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USGS · 70033590

Influence of landscape structure on reef fish assemblages

Abstract

Management of tropical marine environments calls for interdisciplinary studies and innovative methodologies that consider processes occurring over broad spatial scales. We investigated relationships between landscape structure and reef fish assemblage structure in the US Virgin Islands. Measures of landscape structure were transformed into a reduced set of composite indices using principal component analyses (PCA) to synthesize data on the spatial patterning of the landscape structure of the study reefs. However, composite indices (e.g., habitat diversity) were not particularly informative for predicting reef fish assemblage structure. Rather, relationships were interpreted more easily when functional groups of fishes were related to individual habitat features. In particular, multiple reef fish parameters were strongly associated with reef context. Fishes responded to benthic habitat structure at multiple spatial scales, with various groups of fishes each correlated to a unique suite of variables. Accordingly, future experiments should be designed to test functional relationships based on the ecology of the organisms of interest. Our study demonstrates that landscape-scale habitat features influence reef fish communities, illustrating promise in applying a landscape ecology approach to better understand factors that structure coral reef ecosystems. Furthermore, our findings may prove useful in design of spatially-based conservation approaches such as marine protected areas (MPAs), because landscape-scale metrics may serve as proxies for areas with high species diversity and abundance within the coral reef landscape. ?? 2007 Springer Science+Business Media B.V.

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BibTeXRIS

R. Grober-Dunsmore, T.K. Frazer, J.P. Beets, W.J. Lindberg, P. Zwick, N.A. Funicelli. 2007-09-06. Influence of landscape structure on reef fish assemblages. https://doi.org/10.1007/s10980-007-9147-x

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Leveraging time-series remote sensing to predict post-fire invasive annual grass response in western US rangelands

Context Understanding the relationship between Exotic Annual Grass (EAG) expansion, drought, and fire frequency across western US rangelands is complex, but critical for maintaining ecological integrity. Objectives We quantified the impacts of wildfire on EAGs, with emphasis on understanding the factors driving the spatial and temporal variability of those impacts, highlighting the role of drought. Methods We investigate two study periods using 1985–2024 and 2016–2024 EAG cover datasets to better understand EAG dynamics in relation to fire. We, 1) leverage mapped EAG cover responses to fire in the context of various biophysical variables to model expected change in EAG cover (ΔG) given a fire in a specific location, even if no fire has occurred in that location in the period of record; 2) evaluate the influence of drought and other biophysical variables on the ΔG; 3) combine our ΔG values with burn probability and ecological integrity data to evaluate the profile of EAG risk due to fire in sagebrush habitats. Results Our results show that fire impacts on EAG are often delayed, with limited effects in the first post-fire year followed by increasing cover over subsequent years. Importantly, areas with low pre-fire EAG cover exhibited the greatest potential for post-fire increases, whereas heavily invaded sites showed lower incremental responses, suggesting saturation or carrying-capacity constraints. Conclusions Understanding the connections among EAGs, drought, and wildfire is critical for maintaining the ecological integrity of western rangelands. Identifying the magnitude of fire impacts to EAG cover provides critical insight for land managers.

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Ecological diffusion models are still useful in ecology

Context Connecting animal movement across scales—both among spatial scales and between individual and population level processes—is a central theme in ecology, including the sub-fields of movement and landscape ecology. However, most modeling frameworks operate at either the individual (Lagrangian) or population (Eulerian) level. Objectives We aimed to review how a partial differential equation (PDE) known as the ecological diffusion equation (EDE) offers connections among the scales of animal movement and how it can be embedded in commonly used statistical frameworks (e.g., hierarchical models) and fit to multiple types of data, such as count, presence-only, presence-absence, and telemetry data. In doing so, we also highlight emerging and promising avenues of research related to statistical EDE models. Methods We present how the EDE is derived from a simple set of first principles, can be used to represent both individual animal movement and spatiotemporal population dynamics, and can be fit to data by being applied in a hierarchical statistical framework. Finally, we review literature on recent applications and advances related to EDE models. Results In presenting the derivation of the EDE and reviewing recent advances and applications of EDE models, we established that the EDE can enable a mechanistic understanding of animal movement across scales, as well as the integration of multiple types of data. We also identified a number of topics where EDE models can be advanced to gain additional ecological insight. Conclusions Ecological diffusion is a process that emerges naturally from first principles governing animal movement and can be used to model both individual and population-level spatiotemporal dynamics. Statistical EDE models are related to other modeling frameworks, such as species distribution models, occupancy models, and step-selection analysis. The flexibility of hierarchical statistical modeling allows combining different types of data collected on movement and population dynamics, and the theoretical underpinnings of the EDE allow it to connect ecological processes occurring at fine and coarse spatial scales, as well as individual animal movement and population dynamics.

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Assessing environmental influences on gene flow for a migratory ungulate in a fire-prone landscape

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