Search

Ecologists often estimate population trends of animals in time series of counts using linear regression to estimate parameters in a linear transformation of multiplicative growth models, where logarithms of rates of change in counts in time intervals are used as response variables. We present quantile regression estimates for the median (0.50) and interquartile (0.25, 0.75) relationships as an alternative to mean regression estimates for common density‐dependent and density‐independent population growth models. We demonstrate that the quantile regression estimates are more robust to outliers and require fewer distributional assumptions than conventional mean regression estimates and can provide information on heterogeneous rates of change ignored by mean regression. We provide quantile regression trend estimates for 2 populations of greater sage‐grouse (Centrocercus urophasianus) in Wyoming, USA, and for the Crawford population of Gunnison sage‐grouse (Centrocercus minimus) in southwestern Colorado, USA. Our selected Gompertz models of density dependence for both populations of greater sage‐grouse had smaller negative estimates of density‐dependence terms and less variation in corresponding predicted growth rates (λ) for quantile than mean regression models. In contrast, our selected Gompertz models of density dependence with piecewise linear effects of years for the Crawford population of Gunnison sage‐grouse had predicted changes in λ across years from quantile regressions that varied more than those from mean regression because of heterogeneity in estimated λs that were both less and greater than mean estimates. Our results add to literature establishing that quantile regression provides better behaved estimates than mean regression when there are outlying growth rates, including those induced by adjustments for zeros in the time series of counts. The 0.25 and 0.75 quantiles bracketing the median provide robust estimates of population changes (λ) for the central 50% of time series data and provide a 50% prediction interval for a single new prediction without making parametric distributional assumptions or assuming homogeneous λs. Compared to mean estimates, our quantile regression trend estimates for greater sage‐grouse indicated less variation in density‐dependent λs by minimizing sensitivity to outlying values, and for Gunnison sage‐grouse indicated greater variation in density‐dependent λs associated with heterogeneity among quantiles. [ABSTRACT FROM AUTHOR]

Latitudinal patterns of biodiversity have long been a central topic in ecology and evolutionary biology. However, while most previous studies have focused on native species, little effort has been devoted to latitudinal patterns of plant invasions (with a few exceptions based on data from sparse locations). Using the most up‐to‐date worldwide native and alien plant distribution data from 801 regions (including islands), we compared invasion levels (i.e. alien richness/total richness) in the Northern and Southern Hemispheres and across continental regions and islands around the globe. Results from quantile regressions using B‐splines to model nonlinearity showed (1) declining richness with increasing latitude, although the highest alien richness occurs at around 40 degrees in both hemispheres, (2) decreasing invasion levels towards higher latitudes on islands but a unimodal pattern in invasion level in continental regions in each hemisphere, (3) significantly higher invasion levels on islands than in continental regions and (4) a greater variability in invasion levels on islands at low latitudes than on high‐latitude islands. In continental regions, only the mid‐latitudes had high variability with both low and high invasion levels. Our findings identified latitudes with invasion hotspots where management is urgently needed, and latitudes with many areas of low invasions but high conservation potential where prevention of future invasions should be the priority. [ABSTRACT FROM AUTHOR]

