Nairanjana Dasgupta

Land cover type and fire in Portugal: do fires burn land cover selectively?

The purpose of this study is to investigate if, or under what conditions, fires select given land cover types for burning. If fires burn unselectively then the land cover composition (the proportional area of various land cover types) of individual fires should approximate the land cover composition available in their neighborhood. In this study we test this hypothesis by performing statistical analyses of a data set consisting of paired vectors with the proportions of land cover types present in burned areas and in their respective surroundings. The statistical methods employed (a permutation technique and the Cmax statistic) are commonly used in resource selection studies where data is subject to a unit-sum constraint. The results of the analysis of 506 fires that burned in Portugal in 1991 indicate that fires are selective, with small fires exhibiting stronger land cover preferences than large fires. According to the results of a multiple comparison analysis performed for small fires, there is a marked preference for shrubland followed by other forest cover types, while agriculture is clearly avoided. A similar analysis is performed to test if fire selectivity is related to the ecological region where it occurs. The results obtained in this study contribute to the discussion on the relative importance of fuels as a drivers of fire spread.

Maternal supplementation with CLA decreases milk fat in humans

CLA refers to isomers of octadecadienoic acid with conjugated double bonds. The most abundant form of CLA (rumenic acid (RA): c9,t11-18∶2) is found in milk and beef fat. Further, CLA supplements containing RA and t10,c12−18∶2 are now available. Consumption of commercially produced CLA has been shown to decrease adipose accretion in growing laboratory and production animals and cause milk fat depression in cows. We tested the hypothesis that CLA supplementation would increase milk CLA concentration and decrease milk fat content in humans. Breastfeeding women (n=9) participated in this double-blind, placebo-controlled, crossover study divided into three periods: intervention l (5 d), washout (7 d), and intervention II (5 d). Women were randomized to treatment order. During each intervention period, women consumed 1.5 g of CLA supplement or placebo (olive oil) daily; during the washout period, no supplements were consumed. Milk was collected by complete breast expression on the final day of each period; milk output was estimated by 24-h weighing on the penultimate day of each intervention period. Milk RA and t10,c12−18∶2 concentrations were greater (P<0.05) during the CLA treatment period as compared to the placebo period. Milk fat content was significantly lower during the CLA treatment, as compared to the placebo treatment (P<0.05). Data indicate no effect of treatment on milk output. Therefore, it would be prudent that lactating women not consume commercially available CLA supplements at this time.

Morphological abnormalities in Chironomus tentans exposed to cadmium—and copper-spiked sediments

The induction of mouthpart deformities and the developmental response with exposure to sediments spiked with three concentrations (9, 39, and 61 microgg(-1) Cd dry wt.) of cadmium (Cd) and three concentrations (30, 125, and 215 microgg(-1) Cu dry wt.) of copper (Cu) were investigated. Mouthpart deformity proportions in Chironomus tentans larvae were compared between metal-spiked and control populations and between parent and offspring (F1) populations. Cd- and Cu-treated sediments induced deformities (low Cd=13%, medium Cd=7%, high Cd=4%, low Cu=6%, medium Cu=9%, high Cu=6%) at significantly higher proportions than control (3%) sediments. No negative developmental response was determined. Larval sizes in metal-treated aquaria and control aquaria were not significantly different. F1 larvae from parents reared in medium and high Cu had significantly lower deformity rates than their parents. Our research adds to the growing evidence implicating heavy metals in general, and Cd and Cu specifically, as teratogenic agents.

Influence of Summer and Autumn Nutrition on Body Condition and Reproduction in Lactating Mule Deer

Abstract Recent work suggests that availability and quality of forage in late summer and early autumn, a time when female ungulates face multiple energetic demands, is critical to reproduction in wild ungulates. Therefore, we examined direct links between nutritional quality of diets, body condition, and reproduction of lactating mule deer. Using captive mule deer, we tested the hypothesis that females consuming diets with lower digestible energy (DE; kJ/g) would have lower DE intake rates (DEI; MJ/day), have less body fat and muscle, have later estrus cycles, and have lower pregnancy and twinning rates. Deer fed lower DE diets had lower DEI during summer and autumn. In turn, deer with lower DEI, regardless of diet DE, had lower body mass, body fat, and muscle thickness. When nutritional quality of diets began to decline earlier in the summer, relationships between food quality, DEI, and body condition were stronger. Although DEI did not influence estrus date for deer that became pregnant before 21 December, deer with lower DEI had a lower probability of becoming pregnant and had a lower probability of producing twins. Measures of body condition in October (i.e., body mass, body fat, and muscle depth) predicted pregnancy and twinning rates in mule deer. Serum concentration of hormones leptin and Insulin Growth Factor 1 were not good predictors of body condition or reproduction. These findings suggest that managers concerned with productivity of mule deer populations should consider focusing on assessing and improving quality of forage available in summer and autumn.

