Estimating individual fitness in the wild using capture–recapture data

The concept of Darwinian fitness is central in evolutionary ecology, and its estimation has motivated the development of several approaches. However, measuring individual fitness remains challenging in empirical case studies in the wild. Measuring fitness requires a continuous monitoring of individuals from birth to death, which is very difficult to get in part because individuals may or may not be controlled at each reproductive event and recovered at death. Imperfect detection hampers keeping track of mortality and reproductive events over the whole lifetime of individuals. We propose a new statistical approach to estimate individual fitness while accounting for imperfect detection. Based on hidden process modelling of longitudinal data on marked animals, we show that standard metrics to quantify fitness, namely lifetime reproductive success, individual growth rate and lifetime individual contribution to population growth, can be extended to cope with imperfect detection inherent to most monitoring programs in the wild. We illustrate our approach using data collected on individual roe deer in an intensively monitored population.     

Genetics of dispersal

Dispersal is a process of central importance for the ecological and evolutionary dynamics of populations and communities, because of its diverse consequences for gene flow and demography. It is subject to evolutionary change, which begs the question, what is the genetic basis of this potentially complex trait? To address this question, we (i) review the empirical literature on the genetic basis of dispersal, (ii) explore how theoretical investigations of the evolution of dispersal have represented the genetics of dispersal, and (iii) discuss how the genetic basis of dispersal influences theoretical predictions of the evolution of dispersal and potential consequences. Dispersal has a detectable genetic basis in many organisms, from bacteria to plants and animals. Generally, there is evidence for significant genetic variation for dispersal or dispersal‐related phenotypes or evidence for the micro‐evolution of dispersal in natural populations. Dispersal is typically the outcome of several interacting traits, and this complexity is reflected in its genetic architecture: while some genes of moderate to large effect can influence certain aspects of dispersal, dispersal traits are typically polygenic. Correlations among dispersal traits as well as between dispersal traits and other traits under selection are common, and the genetic basis of dispersal can be highly environment‐dependent. By contrast, models have historically considered a highly simplified genetic architecture of dispersal. It is only recently that models have started to consider multiple loci influencing dispersal, as well as non‐additive effects such as dominance and epistasis, showing that the genetic basis of dispersal can influence evolutionary rates and outcomes, especially under non‐equilibrium conditions. For example, the number of loci controlling dispersal can influence projected rates of dispersal evolution during range shifts and corresponding demographic impacts. Incorporating more realism in the genetic architecture of dispersal is thus necessary to enable models to move beyond the purely theoretical towards making more useful predictions of evolutionary and ecological dynamics under current and future environmental conditions. To inform these advances, empirical studies need to answer outstanding questions concerning whether specific genes underlie dispersal variation, the genetic architecture of context‐dependent dispersal phenotypes and behaviours, and correlations among dispersal and other traits.     

Land use and life history limit migration capacity of eastern tree species

Aim

Temperate tree species overwhelmingly responded to past climate change by migrating rather than adapting. However, past climate change did not have the modern human‐driven patterns of land use and fragmentation, raising questions of whether tree migration will still be able to keep pace with climate. Previous studies using coarse‐grained or randomized landscapes suggest that dispersal may be delayed but have not identified outright barriers to migration. Here, we use real‐world fragmented landscapes at the scale of forest stands to assess the migration capacity of eastern tree species.

Location

Eastern U.S.A.

Time period

Present day to 2100.

Major taxa studied

Eastern U.S. trees.

Methods

We simulated dispersal over 100 years for 15 species common to the mid‐Atlantic region and that are predicted to gain suitable habitat in the northeast. In contrast to previous studies, we incorporated greater realism with species‐specific life histories and real‐world spatial configurations of anthropogenic land use. We used simulation results to calculate dispersal rates for each species and related these to predicted rates of species habitat shift.

Results

Our simulations suggest that land use in the human‐dominated east‐coast corridor slows species dispersal rates by 12–40% and may prevent keeping pace with climate. Species most impacted by anthropogenic land use were often those with the highest predicted species habitat shifts. We identified two major dispersal barriers, the Washington DC metropolitan area and central NY, that severely impeded tree migration.

Main conclusions

Patterns of anthropogenic land use not only slowed migration but also resulted in effective barriers to dispersal. These impacts were exacerbated by tree life histories, such as long ages to maturity and narrow dispersal kernels. Without intervention, the migration lags predicted here may lead to loss in biodiversity and ecosystem functions as current forest species decline, and may contribute to formation of novel communities.     

Creating individual accessible area hypotheses improves stacked species distribution model performance

Aim

Stacked species distribution models (SDMs) are an important step towards estimating species richness, but frequently overpredict this metric and therefore erroneously predict which species comprise a given community. We test the idea that developing hypotheses about accessible area a priori can greatly improve model performance. By integrating dispersal ability via accessible area into SDM creation, we address an often‐overlooked facet of ecological niche modelling.