Three flawed practices associated with model averaging coefficients for predictor variables in regression models commonly occur when making multimodel inferences in analyses of ecological data. Model-averaged regression coefficients based on Akaike information criterion (AIC) weights have been recommended for addressing model uncertainty but they are not valid, interpretable estimates of partial effects for individual predictors when there is multicollinearity among the predictor variables. Multicollinearity implies that the scaling of units in the denominators of the regression coefficients may change across models such that neither the parameters nor their estimates have common scales, therefore averaging them makes no sense. The associated sums of AIC model weights recommended to assess relative importance of individual predictors are really a measure of relative importance of models, with little information about contributions by individual predictors compared to other measures of relative importance based on effects size or variance reduction. Sometimes the model-averaged regression coefficients for predictor variables are incorrectly used to make model-averaged predictions of the response variable when the models are not linear in the parameters. I demonstrate the issues with the first two practices using the college grade point average example extensively analyzed by Burnham and Anderson. I show how partial standard deviations of the predictor variables can be used to detect changing scales of their estimates with multicollinearity. Standardizing estimates based on partial standard deviations for their variables can be used to make the scaling of the estimates commensurate across models, a necessary but not sufficient condition for model averaging of the estimates to be sensible. A unimodal distribution of estimates and valid interpretation of individual parameters are additional requisite conditions. The standardized estimates or equivalently the t statistics on unstandardized estimates also can be used to provide more informative measures of relative importance than sums of AIC weights. Finally, I illustrate how seriously compromised statistical interpretations and predictions can be for all three of these flawed practices by critiquing their use in a recent species distribution modeling technique developed for predicting Greater Sage-Grouse (Centrocercus urophasianus) distribution in Colorado, USA. These model averaging issues are common in other ecological literature and ought to be discontinued if we are to make effective scientific contributions to ecological knowledge and conservation of natural resources. [ABSTRACT FROM AUTHOR]

Abstract: Managers concerned with shrinking habitats and limited resources for wildlife seek effective tools for limiting population growth in some species. Fertility control is one such tool, yet little is known about its impacts on the behavioral ecology of wild, free-roaming animals. We investigated influences of the immunocontraceptive porcine zona pellucida (PZP) on individual and social behavior in bands of feral horses (Equus caballus) in three discrete populations and used 14 hierarchical mixed effect models to gain insight into the influences of PZP treatment on feral horse behavior. A model of body condition was the strongest predictor of feeding, resting, maintenance, and social behaviors, with treated females allocating their time similarly to control females. Time spent feeding declined 11.4% from low condition to high condition females (F 1,154 =26.427, P <0.001) and was partially reciprocated by a 6.0% increase in resting (F 1,154 =7.629, P =0.006), 0.9% increase in maintenance (F 1,154 =7.028, P =0.009), and 1.8% increase in social behavior (F 1,154 =15.064, P <0.001). There was no difference detected in body condition of treated versus control females (F 1,154 =0.033, P =0.856), but females with a dependent foal had lower body condition than those without a foal (F 1,154 =4.512, P =0.038). Herding behavior was best explained by a model of treatment and the interaction of band fidelity and foal presence (AIC c weight=0.660) which estimated no difference in rate of herding behavior directed toward control versus treated females (F 1,102 =0.196, P =0.659), but resident females without a dependent foal were herded 50.9% more than resident females with a foal (F 3,102 =8.269, P <0.001). Treated females received 54.5% more reproductive behaviors from stallions than control mares (F 1,105 =5.155, P =0.025), with the model containing only treatment being the most-supported (AIC c weight=0.530). Treated and control females received harem-tending behaviors from stallions equally (F 1,105 =0.001, P =0.969) and agonistic behaviors from stallions equally (F 1,105 <0.001, P =0.986). Direct effects of PZP treatment on the behavior of feral horses appear to be limited primarily to reproductive behaviors and most other differences detected were attributed to the effects of body condition, band fidelity, or foal presence. PZP is a promising alternative to traditional hormone-based contraceptives and appears to contribute few short-term behavioral modifications in feral horses. [Copyright &y& Elsevier]

Deuterium isotope analyses have revolutionized the study of migratory connectivity because global gradients of deuterium in precipitation (δDP) are expressed on a continental scale. Several authors have constructed continental scale base maps of δDP to provide a spatial reference for studying the movement patterns of migratory species and, although they are very useful, these maps present a static, 40-year average view of the landscape that ignores much underlying inter-annual variation. To more fully understand the consequences of this underlying variation, we analyzed the GNIP deuterium data, the source for all current δDP maps, to estimate the minimum separation in δDP (and latitude) necessary to conclude with a given level of confidence that distinct δDP values represent different geographic sites. Extending analyses of δDP successfully to deuterium in tissues of living organisms, e.g., feathers in migratory birds (δDF), is dependent on the existence of geographic separation of δDP, where every geographic location has a distribution of values associated with temporal variability in δDP. Analyses were conducted for three distinct geographic regions: North America, eastern North America (east of longitude 100°W), and Argentina. At the 80% confidence level, the minimum separation values were 12, 7, and 14° of latitude (equivalent to 53, 31, and 32‰) for North America, eastern North America, and Argentina, respectively. Hence, in eastern North America, for example, one may not be able to accurately assign individual samples to sites separated by less than about 7° of latitude as the distributions of δDP were not distinct at latitudes <7° apart. Moreover, two samples that differ by less than 31‰ cannot be confidently said to originate from different latitudes. These estimates of minimum separation for δDP do not include other known sources of variation in feather deuterium (δDF) and hence are a first order approximation that may be useful, in the absence of more specific information for the system of interest, for planning and interpreting the results of new stable isotope studies. [ABSTRACT FROM AUTHOR]

