Biology subjects

Newman, E. A.

Publications and source records attributed to Newman, E. A..

4 recordsLinked to original sources

bioRxiv2018

Revealing biases in the sampling of ecological interaction networks

The structure of ecological interactions is commonly understood through analyses of interaction networks. However, these analyses may be sensitive to sampling biases in both the interactors (the nodes of the network) and interactions (the links between nodes), because the detectability of species and their interactions is highly heterogeneous. These issues may affect the accuracy of empirically constructed ecological networks. Yet statistical biases introduced by sampling error are difficult to quantify in the absence of full knowledge of the underlying ecological networks structure. To explore properties of large-scale modular networks, we developed EcoNetGen, which constructs and samples networks with predetermined topologies. These networks may represent a wide variety of communities that vary in size and types of ecological interactions. We sampled these networks with different sampling designs that may be employed in field observations. The observed networks generated by each sampling process were then analyzed with respect to the number of components, size of components and other network metrics. We show that the sampling effort needed to estimate underlying network properties accurately depends both on the sampling design and on the underlying network topology. In particular, networks with random or scale-free modules require more complete sampling to reveal their structure, compared to networks whose modules are nested or bipartite. Overall, the modules with nested structure were the easiest to detect, regardless of sampling design. Sampling according to species degree (number of interactions) was consistently found to be the most accurate strategy to estimate network structure. Conversely, sampling according to module (representing different interaction types or taxa) results in a rather complete view of certain modules, but fails to provide a complete picture of the underlying network. We recommend that these findings be incorporated into field sampling design of projects aiming to characterize large species interactions networks to reduce sampling biases.\n\nAuthor SummaryEcological interactions are commonly modeled as interaction networks. Analyses of such networks may be sensitive to sampling biases and detection issues in both the interactors and interactions (nodes and links). Yet, statistical biases introduced by sampling error are difficult to quantify in the absence of full knowledge of the underlying networks structure. For insight into ecological networks, we developed software EcoNetGen (available in R and Python). These allow the generation and sampling of several types of large-scale modular networks with predetermined topologies, representing a wide variety of communities and types of ecological interactions. Networks can be sampled according to designs employed in field observations. We demonstrate, through first uses of this software, that underlying network topology interacts strongly with empirical sampling design, and that constructing empirical networks by starting with highly connected species may be the give the best representation of the underlying network.

ecology↗

bioRxiv2018

Disturbance macroecology: integrating disturbance ecology and macroecology in different-age post-fire stands of a closed-cone pine forest as a case study

Macroecological studies have generally restricted their scope to relatively steady-state systems, and as a result, how biodiversity and abundance metrics are expected to scale in disturbance-dependent ecosystems is unknown. We examine macroecological patterns in a fire-dependent forest of Bishop pine (Pinus muricata). We target two different-aged stands in a stand-replacing fire regime, one a characteristically mature stand with a diverse understory, and one more recently disturbed by a stand-replacing fire (17 years prior to measurement). We compare the stands using macroecological metrics of species richness, abundance and spatial distributions that are predicted by the Maximum Entropy Theory of Ecology (METE), an information-entropy based theory that has proven highly successful in predicting macroecological metrics across a wide variety of systems and taxa. Ecological patterns in the mature stand more closely match METE predictions than do data from the recently disturbed stand. This suggests METEs predictions are more robust in late-successional, slowly changing, or steady-state systems than those in rapid flux with respect to species composition, abundances, and organisms sizes. Our findings highlight the need for a macroecological theory that incorporates natural disturbance and other ecological perturbations into its predictive capabilities, because most natural systems are not in a steady state.

ecology↗

bioRxiv2017

Foxd1 is required for terminal differentiation of anterior hypothalamic neuronal subtypes.

Although the hypothalamus functions as a master homeostat for many behaviors, little is known about the transcriptional networks that control its development. To investigate this question, we analyzed mice deficient for the Forkhead domain transcription factor Foxd1. Foxd1 is selectively expressed in neuroepithelial cells of the prethalamus and hypothalamus prior to the onset of neurogenesis, and is later restricted to neural progenitors of the prethalamus and anterior hypothalamus. During early stages of neurogenesis, we observed that Foxd1-deficient mice showed reduced expression of Six3 and Vax1 in anterior hypothalamus, but overall patterning of the prethalamus and hypothalamus is unaffected. After neurogenesis is complete, however, a progressive reduction and eventual loss of expression of molecular markers of the suprachiasmatic, paraventricular and periventricular hypothalamic is observed. These findings demonstrate that Foxd1 acts in hypothalamic progenitors to allow sustained expression of a subset of genes selectively expressed in mature neurons of the anterior hypothalamus.\n\nHighlightsO_LIFoxd1 is broadly expressed in neuroepithelial cells of the hypothalamus and prethalamus.\nC_LIO_LIFoxd1 mutants show severe defects in anterior hypothalamic development, although prethalamic development is only modestly affected.\nC_LIO_LILoss of Foxd1 results does not affect initial patterning of the hypothalamus, but leads to a progressive loss of expression of markers specific to neurons of the suprachiasmatic, paraventricular and periventricular nuclei.\nC_LIO_LIFoxd1 regulates expression of multiple transcription factors expressed in developing anterior hypothalamus.\nC_LI

developmental biology↗

bioRxiv2017

Effects of anthropogenic wildfire activity in low elevation Pacific island vegetation communities in French Polynesia

Anthropogenic (or human-caused) wildfire is an increasingly important driver of ecological change on Pacific islands including southeastern Polynesia, but fire ecology studies are almost completely absent for this region. Where observations do exist, they mostly represent descriptions of fire effects on plant communities before the introduction of invasive species in the modern era. Understanding the effects of wildfire in southeastern Polynesian island vegetation communities can elucidate which species may become problematic invasives with continued wildfire activity. We investigate the effects of wildfire on vegetation in three low-elevation sites (45-379 m) on the island of Moorea in the Society Islands, French Polynesia, which are already heavily impacted by past human land use and invasive exotic plants, but retain some native flora. In six study areas (3 burned and 3 unburned comparisons), we placed 30 transects across sites and collected species and abundance information at 390 points. We analyzed each local community of plants in three categories: natives, those introduced by Polynesians before European contact (1767 C.E.), and those introduced since European contact. Burned areas had the same or lower mean species richness than paired comparison sites. Although wildfire did not affect the proportions of native and introduced species, it may increase the abundance of introduced species on some sites. Non-metric multidimensional scaling indicates that (not recently modified) comparison plant communities are more distinct from one another than are those on burned sites. We discuss conservation concerns for particular native plants absent from burned sites, as well as invasive species (including Lantana camara and Paraserianthes falcataria) that may be promoted by fire in the Pacific.

ecology↗