Towards a sustainable livestock system: The role of traditional silvopastoral systems in carbon sequestration and forest conservation

Integrative forest management (IFM) aims at integrating biodiversity conservation and global change adaptation into forest management for the sustainable provision of multiple ecosystem services. In this paper, and in the context of the European project TRANSFORMIT, we have developed a novel definition of IFM and a framework of guiding principles and attributes directing decision-making and objectives. IFM is structured around eight guiding principles and corresponding attributes, encompassing key ecological and silvicultural elements including the promotion of habitat retention, site‑adapted native tree species, natural regeneration, structural diversity, and landscape connectivity, while reducing the ecological impact of forestry operations and enhancing resilience to ecological stress and disturbance. Based on the definition, we explored the alignment of existing forest management concepts in Europe with IFM. Through content analysis, the review evaluates a set of widely used forest management concepts in Europe, including Continuous Cover Forestry, Close‑to‑Nature Forest Management, ProSilva, the Stepping Stone Concept, Retention Forestry, TRIAD, Climate‑Smart Forestry, and Closer‑to‑Nature Forest Management. These concepts were compared to IFM through a literature‑based assessment of their theoretical foundations and implementation strategies. While most concepts overlapped to different extents at the level of principles, the application of the corresponding elements varied across concepts. We subsequently attributed divergences in terminology, emphasis, and implementation in Europe to differing contexts, including disturbance regimes and management traditions. By using IFM as a common framework, our study clarified where existing concepts converge or diverge, thereby helping to close gaps between high‑level management goals and their practical implementation. Using IFM as a benchmark allowed us to compare the respective strengths and weaknesses of established concepts and to suggest improvements through a transparent dialogue between science, practice, and policy. To illustrate the potential actions necessary to implement IFM, we present an exemplary theoretical transition pathway for the transformation of disturbance‑prone Norway spruce monocultures toward more diverse, adaptive and resilient forests. The trajectory underscores that successful implementation of IFM relies on context‑specific, multi‑level planning and should be tailored to local ecological realities and stakeholder networks. In conclusion, IFM provides a unifying but non‑prescriptive concept that is capable of integrating diverse forest management objectives. However, effective implementation requires context‑ and region-specific adaptation, coordination across governance levels and sectors (e.g. forestry, conservation, and rural development), and continuous exchange between science and practice. • IFM provides a unifying framework to compare European forest management concepts • European concepts share goals but differ strongly in how integration is operationalized • IFM supports a coherent implementation across scales and management traditions • Transition pathways link integrative principles to concrete management actions • Operationalizing integration remains the key challenge for biodiversity-oriented forestry

What influences the effectiveness of forest conservation interventions in tropical regions? A systematic review

ABSTRACT Protected areas (PAs) provide critical ecosystem services, including global climate regulation through the retention of carbon in biomass and soil. Evaluating the impact of terrestrial PAs on carbon storage is essential for strengthening their role in mitigating climate change, especially in the face of significant Land Use and Land Cover (LULC) changes, which can influence carbon dynamics. This study focuses on Ifrane National Park (INP) in Morocco, where the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model was used to simulate carbon stock changes between 2018 and 2033, with a particular emphasis on Strict PAs. The results reveal a total carbon stock decline of 14.28% within INP over the study period. Forested areas, especially Holm oak forests, showed notable resilience, with their carbon stocks increasing by 20.79% inside Strict PAs and by 46.63% outside these zones. In contrast, cedar forests experienced a decline of 5.24% within Strict PAs and 17.56% outside. This highlights the significant role of Strict PAs in mitigating carbon losses, especially in vulnerable ecosystems like cedar forests. Our study underscores the need to expand Strict PAs and adopt sustainable forest management to enhance carbon sequestration and support climate change mitigation.

Protected areas are widely regarded as a cornerstone of global forest conservation, yet growing mineral demand intensifies mining pressure on them. Here, we present a global assessment of mining-induced forest loss within and beyond protected areas from 2001 to 2020. We find that approximately 11% (~1372 km2) of global mining-induced forest loss occurs within protected areas, with this proportion increasing to ~19% during 2016-2020. Furthermore, ~27% (544) of mines associated with forest loss within protected areas remain active throughout the study period. Mining impacts also extend beyond protected area boundaries, where forest loss in the surrounding 10-km buffer zones (~4113 km2) is nearly three times that observed within protected areas. Temporal analyses further reveal that mining increasingly affects forests with higher biomass density over time. These findings demonstrate growing mining pressure on protected forests and highlight the need to mitigate impacts both within and beyond protected area boundaries.