1. A quantile regression model for counts of breeding Cape Sable seaside sparrows Ammodramus maritimus mirabilis (L.) as a function of water depth and previous year abundance was developed based on extensive surveys, 1992–2005, in the Florida Everglades. The quantile count model extends linear quantile regression methods to discrete response variables, providing a flexible alternative to discrete parametric distributional models, e.g. Poisson, negative binomial and their zero-inflated counterparts. 2. Estimates from our multiplicative model demonstrated that negative effects of increasing water depth in breeding habitat on sparrow numbers were dependent on recent occupation history. Upper 10th percentiles of counts (one to three sparrows) decreased with increasing water depth from 0 to 30 cm when sites were not occupied in previous years. However, upper 40th percentiles of counts (one to six sparrows) decreased with increasing water depth for sites occupied in previous years. 3. Greatest decreases (–50% to –83%) in upper quantiles of sparrow counts occurred as water depths increased from 0 to 15 cm when previous year counts were  1, but a small proportion of sites (5–10%) held at least one sparrow even as water depths increased to 20 or 30 cm. 4. A zero-inflated Poisson regression model provided estimates of conditional means that also decreased with increasing water depth but rates of change were lower and decreased with increasing previous year counts compared to the quantile count model. Quantiles computed for the zero-inflated Poisson model enhanced interpretation of this model but had greater lack-of-fit for water depths > 0 cm and previous year counts  1, conditions where the negative effect of water depths were readily apparent and fitted better with the quantile count model. [ABSTRACT FROM AUTHOR]

Performance of quantile rank score tests used for hypothesis testing and constructing confidence intervals for linear quantile regression estimates (0?=?t?=?1) were evaluated by simulation for models with p ?=?2 and 6 predictors, moderate collinearity among predictors, homogeneous and hetero-geneous errors, small to moderate samples ( n ?=?20–300), and central to upper quantiles (0.50–0.99). Test statistics evaluated were the conventional quantile rank score T statistic distributed as ? 2 random variable with q degrees of freedom (where q parameters are constrained by H 0 :) and an F statistic with its sampling distribution approximated by permutation. The permutation F -test maintained better Type I errors than the T -test for homogeneous error models with smaller n and more extreme quantiles t. An F distributional approximation of the F statistic provided some improvements in Type I errors over the T -test for models with?>?2 parameters, smaller n , and more extreme quantiles but not as much improvement as the permutation approximation. Both rank score tests required weighting to maintain correct Type I errors when heterogeneity under the alternative model increased to 5 standard deviations across the domain of X . A double permutation procedure was developed to provide valid Type I errors for the permutation F -test when null models were forced through the origin. Power was similar for conditions where both T - and F -tests maintained correct Type I errors but the F -test provided some power at smaller n and extreme quantiles when the T -test had no power because of excessively conservative Type I errors. When the double permutation scheme was required for the permutation F -test to maintain valid Type I errors, power was less than for the T -test with decreasing sample size and increasing quantiles. Confidence intervals on parameters and tolerance intervals for future predictions were constructed based on test inversion for an example application relating trout densities to stream channel width:depth. [ABSTRACT FROM AUTHOR]

