Dealing with uncertain absences in habitat modelling: a case study of a rare ground-dwelling parrot

In the development of a species distribution model based on regression techniques such as generalized linear or additive modelling (GLM/GAM), a basic assumption is that records of species presence and absence are real. However, a common concern in many studies examining species distributions is that absences cannot be inferred with certainty. This is particularly the case where the species is rare, difficult to detect and/or does not occupy all available habitat considered suitable. The western ground parrot ( Pezoporus wallicus flaviventris ) of southern Western Australia, Australia, is a case in point, as not only is it rare and difficult to detect, but it is also unlikely to occupy all available suitable habitat. A recent survey of ground parrots provided the opportunity to develop a predictive distribution model. As the data were susceptible to false absences, these were replaced with randomly selected 'pseudo' absences and modelled using GLM. As a comparison, presence-only information was modelled using a relatively new approach, MAXENT, a machine-learning technique that has been shown to perform comparatively well. The predictive performance of both models, as assessed by the receiver operating characteristic plot (ROC) was high (AUC > 0.8), with MAXENT performing only marginally better than the GLM. These approaches both indicated that the ground parrot prefers areas relatively high in altitude, distant from rivers, gently sloping to level habitat, with an intermediate cover of vegetation and where there is a mosaic of vegetation ages. In this case, the use of presence-only information resulted in the identification of important environmental attributes defining the occurrence of the ground parrot, but additional factors that account for the inability of the bird to occupy all suitable habitat should be a component of model refinement.     

Predicting climate-induced range shifts: model differences and model reliability

Predicted changes in the global climate are likely to cause large shifts in the geographic ranges of many plant and animal species. To date, predictions of future range shifts have relied on a variety of modeling approaches with different levels of model accuracy. Using a common data set, we investigated the potential implications of alternative modeling approaches for conclusions about future range shifts and extinctions. Our common data set entailed the current ranges of 100 randomly selected mammal species found in the western hemisphere. Using these range maps, we compared six methods for modeling predicted future ranges. Predicted future distributions differed markedly across the alternative modeling approaches, which in turn resulted in estimates of extinction rates that ranged between 0% and 7%, depending on which model was used. Random forest predictors, a model‐averaging approach, consistently outperformed the other techniques (correctly predicting >99% of current absences and 86% of current presences). We conclude that the types of models used in a study can have dramatic effects on predicted range shifts and extinction rates; and that model‐averaging approaches appear to have the greatest potential for predicting range shifts in the face of climate change.     

Marginal Models Via GLS: A Convenient Yet Neglected Tool for the Analysis of Correlated Data in the Behavioural Sciences

Behavioural research often produces data that have a complicated structure. For instance, data can represent repeated observations of the same individual and suffer from heteroscedasticity as well as other technical snags. The regression analysis of such data is often complicated by the fact that the observations (response variables) are mutually correlated. The correlation structure can be quite complex and might or might not be of direct interest to the user. In any case, one needs to take correlations into account (e.g. by means of random‐effect specification) in order to arrive at correct statistical inference (e.g. for construction of the appropriate test or confidence intervals). Over the last decade, such data have been more and more frequently analysed using repeated‐measures ANOVA and mixed‐effects models. Some researchers invoke the heavy machinery of mixed‐effects modelling to obtain the desired population‐level (marginal) inference, which can be achieved by using simpler tools – namely marginal models. This paper highlights marginal modelling (using generalized least squares [GLS] regression) as an alternative method. In various concrete situations, such marginal models can be based on fewer assumptions and directly generate estimates (population‐level parameters) which are of immediate interest to the behavioural researcher (such as population mean). Sometimes, they might be not only easier to interpret but also easier to specify than their competitors (e.g. mixed‐effects models). Using five examples from behavioural research, we demonstrate the use, advantages, limits and pitfalls of marginal and mixed‐effects models implemented within the functions of the ‘nlme’ package in R.     

