecosystem Dynamics Investigation (GEDI), we demonstrate that PAs have an inconsistent effect on canopy height, structural diversity, and plant area index across the region, varying by both country and PA designation. We found PA impact on preserving ecological structure may have a positive relationship with background deforestation rates, where positive differences between PAs and NPCs were particularly prominent in countries experiencing high deforestation. This highlights the need to take country-wide deforestation rates and socioeconomic pressures into account when understanding PA efficacy, as the lack of difference between PAs and NPCs in low deforestation countries does not necessarily indicate inadequate conservation outcomes. Our results demonstrate that governance type and PA establishment goals affect ecological outcomes, with forest structure consistently higher in National Parks. Our study reveals that when deforestation and country-wide values of structural metrics are factored into PA assessments, PAs in Africa have a higher positive impact than previously identified. Therefore, when assessing PAs effectiveness in regions like West and Central Africa, consideration of country-wide and designation-specific dynamics go beyond global studies to describe PA impact and outcomes.\u003c/p\u003e","manuscriptTitle":"If a tree is “Protected”, is it? Using satellite-borne LiDAR to understand efficacy of protection status in West and Central African Protected Areas","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-10 09:10:25","doi":"10.21203/rs.3.rs-8744926/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"a543c284-f39f-44e7-b4a8-4d3611aaaa0e","owner":[],"postedDate":"February 10th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":62603520,"name":"Earth and environmental sciences/Ecology/Conservation biology"},{"id":62603521,"name":"Earth and environmental sciences/Ecology/Biodiversity"}],"tags":[],"updatedAt":"2026-03-09T15:02:44+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-10 09:10:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8744926","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8744926","identity":"rs-8744926","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}