Effects of concentrated long-chain omega-3 polyunsaturated fatty acid supplementation before radical prostatectomy on prostate cancer proliferation, inflammation, and quality of life: study protocol for a phase IIb, randomized, double-blind, placebo-controlled trial.

BACKGROUND: Prostate cancer is the most commonly diagnosed cancer in north-American men. Few dietary or lifestyle interventions have been tested to prevent prostate cancer progression. Omega-3 fatty acid supplementation represents a promising intervention for prostate cancer patients. The aim of the study is to evaluate the effects of long-chain omega-3 polyunsaturated fatty acids (LCn3), more precisely eicosapentaenoic acid monoacylglyceride (MAG-EPA) supplementation, on prostate cancer proliferation, inflammation mediators and quality of life among men who will undergo radical prostatectomy. METHODS/DESIGN: We propose a phase IIb, randomized, double-blind placebo-controlled trial of MAG-EPA supplementation for 130 men who will undergo radical prostatectomy as treatment for a prostate cancer of Gleason score ≥ 7 in an academic cancer center in Quebec City. Participants will be randomized to 6 capsules of 625 mg of fish oil (MAG-EPA) per capsule containing 500 mg of EPA daily or to identically looking capsules of high oleic acid sunflower oil (HOSO) as placebo. The intervention begins 4 to 10 weeks prior to radical prostatectomy (baseline) and continues for one year after surgery. The primary endpoint is the proliferative index (Ki-67) measured in prostate cancer cells at radical prostatectomy. A secondary endpoint includes prostate tissue levels of inflammatory mediators (cytokines and proteins) at time of radical prostatectomy. Changes in blood levels of inflammatory mediators, relative to baseline levels, at time of radical prostatectomy and 12 months after radical prostatectomy will also be evaluated. Secondary endpoints also include important aspects of psychosocial functioning and quality of life such as depression, anxiety, sleep disturbances, fatigue, cognitive complaints and prostate cancer-specific quality of life domains. The changes in these outcomes, relative to baseline levels, will be evaluated at 3, 6, 9 and 12 months after radical prostatectomy. DISCUSSION: The results from this trial will provide crucial information to clarify the role of omega-3 supplementation on prostate cancer proliferation, inflammation and quality of life. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02333435. Registered on December 17, 2014. Last updated September 6, 2016.

Association measures for bivariate failure times in the presence of a cure fraction.

This paper proposes a new joint model for pairs of failure times in the presence of a cure fraction. The proposed model relaxes some of the assumptions required by the existing approaches. This allows us to add some flexibility to the dependence structure and to widen the range of association measures that can be defined. A numerically stable iterative algorithm based on estimating equations is proposed to estimate the parameters. The estimators are shown to be consistent and asymptotically normal. Simulations show that they have good finite-sample properties. The added flexibility of the proposal is illustrated with an application to data from a diabetes retinopathy study.

On the validity of within-nuclear-family genetic association analysis in samples of extended families.

Splitting extended families into their component nuclear families to apply a genetic association method designed for nuclear families is a widespread practice in familial genetic studies. Dependence among genotypes and phenotypes of nuclear families from the same extended family arises because of genetic linkage of the tested marker with a risk variant or because of familial specificity of genetic effects due to gene-environment interaction. This raises concerns about the validity of inference conducted under the assumption of independence of the nuclear families. We indeed prove theoretically that, in a conditional logistic regression analysis applicable to disease cases and their genotyped parents, the naive model-based estimator of the variance of the coefficient estimates underestimates the true variance. However, simulations with realistic effect sizes of risk variants and variation of this effect from family to family reveal that the underestimation is negligible. The simulations also show the greater efficiency of the model-based variance estimator compared to a robust empirical estimator. Our recommendation is therefore, to use the model-based estimator of variance for inference on effects of genetic variants.

Two cross-sectional studies in south India assessing the effect of an HIV prevention programme for female sex workers on reducing syphilis among their clients.

