Optimising monitoring efforts for secretive snakes: a comparison of occupancy and N-mixture models for assessment of population status

A fifth of reptiles are Data Deficient; many due to unknown population status. Monitoring snake populations can be demanding due to crypsis and low population densities, with insufficient recaptures for abundance estimation via Capture-Mark-Recapture. Alternatively, binomial N-mixture models enable abundance estimation from count data without individual identification, but have rarely been successfully applied to snake populations. We evaluated the suitability of occupancy and N-mixture methods for monitoring an insular population of grass snakes (Natrix helvetica) and considered covariates influencing detection, occupancy and abundance within remaining habitat. Snakes were elusive, with detectability increasing with survey effort (mean: 0.33 ± 0.06 s.e.m.). The probability of a transect being occupied was moderate (mean per kilometre: 0.44 ± 0.19 s.e.m.) and increased with transect length. Abundance estimates indicate a small threatened population associated to our transects (mean: 39, 95% CI: 20–169). Power analysis indicated that the survey effort required to detect occupancy declines would be prohibitive. Occupancy models fitted well, whereas N-mixture models showed poor fit, provided little extra information over occupancy models and were at greater risk of closure violation. Therefore we suggest occupancy models are more appropriate for monitoring snakes and other elusive species, but that population trends may go undetected

Challenges for defining minimal clinically important difference (MCID) after spinal cord injury

Study design:This is a review article.Objectives:This study discusses the following: (1) concepts and constraints for the determination of minimal clinically important difference (MCID), (2) the contrasts between MCID and minimal detectable difference (MDD), (3) MCID within the different domains of International Classification of Functioning, disability and health, (4) the roles of clinical investigators and clinical participants in defining MCID and (5) the implementation of MCID in acute versus chronic spinal cord injury (SCI) studies.Methods:The methods include narrative reviews of SCI outcomes, a 2-day meeting of the authors and statistical methods of analysis representing MDD.Results:The data from SCI study outcomes are dependent on many elements, including the following: the level and severity of SCI, the heterogeneity within each study cohort, the therapeutic target, the nature of the therapy, any confounding influences or comorbidities, the assessment times relative to the date of injury, the outcome measurement instrument and the clinical end-point threshold used to determine a treatment effect. Even if statistically significant differences can be established, this finding does not guarantee that the experimental therapeutic provides a person living with SCI an improved capacity for functional independence and/or an increased quality of life. The MDD statistical concept describes the smallest real change in the specified outcome, beyond measurement error, and it should not be confused with the minimum threshold for demonstrating a clinical benefit or MCID. Unfortunately, MCID and MDD are not uncomplicated estimations; nevertheless, any MCID should exceed the expected MDD plus any probable spontaneous recovery.Conclusion:Estimation of an MCID for SCI remains elusive. In the interim, if the target of a therapeutic is the injured spinal cord, it is most desirable that any improvement in neurological status be correlated with a functional (meaningful) benefit.Spinal Cord advance online publication, 16 December 2014; doi:10.1038/sc.2014.232