Using broad-scale, community survey data to compare species conservation strategies across regions: A case study of the Koala in a set of adjacent 'catchments'

Summary   Managers of wildlife populations with a wide geographical range are understandably interested in the question of whether they can manage a broader population with a single conservation strategy (e.g. covering a set of adjacent management regions, referred to as 'catchments' in Australia) or whether separate strategies are required for individual catchments. We addressed this question using data from a statewide, community wildlife survey to quantify Koala ( Phascolarctos cinereus ) habitat relationships in the catchments of four adjacent Catchment Management Authorities or CMA (>10 000 km²) of New South Wales, Australia and then tested whether these habitat relationships were similar across catchments. Although the results were constrained by the coarse resolution of the community survey and environmental data, we were able to model broad-scale patterns of habitat use. Model explanatory power and cross-regional predictability was low, but consistent with Koala ecology. Two environmental variables emerged as having a strong relationship with Koala presence – mean elevation and percentage of fertile soils – the importance of which varied among catchments depending on land-use patterns. The results highlight the need for local wildlife management plans, not a single plan covering multiple catchments.     

Multiple regression and inference in ecology and conservation biology: further comments on identifying important predictor variables

Ecologists and conservation biologists frequently use multipleregression (MR) to try to identify factors influencing response variables suchas species richness or occurrence. Many frequently used regression methods maygenerate spurious results due to multicollinearity. argued that there are actually two kinds of MR modelling: (1)seeking the best predictive model; and (2) isolating amounts of varianceattributable to each predictor variable. The former has attracted most attentionwith a plethora of criteria (measures of model fit penalized for modelcomplexity – number of parameters) and Bayes-factor-based methods havingbeen proposed, while the latter has been little considered, althoughhierarchical methods seem promising (e.g. hierarchical partitioning). If the twoapproaches agree on which predictor variables to retain, then it is more likelythat meaningful predictor variables (of those considered) have been found. Therehas been a problem in that, while hierarchical partitioning allowed the rankingof predictor variables by amounts of independent explanatory power, there was no(statistical) way to decide which variables to retain. A solution usingrandomization of the data matrix coupled with hierarchical partitioning ispresented, as is an ecological example.     

From mixed effects modeling to spike and slab variable selection: A Bayesian regression model for group testing data

Due to reductions in both time and cost, group testing is a popular alternative to individual-level testing for disease screening. These reductions are obtained by testing pooled biospecimens (eg, blood, urine, swabs, etc.) for the presence of an infectious agent. However, these reductions come at the expense of data complexity, making the task of conducting disease surveillance more tenuous when compared to using individual-level data. This is because an individual's disease status may be obscured by a group testing protocol and the effect of imperfect testing. Furthermore, unlike individual-level testing, a given participant could be involved in multiple testing outcomes and/or may never be tested individually. To circumvent these complexities and to incorporate all available information, we propose a Bayesian generalized linear mixed model that accommodates data arising from any group testing protocol, estimates unknown assay accuracy probabilities and accounts for potential heterogeneity in the covariate effects across population subgroups (eg, clinic sites, etc.); this latter feature is of key interest to practitioners tasked with conducting disease surveillance. To achieve model selection, our proposal uses spike and slab priors for both fixed and random effects. The methodology is illustrated through numerical studies and is applied to chlamydia surveillance data collected in Iowa.     

Large old trees increase growth under shifting climatic constraints: Aligning tree longevity and individual growth dynamics in primary mountain spruce forests

