Geology topics

Jack E. Norland

Publications and source records attributed to Jack E. Norland.

6 recordsLinked to original sources

USGS2022

Phytoplankton community interactions and cyanotoxin mixtures in three recurring surface blooms within one lake

Cyanobacteria can produce numerous secondary metabolites (cyanotoxins) with various toxicities, yet data on cyanotoxins in many lakes are limited. Moreover, little research is available on complex relations among cyanobacteria that produce toxins. Therefore, we studied cyanobacteria and 19 cyanotoxins at three sites with recurring blooms in Kabetogama Lake (USA). Seven of 19 toxins were detected in various combinations. Anabaenopeptin A and B were detected in every sample. Microcystin-YR was detected more frequently than microcystin-LR, unlike other lakes in the region. Microcystin-YR concentrations, however, generally were low; two samples exceeded drinking water guidelines and no samples exceeded recreational guidelines. Anabaenopeptins correlated with six cyanobacterial taxa, most of which lack available literature on peptide production. The potential toxin producing cyanobacteria, Microcystis , was significantly correlated to microcystin-YR. Pseudanabaena sp. and Synechococcus sp. had strong negative correlations with several toxins that may indicate competition or stress between organisms. Non-metric multidimensional scaling identified three cyanobacterial pairs that may reflect symbiotic or antagonistic relations. This study highlights interactions among cyanobacteria and multiple cyanotoxins and the methods used may be useful for uncovering additional patterns in cyanobacteria communities in other systems, leading to further understanding of how those interactions lead to toxin production.

Minnesota↗

USGS2021

Cyanotoxin mixture models: Relating environmental variables and toxin co-occurrence to human exposure risk

Toxic cyanobacterial blooms, often containing multiple toxins, are a serious public health issue. However, there are no known models that predict a cyanotoxin mixture (anatoxin-a, microcystin, saxitoxin). This paper presents two cyanotoxin mixture models (MIX) and compares them to two microcystin (MC) models from data collected in 2016–2017 from three recurring cyanobacterial bloom locations in Kabetogama Lake, Voyageurs National Park (Minnesota, USA). Models include those using near-real-time environmental variables (readily available) and those using additional comprehensive variables (based on laboratory analyses). Comprehensive models (R 2 = 0.87 MC; R 2 = 0.86 MIX) explained more variability than the environmental models (R 2 = 0.58 MC; R 2 = 0.57 MIX). Although neither MIX model was a better fit than the MC models, the MIX models produced no false negatives in the calibration dataset, indicating that all observations above regulatory guidelines were simulated by the MIX models. This is the first known use of Virtual Beach software for a cyanotoxin mixture model, and the methods used in this paper may be applicable to other lakes or beaches.

Minnesota↗

USGS2020

Vegetation sampling and management

What is the utility of vegetation measurements for wildlife managers? In the prairie, savanna, tundra, forest, steppe, and wetland regions of the world, mixtures of plant species provide wildlife with food, cover and, in some circumstances, water; the 3 essential habitat elements necessary to sustain viable wildlife populations. We define habitat in reference to use of a vegetation type by an animal (e.g., deer habitat) and vegetation type when referring to differences in vegetation stands (e.g., marsh vegetation type versus tall grass prairie vegetation type; Hall et al. 1997). In strict definition, the variety of wildlife using plants ranges from snails and voles (Microtus spp.) to bison (Bison bison) and elephants (Loxodonta spp.) in uplands and from mosquitoes and ducks to muskrats (Ondatra zibethicus) and manatees (Trichechus manatus) in wetlands. Through evolutionary processes, some wildlife species are totally dependent on vegetation for all annual life requirements, whereas other species use vegetation only for cover or food. Regardless of the role of vegetation in the sustenance of wildlife, any management or research project that requires evaluation of wildlife and vegetation type relationships on a unit of land will necessitate some form of vegetation measurement. The term vegetation can refer to a single plant or species on a specific site or a community in the landscape. Vegetation may occur naturally or be introduced, and may be live or dead. Uses of vegetation measurements are many: (1) evaluation of vegetation response to management practices, (2) estimation of carrying capacity and/or forage production, (3) characterization of cover and habitat components for an endangered species, or (4) long-term monitoring of the general trend of plant vigor or vegetation type condition. Surveying and measuring quantity and quality of vegetation within habitats are basic to wildlife research and management. Grassland, shrubland, and woodland vegetation types are comprised of populations in which individual plants are usually too numerous to inventory completely. Consequently, wildlife biologists usually use sampling techniques to make inferences about the total plant population within a given vegetation type. Vegetation sampling methodologies have evolved within several ecological disciplines (e.g., plant ecology, forestry, rangeland science) and for a variety of management or research objectives (e.g., estimating forage for ungulates, describing habitat use by passerine birds). Description of every method that has been used to sample vegetation is beyond the scope of this chapter. We describe how to measure vegetation structure, which Dansereau (1957) defined as the spatial organization (distribution) of individuals that form a stand. We have organized this chapter into a description of basic methods of vegetation sampling with examples of how those methods have been applied or modified in wildlife research and management. We assume the investigator/reader has adequate knowledge of the concepts of wildlife ecology, primary habitat requirements of wildlife species under study, and ability to systematically identify the species of wildlife and vascular plants within the geographical area of investigation.

