|Saturday, April 18th|
10:10 AM - 10:30 AM
10:30 AM - 10:50 AM
Protective clothing worn by wildland firefighters (WLFF) may increase physiological strain and heat stress factors due to increased insulation and decreased ventilation. PURPOSE: To examine the effects of a flame resistant synthetic material base layer on heat stress factors. METHODS: Ten recreationally active males (25 ± 6.1 yrs, 80.9 ± 8.4 kg, 11.1 ± 5.3% fat, 4.4 ± 0.6 L·min-1 VO2 max) completed two trials of intermittent (50 min walking, 10 min sitting) treadmill walking (2.5mph, 4% grade) over 3 hours in a climate chamber (35⁰C, 30% RH). Participants wore standard WLFF Nomex green pants, yellow shirt with either a 100% cotton base layer (C) or a flame resistant synthetic material base layer (S), while carrying a 35lb pack, hard hat, and gloves. Exercise was followed by a 30 minute rest period without pack, hard hat, gloves, or Nomex yellow shirt. Core (Tc) and skin (Tsk) temperature were measured continuously throughout the trial. Skin blood flow (SBF) and skin temperature () was recorded via laser doppler for two minutes prior to walking, five minutes during each break, and three, five minute periods during the 30 minutes following exercise. Physiological strain index (PSI) was calculated. Water was scripted at 8 ml/kg/hr. Repeated measures ANOVAs were performed using SPSS 22.0. RESULTS: Significant main effects for time were found on Tc (p≤0.001) and Tsk (p=0.003). No significant trialXtime interactions were found in Tc (p=0.077) and Tsk (p=0.086). SBF showed significant main effects for time (p=0.001) and a trialXtime interaction (p=0.001). Significant main effects for time were found on (p=0.001). Comparisons for SBF and were made between peaks, nadirs, and the three post-exercise periods for C and S. Significant main effects for time were found on SBF peaks (p=0.001), nadirs (p=0.028), and posts (p=0.001). Significant main effects for time were found on peaks (p=0.019) and posts (p=0.001). No significant trialXtime interactions were found between C and S. Significant main effects for time and trial were found on PSI (p≤0.001 and p=0.04, respectively). CONCLUSION: These data indicate that a flame resistant synthetic base layer may elevate SBF and possibly jeopardize indices of heat stress.
10:50 AM - 11:10 AM
INTRODUCTION : Over 70 % of Botrychium in Montana are considered “Species of Concern” by the Montana Natural Heritage Program, meaning they are in jeopardy of extirpation due to habitat destruction, limited range, or small population size.12 Thus, developing an effective conservation plan for Montana that maintains current Botrychium sp. diversity and preserves their distribution is critical.5, 4 Unfortunately, Botrychium sp. periodically abstain from producing aboveground fronds due to their symbiosis with arbuscular mycorrhiza, complicating long-term data collection strategies.9, 8, 4 Ergo, Botrychium sp. make an ideal candidate for a species distribution model (SDM) because it can provide statistical evidence linking observed distribution with environmental variables.7, 3, 2 Frequently applied in both conservation and management activities, an SDM can predict potential distribution over an area which is pivotal to conservation planning.5, 13 Studies have yet to employ this technique for Botrychium sp. and thus, there is no way to assess status or identify threats, putting Botrychium sp. at serious risk.5, 12
An SDM was used to identify environmental predictors for Botrychium sp. habitat in order to construct a predictive distribution map for Botrychium sp. in Lincoln County, Montana.