ABSTRACT Forest road surfaces are subject to continuous deterioration from environmental factors, even in the absence of regular traffic, creating significant challenges for safety and maintenance planning. Traditional manual inspections are labor-intensive, costly, and often impractical for extensive networks. This study introduces an Artificial Intelligence (AI)-powered framework, leveraging advanced computer vision techniques, to automate the visual inspection of forest road surfaces using high-resolution UAV imagery. We evaluated three state-of-the-art deep learning models, YOLOv5, YOLOv8, and Faster R-CNN, on a custom dataset of potholes, rutting, and stone protrusions from a mountainous forest road in Iran. The YOLOv5 model demonstrated superior performance, achieving a mean Average Precision (mAP@0.50) of 0.56 and an overall recall of 0.459. It proved highly effective at detecting common distresses like rutting and potholes, while also offering the most robust performance for challenging stone protrusions. These findings validate the integration of AI and computer vision as a transformative tool, providing forest engineers with a scalable, objective, and data-driven methodology to enhance safety, optimize maintenance budgets, and support sustainable forest management.

ABSTRACT Allantoma decandra is an abundant Amazonian hardwood tree species, which has been exploited in sustainable forest management areas. This study investigated the potential of this underutilized species for cross-laminated timber (CLT), comparing bonded systems with adhesive-free nailed (NCLT) and dowelled (DCLT) alternatives. Initially, the wood was submitted to physical and mechanical property tests. Subsequently, ten different models and sizes of connectors (nails, dowels and adhesives) were tested to determine their mechanical resistance. Finally, three-layer CLT were produced using the best-performing nail, dowel and adhesive, which submitted to bending and rolling shear tests. A. decandra demonstrated high mechanical properties, classified as D30 (NBR 7190) and VI (PRG-320). 18 × 30 Ring-shank nails, 20 mm dowels, and PVAc adhesive obtained the highest strength values. Bonded CLT achieved the highest mean bending stiffness (15,367 N/mm²) and mean bending strength (76.5 N/mm²), exceeding most commercial products and most literature results. Although NCLT and DCLT exhibited significantly lower stiffness, they demonstrated high ductility and rolling shear resistance (>2.4 N/mm²), indicating viability for semi-structural applications. The study concludes that the wood of A. decandra offers potential for engineered wood products, though industrial feasibility requires optimizing bonding protocols and refining mechanical connectors to mitigate interlaminar slip.

Continuous forest monitoring using machine learning algorithms is more reliable for land use planning and management. In this study, we assess forest cover change, associated carbon stocks, and future predictions in southwestern Ethiopia using Landsat 8 Operational Land Imager and thermal infrared sensor satellite data of three epochs (2018, 2022, 2025). Multi-temporal land use land cover (LULC) classifications were performed using three machine learning models: classification and regression trees (CART), random forest (RF), and support vector machine (SVM). Classification accuracies were evaluated using overall accuracy. The results show that the RF algorithm offers the most consistent and reliable performance, with greater than 92% overall accuracy. LULC analyses reveal a significant and stable increase in forest areas during the period, while agricultural and grassland areas show varying levels of variability depending on the classifier. Forest cover assessment shows that the net forest gains are much higher than losses. Forest cover increased markedly between 2018 and 2025, by 15.4%, 23.3%, and 23.4% according to the CART, RF, and SVM models, respectively. The result also reveals that the carbon sequestration potential increases as a result of the Green Legacy Initiative (GLI), which increases forest cover. Projections for 2035 indicate that forest areas and carbon sinks are expected to continue increasing; however, the results vary depending on the algorithm used (CART, RF, and SVM). Therefore, this study quantitatively demonstrates the spatial and climatic impacts of afforestation and restoration activities under the Ethiopian GLI; it provides a scientific basis for sustainable forest management and for combating climate change.

