Predicting Species Distributions Research Articles

Activity niches outperform thermal physiological limits in predicting global ant distributions

AbstractAimThermal physiology is commonly used in mechanistic models to predict species distribution and project distribution change. Such thermal constraints for ants are often measured under laboratory conditions as critical thermal limits (CTmax and CTmin), but have also been observed in the field as foraging thermal limits (FTmin and FTmax). Here we compared distribution projections based on ant physiological and behavioural thermal limits with their realized distributions to assess the validity of using ecophysiological models to predict species ranges over large scales.LocationGlobal.TaxonAnts.MethodsFrom published literature, we compiled a database of 148 lab‐measured critical thermal limits and 137 field‐observed foraging thermal limits for 20 ant genera distributed around the world. We projected their potential ranges using active hour thresholds by matching their thermal limits or preferred thermal breadth (incorporating thermal optima) with hourly surface temperature data. We then compared the projections against a comprehensive global database of ant distributions to assess the validity of physiologically and behaviourally based models.ResultsWe found that surface temperature plays a clear role in generic‐level ant biogeography. Projections based on foraging thermal limits were more conservative in constraining ranges to observed distributions, while projections based on critical thermal limits often overestimated ranges. Furthermore, thermal limit models can be improved by incorporating behaviourally derived thermal optima.Main conclusionsOur results suggest that temperature‐dependent activity niches and thermal optima better reveal species realized thermal niches than critical thermal limits, and hence can improve predictions of species distributions and future distribution changes under climate change.

A Leaf Selfie: Using a Smartphone to Quantify Leaf Vulnerability to Hydraulic Dysfunction.

Accurate predictions of species distribution under current and future climate conditions require modeling efforts based on clear mechanistic relationships between climate variables and plant physiological functions. Vulnerability of leaves to xylem embolism is a key mechanistic trait that might be included in these modeling efforts. Here, we propose a simple set-up to measure leaf vulnerability to embolism on the basis of the optical method using a smartphone, a light source, and a notebook. Our data show that this proposed set-up can adequately quantify the vulnerability to xylem embolism of leaf major veins in Populus nigra and Ostrya carpinifolia, producing values consistent with those obtained in temperate tree species with other methods, allowing virtually any laboratory to quantify species-specific drought tolerance on the basis of a sound mechanistic trait.

Effects of Variability in Ship Traffic and Whale Distributions on the Risk of Ships Striking Whales

Assessments of ship-strike risk for large whales typically use a single year of ship traffic data and averaged predictions of species distributions. Consequently, they do not account for variability in ship traffic or species distributions. Variability could reduce the effectiveness of static management measures designed to mitigate ship-strike risk. We explore the consequences of interannual variability on ship-strike risk using multiple years of both ship traffic data and predicted fin, humpback, and blue whale distributions off California. Specifically, risk was estimated in four regions that are important for ship-strike risk management. We estimated risk by multiplying the predicted number of whales by the cumulative distance travelled by ships. To overcome the temporal mismatch between the ship traffic and whale data available off California, we classified the time series of ship traffic data into nearshore and offshore traffic scenarios using the percentage of ship traffic travelling more than 24nmi from the mainland coast, which was the boundary of a clean fuel rule implemented in 2009 that altered ship traffic patterns. We found that risk for fin and humpback whale populations off California increased as these species recovered from whaling. We also found that broad-scale, northward shifts in blue whale distributions throughout the North Pacific, likely in response to changes in oceanographic conditions, were associated with increased ship-strike risk off northern California. The magnitude of ship-strike risk for fin, humpback, and blue whales was influenced by the ship traffic scenarios. Interannual variability in predicted whale distributions also influenced the magnitude of ship-strike risk, but generally did not change whether the nearshore or offshore traffic scenario had higher risk. The consistency in the highest risk from the traffic scenarios likely occurred because areas containing the highest predicted number of whales were generally the same across years. The consistency in risk from the traffic scenarios suggests that static spatial management measures (e.g., changing shipping lanes or creating areas to be avoided) can provide an effective means of mitigating risk resulting from ship traffic variability off California.

Vegetation structure and ground cover attributes describe the occurrence of a newly discovered carnivorous marsupial on the Tweed Shield Volcano caldera, the endangered black-tailed dusky antechinus (Antechinus arktos).