BACKGROUND : In fall 2014, a preliminary feasibility analysis focused on evaluating correction techniques to mitigate the sampling bias found in Botrychium sp. occurrence data from Lincoln County, Montana using MaxEnt. Maximum Entropy (MaxEnt) is an SDM that associates the spatial location of occurrence data with environmental variables to predict habitat suitability at unsurveyed locations.13 Sampling bias arises when observation data is preferentially collected from certain areas because they are for example, more convenient to reach.10, 2, 6 Eight explanatory variables were considered as input to the model: elevation, aspect, slope, soils, geology, mean May precipitation, mean June temperature, and land cover.4, 14 Using techniques assembled from similar studies, sampling bias correction schemes were created that split the data into two categories of locational uncertainty, subsampled the data at two resolutions, or restricted the background sampling extent to the known distribution of Botrychium sp.3, 10, 2, 6 Sampling bias was said to be resolved if the dataset exhibited a random distribution after application of a correction technique. Three optimal datasets were produced from the corrective procedures: points split based on locational uncertainty and then subsampled, points split based on locational uncertainty alone, and the complete Botrychium sp. dataset. The split and subsampled dataset performed poorly, creating a suitability map that grossly over-predicted potential habitat. Because the original sampling bias was so strong, this correction produced a subset of data that was no longer representative of the entire study area. Consequently, future analyses should continue to experiment with subsampling but refrain from splitting data into locational uncertainty classes.
RESEARCH PLAN :
(1) Correction of sampling bias in observation data: (i) Splitting - data will not be split based upon locational uncertainty. Further scrutiny will be given to the process of eliminating redundant points; relevant data was incidentally removed during this process previously. (ii) Subsampling - there exist two main clusters of points in the survey area; limiting subsampling to only the clustered regions will be further examined. (iii) Background Extent - quantification of excluding unsuitable habitat like lakes, rivers, and roads, and high elevation sites will be examined.
(2) Specification of environmental variables: Categories of environmental variables were assembled ranging from standard climatic variables, edaphic variables, and climatic variables that influence edaphic variables.
(3) Testing of MaxEnt’s internal settings: MaxEnt has the ability to modify the number of runs, the type of evaluation procedure, the number of background points sampled, and more. These can considerably alter results.13
CONCLUSION : This analysis is innovative in its application of an SDM to address deficiencies in knowledge about an enigmatic subgenus of Ophioglossaceae known as Botrychium. It will be one of the first studies to provide statistical support for linking environmental variables to observed Botrychium sp. habitat. Furthermore, this research will enhance our understanding of the ecological mechanisms behind symbiotic relationships between plants and arbuscular mycorrhiza. It will fill an important knowledge gap on the current distribution of Botrychium sp. in Lincoln County, MT and has potential to be expanded to other counties in Montana as well. It will help us monitor the health of this subgenus through changing disturbance and climatic regimes and create a conservation strategy that is relevant for maintaining current populations across the state.
1 Ahlenslager, Kathy, and Laura Potash. “Conservation Assessment for 13 Species of Moonworts (Botrychium Swartz Subgenus Botrychium).” Report Submitted to USDA Forest Service Region 6 and USDI Bureau of Land Management, Oregon and Washington. April 18, 2007. 2 Boria, Robert A., Link E. Olson, Steven M. Goodman, and Robert P. Anderson. “Spatial Filtering to Reduce Sampling Bias Can Improve the Performance of Ecological Niche Models.” Ecological Modelling, 275(2014): 73-77.
3Burbach, Thor. “The Influence of Environmental Variables on Predicting Rare-Plant Habitat in the Nez Perce National Forest.” Master’s Thesis, University of Montana, 2011. 4 Farrar, Donald R. "Systematics of moonworts Botrychium subgenus Botrychium.” Iowa : Iowa State University, Department of Ecology, Evolution, and Organismal Biology, 2006. 5 Fielding, Alan H. and John F. Bell. “A Review of Methods for the Assessment of Prediction Errors in Conservation Presence/Absence Models.” Environmental Conservation, 24(1997): 38-49. 6 Fourcade, Yoan, Jan O. Engler, Dennis Rodder, and Jean Secondi. “Mapping Species Distributions with MAXENT Using a Geographically Based Sample of Presence Data : A Performance Assessment of Methods for Correcting Sampling Bias.” PLOS ONE 9, 3(2014) : e97122. 7 Guisan, Antoine, and Niklaus E. Zimmermann. “Predictive habitat distribution models in ecology.” Ecological Modelling, 135(2000): 147-186.
