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Distributions of mammals in Southeast Asia: The role of the legacy of climate and species body mass
(2019)
Aim Current species distributions are shaped by present and past biotic and abiotic factors. Here, we assessed whether abiotic factors (habitat availability) in combination with past connectivity and a biotic factor (body mass) can explain the unique distribution pattern of Southeast Asian mammals, which are separated by the enigmatic biogeographic transition zone, the Isthmus of Kra (IoK), for which no strong geophysical barrier exists. Location Southeast Asia. Taxon Mammals. Methods We projected habitat suitability for 125 mammal species using climate data for the present period and for two historic periods: mid-Holocene (6 ka) and last glacial maximum (LGM 21 ka). Next, we employed a phylogenetic linear model to assess how present species distributions were affected by the suitability of areas in these different periods, habitat connectivity during LGM and species body mass. Results Our results show that cooler climate during LGM provided suitable habitat south of IoK for species presently distributed north of IoK (in mainland Indochina). However, the potentially suitable habitat for these Indochinese species did not stretch very far southwards onto the exposed Sunda Shelf. Instead, we found that the emerged landmasses connecting Borneo and Sumatra provided suitable habitat for forest dependent Sundaic species. We show that for species whose current distribution ranges are mainly located in Indochina, the area of the distribution range that is located south of IoK is explained by the suitability of habitat in the past and present in combination with the species body mass. Main conclusions We demonstrate that a strong geophysical barrier may not be necessary for maintaining a biogeographic transition zone for mammals, but that instead a combination of abiotic and biotic factors may suffice.
Understanding the drivers underlying disease dynamics is still a major challenge in disease ecology, especially in the case of long-term disease persistence. Even though there is a strong consensus that density-dependent factors play an important role for the spread of diseases, the main drivers are still discussed and, more importantly, might differ between invasion and persistence periods. Here, we analysed long-term outbreak data of classical swine fever, an important disease in both wild boar and livestock, prevalent in the wild boar population from 1993 to 2000 in Mecklenburg-Vorpommern, Germany. We report outbreak characteristics and results from generalized linear mixed models to reveal what factors affected infection risk on both the landscape and the individual level. Spatiotemporal outbreak dynamics showed an initial wave-like spread with high incidence during the invasion period followed by a drop of incidence and an increase in seroprevalence during the persistence period. Velocity of spread increased with time during the first year of outbreak and decreased linearly afterwards, being on average 7.6 km per quarter. Landscape- and individual-level analyses of infection risk indicate contrasting seasonal patterns. During the persistence period, infection risk on the landscape level was highest during autumn and winter seasons, probably related to spatial behaviour such as increased long-distance movements and contacts induced by rutting and escaping movements. In contrast, individual-level infection risk peaked in spring, probably related to the concurrent birth season leading to higher densities, and was significantly higher in piglets than in reproductive animals. Our findings highlight that it is important to investigate both individual- and landscape-level patterns of infection risk to understand long-term persistence of wildlife diseases and to guide respective management actions. Furthermore, we highlight that exploring different temporal aggregation of the data helps to reveal important seasonal patterns, which might be masked otherwise.
Biological responses to climate change have been widely documented across taxa and regions, but it remains unclear whether species are maintaining a good match between phenotype and environment, i.e. whether observed trait changes are adaptive. Here we reviewed 10,090 abstracts and extracted data from 71 studies reported in 58 relevant publications, to assess quantitatively whether phenotypic trait changes associated with climate change are adaptive in animals. A meta-analysis focussing on birds, the taxon best represented in our dataset, suggests that global warming has not systematically affected morphological traits, but has advanced phenological traits. We demonstrate that these advances are adaptive for some species, but imperfect as evidenced by the observed consistent selection for earlier timing. Application of a theoretical model indicates that the evolutionary load imposed by incomplete adaptive responses to ongoing climate change may already be threatening the persistence of species.