OBJECTIVE: To assess the impact of the Avahan HIV prevention programme for female sex workers (FSWs) in south India on reducing syphilis prevalence among their clients, by comparing rates of syphilis over time as reported in two large-scale surveys of FSWs' clients. METHODS: A random-effect multilevel logistic regression analysis was performed using syphilis as the dependent variable, with individual independent variables (from the two survey rounds) at level 1 and the district-level programme (from the Avahan computerised monitoring and information system) and contextual variables (from Indian government datasets) at level 2. Programme variables included their 2006 value and their difference in value between 2008 and 2006, as well as the interaction between the latter and the study round. The analysis also controlled for baseline syphilis prevalence and its interaction with the study round. RESULTS: Syphilis decreased significantly among FSWs' clients, from 4.8% (round 1) to 2.6% (round 2), p<0.001. The OR of the interaction term between the difference in programme coverage of FSWs and the round was 0.98 (p=0.023), suggesting that increased coverage was associated with a reduced incidence of syphilis. CONCLUSIONS: This study suggests that the Avahan intervention programme among FSWs reduced syphilis rates among their clients.

Increased HIV prevention program coverage and decline in HIV prevalence among female sex workers in south India.

BACKGROUND: As one way of assessing the impact of Avahan, the India AIDS Initiative of the Bill & Melinda Gates Foundation, we examined the association between HIV prevention program indicators and changes in HIV prevalence among female sex workers (FSWs) between 2005 and 2009. METHODS: We conducted a secondary data analysis from 2 large cross-sectional surveys (2005-2006 and 2008-2009) across 24 districts in south India (n = 11,000 per round). A random-effect multilevel logistic regression analysis was performed using HIV as the outcome, with individual independent variables (from both surveys) at level 1 and district-level FSW-specific program indicators and contextual variables at level 2. Program indicators included their 2006 value, the difference in their values between 2008 and 2006, and the interaction between this difference and study round. RESULTS: HIV prevalence among FSWs decreased from 17.0% to 14.2% (P < 0.001). This decline varied significantly (P = 0.006) across levels of difference in program coverage (% of FSWs contacted by the program in a given year). Odds ratios comparing HIV prevalence between rounds changed with the level of increase in coverage and were statistically significant with coverage increase ≥ quartile (Q) 1: odds ratio, 0.85 at Q1; 0.78 at Q2; 0.66 at Q3; and 0.51 at Q4. CONCLUSIONS: These findings suggest that increased program coverage was associated with declining HIV prevalence among FSWs covered by the Avahan program. The triangulation of our results with those from other approaches used in evaluating Avahan suggests a major impact of this intervention on the HIV epidemic in southern India.

Assessing outcomes of large-scale public health interventions in the absence of baseline data using a mixture of Cox and binomial regressions.

BACKGROUND: Large-scale public health interventions with rapid scale-up are increasingly being implemented worldwide. Such implementation allows for a large target population to be reached in a short period of time. But when the time comes to investigate the effectiveness of these interventions, the rapid scale-up creates several methodological challenges, such as the lack of baseline data and the absence of control groups. One example of such an intervention is Avahan, the India HIV/AIDS initiative of the Bill & Melinda Gates Foundation. One question of interest is the effect of Avahan on condom use by female sex workers with their clients. By retrospectively reconstructing condom use and sex work history from survey data, it is possible to estimate how condom use rates evolve over time. However formal inference about how this rate changes at a given point in calendar time remains challenging. METHODS: We propose a new statistical procedure based on a mixture of binomial regression and Cox regression. We compare this new method to an existing approach based on generalized estimating equations through simulations and application to Indian data. RESULTS: Both methods are unbiased, but the proposed method is more powerful than the existing method, especially when initial condom use is high. When applied to the Indian data, the new method mostly agrees with the existing method, but seems to have corrected some implausible results of the latter in a few districts. We also show how the new method can be used to analyze the data of all districts combined. CONCLUSIONS: The use of both methods can be recommended for exploratory data analysis. However for formal statistical inference, the new method has better power.

