Model-based control of observer bias for the analysis of presence-only data in ecology

Presence-only data, where information is available concerning species presence but not species absence, are subject to bias due to observers being more likely to visit and record sightings at some locations than others (hereafter "observer bias"). In this paper, we describe and evaluate a model-based approach to accounting for observer bias directly - by modelling presence locations as a function of known observer bias variables (such as accessibility variables) in addition to environmental variables, then conditioning on a common level of bias to make predictions of species occurrence free of such observer bias. We implement this idea using point process models with a LASSO penalty, a new presence-only method related to maximum entropy modelling, that implicitly addresses the "pseudo-absence problem" of where to locate pseudo-absences (and how many). The proposed method of bias-correction is evaluated using systematically collected presence/absence data for 62 plant species endemic to the Blue Mountains near Sydney, Australia. It is shown that modelling and controlling for observer bias significantly improves the accuracy of predictions made using presence-only data, and usually improves predictions as compared to pseudo-absence or "inventory" methods of bias correction based on absences from non-target species. Future research will consider the potential for improving the proposed bias-correction approach by estimating the observer bias simultaneously across multiple species. © 2013 Warton et al

Fitting Log-Gaussian Cox Processes Using Generalized Additive Model Software

While log-Gaussian Cox process regression models are useful tools for modeling point patterns, they can be technically difficult to fit and require users to learn/adopt bespoke software. We show that, for suitably formatted data, we can actually fit these models using generalized additive model software, via a simple line of code, demonstrated on R by the popular mgcv package. We are able to do this because a common and computationally efficient way to fit a log-Gaussian Cox process model is to use a basis function expansion to approximate the Gaussian random field, as is provided by a generic bivariate smoother over geographic space. We further show that if basis functions are parameterized appropriately then we can estimate parameters in the spatial covariance function for the latent random field using a generalized additive model. We use simulation to show that this approach leads to model fits of comparable quality to state-of-the-art software, often more quickly. But we see the main advance from this work as lowering the technology barrier to spatial statistics for applied researchers, many of whom are already familiar with generalized additive model software

Untangling direct species associations from indirect mediator species effects with graphical models

Ecologists often investigate co-occurrence patterns in multi-species data in order to gain insight into the ecological causes of observed co-occurrences. Apart from direct associations between the two species of interest, they may co-occur because of indirect effects, where both species respond to another variable, whether environmental or biotic (e.g. a mediator species). A wide variety of methods are now available for modelling how environmental filtering drives species distributions. In contrast, methods for studying other causes of co-occurence are much more limited. “Graphical” methods, which can be used to study how mediator species impact co-occurrence patterns, have recently been proposed for use in ecology. However, available methods are limited to presence/absence data or methods assuming multivariate normality, which is problematic when analysing abundances. We propose Gaussian copula graphical models (GCGMs) for studying the effect of mediator species on co-occurence patterns. GCGMs are a flexible type of graphical model which naturally accommodates all data types, for example binary (presence/absence), counts, as well as ordinal data and biomass, in a unified framework. Simulations demonstrate that GCGMs can be applied to a much broader range of data types than the methods currently used in ecology, and perform as well as or better than existing methods in many settings. We apply GCGMs to counts of hunting spiders, in order to visualise associations between species. We also analyse abundance data of New Zealand native forest cover (on an ordinal scale) to show how GCGMs can be used analyse large and complex datasets. In these data, we were able to reproduce known species relationships as well as generate new ecological hypotheses about species associations

Tropical plants do not have narrower temperature tolerances, but are more at risk from warming because they are close to their upper thermal limits

Aim: Tropical species are thought to be more susceptible to climate warming than are higher latitude species. This prediction is largely based on the assumption that tropical species can tolerate a narrower range of temperatures. While this prediction holds for some animal taxa, we do not yet know the latitudinal trends in temperature tolerance for plants. We aim to address this knowledge gap and establish if there is a global trend in plant warming risk. Location: Global. Time period: Present–2070. Major taxa studied: Plants. Methods: We used 9,737 records for 1,312 species from the Kew Gardens’ global germination database to quantify global patterns in germination temperature. Results: We found no evidence for a latitudinal gradient in the breadth of temperatures at which plant species can germinate. However, tropical plants are predicted to face the greatest risk from climate warming, because they experience temperatures closer to their upper germination limits. By 2070, over half (79/142) of tropical plant species are predicted to experience temperatures exceeding their optimum germination temperatures, with some even exceeding their maximum germination temperature (41/190). Conversely, 95% of species at latitudes above 45° are predicted to benefit from warming, with environmental temperatures shifting closer to the species’ optimal germination temperatures. Main conclusions: The prediction that tropical plant species would be most at risk under future climate warming was supported by our data, but through a different mechanism to that generally assumed

Leaf economics fundamentals explained by optimality principles

The life span of leaves increases with their mass per unit area (LMA). It is unclear why. Here, we show that this empirical generalization (the foundation of the worldwide leaf economics spectrum) is a consequence of natural selection, maximizing average net carbon gain over the leaf life cycle. Analyzing two large leaf trait datasets, we show that evergreen and deciduous species with diverse construction costs (assumed proportional to LMA) are selected by light, temperature, and growing-season length in different, but predictable, ways. We quantitatively explain the observed divergent latitudinal trends in evergreen and deciduous LMA and show how local distributions of LMA arise by selection under different environmental conditions acting on the species pool. These results illustrate how optimality principles can underpin a new theory for plant geography and terrestrial carbon dynamics