We simulated the effects of missing information on statistical distributions of animal response that covaried with measured predictors of habitat to evaluate the utility and performance of quantile regression for providing more useful intervals of uncertainty in habitat relationships. These procedures were evaluated for conditions in which heterogeneity and hidden bias were induced by confounding with missing variables associated with other important processes, a problem common in statistical modeling of ecological phenomena. Simulations for a large (N = 10000) finite population representing grid lo- cations on a landscape demonstrated various forms of hidden bias that might occur when the effect of a measured habitat variable on some animal was confounded with the effect of another unmeasured variable. Quantile (0 ≤ τ ≤ 1) regression parameters for linear models that excluded the important, unmeasured variable revealed bias relative to parameters from the generating model. Depending on whether interactions of the measured and unmeasured variables were negative (interference interactions) or positive (facilitation interactions) in simulations without spatial structuring, either upper (τ > 0.5) or lower (τ < 0.5) quantile regression parameters were less biased than mean rate parameters. Heterogeneous, nonlinear response patterns occurred with correlations between the measured and unmeasured variables. When the unmeasured variable was spatially structured, variation in parameters across quantiles associated with heterogeneous effects of the habitat variable was reduced by modeling the spatial trend surface as a cubic polynomial of location co- ordinates, but substantial hidden bias remained. Sampling (n 20-300) simulations demonstrated that regression quantile estimates and confidence intervals constructed by inverting weighted rank score tests provided valid coverage of these parameters. Local forms of quantile weighting were required for obtaining correct Type I error rates and confidence interval coverage. Quantile regression was used to estimate effects of physical habitat resources on a bivalve (Macomona liliana) in the spatially structured landscape on a sandflat in a New Zealand harbor. Confidence intervals around predicted 0.10 and 0.90 quantiles were used to estimate sampling intervals containing 80% of the variation in densities in relation to bed elevation. Spatially structured variation in bivalve counts estimated by a cubic polynomial trend surface remained after accounting for the nonlinear effects of bed elevation, indicating the existence of important spatially structured processes that were not adequately represented by the measured habitat variables. [ABSTRACT FROM AUTHOR]

Focuses on a study which examined the effects of emergency haying on vegetative characteristics within the selected Conservation Reserve Program grasslands in the northern Great Plains of North and South Dakota. Methods; Results of the study; Discussion on the findings.

Rates of change in final summer densities of two desert annuals, Eriogonum abertianum and Haplopappus gracilis , as constrained by their initial winter germination densities were estimated with regression quantiles and compared with mechanistic fits based on a self-thinning rule proposed by Guo et al. (1998); Oikos 83: 237–245). The allometric relation used was equivalent to S =N f (N i )-1=c f (N i )-1, where S is the ratio of final to initial densities (survivorship), c f is a constant that is a final density specific to the species and environment, N i is the initial plant density, and N f is final plant density. We used regression quantiles to estimate c f assuming the exponent of -1 was fixed (model 1, N f (N i )-1=c f (N i )-1) and also obtained estimates by treating the exponent as a parameter to estimate (model 2, N f (N i )-1=c f (N i )λ). Regression quantiles allow rates of change to be estimated through any part of a data distribution conditional on some linear function of covariates. We focused on estimates for upper (90–99th) quantiles near the boundary of the summer density distributions where we expected effects of self-thinning to operate as the primary constraint on plant performance. Allometric functions estimated with regression quantiles were similar to functions fit by Guo et al. (1998) when the exponent was constrained to -1. However, the data were more consistent with estimates for model (2), where exponents were closer to -0.4 than -1, although model fit was not as good at higher initial plant densities as when the exponent was fixed at -1. An exponential form (model 3, N f (N i )-1=c f (N i )λ eγNi) that is a generalization of the discrete logistic growth function, where estimates of λ were -0.23 to -0.28 and estimates of γ were -0.003 to -0.006, provided better fit from low to high initial germination densities. Model 3 predictions were consistent with an interpretation that final summer densities were constrained by initial germination densities when these were low (<40 per 0.25 m2 for Eriogonum and <100 per 0.25 m2 for Haplopappus ) and were constrained by the self-thinning process at higher germination densities. Our exponential model (3) estimated with regression quantiles had similar form to the mechanistic relation of Guo et al. (1998) when plotted as a survivorship function, but avoided the unrealistic assumption that all populations attained a similar final density, and was based on a statistical model that has formal rules for estimation and inference. [ABSTRACT FROM AUTHOR]

Compares two measures of tree cover used in forest wildlife habitat models, canopy cover and tree basal area, using as example, subalpine forests in the central Rocky Mountains. Why correlations between basal area and canopy cover for a given tree species exists; Statistical information related to this issue.