DNA, Words and Models, Statistics of Exceptional Words

the intended audience. Several of the topics (such as martingales, stochastic differential equations, and multivariate time series) are rather advanced. The authors have both contributed associated S+ software, much of which is available as an add-on (for purchase) module (S+FinMetrics). The book covers some aspects of financial time series, associated analysis challenges, and S+ software. I would prefer that some of the financial time series descriptions were collected in a convenient location, say in one chapter. Instead, various time series concepts are introduced in many chapters, organized somewhat according to the recommended analysis technique(s). The index omits many of the covered topics, so this forced me to thumb through the many pages several times when searching various topics. There is enough S+ detail to increase one’s S+ skills, although there is only a very brief attempt in Chapter 1 to provide systematic S+ instruction, essentially on an as-needed basis for understanding how to use S+FinMetrics. I like the style (one example is on p. 2) of showing an example of S+ coding that does not work, along with effective but brief explanation. The 23 chapters and 900 pages are too dense to list and summarize here. Instead I will list a few things I like and do not like about the book. I like the reference style with well-written concise explanations. For example, I was not aware of a generalization to the Fisher–Tippet result that extends results for extreme values from the independent and identically distributed case to the stationary case; this is one of many practical examples (what is the distribution for my maximum gain or loss in a certain investment over time) that cites rigorous statistical literature. I also like the 1-page discussion of the logic behind an “investment’s β .” Writing the “famous” capital asset pricing model (CAPM) as Rit − rft = αi + βi(RMt − rft) + εit , i = 1, . . . ,N; t = 1, . . . ,T , will not intimidate a statistician although most statisticians will need help with the financial logic. How many of us have seen a clear description of the CAPM in a typical investment firm’s glossy newsletter? Here, Rit is the return on asset i between time periods t − 1 and t,RMt is the return on a relevant market index (that the asset i is being compared to), rft is the rate of return on a risk-free asset between time periods t − 1 and t and εit ∼ GWN(0, σ 2), which is the text’s notation for Gaussian white noise. Perhaps readers of this review will appreciate seeing the CAPM and can interpret it without the help provided in the text. This is one of the simplest financial examples in the text, so be aware that the mathematical and statistical subjects covered are (to me) rather advanced. The well-written reference style avoids technical details however. I also mention the CAPM here because I am reminded of Dr. Fred Leyseiffer’s comment to me when I was a first-year graduate student in the Florida State University statistics department. “They never care about money.” This referred to PhD students who chained themselves to their office desk for 14 hours per day. Well, I now care about money to the extent that I wanted to know how to interpret a “fund’s β” so the text is useful for that rather trivial reason alone. Another good aspect of this text is its surprisingly wide and reasonably deep coverage of a lot of practical and theoretical time series concepts. To be balanced, I must mention a few suggested improvements. First, I could not get a sense when real progress had been made with financial time series forecasting using relatively new approaches such as GARCH (generalized autoregressive conditional heteroskedasticity) models. GARCH dates to 1981 and today is often cited, and I have no doubt that GARCH models fit many time series better than standard ARMA (autoregressive moving average) models. Maybe that justifies their popularity, but perhaps a revision could show or at least cite practical comparisons among forecasts from competing models when applied to real data (there are many real data examples but I did not find any performance comparisons among methods). The GARCH case is but one such example where there is not a practical demonstration of a performance advantage that might matter in a financial sense using real data. To rephrase this small suggestion, could a revision include examples where a basic, naive approach is outperformed in a practical sense by one of the available advanced methods? There are many examples where models were extended to capture various features of the data, such as GARCH. And, note that the S+FinMetrics module provides GARCH model simulation and selection features, among much other S+ functionality for rather advance methods. Second, there are some occasional loose descriptions. For example, on page 68 in describing the shape of the autocorrelation plot (ACF) from lags 1 to 25, the text indicates that the slowly varying ACF values indicate “persistence,” but leaves the reader hanging and never returns to this example interest rate differential data having persistent ACF values. Chapter 8 involves long memory time series where persistence is given a definition, so a reread of page 68 after reading Chapter 8 is suggested. There is also lack of an adequate discussion of why and to what extent the partial ACF is more effective than the ACF for choosing an AR order for such data. In short, the text is not recommended for learning ARMA or other time series concepts, although it would be a fantastic complement to traditional texts that are often used to teach time series (Chatfield 1980). Third, I note that S+FinMetrics does not offer an ARMA-based disaggregation function. Disaggregation is discussed (pp. 35–38), but the function options do not include concepts such as those in (Al-Osh 1989). It could be that the basic function options that are provided are nearly as effective and much simpler to understand and implement. Overall, it is a pleasure to strongly recommend this text, and to include statisticians such as myself among the pleased audience. The statistical technical detail is approximately on the level of (Venables and Ripley 1999). I think the financial subject matter is at an appropriate level, and is perhaps written more for the intended statistical audience than for financial students. Finally, I note that the first author’s website invites suggested improvements; errors are being collected presumably to improve a third edition. Datasets and S+FinMetrics are available through Insightful.