In a world of accelerating changes in environmental conditions driving tree growth, tradeoffs between tree growth rate and longevity could curtail the abundance of large old trees (LOTs), with potentially dire consequences for biodiversity and carbon storage. However, the influence of tree-level tradeoffs on forest structure at landscape scales will also depend on disturbances, which shape tree size and age distribution, and on whether LOTs can benefit from improved growing conditions due to climate warming. We analyzed temporal and spatial variation in radial growth patterns from ~5000 Norway spruce (Picea abies [L.] H. Karst) live and dead trees from the Western Carpathian primary spruce forest stands. We applied mixed-linear modeling to quantify the importance of LOT growth histories and stand dynamics (i.e., competition and disturbance factors) on lifespan. Finally, we assessed regional synchronization in radial growth variability over the 20th century, and modeled the effects of stand dynamics and climate on LOTs recent growth trends. Tree age varied considerably among forest stands, implying an important role of disturbance as an age constraint. Slow juvenile growth and longer period of suppressed growth prolonged tree lifespan, while increasing disturbance severity and shorter time since last disturbance decreased it. The highest age was not achieved only by trees with continuous slow growth, but those with slow juvenile growth followed by subsequent growth releases. Growth trend analysis demonstrated an increase in absolute growth rates in response to climate warming, with late summer temperatures driving the recent growth trend. Contrary to our expectation that LOTs would eventually exhibit declining growth rates, the oldest LOTs (>400 years) continuously increase growth throughout their lives, indicating a high phenotypic plasticity of LOTs for increasing biomass, and a strong carbon sink role of primary spruce forests under rising temperatures, intensifying droughts, and increasing bark beetle outbreaks.     

长白山阔叶红松林乔木幼苗组成及多度格局的影响因素

以长白山原始阔叶红松林样地为平台,以样方中胸径小于1 cm的乔木幼苗为研究对象,基于2018年两次的幼苗调查数据,利用广义线性混合模型分析影响群落乔木幼苗多度的生物邻体和生境因素。结果表明:该样地所有幼苗样方共调查到10064株乔木幼苗,累计17个物种,分属9科9属,水曲柳幼苗多度极高,在乔木树种幼苗中占优势地位;在群落水平,幼苗多度与异种大树效应、草本密度和林冠开阔度呈显著正相关,与草本盖度和土壤含水量呈显著负相关;在物种水平,水曲柳和紫椴幼苗多度的影响因素与群落水平筛选后的结果一致,红松幼苗多度与异种大树效应呈正相关,与同种大树效应及林冠开阔度呈负相关。该研究证实了生物邻体和生境因素共同影响幼苗多度格局,并且生物邻体和生境因素的相对重要性随幼苗物种种类不同而变化。     

Projecting future expansion of invasive species: comparing and improving methodologies for species distribution modeling

Modeling the distributions of species, especially of invasive species in non‐native ranges, involves multiple challenges. Here, we developed some novel approaches to species distribution modeling aimed at reducing the influences of such challenges and improving the realism of projections. We estimated species–environment relationships for Parthenium hysterophorus L. (Asteraceae) with four modeling methods run with multiple scenarios of (i) sources of occurrences and geographically isolated background ranges for absences, (ii) approaches to drawing background (absence) points, and (iii) alternate sets of predictor variables. We further tested various quantitative metrics of model evaluation against biological insight. Model projections were very sensitive to the choice of training dataset. Model accuracy was much improved using a global dataset for model training, rather than restricting data input to the species’ native range. AUC score was a poor metric for model evaluation and, if used alone, was not a useful criterion for assessing model performance. Projections away from the sampled space (i.e., into areas of potential future invasion) were very different depending on the modeling methods used, raising questions about the reliability of ensemble projections. Generalized linear models gave very unrealistic projections far away from the training region. Models that efficiently fit the dominant pattern, but exclude highly local patterns in the dataset and capture interactions as they appear in data (e.g., boosted regression trees), improved generalization of the models. Biological knowledge of the species and its distribution was important in refining choices about the best set of projections. A post hoc test conducted on a new Parthenium dataset from Nepal validated excellent predictive performance of our ‘best’ model. We showed that vast stretches of currently uninvaded geographic areas on multiple continents harbor highly suitable habitats for parthenium. However, discrepancies between model predictions and parthenium invasion in Australia indicate successful management for this globally significant weed.     