The black‐tailed dusky antechinus (Antechinus arktos) is a recently discovered, endangered, carnivorous marsupial mammal endemic to the Tweed Shield Volcano caldera, straddling the border between Queensland and New South Wales in eastern Australia. The species' preference for cool, high‐altitude habitats makes it particularly vulnerable to a shifting climate as these habitats recede. Aside from basic breeding and dietary patterns, the species' ecology is largely unknown. Understanding fine‐scale habitat attributes preferred by this endangered mammal is critical to employ successful conservation management. Here, we assess vegetation attributes of known habitats over three sites at Springbrook and Border Ranges National Parks, including detailed structure data and broad floristic assessment.Floristic compositional assessment of the high‐altitude cloud rainforest indicated broad similarities. However, only 22% of plant species were shared between all sites indicating a high level of local endemism. This suggests a diverse assemblage of vegetation across A. arktos habitats.Habitat characteristics were related to capture records of A. arktos to determine potential fine‐scale structural habitat requirements. Percentage of rock cover and leaf litter were the strongest predictors of A. arktos captures across survey sites, suggesting a need for foraging substrate and cover. Habitat characteristics described here will inform predictive species distribution models of this federally endangered species and are applicable to other mammal conservation programs.

An evaluation of central Iran’s protected areas under different climate change scenarios (A Case on Markazi and Hamedan provinces)

Global climate change poses a new challenge for species and can even push some species toward an extinction vortex. The most affected organisms are those with narrow tolerance to the climatic factors but many large mammals such as ungulates with a wider ecological niche are also being affected indirectly. Our research mainly used wild sheep in central Iran as a model species to explore how the suitable habitats will change under different climatic scenarios and to determine if current borders of protected areas will adequately protect habitat requirements. To create habitat models we used animal-vehicle collision points as an input for species presence data. We ran habitat models using MaxEnt modeling approach under different climatic scenarios of the past, present and future (under the climatic scenarios for minimum (RCP2.6) and maximum (RCP8.5) CO2 concentration trajectories). We tried to estimate the overlap and the width of the ecological niche using relevant metrics. In order to analyze the effectiveness of the protected areas, suitable maps were concerted to binary maps using True Skill Statistic (TSS) threshold and measured the similarity of the binary maps for each scenario using Kappa index. In order to assess the competence of the present protected areas boundary in covering the distribution of species, two different scenarios were employed, which are ensemble scenario 1: an ensemble of the binary maps of the species distribution in Mid-Holocene, present, and RCP2.6; and ensemble scenario 2: an ensemble of binary suitability maps in Mid-Holocene, present, and RCP8.5. Then, the borders of modeled habitats with the boundaries of 23 existing protected areas in two central provinces in Iran were compared. The predicted species distribution under scenario 1 (RCP2.6) was mostly similar to its current distribution (Kappa = 0.53) while the output model under scenario 2 (RCP8.5) indicated a decline in the species distribution range. Under the first ensemble scenario, current borders of the protected areas in Hamedan province showed better efficiency to cover the model species distribution range. Analyzing MaxEnt spatial models under the second climatic scenario suggested that protected areas in both Markazi and Hamedan provinces will not cover “high suitability” areas in the future. Modeling the efficiency of the current protected areas under predicted future climatic scenarios can help the related authorities to plan conservation activities more efficiently.

Using virtual reality and thermal imagery to improve statistical modelling of vulnerable and protected species.

Biodiversity loss and sparse observational data mean that critical conservation decisions may be based on little to no information. Emerging technologies, such as airborne thermal imaging and virtual reality, may facilitate species monitoring and improve predictions of species distribution. Here we combined these two technologies to predict the distribution of koalas, specialized arboreal foliovores facing population declines in many parts of eastern Australia. For a study area in southeast Australia, we complemented ground-survey records with presence and absence observations from thermal-imagery obtained using Remotely-Piloted Aircraft Systems. These field observations were further complemented with information elicited from koala experts, who were immersed in 360-degree images of the study area. The experts were asked to state the probability of habitat suitability and koala presence at the sites they viewed and to assign each probability a confidence rating. We fit logistic regression models to the ground survey data and the ground plus thermal-imagery survey data and a Beta regression model to the expert elicitation data. We then combined parameter estimates from the expert-elicitation model with those from each of the survey models to predict koala presence and absence in the study area. The model that combined the ground, thermal-imagery and expert-elicitation data substantially reduced the uncertainty around parameter estimates and increased the accuracy of classifications (koala presence vs absence), relative to the model based on ground-survey data alone. Our findings suggest that data elicited from experts using virtual reality technology can be combined with data from other emerging technologies, such as airborne thermal-imagery, using traditional statistical models, to increase the information available for species distribution modelling and the conservation of vulnerable and protected species.