ABSTRACT Forest aboveground biomass (AGB) is a critical indicator of forest ecosystem structure and function. Accurate quantification of forest AGB and projection of its dynamics are essential for sustainable forest management and climate regulation. Subtropical evergreen broadleaf forests (SEBFs) represent a significant forest carbon sink; however, estimates of the carbon parameters in SEBFs are highly uncertain, and projections of the carbon sequestration potential (CSP) remain scarce. This study employed a dynamic global vegetation model (DGVM) capable of simulating and predicting long-term structural changes and successional dynamics in vegetation ecosystems to estimate and project forest AGB and CSP in Hunan Province, China – a typical SEBF region. The model was integrated with a high-resolution remote-sensing-derived canopy height map to enhance the accuracy and spatial detail of the results. The key findings include: (1) The total forest AGB of Hunan Province in 2017 was 1482 Tg, concentrated primarily in the northwestern forested areas; (2) The region’s total forest AGB stock by 2100 reached 3189 Tg, which doubles the 2017 AGB stock, highlighting substantial carbon sink capacity, with evergreen broadleaf species dominating early successional stages and coniferous species prevailing during later succession; (3) Validation against field plot data confirmed robust model initialization performance (Pearson’s r = 0.69, RMSE = 36.37 Mg/ha, Bias = 12.02 Mg/ha). Furthermore, compared with existing regional AGB products and DGVM-based studies, this study provides superior spatial resolution, seamless coverage, and continuous future projections. Correlation analysis revealed that model simulation performance is strongly influenced by environmental variables and forest structure inputs. This research establishes an integrated framework combining multi-source data and methods to achieve high-resolution estimation of forest AGB and CSP, accompanied by multi-scale evaluation and analysis. It underscores the importance of enhancing data quality and assimilating remote sensing observations to improve DGVM reliability for future carbon cycle research and climate mitigation applications.

ABSTRACT Tropical forests are crucial ecological resources but are face severe threats from human activities. Assessing forest cover is essential for monitoring and for the development of effective sustainable forest management approaches. This study analyses the changes in forest canopy density and land surface temperature (LST) in Kolasib district of Mizoram, India, over a period of three decades (1995–2025), utilizing Landsat satellite imagery via Google Earth Engine (GEE). The Forest Canopy Density (FCD) model was computed by integrating the Normalized Difference Vegetation Index (NDVI), Advanced Vegetation Index (AVI), Barren Index (BI), and Shadow Index (SI). A Mann-Kendall trend test and Pearson correlation were also applied to the long-term data. The results revealed a 1.83% increase in non-forest areas and a 0.21% decrease in dense forest, primarily driven by infrastructure and shifting cultivation. Conversely, moderate forest increased by 2.41% due to vegetation recovery and land‑use policies. The Mann-Kendall trend analysis shows that LST is increasing by 0.042°C annually, accumulating in a 1.26°C increase over the three decades. Although NDVI has improved over recent years, the thermal footprint remains elevated, proving that forest conversion permanently alters the regional microclimate. These findings emphasize the necessity of sustainable land-use planning for continued forest management and conservation.

ABSTRACT This study seamlessly integrates Geographic Information System (GIS) with an enhanced Particle Swarm Optimization (PSO) algorithm to conduct a detailed geospatial analysis of pine wood nematode disease, a severe forest affliction. Our research team curated a comprehensive disease database and utilized GIS technology to delve into the spatial distribution characteristics of the disease. By merging this data with the PSO algorithm, we developed a disease recognition model capable of precise detection and analysis of the disease. Experimental outcomes highlight that the fusion of GIS and Memetic Computing (MC) methods provides a more accurate reflection of the spatiotemporal distribution of the disease. Notably, the recognition model forged with the PSO algorithm achieved an exceptional accuracy rate of 98.41%, outperforming other methodologies. This research not only deepens the understanding of the spatiotemporal patterns of pine wood nematode disease but also offers a scientific foundation for the formulation of more effective prevention and control strategies, significantly contributing to the monitoring and management of forest diseases.