A phase IIb randomized placebo-controlled trial testing the effect of MAG-EPA long-chain omega-3 fatty acid dietary supplement on prostate cancer proliferation.

BACKGROUND: High prostate eicosapentaenoic fatty acid (EPA) levels were associated with a significant reduction of upgrading to grade group (GG) ≥ 2 prostate cancer in men under active surveillance. We aimed to evaluate the effect of MAG-EPA long-chain omega-3 fatty acid dietary supplement on prostate cancer proliferation. METHODS: A phase II double-blind randomized placebo-controlled trial was conducted in 130 men diagnosed with GG ≥ 2 prostate cancer and undergoing radical prostatectomy between 2015-2017 (Clinicaltrials.gov: NCT02333435). Participants were randomized to receive 3 g daily of either MAG-EPA (n = 65) or placebo (n = 65) for 7 weeks (range 4-10) prior to radical prostatectomy. The primary outcome was the cancer proliferation index quantified by automated image analysis of tumor nuclear Ki-67 expression using standardized prostatectomy tissue microarrays. Additional planned outcomes at surgery are reported including plasma levels of 27 inflammatory cytokines and fatty acid profiles in circulating red blood cells membranes and prostate tissue. RESULTS: Cancer proliferation index measured by Ki-67 expression was not statistically different between the intervention (3.10%) and placebo (2.85%) groups (p = 0.64). In the per protocol analyses, the adjusted estimated effect of MAG-EPA was greater but remained non-significant. Secondary outcome was the changes in plasma levels of 27 cytokines, of which only IL-7 was higher in MAG-EPA group compared to placebo (p = 0.026). Men randomized to MAG-EPA prior to surgery had four-fold higher EPA levels in prostate tissue compared to those on placebo. CONCLUSIONS: This MAG-EPA intervention did not affect the primary outcome of prostate cancer proliferation according to nuclear Ki-67 expression. More studies are needed to decipher the effects of long-chain omega-3 fatty acid dietary supplementation in men with prostate cancer.

It is thought that our diet can impact our risk of cancer and affect outcomes in patients with cancer. Omega-3 fatty acids, mostly found in fatty fish, might be beneficial by protecting against prostate cancer and its adverse outcomes. We conducted a clinical trial to test the effects of an omega-3 dietary supplement (MAG-EPA) in men with prostate cancer. We randomly allocated 130 men to receive either MAG-EPA or a placebo for 7 weeks before their prostate cancer surgery. We measured a marker of how much tumor cells were proliferating (or growing in number) at the point of surgery, which might indicate how aggressive their disease was. However, the supplement did not affect tumor cell proliferation. The supplement was therefore not beneficial in this group of patients and further studies  are needed to test and confirm the effects of MAG-EPA on prostate cancer cells.

A two-stage, single-arm, phase II study of EGCG-enriched green tea drink as a maintenance therapy in women with advanced stage ovarian cancer.

OBJECTIVES: A two-stage, single-arm, phase II study was conducted to assess the effectiveness and safety of an epigallocatechin gallate (EGCG)-enriched tea drink, the double-brewed green tea (DBGT), as a maintenance treatment in women with advanced stage serous or endometrioid ovarian cancer (clinicaltrials.gov, NCT00721890). METHODS: Eligible women had FIGO stage III-IV serous or endometrioid ovarian cancer. They had to undergo complete response after debulking surgery followed by 6 to 8 cycles of platinum/taxane chemotherapy at the Centre Hospitalier Universitaire de Québec. They all had to drink the DBGT, 500 mL daily until recurrence or during a follow-up of 18 months. The primary endpoint was the absence of recurrence at 18 months. Statistical analyses were done according to the principle of intention to treat. Using a two-stage design, the first stage consisted of 16 enrolled patients. At the end of the follow-up, if 7 or fewer patients were free of recurrence, the trial stopped. Otherwise, accrual would continue to a total of 46 patients. RESULTS: During the first stage of the study, only 5 of the 16 women remained free of recurrence 18 months after complete response. Accordingly, the clinical trial was terminated. Women's adherence to DBGT was high (median daily intake during intervention, 98.1%, interquartile range: 89.7-100%), but 6 women discontinued the intervention before the end of their follow-up. No severe toxicity was reported. CONCLUSIONS: DBGT supplementation does not appear to be a promising maintenance intervention in women with advanced stage ovarian cancer after standard treatment.

