Is behavioral ecology important for understanding and predicting population dynamics?

Population ecology is a discipline that studies changes in the number and composition (age, sex) of the individuals that form a population. Many of the mechanisms that generate these changes are associated with individual behavior, for example how individuals defend their territories, find mates or disperse. Therefore, it is important to model population dynamics considering the potential influence of behavior on the modeled dynamics. This study illustrates the diversity of behaviors that influence population dynamics describing several methods that allow integrating behavior into population models and range from simpler models that only consider the number of individuals to complex individual-based models that capture great levels of detail. A series of examples shows the importance of explicitly considering behavior in population modeling to avoid reaching erroneous conclusions. This integration is particularly relevant for conservation, as incorrect predictions regarding the dynamics of populations of conservation interest can lead to inadequate assessment and management. Improved predictions can favor effective protection of species and better use of the limited financial and human conservation resources

Generalized drivers in the mammalian endangerment process

An important challenge for conservation today is to understand the endangerment process and identify any generalized patterns in how threats occur and aggregate across taxa. Here we use a global database describing main current external threats in mammals to evaluate the prevalence of distinct threatening processes, primarily of anthropogenic origin, and to identify generalized drivers of extinction and their association with vulnerability status and intrinsic species' traits. We detect several primary threat combinations that are generally associated with distinct species. In particular, large and widely distributed mammals are affected by combinations of direct exploitation and threats associated with increasing landscape modification that go from logging to intense human land-use. Meanwhile, small, narrowly distributed species are affected by intensifying levels of landscape modification but are not directly exploited. In general more vulnerable species are affected by a greater number of threats, suggesting increased extinction risk is associated with the accumulation of external threats. Overall, our findings show that endangerment in mammals is strongly associated with increasing habitat loss and degradation caused by human land-use intensification. For large and widely distributed mammals there is the additional risk of being hunted

From conference abstract to publication in the conservation science literature

Every two years, the conservation community comes together at The Society for Conservation Biology's International Congress for Conservation Biology (ICCB) to share new developments in conservation science and practice. Publication of content presented at conferences in scientific journals adds to a permanent record and helps increase its potential impact. However, quantitative research on publication rates for meetings relevant to conservation is lacking. We provide a data-driven exploration of the presentations at the 25th ICCB held in Auckland, New Zealand in 2011. To study publication rates and presenter demographics, we recorded titles, number of authors, presenter affiliations, gender, country of study region, publication status, and the elapsed time between presentation and publication. Of the 980 contributions (782 talks and 198 posters), 587 (60%) became publications. We found a mean time to publication of 13.7 months for all published abstracts, and 21.3 months when excluding abstracts published before the meeting. The gender breakdown of presenters was almost even (53% male, 47% female), but the representation of the countries where the presenting authors were based at was biased. The political units with the most contributions were by far the USA, Australia, New Zealand, and the UK. Presenters based in English-speaking countries made up 74% of the total sample, but this did not influence the likelihood of their abstract becoming a publication. Understanding the presentation to publication process in conservation is useful to identify biases and potential challenges that need to be addressed to make conference communications permanent and increase their reach beyond those in attendance

Which intrinsic traits predict vulnerability to extinction depends on the actual threatening processes

Understanding what makes some species more vulnerable to extinction than others is an important challenge for conservation. Many comparative analyses have addressed this issue exploring how intrinsic and extrinsic traits associate with general estimates of vulnerability. However, these general estimates do not consider the actual threats that drive species to extinction and hence, are more difficult to translate into effective management. We provide an updated description of the types and spatial distribution of threats that affect mammals globally using data from the IUCN for 5941 species of mammals. Using these data we explore the links between intrinsic species traits and specific threats in order to identify key intrinsic features associated with particular drivers of extinction. We find that families formed by small-size habitat specialists are more likely to be threatened by habitat-modifying processes; whereas, families formed by larger mammals with small litter sizes are more likely to be threatened by processes that directly affect survival. These results highlight the importance of considering the actual threatening process in comparative studies. We also discuss the need to standardize and rank threat importance in global assessments such as the IUCN Red List to improve our ability to understand what makes some species more vulnerable to extinction than others

Socioeconomic correlates of global mammalian conservation status

The main causes of biodiversity decline are related to human use of resources, which is ultimately triggered by the socioeconomic decisions made by individuals and nations. Characterizing the socioeconomic attributes of areas in which biodiversity is most threatened can help us identify decisions and conditions that promote the presence or absence of threats and potentially suggest more sustainable strategies. In this study we explored how diverse indicators of social and economic development correlate with the conservation status of terrestrial mammals within countries explicitly exploring hypothesized linear and quadratic relationships. First, comparing countries with and without threatened mammals we found that those without threatened species are a disparate group formed by European countries and Small Island Developing States (SIDS) with little in common besides their slow population growth and a past of human impacts. Second, focusing on countries with threatened mammals we found that those with a more threatened mammalian biota have mainly rural populations, are predominantly exporters of goods and services, receive low to intermediate economic benefits from international tourism, and have medium to high human life expectancy. Overall, these results provide a comprehensive characterization of the socioeconomic profiles linked to mammalian conservation status of the world's nations, highlighting the importance of transborder impacts reflected by the international flux of goods, services and people. Further studies would be necessary to unravel the actual mechanisms and threats that link these socioeconomic profiles and indicators with mammalian conservation. Nevertheless, this study presents a broad and complete characterization that offers testable hypotheses regarding how socioeconomic development associates with biodiversity