Book chapter↗

USGS2019

Phytoplankton community and algal toxicity at a recurring bloom in Sullivan Bay, Kabetogama Lake, Minnesota, USA

Kabetogama Lake in Voyageurs National Park, Minnesota, USA suffers from recurring late summer algal blooms that often contain toxin-producing cyanobacteria. Previous research identified the toxin microcystin in blooms, but we wanted to better understand how the algal and cyanobacterial community changed throughout an open water season and how changes in community structure were related to toxin production. Therefore, we sampled one recurring bloom location throughout the entire open water season. The uniqueness of this study is the absence of urban and agricultural nutrient sources, the remote location, and the collection of samples before any visible blooms were present. Through quantitative polymerase chain reaction (qPCR), we discovered that toxin-forming cyanobacteria were present before visible blooms and toxins not previously detected in this region (anatoxin-a and saxitoxin) were present, indicating that sampling for additional toxins and sampling earlier in the season may be necessary to assess ecosystems and human health risk.

Minnesota↗

USGS2018

Prairie reconstruction unpredictability and complexity: What is the rate of reconstruction failures?

The outcomes of prairie reconstructions are subject to both unpredictability and complexity. Prairie, tallgrass, and mixed grass reconstruction is defined as the planting of a native herbaceous seed mixture composed of multiple prairie species (10 or more) in an area where the land has been heavily cultivated or anthropogenically disturbed. Because of the unpredictability and complexity inherent in reconstructions, some outcomes end up being failures dominated by exotic species. We propose that these failures follow a fat-tailed distribution as found in other complex systems. Fat-tailed distributions follow the Pareto principle, where 80% of the time reconstructions work as expected but 20% of the time they are surprising and far from the typical response. Therefore, we suggest managers be informed that reconstruction failures follow fat-tailed distributions as opposed to assuming reconstructions are simple and predictable with few failures. Once managers realize failures are inherent in reconstructions, resources can be allocated to more effective methods of dealing with failures rather than working to perfect the predictability of reconstructions. We suggest implementing adaptive management, especially where unpredictability is high, as a way to learn from failures. Combining learning from adaptive management with a reconstruction design process, in which goals and constraints are iteratively adjusted, can be a way to deal with failures and develop better outcomes.

Ecological Restoration↗

USGS1994

Niche relationships within a guild of ungulate species in Yellowstone National Park, Wyoming, following release from artificial controls

Niche relationships and diet overlaps were compared among elk (Cervus elaphus), bison (Bison bison), bighorn sheep (Ovis canadensis), mule deer (Odocoileus hemionus), and pronghorn antelope (Antilocapra americana) between 1967–1970 and 1986–1988, a period when total ungulate numbers nearly tripled on Yellowstone's northern range. Ungulate species ratios on Yellowstone's northern winter range during the latter period were 100 elk : 10 mule deer : 3 bison : 2 pronghorns : 1 bighorns. Elk numbers were positively correlated to bison, mule deer, and pronghorn numbers (r2 = 0.76, 0.97, and 0.48, respectively, P < 0.01). Few other changes in habitat use or habitat overlap occurred, and diets for only 2 of the 10 species pairs, elk-bighorn (Spearman's rank order coefficient (RHO) = 0.55, P < 0.05) and mule deer – pronghorn (RHO = 0.64, P < 0.05), were significantly associated with each other. Bison consumed more grass and fewer sedges, mule deer more fringed sage (Artemisia frigida) and more rabbit-brush (Chrysothamnus spp.), and bighorn sheep more grasses and fewer sedges, while pronghorns ate less saltsage (Atriplex nuttalli) but more big sagebrush (Artemisia tridentata) during 1986–1988 than during 1967–1970. Bison expanded their range and bison and bighorn sheep used a wider variety of habitats. We found little evidence of change in competitive interactions between ungulate species. A few diet and habitat overlaps increased, the opposite of the prediction from the competitive exclusion principle amongst species, suggesting that intraspecific competition was more important. Several explanations are proposed for the lack of changes in niche relationships during a period of near tripling in density of the ungulate guild.

Wyoming↗