Effects of Concentrated Long-Chain Omega-3 Polyunsaturated Fatty Acid Supplementation on Quality of Life after Radical Prostatectomy: A Phase II Randomized Placebo-Controlled Trial (RCT-EPA).

Prostate cancer (PCa) and associated treatments incur symptoms that may impact patients' quality of life. Studies have shown beneficial relationships between diet, especially omega-3 fatty acids, and these symptoms. Unfortunately, only few data describing the relationship between long-chain omega-3 fatty acids (LCn3) and PCa-related symptoms in patients are available. The purpose of this study was to evaluate the effects of LCn3 supplementation on PCa-specific quality of life in 130 men treated by radical prostatectomy. Men were randomized to receive a daily dose of either 3.75 g of fish oil or a placebo starting 7 weeks before surgery and for up to one-year post-surgery. Quality of life was assessed using the validated EPIC-26 and IPSS questionnaires at randomization, at surgery, and every 3 months following surgery. Between-group differences were assessed using linear mixed models. Intention-to-treat analyses showed no significant difference between the two groups. However, at 12-month follow-up, per-protocol analyses showed a significantly greater increase in the urinary irritation function score (better urinary function) (MD = 5.5, p = 0.03) for the LCn3 group compared to placebo. These results suggest that LCn3 supplementation may improve the urinary irritation function in men with PCa treated by radical prostatectomy and support to conduct of larger-scale studies.

Multi-mode movement decisions across widely ranging behavioral processes.

Movement of organisms plays a fundamental role in the evolution and diversity of life. Animals typically move at an irregular pace over time and space, alternating among movement states. Understanding movement decisions and developing mechanistic models of animal distribution dynamics can thus be contingent to adequate discrimination of behavioral phases. Existing methods to disentangle movement states typically require a follow-up analysis to identify state-dependent drivers of animal movement, which overlooks statistical uncertainty that comes with the state delineation process. Here, we developed population-level, multi-state step selection functions (HMM-SSF) that can identify simultaneously the different behavioral bouts and the specific underlying behavior-habitat relationship. Using simulated data and relocation data from mule deer (Odocoileus hemionus), plains bison (Bison bison bison) and plains zebra (Equus quagga), we illustrated the HMM-SSF robustness, versatility, and predictive ability for animals involved in distinct behavioral processes: foraging, migrating and avoiding a nearby predator. Individuals displayed different habitat selection pattern during the encamped and the travelling phase. Some landscape attributes switched from being selected to avoided, depending on the movement phase. We further showed that HMM-SSF can detect multi-modes of movement triggered by predators, with prey switching to the travelling phase when predators are in close vicinity. HMM-SSFs thus can be used to gain a mechanistic understanding of how animals use their environment in relation to the complex interplay between their needs to move, their knowledge of the environment and navigation capacity, their motion capacity and the external factors related to landscape heterogeneity.

Confounding adjustment methods for multi-level treatment comparisons under lack of positivity and unknown model specification.

Imbalances in covariates between treatment groups are frequent in observational studies and can lead to biased comparisons. Various adjustment methods can be employed to correct these biases in the context of multi-level treatments (> 2). Analytical challenges, such as positivity violations and incorrect model specification due to unknown functional relationships between covariates and treatment or outcome, may affect their ability to yield unbiased results. Such challenges were expected in a comparison of fire-suppression interventions for preventing fire growth. We identified the overlap weights, augmented overlap weights, bias-corrected matching and targeted maximum likelihood as methods with the best potential to address those challenges. A simple variance estimator for the overlap weight estimators that can naturally be combined with machine learning is proposed. In a simulation study, we investigated the performance of these methods as well as those of simpler alternatives. Adjustment methods that included an outcome modeling component performed better than those that focused on the treatment mechanism in our simulations. Additionally, machine learning implementation was observed to efficiently compensate for the unknown model specification for the former methods, but not the latter. Based on these results, we compared the effectiveness of fire-suppression interventions using the augmented overlap weight estimator.