Identifying and applying an optimum set of environmental variables in species distribution models

ABSTRACTPredictive models such as species distribution models (SDMs) are increasingly applied to inform conservation efforts and management decisions. A thoughtful predictor choice, and hence the variable selection process, is a challenge when modeling large communities. Often, variable choice is made for an entire community and not for specific species, resulting in less appropriate predictors for at least some species and affecting model performance and predicted distributions. Using 2 German river catchments as a model system, we investigated (1) application of boosted regression trees (BRTs) as a variable selection procedure to choose the optimal set of environmental predictors and (2) whether model performance is increased by applying custom-made predictor sets to individual species. From a community of 67 benthic macroinvertebrate species, 10 increased in accuracy with the customized predictor set and 10 species decreased in accuracy. Notably, current preference, stream notation, and functional group differed between these species’ groups, which correspond to varied environmental conditions in their known occurrence sites. The species that increased in accuracy showed a preference toward lowland conditions and were far less widespread than the species that decreased in accuracy. We conclude that BRTs are a useful tool for selecting variables for SDMs on large communities. Also, for specialist, rare, or invasive species, determining a species-specific custom-made predictor set may be preferable because species’ preferences may not be representative of the entire study area. Our study describes a structured variable selection approach that can be readily implemented to predict species distributions informative for river management decisions.

Using stable isotopes to estimate migratory connectivity for a patchily distributed, wetland-associated Neotropical migrant

AbstractEstimates of migratory connectivity are needed for full annual cycle population models of migratory bird species experiencing rapid declines in abundance. One technique to determine migratory connectivity is through stable isotope analysis. This low-resolution method may be influenced by how data are calibrated between isotopes measured in precipitation and those measured in feathers, and can be informed by incorporating relative abundance into the assignment model. eBird abundance maps are a new tool combining citizen science data into a predictive species distribution model. In the Prothonotary Warbler (Protonotaria citrea), a wetland-associated songbird with a patchy breeding distribution, we sought to use stable-hydrogen isotope analysis informed by a species-specific calibration equation and eBird abundance data to determine the strength of migratory connectivity. We developed a species-specific calibration equation using known-origin samples from the breeding grounds and found that stable-hydrogen isotope values measured in precipitation explained 50% of the variation in stable-hydrogen isotope values among feathers. We found that the assignment model incorporating eBird abundance data correctly identified the true origins of 66% of individuals, and that the average assignment area (as a measure of precision) was 64% of the breeding distribution. These results represented a 7% increase in precision and a 3% decrease in accuracy when compared to a model that was not informed by abundance. Based on these models, wintering populations from 6 countries represented a mix of likely breeding origins, suggesting low migratory connectivity for Prothonotary Warblers. We found evidence that wintering latitude was related to likely breeding origin, with individuals at western wintering locations more likely to have southern breeding origins, but this relationship was weak. These results corroborate studies using archival light-level geolocators and high-resolution genetic markers, which also demonstrated weak migratory connectivity in this species. For patchily distributed species, eBird abundance data may not provide a useful increase in precision and accuracy for isotope assignments.

The Influence of New Surveillance Data on Predictive Species Distribution Modeling of Aedes aegypti and Aedes albopictus in the United States.

The recent emergence or reemergence of various vector-borne diseases makes the knowledge of disease vectors’ presence and distribution of paramount concern for protecting national human and animal health. While several studies have modeled Aedes aegypti or Aedes albopictus distributions in the past five years, studies at a large scale can miss the complexities that contribute to a species’ distribution. Many localities in the United States have lacked or had sporadic surveillance conducted for these two species. To address these gaps in the current knowledge of Ae. aegypti and Ae. albopictus distributions in the United States, surveillance was focused on areas in Texas at the margins of their known ranges and in localities that had little or no surveillance conducted in the past. This information was used with a global database of occurrence records to create a predictive model of these two species’ distributions in the United States. Additionally, the surveillance data from Texas was used to determine the influence of new data from the margins of a species’ known range on predicted species’ suitability maps. This information is critical in determining where to focus resources for the future and continued surveillance for these two species of medical concern.