Roadkill risk and population vulnerability in European birds and mammals

Roads represent a threat to biodiversity, primarily through increased mortality from collisions with vehicles. Although estimating roadkill rates is an important first step, how roads affect long-term population persistence must also be assessed. We developed a trait-based model to predict roadkill rates for terrestrial bird and mammalian species in Europe and used a generalized population model to estimate their long-term vulnerability to road mortality. We found that ~194 million birds and ~29 million mammals may be killed each year on European roads. The species that were predicted to experience the highest mortality rates due to roads were not necessarily the same as those whose long-term persistence was most vulnerable to road mortality. When evaluating which species or areas could be most affected by road development projects, failure to consider how roadkill affects populations may result in misidentifying appropriate targets for mitigation

Automated synthesis of biodiversity knowledge requires better tools and standardised research output

As the impact of anthropogenic activity on the environment has grown, research into biodiversity change and associated threats has also accelerated. Synthesising this vast literature is important for understanding the drivers of biodiversity change and identifying those actions that will mitigate further ecological losses. However, keeping pace with an ever-increasing publication rate presents a substantial challenge to efficient syntheses, an issue which could be partly addressed by increasing levels of automation in the synthesis pipeline. Here, we evaluate the potential for automated tools to extract ecologically important information from the abstracts of articles compiled in the Living Planet Database. Specifically, we focused on extracting key information on taxonomy (studied species names), geographic location and estimated population trend, assessing the accuracy of automated versus manual information extraction, the potential for automated tools to introduce biases into syntheses, and evaluating if synthesising abstracts was enough to capture the key information from the full article. Taxonomic and geographic extraction tools performed reasonably well, although information on studied species was sometimes limited in the abstract (compared to the main text) preventing fast extraction. In contrast, extraction of trends was less successful, highlighting the challenges involved in automating information extraction from abstracts, such as deficiencies in the algorithms, linguistic complexity associated with ecological findings, and limited information when compared to the main text. In light of these results, we cautiously advocate for a wider use of automated taxonomic and geographic parsing tools for ecological synthesis. Additionally, to further the use of automated synthesis within ecology, we recommend a dual approach: development of improved computational tools to reduce biases; and enhanced protocols for abstracts (and associated metadata) to ensure key information is included in a format that facilitates machine-readability

Human activity is altering the world’s zoogeographical regions

Zoogeographical regions, or zooregions, are areas of the Earth defined by species pools that reflect ecological, historical, and evolutionary processes acting over millions of years. Consequently, researchers have assumed that zooregions are robust and unlikely to change on a human timescale. However, the increasing number of human-mediated introductions and extinctions can challenge this assumption. By delineating zooregions with a network-based algorithm, here we show that introductions and extinctions are altering the zooregions we know today. Introductions are homogenising the Eurasian and African mammal zooregions and also triggering less intuitive effects in birds and amphibians, such as dividing and redefining zooregions representing the Old and New World. Furthermore, these Old and New World amphibian zooregions are no longer detected when considering introductions plus extinctions of the most threatened species. Our findings highlight the profound and far-reaching impact of human activity and call for identifying and protecting the uniqueness of biotic assemblages

Handling missing values in trait data

Aim Trait data are widely used in ecological and evolutionary phylogenetic comparative studies, but often values are not available for all species of interest. Researchers traditionally have excluded species without data from analyses, but estimation of missing values using imputation has been proposed as a better approach. However, imputation methods have largely been designed for randomly missing data, yet trait data are often not missing at random (e.g. more data for bigger species). Here we evaluate the performance of approaches for handling missing values considering biased datasets. Location Any Time period Any Major taxa studied Any Methods We simulated continuous traits and separate response variables to test performance of nine imputation methods and complete-case analysis (excluding missing values from the dataset) under biased missing data scenarios. We characterized performance by estimating error in imputed trait values (deviation from the true value) and inferred trait-response relationships (deviation from the true relationship between a trait and response). Results Generally, Rphylopars imputation produced the most accurate estimate of missing values and best preserved the response-trait slope. However, estimates of missing data were still inaccurate, even with only 5% of values missing. Under severe biases, errors were high with every approach. Imputation was not always the best option, with complete-case analysis frequently outperforming Mice imputation, and to a lesser degree BHPMF imputation. Mice, a popular approach, performed poorly when the response variable was excluded from the imputation model. Main conclusions Imputation can effectively handle missing data under some conditions, but is not always the best solution. None of the methods we tested could effectively deal with severe biases, which may be common in trait datasets. We recommend rigorous data checking for biases before and after imputation and propose variables that can assist researchers working with incomplete datasets to detect data biases and minimise errors

Classecol: classifiers to understand public opinions of nature

1) Human perceptions of nature, once the domain of the social sciences, are now an important part of environmental research. However, the data and tools to tackle this research are lacking or are difficult to apply. 2) Here, we present a collection of text classifier models to identify text relevant to the broad topics of hunting and nature, describing whether opinions are pro- or against-hunting, or show interest, concern, or dislike of nature. The methods also include a biographical classification – describing whether the author of the text is a person, nature expert, nature organisation, or ‘Other’. The classifiers were developed using an extensive social media dataset, and are designed to support qualitative analysis of big data (especially from Twitter). 3) The classifiers accurately identified biographies, text related to hunting and nature, and the stance towards hunting and nature (weighted F-scores: 0.79 - 0.99; 1 indicates perfect accuracy). 4) These classifiers, alongside an array of other text processing and analysis functions, are presented in the form of an R package classecol. classecol also acts as a proof of concept that nature related text classifiers can be developed with high accuracy