Variance estimation and confidence intervals for the standardized mortality ratio with application to the assessment of a cancer screening program.

The effect of a cancer screening program can be measured through the standardized mortality ratio (SMR) statistic. The numerator of the SMR is the observed number of deaths from the screened disease among participants in the screening program, whereas the denominator of the SMR is an estimate of the expected number of deaths in these participants under the assumption that the screening program has no effect. In this article, we propose a variance estimator for the denominator of the SMR when this expected number of deaths is estimated with Sasieni's method. We give both a general formula for this variance as well as formulas for specific disease incidence and survival estimators. We show how this new variance estimator can be used to build confidence intervals for the SMR. We investigate the coverage properties of various types of confidence intervals by simulation and find that intervals that make use of the proposed variance estimator perform well. We illustrate the method by applying it to the Québec Breast Cancer Screening program.

A generalization of Turnbull's estimator for nonparametric estimation of the conditional survival function with interval-censored data.

Simple nonparametric estimates of the conditional distribution of a response variable given a covariate are often useful for data exploration purposes or to help with the specification or validation of a parametric or semi-parametric regression model. In this paper we propose such an estimator in the case where the response variable is interval-censored and the covariate is continuous. Our approach consists in adding weights that depend on the covariate value in the self-consistency equation proposed by Turnbull (J R Stat Soc Ser B 38:290-295, 1976), which results in an estimator that is no more difficult to implement than Turnbull's estimator itself. We show the convergence of our algorithm and that our estimator reduces to the generalized Kaplan-Meier estimator (Beran, Nonparametric regression with randomly censored survival data, 1981) when the data are either complete or right-censored. We demonstrate by simulation that the estimator, bootstrap variance estimation and bandwidth selection (by rule of thumb or cross-validation) all perform well in finite samples. We illustrate the method by applying it to a dataset from a study on the incidence of HIV in a group of female sex workers from Kinshasa.

Mixed conditional logistic regression for habitat selection studies.

1. Resource selection functions (RSFs) are becoming a dominant tool in habitat selection studies. RSF coefficients can be estimated with unconditional (standard) and conditional logistic regressions. While the advantage of mixed-effects models is recognized for standard logistic regression, mixed conditional logistic regression remains largely overlooked in ecological studies. 2. We demonstrate the significance of mixed conditional logistic regression for habitat selection studies. First, we use spatially explicit models to illustrate how mixed-effects RSFs can be useful in the presence of inter-individual heterogeneity in selection and when the assumption of independence from irrelevant alternatives (IIA) is violated. The IIA hypothesis states that the strength of preference for habitat type A over habitat type B does not depend on the other habitat types also available. Secondly, we demonstrate the significance of mixed-effects models to evaluate habitat selection of free-ranging bison Bison bison. 3. When movement rules were homogeneous among individuals and the IIA assumption was respected, fixed-effects RSFs adequately described habitat selection by simulated animals. In situations violating the inter-individual homogeneity and IIA assumptions, however, RSFs were best estimated with mixed-effects regressions, and fixed-effects models could even provide faulty conclusions. 4. Mixed-effects models indicate that bison did not select farmlands, but exhibited strong inter-individual variations in their response to farmlands. Less than half of the bison preferred farmlands over forests. Conversely, the fixed-effect model simply suggested an overall selection for farmlands. 5. Conditional logistic regression is recognized as a powerful approach to evaluate habitat selection when resource availability changes. This regression is increasingly used in ecological studies, but almost exclusively in the context of fixed-effects models. Fitness maximization can imply differences in trade-offs among individuals, which can yield inter-individual differences in selection and lead to departure from IIA. These situations are best modelled with mixed-effects models. Mixed-effects conditional logistic regression should become a valuable tool for ecological research.