Global patterns of the leaf economics spectrum in wetlands

Contains fulltext : 226771.pdf (publisher's version ) (Open Access

Collision Mortality Has No Discernible Effect on Population Trends of North American Birds

Avian biodiversity is threatened by numerous anthropogenic factors and migratory species are especially at risk. Migrating birds frequently collide with manmade structures and such losses are believed to represent the majority of anthropogenic mortality for North American birds. However, estimates of total collision mortality range across several orders of magnitude and effects on population dynamics remain unknown. Herein, we develop a novel method to assess relative vulnerability to anthropogenic threats, which we demonstrate using 243,103 collision records from 188 species of eastern North American landbirds. After correcting mortality estimates for variation attributable to population size and geographic overlap with potential collision structures, we found that per capita vulnerability to collision with buildings and towers varied over more than four orders of magnitude among species. Species that migrate long distances or at night were much more likely to be killed by collisions than year-round residents or diurnal migrants. However, there was no correlation between relative collision mortality and long-term population trends for these same species. Thus, although millions of North American birds are killed annually by collisions with manmade structures, this source of mortality has no discernible effect on populations

Three-Dimensional Geometric Analysis of Felid Limb Bone Allometry

Studies of bone allometry typically use simple measurements taken in a small number of locations per bone; often the midshaft diameter or joint surface area is compared to body mass or bone length. However, bones must fulfil multiple roles simultaneously with minimum cost to the animal while meeting the structural requirements imposed by behaviour and locomotion, and not exceeding its capacity for adaptation and repair. We use entire bone volumes from the forelimbs and hindlimbs of Felidae (cats) to investigate regional complexities in bone allometry.Computed tomographic (CT) images (16435 slices in 116 stacks) were made of 9 limb bones from each of 13 individuals of 9 feline species ranging in size from domestic cat (Felis catus) to tiger (Panthera tigris). Eleven geometric parameters were calculated for every CT slice and scaling exponents calculated at 5% increments along the entire length of each bone. Three-dimensional moments of inertia were calculated for each bone volume, and spherical radii were measured in the glenoid cavity, humeral head and femoral head. Allometry of the midshaft, moments of inertia and joint radii were determined. Allometry was highly variable and related to local bone function, with joint surfaces and muscle attachment sites generally showing stronger positive allometry than the midshaft.Examining whole bones revealed that bone allometry is strongly affected by regional variations in bone function, presumably through mechanical effects on bone modelling. Bone's phenotypic plasticity may be an advantage during rapid evolutionary divergence by allowing exploitation of the full size range that a morphotype can occupy. Felids show bone allometry rather than postural change across their size range, unlike similar-sized animals

The Allometry of Host-Pathogen Interactions

Understanding the mechanisms that control rates of disease progression in humans and other species is an important area of research relevant to epidemiology and to translating studies in small laboratory animals to humans. Body size and metabolic rate influence a great number of biological rates and times. We hypothesize that body size and metabolic rate affect rates of pathogenesis, specifically the times between infection and first symptoms or death.We conducted a literature search to find estimates of the time from infection to first symptoms (t(S)) and to death (t(D)) for five pathogens infecting a variety of bird and mammal hosts. A broad sampling of diseases (1 bacterial, 1 prion, 3 viruses) indicates that pathogenesis is controlled by the scaling of host metabolism. We find that the time for symptoms to appear is a constant fraction of time to death in all but one disease. Our findings also predict that many population-level attributes of disease dynamics are likely to be expressed as dimensionless quantities that are independent of host body size.Our results show that much variability in host pathogenesis can be described by simple power functions consistent with the scaling of host metabolic rate. Assessing how disease progression is controlled by geometric relationships will be important for future research. To our knowledge this is the first study to report the allometric scaling of host/pathogen interactions

Anyone with a Long-Face? Craniofacial Evolutionary Allometry (CREA) in a Family of Short-Faced Mammals, the Felidae

Among adults of closely related species, a trend in craniofacial evolutionary allometry (CREA) for larger taxa to be long-faced and smaller ones to have paedomorphic aspects, such as proportionally smaller snouts and larger braincases, has been demonstrated in some mammals and two bird lineages. Nevertheless, whether this may represent a ‘rule’ with few exceptions is still an open question. In this context, Felidae is a particularly interesting family to study because, although its members are short-faced, previous research did suggest relative facial elongation in larger living representatives. Using geometric morphometrics, based on two sets of anatomical landmarks, and traditional morphometrics, for comparing relative lengths of the palate and basicranium, we performed a series of standard and comparative allometric regressions in the Felidae and its two subfamilies. All analyses consistently supported the CREA pattern, with only one minor exception in the geometric morphometric analysis of Pantherinae: the genus Neofelis. With its unusually long canines, Neofelis species seem to have a relatively narrow cranium and long face, despite being smaller than other big cats. In spite of this, overall, our findings strengthen the possibility that the CREA pattern might indeed be a ‘rule’ among mammals, raising questions on the processes behind it and suggesting future directions for its study