Fine-scale Landscape Variability of Cotonou City, Benin: Insights From Three Contrasted Urban Neighborhoods

Urbanization consist in a complex and deep human-driven environmental change that strongly impacts the ecology and evolution of living organisms, including pathogens, reservoir and vector species hence human health. Quantitative proxies of urban landscapes may be very useful to sum-up such a complexity and to guide fundamental and applied research as well as urban planning programs. Geographic Information Systems (GIS) provide landscape and uses metrics which can be investigated through multivariate analyses, thus providing pertinent synthetic landscape descriptors. As such, our study describes the fine-scale modelling of three urban neighborhoods of Cotonou city, Benin, using GIS, landscape metrics and Principal Component Analysis (PCA). Spatial variability between and within neighborhoods revealed different levels of variability, with elements differentiating the three areas from each other, while local neighbourhood-specific variations were also evidenced. We found that Cotonou landscapes are strongly influenced by their history, the natural environment in which they develop as well as the urban planning trajectories. This case study shows that PCA-analyzed of GIS-based metrics may be very relevant to describe and understand the variability of urban landscapes at different scales, thus constituting a valuable tool for urban management of African cities.

Regional, multi-decadal analysis reveals that stream temperature increases faster than air temperature

Stream temperature appears to be increasing globally, but its rate remains poorly constrained due to a paucity of long-term data and difficulty in parsing effects of hydroclimate and landscape variability. Here, we address these issues using the physically-based thermal model T-NET (Temperature-NETwork) coupled with the EROS semi-distributed hydrological model to reconstruct past daily stream temperature and streamflow at the scale of the entire Loire River basin in France (105 km2 with 52278 reaches). Stream temperature increased for almost all reaches in all seasons (mean = +0.38 °C/decade) over the 1963–2019 period. Increases were greatest in spring and summer with a median increase of +0.38 °C (range = +0.11– +0.76 °C) and +0.44 °C (+0.08– +1.02 °C) per decade, respectively. Rates of stream temperature increases were greater than for air temperature across seasons for 50–86 % of reaches. Spring and summer increases were typically the greatest in the southern headwaters (up to +1 °C/decade) and in the largest rivers (Strahler order > 5). Importantly, air temperature and streamflow exerted joint influence on stream temperature trends, where the greatest stream temperature increases were accompanied by similar trends in air temperature (up to +0.71 °C/decade) and the greatest decreases in streamflow (up to −16 %/decade). Indeed, for the majority of reaches, positive stream temperature anomalies exhibited synchrony with positive anomalies in air temperature and negative anomalies in streamflow, highlighting the dual control exerted by these hydroclimatic drivers. Moreover, spring and summer stream temperature, air temperature, and streamflow time series exhibited common change-points occurring in the late 1980s, suggesting a temporal coherence between changes in the hydroclimatic drivers and a rapid stream temperature response. Critically, riparian vegetation shading mitigated stream temperature increases by up to 16 % in smaller streams (i.e., < 30 km from the source). Our results provide strong support for basin-wide increases in stream temperature due to joint effects of rising air temperature and reduced streamflow. We suggest that some of these climate change-induced effects can be mitigated through the restoration and maintenance of riparian forests, and call for continued high-resolution monitoring of stream temperature at large scales.

Using Machine Learning to Map Western Australian Landscapes for Mineral Exploration