Group-size-mediated habitat selection and group fusion-fission dynamics of bison under predation risk.

For gregarious animals the cost-benefit trade-offs that drive habitat selection may vary dynamically with group size, which plays an important role in foraging and predator avoidance strategies. We examined how habitat selection by bison (Bison bison) varied as a function of group size and interpreted these patterns by testing whether habitat selection was more strongly driven by the competing demands of forage intake vs. predator avoidance behavior. We developed an analytical framework that integrated group size into resource selection functions (RSFs). These group-size-dependent RSFs were based on a matched case-control design and were estimated using conditional logistic regression (mixed and population-averaged models). Fitting RSF models to bison revealed that bison groups responded to multiple aspects of landscape heterogeneity and that selection varied seasonally and as a function of group size. For example, roads were selected in summer, but not in winter. Bison groups avoided areas of high snow water equivalent in winter. They selected areas composed of a large proportion of meadow area within a 700-m radius, and within those areas, bison selected meadows. Importantly, the strength of selection for meadows varied as a function of group size, with stronger selection being observed in larger groups. Hence the bison-habitat relationship depended in part on the dynamics of group formation and division. Group formation was most likely in meadows. In contrast, risk of group fission increased when bison moved into the forest and was higher during the time of day when movements are generally longer and more variable among individuals. We also found that stronger selection for meadows by large rather than small bison groups was caused by longer residence time in individual meadows by larger groups and that departure from meadows appears unlikely to result from a depression in food intake rate. These group-size-dependent patterns were consistent with the hypothesis that avoidance of predation risk is the strongest driver of habitat selection.

Redefining the maximum sustainable yield for the Schaefer population model including multiplicative environmental noise.

The focus of this article is to investigate the biological reference points, such as the maximum sustainable yield (MSY), in a common Schaefer (logistic) surplus production model in the presence of a multiplicative environmental noise. This type of model is used in fisheries stock assessment as a first-hand tool for biomass modelling. Under the assumption that catches are proportional to the biomass, we derive new conditions on the environmental noise distribution such that stationarity exists and extinction is avoided. We then get new explicit results about the stationary behavior of the biomass distribution for a particular specification of the noise, namely the biomass distribution itself and a redefinition of the MSY and related quantities that now depend on the value of the variance of the noise. Consequently, we obtain a more precise vision of how less optimistic the stochastic version of the MSY can be than the traditionally used (deterministic) MSY. In addition, we give empirical conditions on the error variance to approximate our specific noise by a lognormal noise, the latter being more natural and leading to easier inference in this context. These conditions are mild enough to make the explicit results of this paper valid in a number of practical applications. The outcomes of two case-studies about northwest Atlantic haddock [Spencer, P.D., Collie, J.S., 1997. Effect of nonlinear predation rates on rebuilding the Georges Bank haddock (Melanogrammus aeglefinus) stock. Can. J. Fish. Aquat. Sci. 54, 2920-2929] and South Atlantic albacore tuna [Millar, R.B., Meyer, R., 2000. Non-linear state space modelling of fisheries biomass dynamics by using Metropolis-Hastings within-Gibbs sampling. Appl. Stat. 49, 327-342] are used to illustrate the impact of our results in bioeconomic terms.

Inference methods for the conditional logistic regression model with longitudinal data.

This paper considers inference methods for case-control logistic regression in longitudinal setups. The motivation is provided by an analysis of plains bison spatial location as a function of habitat heterogeneity. The sampling is done according to a longitudinal matched case-control design in which, at certain time points, exactly one case, the actual location of an animal, is matched to a number of controls, the alternative locations that could have been reached. We develop inference methods for the conditional logistic regression model in this setup, which can be formulated within a generalized estimating equation (GEE) framework. This permits the use of statistical techniques developed for GEE-based inference, such as robust variance estimators and model selection criteria adapted for non-independent data. The performance of the methods is investigated in a simulation study and illustrated with the bison data analysis.