Induction of morphological deformities in Chironomus tentans exposed to zinc‐ and lead‐spiked sediments

Laboratory experiments were used to assess morphological responses of Chironomus tentans larvae exposed to three levels of zinc and lead. Chironomus tentans egg masses were placed into triplicate control and metal-spiked aquaria containing the measured concentrations 1,442, 3,383, and 5,562 microg/g Pb dry weight and 1,723, 3,743, and 5,252 microg/g Zn dry weight. Larvae were collected at 10-d intervals after egg masses were placed in aquaria until final emergence. Larvae were screened for mouthpart deformities and metal body burdens. Deformities increased with time of exposure in both Zn and Pb tanks. Deformity rates between the three Zn concentrations differed statistically, with low and medium Zn levels containing the highest overall deformity rates of 12%. Deformity rates for larvae held in the Pb aquaria were found to differ significantly. Larvae in the low-Pb tanks had a deformity rate of 9%. Larvae and water from both the Zn and Pb aquaria had increasing metal concentrations with increasing sediment metal concentration. Results demonstrate that Zn and Pb each induce chironomid mouthpart deformities at various concentrations. However, a clear dose-related response was not demonstrated. Our research provides more support for the potential use of chironomid deformities as a tool for the assessment of heavy metal pollution in aquatic systems.

Forage quality's influence on mule deer fawns†

ABSTRACT In many mule deer (Odocoileus hemionus) populations, recruitment of fawns drives population dynamics. The quality of food available to females and their fawns in summer and autumn may play an important role in fawn recruitment. We examined direct links between digestible energy (DE) content of food and the DE intake of females on the nutrient concentration of milk and between the nursing behavior, DE intake, growth, and survival in captive mule deer fawns. We offered females and their fawns diets that simulated the natural decline in DE content of forage from mid-summer to late autumn in many western landscapes. Fawns fed a higher DE diet weighed 14% more at the onset of winter, had fewer unsuccessful nursing attempts, consumed milk with more protein and energy, and had higher survival than fawns fed a low DE diet. Differences between fawn performances among treatments were greatest when diet quality began decreasing earlier in the summer. Because our results indicate that summer and autumn nutrition is likely to influence fawn recruitment, wildlife biologists should include metrics for summer precipitation and late autumn fawn mass in population models, and land managers should focus on methods for improving the nutritional carrying capacity of summer and early autumn habitats.

Effects of exposure to a combination of zinc‐ and lead‐spiked sediments on mouthpart development and growth in Chironomus tentans

Exposures to either zinc or lead in contaminated sediments have been shown to induce characteristic deformities in larval chironomids. This study examined the effects of exposure to lead and zinc in combination on Chironomus tentans larvae. Proportions of mouthpart deformities in populations of larvae reared in sediments containing nominal combinations of lead and zinc were tested for additive, synergistic, and antagonistic interactions using logistic regression. Metal body burdens, body size measurements, and survival were used to evaluate toxicity and developmental impacts. Results demonstrate zinc and lead mixtures produce fewer deformities than the individual metal, so their interaction may be characterized as antagonistic. However, exposure to the metal mixtures also caused delayed development and failure to hatch. The apparent decline in deformities may be an artifact of higher mortalities or developmental effects. This research provides better understanding of some of the problems and considerations for use of chironomid population deformity proportions in bioassessments for sediment metal contamination.

A single-step method for identifying individual resources

Many methods of analysis have been used to test whether animals use resources in proportion to availability. Many of the most commonly used methods have been criticized because they ignore the induced correlation among cells in the multinomial classification table due to the unit-sum constraint. Some methods are also flawed because they assume that relocation data can be pooled across animals even though the animals may select habitats differently. We propose a method, based on the maximum of the joint chi-square, to compare habitat usage to availability for individual animals. This method is combined with a follow-up multiple comparison technique that takes account of the unit-sum constraint. We compare our proposed method to a method based on the sum of individual chi-squares and a method based on compositional analysis by analyzing data on gray partridge and ringnecked pheasant resource use. Simulation is also used to compare methods by evaluating their Type I error rates.

Optimizing experimental design using the house mouse (Mus musculus L.) as a model for determining grain feeding preferences.

There is little research evaluating flavor preferences among wheat varieties. We previously demonstrated that mice exert very strong preferences when given binary mixtures of wheat varieties. We plan to utilize mice to identify wheat genes associated with flavor, and then relate this back to human preferences. Here we explore the effects of experimental design including the number of days (from 1 to 4) and number of mice (from 2 to 15) in order to identify designs that provide significant statistical inferences while minimizing requirements for labor and animals. When mice expressed a significant preference between 2 wheat varieties, increasing the number of days (for a given number of mice) increased the significance level (decreased P-values) for their preference, as expected, but with diminishing benefit as more days were added. However, increasing the number of mice (for a given number of days) provided a more dramatic log-linear decrease in P-values and thus increased statistical power. In conclusion, when evaluating mouse feeding preferences in binary mixtures of grain, an efficient experimental design would emphasize fewer days rather than fewer animals thus shortening the experiment duration and reducing the overall requirement for labor and animals.