Anthropogenic pressures increasingly alter natural systems. Therefore, understanding the resilience of agent-based complex systems such as ecosystems, i.e. their ability to absorb these pressures and sustain their functioning and services, is a major challenge. However, the mechanisms underlying resilience are still poorly understood. A main reason for this is the multidimensionality of both resilience, embracing the three fundamental stability properties recovery, resistance and persistence, and of the specific situations for which stability properties can be assessed. Agent-based models (ABM) complement empirical research which is, for logistic reasons, limited in coping with these multiple dimensions. Besides their ability to integrate multidimensionality through extensive manipulation in a fully controlled system, ABMs can capture the emergence of system resilience from individual interactions and feedbacks across different levels of organization. To assess the extent to which this potential of ABMs has already been exploited, we reviewed the state of the art in exploring resilience and its multidimensionality in ecological and socio-ecological systems with ABMs. We found that the potential of ABMs is not utilized in most models, as they typically focus on a single dimension of resilience by using variability as a proxy for persistence, and are limited to one reference state, disturbance type and scale. Moreover, only few studies explicitly test the ability of different mechanisms to support resilience. To overcome these limitations, we recommend to simultaneously assess multiple stability properties for different situations and under consideration of the mechanisms that are hypothesised to render a system resilient. This will help us to better exploit the potential of ABMs to understand and quantify resilience mechanisms, and hence support solving real-world problems related to the resilience of agent-based complex systems.
Ecosystems respond in various ways to disturbances. Quantifying ecological stability therefore requires inspecting multiple stability properties, such as resistance, recovery, persistence and invariability. Correlations among these properties can reduce the dimensionality of stability, simplifying the study of environmental effects on ecosystems. A key question is how the kind of disturbance affects these correlations. We here investigated the effect of three disturbance types (random, species-specific, local) applied at four intensity levels, on the dimensionality of stability at the population and community level. We used previously parameterized models that represent five natural communities, varying in species richness and the number of trophic levels. We found that disturbance type but not intensity affected the dimensionality of stability and only at the population level. The dimensionality of stability also varied greatly among species and communities. Therefore, studying stability cannot be simplified to using a single metric and multi-dimensional assessments are still to be recommended.
Evolutionary history and conservation significance of the Javan leopard Panthera pardus melas
(2016)
The leopard Panthera pardus is widely distributed across Africa and Asia; however, there is a gap in its natural distribution in Southeast Asia, where it occurs on the mainland and on Java but not on the interjacent island of Sumatra. Several scenarios have been proposed to explain this distribution gap. Here, we complemented an existing dataset of 68 leopard mtDNA sequences from Africa and Asia with mtDNA sequences (NADH5+ ctrl, 724bp) from 19 Javan leopards, and hindcasted leopard distribution to the Pleistocene to gain further insights into the evolutionary history of the Javan leopard. Our data confirmed that Javan leopards are evolutionarily distinct from other Asian leopards, and that they have been present on Java since the Middle Pleistocene. Species distribution projections suggest that Java was likely colonized via a Malaya-Java land bridge that by-passed Sumatra, as suitable conditions for leopards during Pleistocene glacial periods were restricted to northern and western Sumatra. As fossil evidence supports the presence of leopards on Sumatra at the beginning of the Late Pleistocene, our projections are consistent with a scenario involving the extinction of leopards on Sumatra as a consequence of the Toba super volcanic eruption (similar to 74kya). The impact of this eruption was minor on Java, suggesting that leopards managed to survive here. Currently, only a few hundred leopards still live in the wild and only about 50 are managed in captivity. Therefore, this unique and distinctive subspecies requires urgent, concerted conservation efforts, integrating insitu and ex situ conservation management activities in a One Plan Approach to species conservation management.