Reduction of the gas-phase reaction mechanism of iso-octane for the catalytic oxidation and non-catalytic applications

In this study, catalytic oxidation of iso-octane is investigated for a very fuel-rich and for a fuel-lean condition in a batch reactor. For this purpose, skeletal mechanisms are derived and their results are compared with detailed reaction mechanism results. It is shown that skeletal mechanisms, which have 61–62% less species and 57–59% less reactions than the detailed reaction mechanism, allow an accurate prediction of species distributions in the batch reactor. Hydrogen selectivity decrease and soot precursors increase at fuel-rich condition. Further, the behavior of a gas composition downstream a catalyst is investigated for fuel-rich conditions in a plug-flow reactor. Skeletal mechanism yields similar results to the detailed reaction mechanism and experiment in plug-flow reactor. The results show that iso-octane is completely converted in the beginning of the reactor (0.01–0.05 m) for fuel-rich conditions, while other hydrocarbon species like methane, ethylene, propylene and acetylene are produced within 0.05 m.

Habitat suitability and environmental niche comparison of cold-water coral species along the Brazilian continental margin

In face of increasing anthropogenic disturbance in the deep sea, it is a priority to better understand the regional distribution of cold-water corals (CWC). These organisms create some of the most species-rich habitats in the deep sea and, for this reason, they must be properly protected and managed. In this study, we aimed to identify suitable habitat for multiple CWC taxa off the Brazilian continental margin and compare their environmental niches. Habitat suitability models were developed using the Maxent approach, which allowed for the prediction of species distribution and for the identification of potential ‘hot spot’ areas that may be important for biodiversity conservation. Ecological niches were determined by a PCA-env approach, and niche similarity and equivalence were evaluated based on niche overlap using the Schoener's D metric. Potentially suitable habitat for Octocorallia covered a broad latitudinal range encompassing nearly the entire Brazilian continental margin, whereas Scleractinia had greater potentially suitable habitat in the Central and Southern areas. Scleractinian species were observed to slightly differ in their environmental niche, with non-reef-forming species being more tolerant to a wider range of environmental conditions in comparison with reef-forming species, inhabiting a wider area of the South American continental margin. Due to the high potential suitability for several CWC species, the Central and Southern parts of the Brazilian continental margin should be considered as potential areas high CWC diversity. Considering the current state of the art and strategic assessment tools, these areas are important targets for conservation, management, and environmental impact assessment. Most reef-forming species had similar but not directly equivalent ecological niches, indicating that mapping efforts and management planning should consider CWCs at the species level.

Contrasting microclimates among hedgerows and woodlands across temperate Europe

Hedgerows have the potential to facilitate the persistence and migration of species across landscapes, mostly due to benign microclimatic conditions. This thermal buffering function may become even more important in the future for species migration under climate change. Unfortunately, there is a lack of empirical studies quantifying the microclimate of hedgerows, particularly at broad geographical scales.Here we monitored sub-canopy temperatures using 168 miniature temperature sensors distributed along woodland-hedgerow transects, and spanning a 1600-km macroclimatic gradient across Europe. First, we assessed the variation in the temperature offset (that is, the difference between sub-canopy and corresponding macroclimate temperatures) for minimum, mean and maximum temperatures along the woodland-hedgerow transects. Next, we linked the observed patterns to macroclimate temperatures as well as canopy structure, overstorey composition and hedgerow characteristics.The sub-canopy versus macroclimate temperature offset was on average 0.10 °C lower in hedgerows than in woodlands. Minimum winter temperatures were consistently lower by 0.10 °C in hedgerows than in woodlands, while maximum summer temperatures were 0.80 °C higher, albeit mainly around the woodland-hedgerow ecotone. The temperature offset was often negatively correlated with macroclimate temperatures. The slope of this relationship was lower for maximum temperatures in hedgerows than in woodlands. During summer, canopy cover, tree height and hedgerow width had strong cooling effects on maximum mid-day temperatures in hedgerows. The effects of shrub height, shrub cover and shade-casting ability, however, were not significant.To our knowledge, this is the first study to quantify hedgerow microclimates along a continental-scale environmental gradient. We show that hedgerows are less efficient thermal insulators than woodlands, especially at high ambient temperatures (e.g. on warm summer days). This knowledge will not only result in better predictions of species distribution across fragmented landscapes, but will also help to elaborate efficient strategies for biodiversity conservation and landscape planning.

