Land Cover Classification of Complex Agroecosystems in the Non-Protected Highlands of the Galapagos Islands

Ladybird Beetle Diversity in Natural and Human-Modified Habitats in the San Cristóbal Island, Galapagos, Ecuador

This study investigates the species richness and distribution of ladybird beetles (Coccinellidae) across various habitats on San Cristóbal Island in the Galápagos Archipelago, Ecuador. Through extensive field surveys, we catalogued nineteen species, including four previously known species (two endemics, Psyllobora bisigma and Scymnobius scalesius, and two natives, Cycloneda sanguinea and Tenuisvalvae bromelicola). We also identified nine possibly native species reported for the first time in the Galapagos islands in this study or correspond to the first voucher specimens for the island. We collected three previously reported non-native species: Cheilomenes sexmaculata, Novius cardinalis, and Paraneda guticollis. Three species belonging to the genera Stethorus, Calloeneis, and Delphastus remain undetermined, pending further taxonomic analyses. Our findings reveal a rich and complex community with notable differences in species abundance and habitat preference. Endemic species were found to be particularly scarce and restricted mainly to crops undergoing forest regeneration and deciduous forests, emphasising their vulnerability and specialised habitat requirements. The native Cycloneda sanguinea emerged as the most prevalent species, exhibiting broad ecological adaptability. Non-native species, like Cheilomenes sexmaculata, were predominantly found in disturbed habitats, with some showing early signs of spreading into more natural environments, raising concerns about their potential impact on local biodiversity. These findings contribute valuable knowledge to understanding Coccinellidae diversity on San Cristóbal Island and highlight the importance of continued monitoring, particularly in the face of ongoing environmental change and the introduction of non-native species. This study underscores the need for targeted conservation efforts to protect the unique and fragile ecosystems of the Galápagos Archipelago.

Preliminary insights of the genetic diversity and invasion pathways of Cedrela odorata in the Galapagos Islands, Ecuador

Cedrela odorata is considered the second most invasive tree species of the Galapagos Islands. Although it is listed in CITES Appendix II and there are population losses in mainland Ecuador, in Galapagos it is paradoxically a species of concern due to its invasive potential. Genetic studies can shed light on the invasion history of introduced species causing effects on unique ecosystems like the Galapagos. We analyzed nine microsatellite markers in C. odorata individuals from Galapagos and mainland Ecuador to describe the genetic diversity and population structure of C. odorata in the Galapagos and to explore the origin and invasion history of this species. The genetic diversity found for C. odorata in Galapagos (H e = 0.55) was lower than reported in the mainland (H e = 0.81), but higher than other invasive insular plant species, which could indicate multiple introductions. Our results suggest that Ecuador's northern Coastal region is the most likely origin of the Galapagos C. odorata, although further genomic studies, like Whole Genome Sequencing, Rad-Seq, and/or Whole Genome SNP analyses, are needed to confirm this finding. Moreover, according to our proposed pathway scenarios, C. odorata was first introduced to San Cristobal and/or Santa Cruz from mainland Ecuador. After these initial introductions, C. odorata appears to have arrived to Isabela and Floreana from either San Cristobal or Santa Cruz. Here, we report the first genetic study of C. odorata in the Galapagos and the first attempt to unravel the invasion history of this species. The information obtained in this research could support management and control strategies to lessen the impact that C. odorata has on the islands' local flora and fauna.

Potential model of Scalesia pedunculata carbon sequestration through restoration efforts in agricultural fields of Galapagos

Scalesia pendunculata Hook.f. is the dominant tree in several highlands' areas of the Galapagos Archipelago, yet in inhabited islands the conversion to agricultural fields has reduced its cover. The transition to agroforestry systems including the species shows promising scenarios to restore its cover and to provide ecosystem services such as carbon sequestration. Here, based on field gathered data, we model the potential contribution of S. pedunculata stands in the carbon sequestration of Galapagos. Between 2013-2021, 426 S. pedunculata seedlings were planted in the highlands of Santa Cruz and Floreana islands using several restoration technologies, and their height and survival were monitored every three months. A sub-sample of 276 trees alive since 2020 was used to estimate the DBH based on plant age and height. Based on scientific literature, biomass and carbon content were estimated across time. The final modelling included the density of plants in the restoration sites, estimated DBH, potential survival by restoration treatment, and a Brownian noise to add stochastic events. Overall, survival of S. pedunculata was high in control and slightly increased by most restoration treatments. A stand of 530 trees/ha was projected to sequester ~21 Mg C/ha in 10 years. If this is replicated over all Galapagos coffee production would contribute to the reduction of -1.062% of the Galapagos carbon footprint for the same period. This study adds to compiling benefits of restoring Galapagos flora.