ABSTRACT Tropical forests are central to global sustainability, underpinning biodiversity conservation, climate change mitigation, and disaster risk reduction. Yet, it remains unclear whether recent efforts have reduced tropical forest loss (TFL) and improved protection of ecologically and socially important areas. This study provides a global, spatially explicit assessment of TFL before (2011–2015) and after (2016–2022) the mid-2010s, a period coinciding with major global sustainability, climate, and biodiversity commitments. It examines how TFL relates to climate change mitigation, biodiversity conservation, and disaster (landslide) risk reduction using an integrated ecosystem co-benefit index, as well as to tropical terrestrial protected areas (PAs). Although annual TFL exhibited a statistically significant downward trend during 2016–2022, average annual TFL was 34% higher than in 2011–2015, increasing from 11.69 ± 2.13 to 15.69 ± 2.45 Mha/year, with significant increases in tropical South America and Africa. Across both periods, higher TFL consistently occurred in areas with moderate ecosystem co-benefits. Despite an 8% expansion of PAs and their greater representation in high co-benefit areas, the average annual TFL within PAs increased by 71%, from 1.12 ± 0.19 to 1.92 ± 0.41 Mha/year, while the average annual share of TFL within PAs rose from 9.6 ± 0.5% to 12.2 ± 0.9%. These findings indicate that recent slowdowns in TFL are modest and insufficient to offset persistent pressures. This underscores the need to strengthen sustainable forest management by addressing the broader socio-economic and institutional conditions that drive and perpetuate TFL.

Beneficial fungi are increasingly recognized as key modulators of plant growth and stress resilience, yet their functional roles in woody tree species remain poorly understood. Here, we examined the physiological, nutritional, and metabolic responses of sengon (Falcataria moluccana) to two plant growth-promoting fungi, Penicillium olsonii TLL1 and Aspergillus aculeatus TLL1, during a 6-month greenhouse experiment. Inoculation with A. aculeatus significantly enhanced leaf number, and shoot and root biomass compared with uninoculated controls, whereas P. olsonii showed more limited growth effects. Fungal inoculation led to a marked increase in root nutrient accumulation, including nitrogen (~2.5-fold), phosphorus (~1.5-fold), magnesium (~1.75-fold), and zinc (~2.5-fold), relative to controls, indicating improved nutrient acquisition. Untargeted metabolomic profiling revealed extensive metabolic reprogramming, with over 1,600 metabolites detected in negative ion mode and 2,800 metabolites in positive ion mode. Comparative analysis identified 367 and 206 differentially regulated metabolites in P. olsonii- and A. aculeatus-treated plants, respectively. Notably, enhanced nutrient status was accompanied by coordinated shifts in hormone-associated and secondary metabolic pathways, including phenylpropanoid-, lignin-, and terpenoid-related metabolites, consistent with a defense-primed metabolic state rather than constitutive defense activation. Together, these findings demonstrate that beneficial fungi reprogram tree metabolism by coupling improved nutrient acquisition with metabolic allocation toward growth and inducible defense, highlighting their potential role in sustainable forestry systems with tangible yield benefits.IMPORTANCEFast-growing tropical trees are central to reforestation and sustainable forestry, yet their productivity is often constrained by nutrient availability and vulnerability to pests and diseases. While beneficial microbes are widely used in annual crops, their functional impact on long-lived woody plants remains largely unexplored. This study shows that plant growth-promoting fungi do more than stimulate tree growth-they quantitatively enhance root nutrient acquisition and translate this nutritional advantage into systemic metabolic reprogramming. By integrating long-term growth measurements, elemental profiling, and metabolomics, we reveal that fungal colonization increases the bioavailability of key macro- and micronutrients in roots and supports the allocation of metabolic resources toward lignification, secondary metabolism, and inducible defense without compromising biomass accumulation. These findings provide mechanistic insight into how nutrient status and metabolism are coordinated in tree-microbe interactions and support the rational use of beneficial fungi in forestry and agroforestry applications.

ABSTRACTGlobal climate change exerts a far‐reaching influence on the geographical distribution, phenological patterns, and genetic diversity of plants, presenting a grave challenge to the survival and evolution of species. Owing to its robust ecological adaptability, restricted dispersal capacity, and abundant intraspecific variation, Cornus kousa subsp. chinensis has emerged as an ideal subject for investigating the responses of woody plants to climate change. Consequently, this study employed the landscape genomics approach to evaluate the survival risks faced by C. kousa under the present and future (2060–2080) climate scenarios (RCP45 and RCP85). A total of 348 leaf samples were gathered from 25 populations of C. kousa spanning diverse regions of China. The research revealed that, under the prevailing climate conditions, the genetic diversity within the populations is remarkably abundant. Nevertheless, the POPs model forecasts that the genetic structure will undergo substantial alterations in future climate scenarios. Notably, 93.6% of the populations will be grouped into a solitary common cluster, and the genetic variation will be conspicuously homogenized. Through BayeScan and Latent Factor Mixed Model (LFMM) analyses, 67 Single Nucleotide Polymorphism (SNP) loci significantly associated with climate factors were identified. Among these loci, 90.3% are closely correlated with the annual mean temperature (Bio 1), suggesting that temperature serves as the primary driving force for adaptive selection at the genomic level. In summary, our findings not only offer valuable perspectives for the conservation of the genetic resources of the C. kousa population but also provide significant reference value for the formulation of subtropical forest conservation strategies.