Landscapes evolve due to climatic conditions, tectonic activity, geological features, biological activity, and sedimentary dynamics. Geological processes at depth ultimately control and are linked to the resulting surface features. Large regions in Australia, West Africa, India, and China are blanketed by cover (intensely weathered surface material and/or later sediment deposition, both up to hundreds of metres thick). Mineral exploration through cover poses a significant technological challenge worldwide. Classifying and understanding landscape types and their variability is of key importance for mineral exploration in covered regions. Landscape variability expresses how near-surface geochemistry is linked to underlying lithologies. Therefore, landscape variability mapping should inform surface geochemical sampling strategies for mineral exploration. Advances in satellite imaging and computing power have enabled the creation of large geospatial data sets, the sheer size of which necessitates automated processing. In this study, we describe a methodology to enable the automated mapping of landscape pattern domains using machine learning (ML) algorithms. From a freely available digital elevation model, derived data, and sample landclass boundaries provided by domain experts, our algorithm produces a dense map of the model region in Western Australia. Both random forest and support vector machine classification achieve approximately 98% classification accuracy with a reasonable runtime of 48 minutes on a single Intel® Core™ i7-8550U CPU core. We discuss computational resources and study the effect of grid resolution. Larger tiles result in a more contiguous map, whereas smaller tiles result in a more detailed and, at some point, noisy map. Diversity and distribution of landscapes mapped in this study support previous results. In addition, our results are consistent with the geological trends and main basement features in the region. Mapping landscape variability at a large scale can be used globally as a fundamental tool for guiding more efficient mineral exploration programs in regions under cover.

The global scientific literature on applications and trends in the use of functional morphological groups in phytoplankton studies

Aim The application of deconstructive approaches in aquatic ecology has been increasing recently. Especially for phytoplankton, some functional classifications summarize similar traits of a group of species to understand organisms’ response to landscape variability. One of these approaches deals with phytoplankton functional classification based on morphology (MBFG - Morphologically Based Functional Groups). Focusing on this approach, we systematic mapping the scientific literature to reveal this functional framework´s applications for freshwater phytoplankton. Methods For this study, we selected from the Thomson ISI Web of Science database all articles published between 2010 and 2018 dealing with MBFG. We recorded 179 manuscripts citing the phytoplankton functional classification based on morphology and, among them, we excluded three due to lack of access to information. Results A clear temporal trend occurred with an increase in citations involving the morphological approach, with Brazil, Uruguay, and China as the countries with the highest number of studies. Of the total records, 60 manuscripts applied morphological classification in their studies, of which 23 manuscripts comprised comparative studies with other functional approaches. Most applications were for phytoplankton in lakes, with biomass being the most used metric for framing taxa in MBFG. The most often recorded groups are MBFG IV (medium-sized organisms without specialization), VII (large mucilaginous colonies), and III (large filamentous organisms with aerotopes). Conclusion This study showed an increasing trend in the number of studies that used the functional approach based on MBFG. We believe that deconstructive approaches, such as MBFG, help assess issues of interest in phytoplankton ecology.

Relationships between regional coastal land cover distributions and elevation reveal data uncertainty in a sea-level rise impacts model

Understanding land loss or resilience in response to sea-level rise (SLR) requires spatially extensive and continuous datasets to capture landscape variability. We investigate the sensitivity and skill of a model that predicts dynamic response likelihood to SLR across the northeastern US by exploring several data inputs and outcomes. Using elevation and land cover datasets, we determine where data error is likely, quantify its effect on predictions, and evaluate its influence on prediction confidence. Results show data error is concentrated in low-lying areas with little impact on prediction skill, as the inherent correlation between the datasets can be exploited to reduce data uncertainty using Bayesian inference. This suggests the approach may be extended to regions with limited data availability and/or poor quality. Furthermore, we verify that model sensitivity in these first-order landscape change assessments is well-matched to larger coastal process uncertainties, for which process-based models are important complements to further reduce uncertainty.

Relationships between regional coastal land cover distributions and elevation reveal data uncertainty in a sea-level rise impacts model

Understanding land loss or resilience in response to sea-level rise (SLR) requires spatially extensive and continuous datasets to capture landscape variability. We investigate sensitivity and skill of a model that predicts dynamic response likelihood to SLR across the northeastern U.S. by exploring several data inputs and outcomes. Using elevation and land cover datasets, we determine where data error is likely, quantify its effect on predictions, and evaluate its influence on prediction confidence. Results show data error is concentrated in low-lying areas with little impact on prediction skill, as the inherent correlation between the datasets can be exploited to reduce data uncertainty using Bayesian inference. This suggests the approach may be extended to regions with limited data availability and/or poor quality. Furthermore, we verify that model sensitivity in these first-order landscape change assessments is well-matched to larger coastal process uncertainties, for which process-based models are important complements to further reduce uncertainty.