Linking animal migration and ecosystem processes: Data-driven simulation of propagule dispersal by migratory herbivores

Animal migration is a key process underlying active subsidies and species dispersal over long distances, which affects the connectivity and functioning of ecosystems. Despite much research describing patterns of where animals migrate, we still lack a framework for quantifying and predicting how animal migration affects ecosystem processes. In this study, we aim to integrate animal movement behavior and ecosystem functioning by developing a predictive modeling framework that can inform ecosystem management and conservation.

We propose a framework to model individual‐level migration trajectories between populations' seasonal ranges as well as the resulting dispersal and fate of propagules carried by the migratory animals, which can be calibrated using empirical data at every step of the modeling process. As a case study, we applied our framework to model the spread of guava seeds, Psidium guajava, by a population of migratory Galapagos tortoises, Chelonoidis porteri, across Santa Cruz Island. Galapagos tortoises are large herbivores that transport seeds and nutrients across the island, while Guava is one of the most problematic invasive species in the Galapagos archipelago.

Our model can predict the pattern of spread of guava seeds alongside tortoises' downslope migration range, and it identified areas most likely to see establishment success. Our results show that Galapagos tortoises' seed dispersal may particularly contribute to guava range expansion on Santa Cruz Island, due to both long gut retention time and tortoise's long‐distance migration across vegetation zones. In particular, we predict that tortoises are dispersing a significant amount of guava seeds into the Galapagos National Park, which has important consequences for the native flora.

The flexibility and modularity of our framework allow for the integration of multiple data sources. It also allows for a wide range of applications to investigate how migratory animals affect ecosystem processes, including propagule dispersal but also other processes such as nutrient transport across ecosystems. Our framework is also a valuable tool for predicting how animal‐mediated propagule dispersal can be affected by environmental change. These different applications can have important conservation implications for the management of ecosystems that include migratory animals.

History of Land Cover Change on Santa Cruz Island, Galapagos

Islands are particularly vulnerable to the effects of land cover change due to their limited size and remoteness. This study analyzes vegetation cover change in the agricultural area of Santa Cruz (Galapagos Archipelago) between 1961 and 2018. To reconstruct multitemporal land cover change from existing land cover products, a multisource data integration procedure was followed to reduce imprecision and inconsistencies that may result from the comparison of heterogeneous datasets. The conversion of native forests and grasslands into agricultural land was the principal land cover change in the non-protected area. In 1961, about 94% of the non-protected area was still covered by native vegetation, whereas this had decreased to only 7% in 2018. Most of the agricultural expansion took place in the 1960s and 1970s, and it created an anthropogenic landscape where 67% of the area is covered by agricultural land and 26% by invasive species. Early clearance of native vegetation took place in the more accessible—less rugged—areas with deeper-than-average and well-drained soils. The first wave of settlement consisted of large and isolated farmsteads, with 19% of the farms being larger than 100 ha and specializing in diary and meat production. Over the period of 1961–1987, the number of farms doubled from less than 100 to more than 200, while the average farm size decreased from 90 to 60 ha/farmstead. Due to labor constraints in the agricultural sector, these farms opted for less labor-intensive activities such as livestock farming. New farms (popping up in the 1990s and 2000s) are generally small in size, with <5 ha per farmstead, and settled in areas with less favorable biophysical conditions and lower accessibility to markets. From the 1990s onwards, the surge of alternative income opportunities in the tourism and travel-related sector reduced pressure on the natural resources in the non-protected area.

Analyzing Variations in Size and Intensities in Land Use Dynamics for Sustainable Land Use Management: A Case of the Coastal Landscapes of South-Western Ghana

Land use/land cover change (LULCC) studies are gaining prominence among environmentalist and land use planners. This is due to the effects of LULCCs on natural ecosystems and livelihoods. In the coastal landscape of south-western Ghana, there exist knowledge gaps in the variations in size and intensities in LULCCs and the degree of change among land cover types in LULCC studies. Such studies are important for identifying periods of rapid land cover transitions and their implications on the landscape. Using change detection, intensity analysis and informal stakeholder conversations, the land use system dynamics of the study landscape was analyzed over a 34-year period to assess the variations in size and intensities in LULC transitions and its implications. The results showed a dynamic landscape driven primarily by rubber and settlement expansions. Rubber and settlement increased threefold (172.65%) and fourfold (449.93%) in the 34-year period mainly due to rubber outgrower scheme and onshore infrastructural developments, respectively. Gains in rubber and settlement targeted arable lands. The LULCC implies local food insecurity issues, declines in ecosystem services and compromised livelihoods, hence, the enforcement of the Land Use and Spatial Planning Act (2016) is recommended in land use planning in the coastal landscapes of south-western Ghana.