ABSTRACT Forests are critical to environmental sustainability, yet they face significant threats from exploitation worldwide. This research addresses these gaps by offering a thorough examination of the factors driving forest overexploitation, its socio-environmental effects, and the promise of co-management strategies to promote sustainable forest resource development in Enugu State. The study adopted both quantitative and qualitative research methods. A sample of 1,043 respondents participated in the quantitative survey, while 20 key informants were involved in in-depth interviews for the qualitative aspect of the study, which were drawn from Enugu State, Nigeria. Purposive and multi-stage sampling techniques were used to reach the respondents. The quantitative data were descriptively analyzed using tables and percentages, while a thematic method of analysis was employed for the qualitative data. Findings revealed that illegal logging (81%), poverty (73.1%), and firewood/charcoal production (79.6%) are the primary drivers of deforestation. These factors contribute to significant environmental consequences, including forest tree loss (96.5%), biodiversity extinction (94.5%), increased global warming (77%), and soil erosion (82.6%). The study recommends the adoption of a co-management framework that integrates government oversight with active community participation to ensure effective forest conservation. This approach, supported by improved funding, awareness creation and enforcement mechanisms, offers a viable pathway toward sustainable forest governance and environmental resilience in Enugu State and similar ecological zones.

Insect declines are of global concern, yet no long-term ecological studies (LTER) have confirmed this trend on islands. This study utilises the first available LTER data on island arthropods, targeting epigeal and canopy species from the Azores Archipelago (Portugal), and covering over 20 years in three distinct sampling events from 30 standard sites. We investigate changes in abundance, biomass, and species richness within native forest arthropod communities, focusing on the proportions of endemic and introduced species, and temporal patterns among single-island endemics and forest-dependent endemics. Results reveal significant temporal variability, but overall abundance, biomass, and species richness remain stable across endemic and native non-endemic taxa. Among the species studied, 28% declined, 17% increased, and 55% showed no significant differences. Exotic invasions and related extinctions appear minimal. Forest-dependent endemic species declined below anticipated levels, suggesting that the extinction debt for these species may be less severe than initially expected. Nonetheless, some forest specialists have declined significantly, and seven species, not seen over 20 years, are considered to be extinct. The three-decade-long conservation of Azorean native forests may have contributed to the stability of some populations, thus these findings underscore the need for continued and enhanced conservation efforts of insular forest-associated diversity.

Understanding how forest structure and species composition respond to anthropogenic pressure is essential for sustainable forest management in tropical protected landscapes. This study assessed variations in forest structure and plant species composition along disturbance gradients in the Lagonoy Natural Biotic area (LNBA), a multiple-use natural biotic area characterized by long-term human occupation. Vegetation data were collected from systematically established plots across upland and riparian forest zones, documenting species identity, abundance, and community composition. Anthropogenic pressure gradients were represented using indirect but robust proxies, including spatial accessibility, altitude, proximity to settlements and markets, forest resource dependence, and length of community residency. Results showed pronounced compositional and structural differentiation across pressure gradients. Forest areas characterized by higher accessibility and stronger livelihood dependence exhibited lower species diversity, reduced community heterogeneity, and a greater dominance of disturbance-tolerant and early-successional taxa. Multivariate ordination analyses revealed clear separation of plant communities along anthropogenic pressure gradients, indicating consistent ecosystem responses to cumulative human use. Cluster analysis further identified distinct forest condition classes corresponding to low-, moderate-, and high-pressure environments. Indicator species analysis demonstrated that late-successional and native species were strongly associated with less disturbed forest segments, whereas generalist and pioneer species dominated highly accessible areas. These findings indicate that anthropogenic pressure drives measurable simplification of forest structure and reorganization of species composition within protected landscapes. The study demonstrates the utility of integrating vegetation data with socio-spatial disturbance proxies to detect forest degradation patterns in data-limited tropical contexts. This approach provides a practical and scalable framework for informing adaptive forest management and conservation planning in human-inhabited tropical forests.