Innovation

By limiting the training and transference areas to theoretically accessible areas, we are creating more accurate SDMs on the basis of a taxon's explorable environments. This limitation of space and environment is a more accurate reflection of a taxon's true dispersal properties and more accurately reflects the geographical and environmental space to which a taxon is exposed. Here, we compare the predictive performance of stacked SDMs derived from spatially constrained and unconstrained training areas.

Main conclusions

Restricting a species’ training and transference areas to a theoretically accessible area greatly improves model performance. Stacked SDMs drawn from spatially restricted training areas predicted species richness and community composition more accurately than non‐restricted stacked SDMs. These accessible area‐based restrictions mimic true dispersal barriers to species and limit training areas to the suite of environments to those which a species is exposed to in nature. Furthermore, these restrictions serve to ‘clip’ predictions in geographical space, thus removing overpredictions in adjacent geographical regions where the species is known to be absent.     

Homogeneous vs. patient specific breast models for Monte Carlo evaluation of mean glandular dose in mammography

PurposeTo compare, via Monte Carlo simulations, homogeneous and non-homogenous breast models adopted for mean glandular dose (MGD) estimates in mammography vs. patient specific digital breast phantoms.MethodsWe developed a GEANT4 Monte Carlo code simulating four homogenous cylindrical breast models featured as follows: (1) semi-cylindrical section enveloped in a 5-mm adipose layer; (2) semi-elliptical section with a 4-mm thick skin; (3) semi-cylindrical section with a 1.45-mm skin layer; (4) semi-cylindrical section in a 1.45-mm skin layer and 2-mm subcutaneous adipose layer. Twenty patient specific digital breast phantoms produced from a dedicated CT scanner were assumed as reference in the comparison. We simulated two spectra produced from two anode/filter combinations. An additional digital breast phantom was produced via BreastSimulator software.ResultsWith reference to the results for patient-specific breast phantoms and for W/Al spectra, models #1 and #3 showed higher MGD values by about 1% (ranges [–33%; +28%] and [−31%; +30%], respectively), while for model #4 it was 2% lower (range [−34%; +26%]) and for model #2 –11% (range [−39%; +14%]), on average. On the other hand, for W/Rh spectra, models #1 and #4 showed lower MGD values by 2% and 1%, while for model #2 and #3 it was 14% and 8% lower, respectively (ranges [−43%; +13%] and [−41%; +21%]). The simulation with the digital breast phantom produced with BreastSimulator showed a MGD overestimation of +33%.ConclusionsThe homogeneous breast models led to maximum MGD underestimation and overestimation of 43% and 28%, respectively, when compared to patient specific breast phantoms derived from clinical CT scans.     

Matching effort to threat: Strategies to increase the scale and effectiveness of revegetation in southern Australia

The past 30 years of restoration activities in Australia has been mere cautious fiddling in the face of continental‐wide native habitat loss, fragmentation and degradation. A fundamental principle of conservation is to address threats at the scale of the threatening processes. This is still not happening for two reasons: we do not know how to effectively restore at scale, and if we did, there is no demand because it does not pay. The first problem of developing the technologies needed for large‐scale revegetation will largely be solved if we collectively demand revegetation at scale. Such a scale of demand by Australian governments (taxpayers) is only likely if a permanent Natural Heritage Trust is created for long‐term funding complemented by a price on carbon pollution. That will take a lot of political will. The other big opportunity is to create demand for large‐scale revegetation cofunded by farmers to improve their farm's long‐term productivity, resilience and economic viability. This requires sustained R&D supported by novel partnerships with the massive Australian agricultural market. Native vegetation must move from the margins to the mainstream if scale is to be achieved.     

Prediction of enteric methane production,yield, and intensity in dairy cattle using an intercontinental database

Enteric methane (CH₄) production from cattle contributes to global greenhouse gas emissions. Measurement of enteric CH₄ is complex, expensive, and impractical at large scales; therefore, models are commonly used to predict CH₄ production. However, building robust prediction models requires extensive data from animals under different management systems worldwide. The objectives of this study were to (1) collate a global database of enteric CH₄ production from individual lactating dairy cattle; (2) determine the availability of key variables for predicting enteric CH₄ production (g/day per cow), yield [g/kg dry matter intake (DMI)], and intensity (g/kg energy corrected milk) and their respective relationships; (3) develop intercontinental and regional models and cross‐validate their performance; and (4) assess the trade‐off between availability of on‐farm inputs and CH₄ prediction accuracy. The intercontinental database covered Europe (EU), the United States (US), and Australia (AU). A sequential approach was taken by incrementally adding key variables to develop models with increasing complexity. Methane emissions were predicted by fitting linear mixed models. Within model categories, an intercontinental model with the most available independent variables performed best with root mean square prediction error (RMSPE) as a percentage of mean observed value of 16.6%, 14.7%, and 19.8% for intercontinental, EU, and United States regions, respectively. Less complex models requiring only DMI had predictive ability comparable to complex models. Enteric CH₄ production, yield, and intensity prediction models developed on an intercontinental basis had similar performance across regions, however, intercepts and slopes were different with implications for prediction. Revised CH₄ emission conversion factors for specific regions are required to improve CH₄ production estimates in national inventories. In conclusion, information on DMI is required for good prediction, and other factors such as dietary neutral detergent fiber (NDF) concentration, improve the prediction. For enteric CH₄ yield and intensity prediction, information on milk yield and composition is required for better estimation.     