U-shaped deep-learning models for island ecosystem type classification, a case study in Con Dao Island of Vietnam

The monitoring of ecosystem dynamics utilises time and resources from scientists and land-use managers, especially in wetland ecosystems in islands that have been affected significantly by both the current state of oceans and human-made activities. Deep-learning models for natural and anthropogenic ecosystem type classification, based on remote sensing data, have become a tool to potentially replace manual image interpretation. This study proposes a U-Net model to develop a deep learning model for classifying 10 island ecosystems with cloud- and shadow-based data using Sentinel-2, ALOS and NOAA remote sensing data. We tested and compared different optimiser methods with two benchmark methods, including support vector machines and random forests. In total, 48 U-Net models were trained and compared. The U-Net model with the Adadelta optimiser and 64 filters showed the best result, because it could classify all island ecosystems with 93 percent accuracy and a loss function value of 0.17. The model was used to classify and successfully manage ecosystems on a particular island in Vietnam. Compared to island ecosystems, it is not easy to detect coral reefs due to seasonal ocean currents. However, the trained deep-learning models proved to have high performances compared to the two traditional methods. The best U-Net model, which needs about two minutes to create a new classification, could become a suitable tool for island research and management in the future.

Mapping Invasive Plant Species with Hyperspectral Data Based on Iterative Accuracy Assessment Techniques

Recent developments in computer hardware made it possible to assess the viability of permutation-based approaches in image classification. Such approaches sample a reference dataset multiple times in order to train an arbitrary number of machine learning models while assessing their accuracy. So-called iterative accuracy assessment techniques or Monte-Carlo-based approaches can be a useful tool when it comes to assessment of algorithm/model performance but are lacking when it comes to actual image classification and map creation. Due to the multitude of models trained, one has to somehow reason which one of them, if any, should be used in the creation of a map. This poses an interesting challenge since there is a clear disconnect between algorithm assessment and the act of map creation. Our work shows one of the ways this disconnect can be bridged. We calculate how often a given pixel was classified as given class in all variations of a multitude of post-classification images delivered by models trained during the iterative assessment procedure. As a classification problem, a mapping of Calamagrostis epigejos, Rubus spp., Solidago spp. invasive plant species using three HySpex hyperspectral datasets collected in June, August and September was used. As a classification algorithm, the support vector machine approach was chosen, with training hyperparameters obtained using a grid search approach. The resulting maps obtained F1-scores ranging from 0.87 to 0.89 for Calamagrostis epigejos, 0.89 to 0.97 for Rubus spp. and 0.99 for Solidago spp.

Scientific Research in Ecuador: A Bibliometric Analysis

Ecuador has shown a growth in its scientific production since 2011, representing 85% of the total historical production. These investigations are reflected in scientific publications, which address world interest topics and serve as a link for the university, business, and society. This work aims to analyze the scientific production generated by Ecuador in the period of 1920–2020 using bibliometric methods to evaluate its intellectual structure and performance. The methodology applied in this study includes: (i) terms definition and search criteria; (ii) database selection, initial search, and document compilation; (iii) data extraction and software selection; and finally, (iv) analysis of results. The results show that scientific production has been consolidated in 30,205 documents, developed in 27 subject areas, in 13 languages under the contribution of 84 countries. This intellectual structure is in harmony with the global context when presenting research topics related to “Biology and regional climate change”, “Higher education and its various approaches”, “Technology and Computer Science”, “Medicine”, “Energy, food and water”, and ”Development and applications on the Web”. Topics framed in the Sustainable Development Goals (SDGs), sustainability, climate change, and others. This study contributes to the academic community, considering current re-search issues and global concerns, the collaboration between universities and countries that allow establishing future collaboration links.