Bamboo plays an important role in forest ecosystem conservation and carbon sequestration, but the effects of foliar nano-silica on nutrient accumulation and stoichiometric regulation in bamboo remain unclear. In this study, six foliar SiO₂ nanoparticle (SiO₂ NP) concentrations, 0, 100, 200, 300, 400, and 500mg·L⁻¹, were applied to Dendrocalamus brandisii seedlings. Leaf SPAD values, SiO₂ content, C, N, P, and K concentrations, and related stoichiometric ratios were determined. Results showed that the 300mg·L⁻¹ treatment (T3) produced the most favorable overall response, particularly enhancing leaf SPAD values, SiO₂, P, and K accumulation and maintaining relatively high N content, while altering multiple stoichiometric ratios among SiO₂, C, N, P, and K. Correlation and PCA analyses indicated that SPAD was closely associated with SiO₂, N, and P contents. In contrast, the highest concentration, 500mg·L⁻¹, reduced SPAD values and nutrient accumulation, suggesting that excessive nano-silica application may negatively affect seedling performance. These findings indicate that moderate SiO₂ NP application may improve leaf nutrient status and C: N: P: K stoichiometric coordination in D. brandisii seedlings, providing a potential supplementary approach for improving bamboo seedling quality and supporting conservation-oriented bamboo plantation management.

Abstract This study presents SST (Species Selection Tool), a climate-based framework designed to support species selection across geographic regions and climate scenarios. The tool was developed to enable breeders to examine species selection scenarios across countries and climate horizons. Beyond identifying promising species for testing, SST prioritizes candidate species, highlights opportunities for germplasm exchange, anticipates climate-driven shifts, and maps environmental clusters and adapted genetic resources to guide near- and long-term breeding strategies. Built with the Shiny framework in R, SST allows users to upload tabular data in CSV format for flexible analyses. We demonstrate its application for a Eucalyptus breeding example in Brazil, using occurrence data from GBIF and environmental covariates from TerraClimate and CHIRPS, and complemented by CMIP6 future climate projections. Temperature and precipitation data were used to compute 19 bioclimatic variables (BIO1–BIO19) for macroenvironmental classification and species suitability assessment. SST integrates five analytical indices, scaled from 0 to 1, producing an overall suitability index and ranking species accordingly. The tool identified E. urophylla , E. brassiana , E. deglupta , and E. pellita as the most suitable for the study area in the Maranhão State, Brazil. Open-source and user-friendly, SST accelerates breeding decisions, supports climate-adaptive planning, and provides access to advanced analytical tools. The tool is a useful contribution to forest management and forest tree breeding, supporting data-driven strategies for sustainable forestry under changing climates.

Giant kelp, Macrocystis pyrifera (order Laminariales), occurs across the temperate coasts of the Northern and Southern Hemispheres and is at high risk from ocean warming. Few giant kelp forests remain across the Southeast Australian shelf, and some sites are currently under active restoration. Genomic resources can greatly aid in the conservation of remnant populations and enhance restoration efforts. Reference genomes are a fundamental resource as they are either prerequisites for, or substantially improve, many analyses used in conservation genomics. A single reference genome is available for giant kelp, assembled from a Californian haploid specimen. However, increasing evidence of genetic divergence between Northern and Southern Hemisphere populations highlights the need for regionally representative reference genomes. We present two genome assemblies from the vegetative tissue (diploid sporophyte) of Australian giant kelp specimens. We performed de novo genome assembly using long-read sequencing (PacBio HiFi and ONT R10.4 Simplex) and used the ONT reads for scaffolding, assembling 98-99% of the genomes into 35 pseudo-chromosomes. Genome sizes ranged from 528 to 534 Mbp, with BUSCO completeness of 96-97% and QV scores of 51-52. Functional annotation identified 17,330 - 17,832 genes in the Australian assemblies. Genomic divergence between Australian and Californian genomes was seven-fold greater than between Australian genomes (1.5% vs. 0.2%), supporting a northeast-southwest Pacific genetic divergence. Differences in gene ontology enrichment patterns were also observed between Australian and Californian genomes, reflected by differing patterns of enrichment in gene ontologies linked to energy metabolism, proteostasis and stress responses. These two new genome assemblies will serve as valuable resources for ongoing research into Australian giant kelp genetics, while providing the basis for genomics-guided conservation and restoration of remnant giant kelp forests in Australia.