长白山阔叶红松林中影响乔木幼苗存活的关键因素

应用广义线性混合模型,对长白山阔叶红松林中影响幼苗存活的生物因素和非生物因素进行分析。结果表明:(1)在群落水平上,幼苗存活率和生物因素中同种幼苗邻居显著负相关,说明在阔叶红松林群落中存在负密度制约效应。(2)生物因子和非生物因子对不同年龄阶段的幼苗存活率影响不同。对于1年生的幼苗,幼苗存活率与异种大树邻居呈显著负相关,与同种大树邻居呈显著正相关;对于2—3年生的幼苗,其存活率和同种幼苗邻居、同种大树邻居均呈显著负相关,和非生物因子相关不显著;对于4年生以上的幼苗,其存活率和土壤主成分分析的PC1(低的有机质、速效钾、速效氮等比较贫瘠的土壤)显著负相关。(3)种子的传播方式不同,幼苗存活率的影响因子也不同。对于风传播的物种,存活率与同种幼苗邻居密度显著正相关。对于重力传播的物种,幼苗存活率与土壤PC3(高的全氮和速效氮,含氮较高的土壤)、异种大树邻居、草本密度呈负相关,与林冠开阔度和草本盖度成正相关。(4)对于不同的物种,影响幼苗存活的因素也不同。紫椴的幼苗存活率与土壤PC3、异种大树邻居、草本密度呈显著负相关,与土壤主成分PC2(高的有机质和全氮等养分比较好的土壤)呈显著正相关。     

When three traits make a line: evolution of phenotypic plasticity and genetic assimilation through linear reaction norms in stochastic environments

Genetic assimilation emerges from selection on phenotypic plasticity. Yet, commonly used quantitative genetics models of linear reaction norms considering intercept and slope as traits do not mimic the full process of genetic assimilation. We argue that intercept–slope reaction norm models are insufficient representations of genetic effects on linear reaction norms and that considering reaction norm intercept as a trait is unfortunate because the definition of this trait relates to a specific environmental value (zero) and confounds genetic effects on reaction norm elevation with genetic effects on environmental perception. Instead, we suggest a model with three traits representing genetic effects that, respectively, (i) are independent of the environment, (ii) alter the sensitivity of the phenotype to the environment and (iii) determine how the organism perceives the environment. The model predicts that, given sufficient additive genetic variation in environmental perception, the environmental value at which reaction norms tend to cross will respond rapidly to selection after an abrupt environmental change, and eventually becomes equal to the new mean environment. This readjustment of the zone of canalization becomes completed without changes in genetic correlations, genetic drift or imposing any fitness costs of maintaining plasticity. The asymptotic evolutionary outcome of this three‐trait linear reaction norm generally entails a lower degree of phenotypic plasticity than the two‐trait model, and maximum expected fitness does not occur at the mean trait values in the population.     

A mitogenomic phylogeny and genetic history of sable (Martes zibellina)

We assessed phylogeny of sable (Martes zibellina, Linnaeus, 1758) by sequence analysis of nearly complete, new mitochondrial genomes in 36 specimens from different localities in northern Eurasia (Primorye, Khabarovsk and Krasnoyarsk regions, the Kamchatka Peninsula, the Kuril Islands and the Urals). Phylogenetic analysis of mtDNA sequences demonstrates that two clades, A and BC, radiated about 200–300 thousand years ago (kya) according to results of Bayesian molecular clock and RelTime analyses of different mitogenome alignments (nearly complete mtDNA sequences, protein-coding region, and synonymous sites), while the age estimates of clades A, B and C fall within the Late Pleistocene (~ 50–140 kya). Bayesian skyline plots (BSPs) of sable population size change based on analysis of nearly complete mtDNAs show an expansion around 40 kya in the warm Karganian time, without a decline of population size around the Last Glacial Maximum (21 kya). The BSPs based on synonymous clock rate indicate that M. zibellina experienced demographic expansions later, approximately 22 kya. The A2a clade that colonized Kamchatka ~ 23–50 kya (depending on the mutation rate used) survived the last glaciation there as demonstrated by the BSP analysis. In addition, we have found evidence of positive selection acting at ND4 and cytochrome b genes, thereby suggesting adaptive evolution of the A2a clade in Kamchatka.