Presents a spatial analysis of sandhill crane nesting habitat in Michigan. Nest sites and random points; Scale and heterogeneity effects on spatial analysis; Crane habitat selection; Use of a geographic information system (GIS) in the analysis; Management implications of study results.

Reports on the winter use of Douglas-fir forests by blue grouse in Colorado. Spatial concentration; Types of stands used; Types of trees in stands; Location of stands; Sizes of trees.

Many amphibians breed in water but live most of their lives in terrestrial habitats. Little is known, however, about the spatial distribution of these habitats or of the distances and directions amphibians move to reach breeding sites. The amphibian community at a small, temporary pond in northcentral Florida was monitored for 5 years. Based on captures and recaptures of more than 2500 striped newts (Notophthalmus perstriatus) and 5700 eastern narrow-mouthed toads (Gastrophryne carolinensis), we tabulated the angles of orientation that these amphibians entered and exited the pond basin. Our results showed that movements of these species between the pond and terrestrial habitats were nonrandom in orientation, but that narrow corridors did not appear to be used. Differences between the species likely reflect differences in habitat preferences, whereas intraspecific differences among years and between the sexes likely reflect variation among individuals. For terrestrial buffer zones to be effective at conserving pond-breeding amphibian communities, they need both a distance and a directional component. The determination of a directional component may be obscured if studies are carried out over a short time span. Conservation efforts for wetland-breeding amphibians that concentrate solely on the wetland likely will fail without consideration of the adjacent terrestrial habitat. [ABSTRACT FROM AUTHOR]

We investigated polychlorinated biphenyl (PCB) bioaccumulation relative to octanol-water partition coefficient (KOW) and organism trophic position (TP) at the Lake Hartwell Superfund site (South Carolina). We measured PCBs (127 congeners) and stable isotopes (δ15N) in sediment, organic matter, phytoplankton, zooplankton, macroinvertebrates, and fish. TP, as calculated from d15N, was significantly, positively related to PCB concentrations, and food web trophic magnification factors (TMFs) ranged from 1.5-6.6 among congeners. TMFs of individual congeners increased strongly with log KOW, as did the predictive power (r²) of individual TP-PCB regression models used to calculate TMFs. We developed log KOW-TMF models for eight food webs with vastly different environments (freshwater, marine, arctic, temperate) and species composition (cold- vs warmblooded consumers). The effect of KOW on congener TMFs varied strongly across food webs (model slopes 0.0-15.0) because the range of TMFs among studies was also highly variable. We standardized TMFs within studies to mean = 0, standard deviation (SD) = 1 to normalize for scale differences and found a remarkably consistent KOW effect on TMFs (no difference in model slopes among food webs). Our findings underscore the importance of hydrophobicity (as characterized by KOW) in regulating bioaccumulation of recalcitrant compounds in aquatic systems, and demonstrate that relationships between chemical KOW and bioaccumulation from field studies are more generalized than previously recognized. [ABSTRACT FROM AUTHOR]

Abstract: Decomposition of senesced culm material of two bulrush species was studied in a surface-flow ammonia-dominated treatment wetland in southern California. Decomposition of the submerged culm material during summer months was relatively rapid (k =0.037 day−1), but slowed under extended submergence (up to 245 days) and during fall and spring sampling periods (k =0.009–0.014 day−1). Stepwise regression of seasonal data indicated that final water temperature and abundance of the culm-mining midge, Glyptotendipes, were significantly associated with culm decomposition. Glyptotendipes abundance, in turn, was correlated with water quality parameters such as conductivity and dissolved oxygen and ammonia concentrations. No differences were detected in decomposition rates between the bulrush species, Schoenoplectus californicus and Schoenoplectus acutus. [Copyright &y& Elsevier]