Multi‐model comparison highlights consistency in predicted effect of warming on a semi‐arid shrub

A number of modeling approaches have been developed to predict the impacts of climate change on species distributions, performance, and abundance. The stronger the agreement from models that represent different processes and are based on distinct and independent sources of information, the greater the confidence we can have in their predictions. Evaluating the level of confidence is particularly important when predictions are used to guide conservation or restoration decisions. We used a multi‐model approach to predict climate change impacts on big sagebrush (Artemisia tridentata), the dominant plant species on roughly 43 million hectares in the western United States and a key resource for many endemic wildlife species. To evaluate the climate sensitivity of A. tridentata, we developed four predictive models, two based on empirically derived spatial and temporal relationships, and two that applied mechanistic approaches to simulate sagebrush recruitment and growth. This approach enabled us to produce an aggregate index of climate change vulnerability and uncertainty based on the level of agreement between models. Despite large differences in model structure, predictions of sagebrush response to climate change were largely consistent. Performance, as measured by change in cover, growth, or recruitment, was predicted to decrease at the warmest sites, but increase throughout the cooler portions of sagebrush's range. A sensitivity analysis indicated that sagebrush performance responds more strongly to changes in temperature than precipitation. Most of the uncertainty in model predictions reflected variation among the ecological models, raising questions about the reliability of forecasts based on a single modeling approach. Our results highlight the value of a multi‐model approach in forecasting climate change impacts and uncertainties and should help land managers to maximize the value of conservation investments.     

Projecting the future of an alpine ungulate under climate change scenarios

Climate change represents a primary threat to species persistence and biodiversity at a global scale. Cold adapted alpine species are especially sensitive to climate change and can offer key “early warning signs” about deleterious effects of predicted change. Among mountain ungulates, survival, a key determinant of demographic performance, may be influenced by future climate in complex, and possibly opposing ways. Demographic data collected from 447 mountain goats in 10 coastal Alaska, USA, populations over a 37‐year time span indicated that survival is highest during low snowfall winters and cool summers. However, general circulation models (GCMs) predict future increase in summer temperature and decline in winter snowfall. To disentangle how these opposing climate‐driven effects influence mountain goat populations, we developed an age‐structured population model to project mountain goat population trajectories for 10 different GCM/emissions scenarios relevant for coastal Alaska. Projected increases in summer temperature had stronger negative effects on population trajectories than the positive demographic effects of reduced winter snowfall. In 5 of the 10 GCM/representative concentration pathway (RCP) scenarios, the net effect of projected climate change was extinction over a 70‐year time window (2015–2085); smaller initial populations were more likely to go extinct faster than larger populations. Using a resource selection modeling approach, we determined that distributional shifts to higher elevation (i.e., “thermoneutral”) summer range was unlikely to be a viable behavioral adaptation strategy; due to the conical shape of mountains, summer range was expected to decline by 17%–86% for 7 of the 10 GCM/RCP scenarios. Projected declines of mountain goat populations are driven by climate‐linked bottom‐up mechanisms and may have wide ranging implications for alpine ecosystems. These analyses elucidate how projected climate change can negatively alter population dynamics of a sentinel alpine species and provide insight into how demographic modeling can be used to assess risk to species persistence.     

Recent Advances in Animal Models of Zika Virus Infection

An infection by Zika virus(ZIKV), a mosquito-borne flavivirus, broke out in South American regions in 2015, and recently showed a tendency of spreading to North America and even worldwide. ZIKV was first detected in 1947 and only 14 human infection cases were reported until 2007. This virus was previously observed to cause only mild flu-like symptoms.However, recent ZIKV infections might be responsible for the increasing cases of neurological disorders such as GuillainBarre′ syndrome and congenital defects, including newborn microcephaly. Therefore, researchers have established several animal models to study ZIKV transmission and pathogenesis, and test therapeutic candidates. This review mainly summarizes the reported animal models of ZIKV infection, including mice and non-human primates.