Survival analysis and classification methods for forest fire size.

Factors affecting wildland-fire size distribution include weather, fuels, and fire suppression activities. We present a novel application of survival analysis to quantify the effects of these factors on a sample of sizes of lightning-caused fires from Alberta, Canada. Two events were observed for each fire: the size at initial assessment (by the first fire fighters to arrive at the scene) and the size at "being held" (a state when no further increase in size is expected). We developed a statistical classifier to try to predict cases where there will be a growth in fire size (i.e., the size at "being held" exceeds the size at initial assessment). Logistic regression was preferred over two alternative classifiers, with covariates consistent with similar past analyses. We conducted survival analysis on the group of fires exhibiting a size increase. A screening process selected three covariates: an index of fire weather at the day the fire started, the fuel type burning at initial assessment, and a factor for the type and capabilities of the method of initial attack. The Cox proportional hazards model performed better than three accelerated failure time alternatives. Both fire weather and fuel type were highly significant, with effects consistent with known fire behaviour. The effects of initial attack method were not statistically significant, but did suggest a reverse causality that could arise if fire management agencies were to dispatch resources based on a-priori assessment of fire growth potentials. We discuss how a more sophisticated analysis of larger data sets could produce unbiased estimates of fire suppression effect under such circumstances.

Robust Inference from Conditional Logistic Regression Applied to Movement and Habitat Selection Analysis.

Conditional logistic regression (CLR) is widely used to analyze habitat selection and movement of animals when resource availability changes over space and time. Observations used for these analyses are typically autocorrelated, which biases model-based variance estimation of CLR parameters. This bias can be corrected using generalized estimating equations (GEE), an approach that requires partitioning the data into independent clusters. Here we establish the link between clustering rules in GEE and their effectiveness to remove statistical biases in variance estimation of CLR parameters. The current lack of guidelines is such that broad variation in clustering rules can be found among studies (e.g., 14-450 clusters) with unknown consequences on the robustness of statistical inference. We simulated datasets reflecting conditions typical of field studies. Longitudinal data were generated based on several parameters of habitat selection with varying strength of autocorrelation and some individuals having more observations than others. We then evaluated how changing the number of clusters impacted the effectiveness of variance estimators. Simulations revealed that 30 clusters were sufficient to get unbiased and relatively precise estimates of variance of parameter estimates. The use of destructive sampling to increase the number of independent clusters was successful at removing statistical bias, but only when observations were temporally autocorrelated and the strength of inter-individual heterogeneity was weak. GEE also provided robust estimates of variance for different magnitudes of unbalanced datasets. Our simulations demonstrate that GEE should be estimated by assigning each individual to a cluster when at least 30 animals are followed, or by using destructive sampling for studies with fewer individuals having intermediate level of behavioural plasticity in selection and temporally autocorrelated observations. The simulations provide valuable information to build reliable habitat selection and movement models that allow for robustness of statistical inference without removing excessive amounts of ecological information.

Forest productivity mitigates human disturbance effects on late-seral prey exposed to apparent competitors and predators.

Primary production can determine the outcome of management actions on ecosystem properties, thereby defining sustainable management. Yet human agencies commonly overlook spatio-temporal variations in productivity by recommending fixed resource extraction thresholds. We studied the influence of forest productivity on habitat disturbance levels that boreal caribou - a threatened, late-seral ungulate under top-down control - should be able to withstand. Based on 10 years of boreal caribou monitoring, we found that adult survival and recruitment to populations decreased with landscape disturbance, but increased with forest productivity. This benefit of productivity reflected the net outcome of an increase in resources for apparent competitors and predators of caribou, and a more rapid return to the safety of mature conifer forests. We estimated 3-fold differences in forest harvesting levels that caribou populations could withstand due to variations in forest productivity. The adjustment of ecosystem provisioning services to local forest productivity should provide strong conservation and socio-economic advantages.