Ecologically relevant references are useful for evaluating ecosystem recovery, but references that are temporally static may be less useful when environmental conditions and disturbances are spatially and temporally heterogeneous. This challenge is particularly acute for ecosystems dominated by sagebrush (Artemisia spp.), where communities may require decades to recover from disturbance. We demonstrated application of a dynamic reference approach to studying sagebrush recovery using three decades of sagebrush cover estimates from remote sensing (1985–2018). We modelled recovery on former oil and gas well pads (n = 1200) across southwestern Wyoming, USA, relative to paired references identified by the Disturbance Automated Reference Toolset. We also used quantile regression to account for unmodelled heterogeneity in recovery, and projected recovery from similar disturbance across the landscape. Responses to weather and site‐level factors often differed among quantiles, and sagebrush recovery on former well pads increased more when paired reference sites had greater sagebrush cover. Little (<5%) of the landscape was projected to recover within 100 years for low to mid quantiles, and recovery often occurred at higher elevations with cool and moist annual conditions. Conversely, 48%–78% of the landscape recovered quickly (within 25 years) for high quantiles of sagebrush cover. Our study demonstrates advantages of using dynamic reference sites when studying vegetation recovery, as well as how additional inferences obtained from quantile regression can inform management. [ABSTRACT FROM AUTHOR]

Atmospheric nitrogen (N) deposition has been shown to decrease plant species richness along regional deposition gradients in Europe and in experimental manipulations. However, the general response of species richness to N deposition across different vegetation types, soil conditions, and climates remains largely unknown even though responses may be contingent on these environmental factors. We assessed the effect of N deposition on herbaceous richness for 15,136 forest, woodland, shrubland, and grassland sites across the continental United States, to address how edaphic and climatic conditions altered vulnerability to this stressor. In our dataset, with N deposition ranging from 1 to 19 kg N⋅ha-1⋅y-1, we found a unimodal relationship; richness increased at low deposition levels and decreased above 8.7 and 13.4 kg N⋅ha-1⋅y-1 in open and closed-canopy vegetation, respectively. N deposition exceeded critical loads for loss of plant species richness in 24% of 15,136 sites examined nationwide. There were negative relationships between species richness and N deposition in 36% of 44 community gradients. Vulnerability to N deposition was consistently higher in more acidic soils whereas the moderating roles of temperature and precipitation varied across scales. We demonstrate here that negative relationships between N deposition and species richness are common, albeit not universal, and that fine-scale processes can moderate vegetation responses to N deposition. Our results highlight the importance of contingent factors when estimating ecosystem vulnerability to N deposition and suggest that N deposition is affecting species richness in forested and nonforested systems across much of the continental United States. [ABSTRACT FROM AUTHOR]

Savannas are globally important ecosystems of great significance to human economies. In these biomes, which are characterized by the co-dominance of trees and grasses, woody cover is a chief determinant of ecosystem properties. The availability of resources (water, nutrients) and disturbance regimes (fire, herbivory) are thought to be important in regulating woody cover, but perceptions differ on which of these are the primary drivers of savanna structure. Here we show, using data from 854 sites across Africa, that maximum woody cover in savannas receiving a mean annual precipitation (MAP) of less than ∼650 mm is constrained by, and increases linearly with, MAP. These arid and semi-arid savannas may be considered ‘stable’ systems in which water constrains woody cover and permits grasses to coexist, while fire, herbivory and soil properties interact to reduce woody cover below the MAP-controlled upper bound. Above a MAP of ∼650 mm, savannas are ‘unstable’ systems in which MAP is sufficient for woody canopy closure, and disturbances (fire, herbivory) are required for the coexistence of trees and grass. These results provide insights into the nature of African savannas and suggest that future changes in precipitation may considerably affect their distribution and dynamics. [ABSTRACT FROM AUTHOR]