8 Johnson-Groh, Cindy L., and Jennifer M. Lee. “Phenology and Demography of Two Species of Botrychium (Ophioglossaceae).” American Journal of Botany, 89(2002a): 1624-1633. 9 Johnson-Groh, Cindy, Chandra Riedel, Laura Schoessler, and Krissa Skogen. “Belowground Distribution and Abundance of Botrychium Gametophytes and Juvenille Sporophytes.” American Fern Journal, 92(2002b): 80-92. 10 Kramer-Schadt, Stephanie, Jürgen Niedballa, John D. Pilgrim, Boris Schröder, Jana Lindenborn, Vanessa Reinfelder, Milena Stillfried, Ilja Heckmann, Anne K. Scharf, Dave M. Augeri, Susan M. Cheyne, Andrew J. Hearn, Joanna Ross, David W. Macdonald, John Mathai, James Eaton, Andrew J. Marshall, Gono Semiadi, Rustam, Henry Bernard, Raymond Alfred, Hiromitsu Samejima, J. W. Duckworth, Christine Breitenmoser-Wuersten, Jerrold L. Belant, Heribert Hofer, and Andreas Wilting. “The Importance of Correcting for Sampling Bias in MaxEnt Species Distribution Models.” Diversity and Distributions, 19 no. 11(2013): 1366–1379. 11 Lesica, Peter, and Kathleen Ahlenslager. “Demography and Life History of Three Sympatrix Species of Botrychium subg. Botrychium in Waterton Lakes National Park, Alberta.” Canadian Journal of Botany, 74(1996): 538-543. 12 Montana Natural Heritage Program. “Montana Plant Species of Concern Report.” State of Montana, 2014. Available at : http://mtnhp.org/SpeciesOfConcern/?AorP=p. 13 Phillips, Steven J., Robert P. Anderson, and Robert E. Schapire. "Maximum entropy modeling of species geographic distributions." Ecological Modelling, 190(2006): 231-259. 14 Roe-Anderson, Susan M., and Darlene Southworth. “Microsite Factors and Spore Dispersal Limit Obligate Mycorrhizal Fern Distribution: Habitat Islands of Botrychium pumicola (Ophioglossaceae).” American Fern Journal, 103(2013): 1-20.
Laurel Anne T. Yorgason, The University Of Montana
11:10 AM - 11:30 AM
The attachment relationship between a mother and her infant provides a foundation for future development (Bowlby, 1951; Sroufe, 2005). A high level of maternal sensitivity has been deemed one of the most important antecedents to a secure attachment (van IJzendoorn & Bakermans-Kranenburg, 2004). Although Mary Ainsworth originally developed a measure of maternal sensitivity other researchers developed measures to determine a mother’s level of sensitivity (Mesman & Emmen, 2013). The Strange Situation Procedure (SSP) was developed to determine the classification of the attachment relationship (Ainsworth, Bell, & Stayton, 1974). Currently these measures focus predominantly on dyads that include an infant at approximately age 12 months. Since the benefit of earlier intervention in problematic parental-infant relationships is evident (Juffer, et al., 2008), discovering ways to accurately measure parental sensitivity at earlier infant ages would be beneficial. This study is unique in that it includes infants who are 4 weeks old. The overall intent of this study is to ascertain the relationship between maternal sensitivity at 4 weeks and attachment classification at 16 months and whether the Ainsworth Maternal Sensitivity Scale (AMSS) (Ainsworth et al., 1974) is a reliable measure for assessing maternal sensitivity at the infant's age of 4 weeks and 16 months. Sixty-eight mothers were videotaped during interaction with their infant at age 4 weeks. Mothers returned with their 16 month-old infant to participate in the SSP to determine attachment security (see Ainsworth & Bell 1970). Maternal sensitivity during the SSP was also coded using the AMSS and previously reported results determined that higher levels of maternal sensitivity at that time was related to secure attachment (Muir, Koester & Yorgason, 2012). Maternal sensitivity was coded during the 4 week infant-mother interaction using the AMSS. Results showed that maternal sensitivity at 4 weeks was not correlated with the maternal sensitivity at 16 months. Maternal sensitivity at 4 weeks was not related to overall attachment classifications at 16 months but specifically deciphered subtypes of secure and disorganized attachment. Development of infant age-specific measures that predict attachment is worth future consideration.