Forage availability has been suggested as one driver of the observed decline in honey bees. However, little is known about the effects of its spatiotemporal variation on colony success. We present a modeling framework for assessing honey bee colony viability in cropping systems. Based on two real farmland structures, we developed a landscape generator to design cropping systems varying in crop species identity, diversity, and relative abundance. The landscape scenarios generated were evaluated using the existing honey bee colony model BEEHAVE, which links foraging to in-hive dynamics. We thereby explored how different cropping systems determine spatiotemporal forage availability and, in turn, honey bee colony viability (e.g., time to extinction, TTE) and resilience (indicated by, e.g., brood mortality). To assess overall colony viability, we developed metrics,P(H)andP(P,)which quantified how much nectar and pollen provided by a cropping system per year was converted into a colony's adult worker population. Both crop species identity and diversity determined the temporal continuity in nectar and pollen supply and thus colony viability. Overall farmland structure and relative crop abundance were less important, but details mattered. For monocultures and for four-crop species systems composed of cereals, oilseed rape, maize, and sunflower,P(H)andP(P)were below the viability threshold. Such cropping systems showed frequent, badly timed, and prolonged forage gaps leading to detrimental cascading effects on life stages and in-hive work force, which critically reduced colony resilience. Four-crop systems composed of rye-grass-dandelion pasture, trefoil-grass pasture, sunflower, and phacelia ensured continuous nectar and pollen supply resulting in TTE > 5 yr, andP(H)(269.5 kg) andP(P)(108 kg) being above viability thresholds for 5 yr. Overall, trefoil-grass pasture, oilseed rape, buckwheat, and phacelia improved the temporal continuity in forage supply and colony's viability. Our results are hypothetical as they are obtained from simplified landscape settings, but they nevertheless match empirical observations, in particular the viability threshold. Our framework can be used to assess the effects of cropping systems on honey bee viability and to develop land-use strategies that help maintain pollination services by avoiding prolonged and badly timed forage gaps.
Studies explaining the choice of model structure for population viability analysis (PVA) are rare and no such study exists for butterfly species, a focal group for conservation. Here, we describe in detail the development of a model to predict population viability of a glacial relict butterfly species, Boloria eunomia, under climate change. We compared four alternative formulations of an individual-based model, differing in the environmental factors acting on the survival of immature life stages: temperature (only temperature impact), weather (temperature, precipitation, and sunshine), temperature and parasitism, and weather and parasitism. Following pattern-oriented modeling, four observed patterns were used to contrast these models: one qualitative (response of population size to habitat parameters) and three quantitative ones describing population dynamics during eight years (mean and variability of population size, and magnitude of the temporal autocorrelation in yearly population growth rates). The four model formulations were not equally able to depict population dynamics under current environmental conditions; the model including only temperature was selected as the most parsimonious model sufficiently well reproducing the empirical patterns. We used all four model formulations to test a range of climate change scenarios that were characterized by changes in both mean and variability of the weather variables. All models predicted adverse effects of climate change and resulted in the same ranking of mean climate change scenarios. However, models differed in their absolute values of population viability measures, underlining the need to explicitly choose the most appropriate model formulation and avoid arbitrary usage of environmental drivers in a model. We conclude that further applications of pattern-oriented modeling to butterfly and other species are likely to help in identifying the key factors impacting the viability of certain taxa, which, ultimately, will aid and speed up informed management decisions for endangered species under climate change.
The potential of ecological models for supporting environmental decision making is increasingly acknowledged. However, it often remains unclear whether a model is realistic and reliable enough. Good practice for developing and testing ecological models has not yet been established. Therefore, TRACE, a general framework for documenting a model's rationale, design, and testing was recently suggested. Originally TRACE was aimed at documenting good modelling practice. However, the word 'documentation' does not convey TRACE's urgency. Therefore, we re-define TRACE as a tool for planning, performing, and documenting good modelling practice. TRACE documents should provide convincing evidence that a model was thoughtfully designed, correctly implemented, thoroughly tested, well understood, and appropriately used for its intended purpose. TRACE documents link the science underlying a model to its application, thereby also linking modellers and model users, for example stakeholders, decision makers, and developers of policies. We report on first experiences in producing TRACE documents. We found that the original idea underlying TRACE was valid, but to make its use more coherent and efficient, an update of its structure and more specific guidance for its use are needed. The updated TRACE format follows the recently developed framework of model 'evaludation': the entire process of establishing model quality and credibility throughout all stages of model development, analysis, and application. TRACE thus becomes a tool for planning, documenting, and assessing model evaludation, which includes understanding the rationale behind a model and its envisaged use. We introduce the new structure and revised terminology of TRACE and provide examples. (C) 2014 Elsevier B.V. All rights reserved.