The Scale-Dependent Role of Biological Traits in Landscape Ecology: A Review

Influence of landscape structure on previous exposure to Leptospira spp. and Brucella abortus in free-living neotropical primates from southern Brazil

The environments in which neotropical primates live have been undergoing an intense fragmentation process, constituting a major threat to the species' survival and causing resource scarcity, social isolation, and difficulty in dispersal, leaving populations increasingly vulnerable. Moreover, the proximity of wild environments to anthropized landscapes can change the dynamics of pathogens and the parasite-host-environment relationship, creating conditions that favor exposure to different pathogens. To investigate the previous exposure of free-living primates in Rio Grande do Sul State (RS), southern Brazil, to the bacterial agents Leptospira spp. and Brucella abortus, we investigated agglutinating antibodies against 23 serovars of Leptospira spp. using the microscopic agglutination test and B. abortus acidified antigen test in primate serum samples; 101 samples from primates captured between 2002 and 2016 in different forest fragments were used: 63 Alouatta caraya, 36 Alouatta guariba clamitans, and 02 Sapajus nigritus cucullatus. In addition, the forest remnants where the primates were sampled were characterized in a multiscale approach in radii ranging from 200 to 1400 m to investigate the potential relationship of previous exposure to the agent with the elements that make up the landscape structure. The serological investigation indicated the presence of antibodies for at least one of the 23 serovars of Leptospira spp. in 36.6% (37/101) of the samples analyzed, with titers ranging from 100 to 1600. The most observed serovars were Panama (17.8%), Ballum (5.9%), Butembo (5.9%), Canicola (5.9%), Hardjo (4.9%), and Tarassovi (3.9%); no samples were seropositive for Brucella abortus. Decreased forest cover and edge density were the landscape factors that had a significant relationship with Leptospira spp. exposure, indicating that habitat fragmentation may influence contact with the pathogen. The data generated in this study demonstrate the importance of understanding how changes in landscape structure affect exposure to pathogenic microorganisms of zoonotic relevance. Hence, improving epidemiological research and understanding primates' ecological role in these settings can help improve environmental surveillance and conservation strategies for primate populations in different landscapes.

Landscape structure shapes the diversity of tree seedlings at multiple spatial scales in a fragmented tropical rainforest

The maintenance of seedling diversity of animal-dispersed tree species is fundamental for the structure and function of forest patches in fragmented tropical rainforests. Nonetheless, the effects of landscape structure at different spatial scales on α- and β-diversity of tree seedling communities are recently explored. Using a multi-scale approach, we assessed the relative effect of landscape composition and configuration on α- and β-diversity of animal-dispersed seedlings within 16 forest patches in the Lacandona rainforest, Mexico. We assessed these effects at 13 spatial scales (from 300 to 1500 m radius, at 100 m intervals) for three metrics of effective number of species considering α- and β-diversity. We found that α-diversity was largely affected by landscape composition and β-diversity by landscape configuration. On the one hand, the amount of secondary forest influenced α-diversity. Additionally, species richness increased in landscapes with highly aggregated forest patches. On the other hand, β-diversity was affected positively by forest fragmentation and negatively by the edge contrast of forest patches with the surrounding matrix. Our findings indicate that landscape configuration is a strong driver of seedling diversity in highly deforested rainforests. Promoting forest patches and secondary forests through payment for ecosystem services' programs, favoring matrix quality within land-sharing schemes of smallholder agriculture and secondary forest management, and identifying restoration opportunities for assisted or unassisted natural regeneration are urgently needed for conservation of seedling diversity in human-modified tropical landscapes.

Back to the future: Reintegrating biology to understand how past eco-evolutionary change can predict future outcomes

During the last few decades, biologists have made remarkable progress in understanding the fundamental processes that shape life. But despite the unprecedented level of knowledge now available, large gaps still remain in our understanding of the complex interplay of eco-evolutionary mechanisms across scales of life. Rapidly changing environments on Earth provide a pressing need to understand the potential implications of eco-evolutionary dynamics, which can be achieved by improving existing eco-evolutionary models and fostering convergence among the sub-fields of biology. We propose a new, data-driven approach that harnesses our knowledge of the functioning of biological systems to expand current conceptual frameworks and develop corresponding models that can more accurately represent and predict future eco-evolutionary outcomes. We suggest a roadmap toward achieving this goal. This long-term vision will move biology in a direction that can wield these predictive models for scientific applications that benefit humanity and increase the resilience of natural biological systems. We identify short, medium, and long-term key objectives to connect our current state of knowledge to this long-term vision, iteratively progressing across three stages: 1) utilizing knowledge of biological systems to better inform eco-evolutionary models, 2) generating models with more accurate predictions, and 3) applying predictive models to benefit the biosphere. Within each stage, we outline avenues of investigation and scientific applications related to the timescales over which evolution occurs, the parameter space of eco-evolutionary processes, and the dynamic interactions between these mechanisms. The ability to accurately model, monitor, and anticipate eco-evolutionary changes would be transformational to humanity's interaction with the global environment, providing novel tools to benefit human health, protect the natural world, and manage our planet's biosphere.

Spatial Pattern Consistency among Different Remote-Sensing Land Cover Datasets: A Case Study in Northern Laos

Comparisons of the accuracy and consistency of different remote-sensing land cover datasets are important for the rational application of multi-source land cover datasets to regional development, or to studies of global or local environmental change. Existing comparisons of accuracy or spatial consistency among land cover datasets primarily use confusion or transfer matrices and focus on the type and area consistency of land cover. However, less attention has been paid to the consistency of spatial patterns, and quantitative analyses of spatial pattern consistency are rare. However, when proportions of land cover types are similar, spatial patterns are essential for studies of the ecological functions of a landscape system. In this study, we used classical landscape indices that quantifies spatial patterns to analyze the spatial pattern consistency among different land cover datasets, and chose three datasets (GlobeLand30-2010, FROM-GLC2010, and SERVIR MEKONG2010) in northern Laos as a case study. We also analyzed spatial pattern consistency at different scales after comparing the landscape indices method with the confusion matrix method. We found that the degree of consistency between GlobeLand30-2010 and SERVIR MEKONG2010 was higher than that of GlobeLand30-2010 and FROM-GLC2010, FROM-GLC2010, and SERVIR MEKONG2010 based on the confusion matrix, mainly because of the best forest consistency and then water. However, the spatial consistency results of the landscape indices analysis show that the three datasets have large differences in the number of patches (NP), patch density (PD), and landscape shape index (LSI) at the original scale of 30 m, and decrease with the increase of the scale. Meanwhile, the aggregation index (AI) shows different changes, such as the changing trend of the forest aggregation index increasing with the scale. Our results suggested that, when using or producing land cover datasets, it is necessary not only to ensure the consistency of landscape types and areas, but also to ensure that differences among spatial patterns are minimized, especially those exacerbated by scale. Attention to these factors will avoid larger deviations and even erroneous conclusions from these data products.