Origin and dispersion pathways of guava in the Galapagos Islands inferred through genetics and historical records

Guava (Psidium guajava) is an aggressive invasive plant in the Galapagos Islands. Determining its provenance and genetic diversity could explain its adaptability and spread, and how this relates to past human activities. With this purpose, we analyzed 11 SSR markers in guava individuals from Isabela, Santa Cruz, San Cristobal, and Floreana islands in the Galapagos, as well as from mainland Ecuador. The mainland guava population appeared genetically differentiated from the Galapagos populations, with higher genetic diversity levels found in the former. We consistently found that the Central Highlands region of mainland Ecuador is one of the most likely origins of the Galapagos populations. Moreover, the guavas from Isabela and Floreana show a potential genetic input from southern mainland Ecuador, while the population from San Cristobal would be linked to the coastal mainland regions. Interestingly, the proposed origins for the Galapagos guava coincide with the first human settlings of the archipelago. Through approximate Bayesian computation, we propose a model where San Cristobal was the first island to be colonized by guava from the mainland, and then, it would have spread to Floreana and finally to Santa Cruz; Isabela would have been seeded from Floreana. An independent trajectory could also have contributed to the invasion of Floreana and Isabela. The pathway shown in our model agrees with the human colonization history of the different islands in the Galapagos. Our model, in conjunction with the clustering patterns of the individuals (based on genetic distances), suggests that guava introduction history in the Galapagos archipelago was driven by either a single event or a series of introduction events in rapid succession. We thus show that genetic analyses supported by historical sources can be used to track the arrival and spread of invasive species in novel habitats and the potential role of human activities in such processes.

A methodological roadmap to quantify animal-vectored spatial ecosystem subsidies

Energy, nutrients and organisms move over landscapes, connecting ecosystems across space and time. Meta-ecosystem theory investigates the emerging properties of local ecosystems coupled spatially by these movements of organisms and matter, by explicitly tracking exchanges of multiple substances across ecosystem borders. To date, meta-ecosystem research has focused mostly on abiotic flows-neglecting biotic nutrient flows. However, recent work has indicated animals act as spatial nutrient vectors when they transport nutrients across landscapes in the form of excreta, egesta and their own bodies. Partly due to its high level of abstraction, there are few empirical tests of meta-ecosystem theory. Furthermore, while animals may be viewed as important mediators of ecosystem functions, better integration of tools is needed to develop predictive insights of their relative roles and impacts on diverse ecosystems. We present a methodological roadmap that explains how to do such integration by discussing how to combine insights from movement, foraging and ecosystem ecology to develop a coherent understanding of animal-vectored nutrient transport on meta-ecosystems processes. We discuss how the slate of newly developed technologies and methods-tracking devices, mechanistic movement models, diet reconstruction techniques and remote sensing-that when integrated have the potential to advance the quantification of animal-vectored nutrient flows and increase the predictive power of meta-ecosystem theory. We demonstrate that by integrating novel and established tools of animal ecology, ecosystem ecology and remote sensing, we can begin to identify and quantify animal-mediated nutrient translocation by large animals. We also provide conceptual examples that show how our proposed integration of methodologies can help investigate ecosystem impacts of large animal movement. We conclude by describing practical advancements to understanding cross-ecosystem contributions of animals on the move. Understanding the mechanisms by which animals shape ecosystem dynamics is important for ongoing conservation, rewilding and restoration initiatives around the world, and for developing more accurate models of ecosystem nutrient budgets. Our roadmap will enable ecologists to better qualify and quantify animal-mediated nutrient translocation for animals on the move.

Drone-Based Participatory Mapping: Examining Local Agricultural Knowledge in the Galapagos

Agriculture is cultural heritage, and studies of agricultural spaces and practices help this heritage to be valued and protected. In the Galapagos Islands, little focus has been placed on local agricultural practices and agroforestry, despite their increasing importance for food security and invasive species management. This article discusses the possibilities for unoccupied aerial vehicle (UAV) high-resolution imagery in examining agricultural and agroforestry spaces, techniques, and practices. It describes and assesses an UAV-assisted participatory methodology for on-farm qualitative research that aims to investigate the visible and invisible features of farming practices. An analysis of the types of responses elicited by different methods of interviews with Galapagos farmers demonstrates how incorporating UAV data affects what we took away from the interview, and how the perceived relationship between farmer and land is reflected. Specifically, we find that when interacting with orthomosaics created from UAV images of their farms, farmers’ responses reveal a greater focus on management strategies at larger spatial and temporal scales. UAV imagery thus supports studies of agricultural heritage not only by recording agricultural spaces but also by revealing agrarian knowledge and practices.