Predictive multi‐scale occupancy models at range‐wide extents: Effects of habitat and human disturbance on distributions of wetland birds

AbstractAimPredicting distributions is fundamental to ecology, yet hindered by spatially restricted sampling, scale‐dependent relationships and detection error associated with field surveys. Predictive species distribution models (SDMs) are nonetheless vital for conservation of many species. We developed a framework for building predictive SDMs with multi‐scale data and used it to develop range‐wide breeding‐season SDMs for 14 marsh bird species of concern.LocationUSA.MethodsWe built SDMs using data from range‐wide surveys conducted over 14 years, and habitat and disturbance covariates measured at multiple spatial scales. We built hierarchical occupancy models that included heterogeneity in detectability during sampling, and used Bayesian model selection to regulate model complexity (covariates and scales) based explicitly on spatial predictive abilities. We thus integrated model selection for optimizing out‐of‐sample prediction, range‐wide sampling over broad conditions, multi‐scale analyses and scale optimization, and species‐specific detectability for a suite of wide‐ranging species.ResultsDistributions of marsh birds were affected by local wetland conditions, but also by agricultural, urban and hydrologic disturbances operating from local scales (100–500 m) to the watershed level. Variables measuring human disturbances improved prediction for most species, and every species was affected by attributes at >1 scale. Five species showed evidence for continental‐scale range contraction during the study.Main conclusionsWe demonstrate how hierarchical occupancy models can be optimized for prediction across a species' range at the extent of a continent while also accounting for imperfect detection, and thus describe a generalizable approach that can be used for any species. We provide the first data‐driven, empirical SDMs built at the range‐wide extent for most of our 14 study species and demonstrate that previous studies focused on local distributions and the effects of fine‐scale wetland vegetation missed important broadscale drivers of occupancy for marsh birds.

Integrating environmental and neighborhood factors in MaxEnt modeling to predict species distributions: A case study of Aedes albopictus in southeastern Pennsylvania.

Aedes albopictus is a viable vector for several infectious diseases such as Zika, West Nile, Dengue viruses and others. Originating from Asia, this invasive species is rapidly expanding into North American temperate areas and urbanized places causing major concerns for public health. Previous analyses show that warm temperatures and high humidity during the mosquito season are ideal conditions for A. albopictus development, while its distribution is correlated with population density. To better understand A. albopictus expansion into urban places it is important to consider the role of both environmental and neighborhood factors. The present study aims to assess the relative importance of both environmental variables and neighborhood factors in the prediction of A. albopictus’ presence in Southeast Pennsylvania using MaxEnt (version 3.4.1) machine-learning algorithm. Three models are developed that include: (1) exclusively environmental variables, (2) exclusively neighborhood factors, and (3) a combination of environmental variables and neighborhood factors. Outcomes from the three models are compared in terms of variable importance, accuracy, and the spatial distribution of predicted A. albopictus’ presence. All three models predicted the presence of A. albopictus in urban centers, however, each to a different spatial extent. The combined model resulted in the highest accuracy (74.7%) compared to the model with only environmental variables (73.5%) and to the model with only neighborhood factors (72.1%) separately. Although the combined model does not essentially increase the accuracy in the prediction, the spatial patterns of mosquito distribution are different when compared to environmental or neighborhood factors alone. Environmental variables help to explain conditions associated with mosquitoes in suburban/rural areas, while neighborhood factors summarize the local conditions that can also impact mosquito habitats in predominantly urban places. Overall, the present study shows that MaxEnt is suitable for integrating neighborhood factors associated with mosquito presence that can complement and improve species distribution modeling.

Thermal niche predictors of alpine plant species.

Within the context of species distribution models, scrutiny arises from the choice of meaningful environmental predictors. Thermal conditions are not the sole driver, but are the most widely acknowledged abiotic driver of plant life within alpine ecosystems. We linked long-term measurements of direct, plant-relevant, near-surface temperatures to plant species frequency. Across 47 sites located along environmental gradients within the Scandinavian mountain chain, the thermal preferences of 26 focal species of vascular plants, lichens, and bryophytes were explored. Based on partial least-squares regression, we applied a relative importance analysis to derive inductively the thermal variables that were best related to a species' frequency. To discover potential seasonal variability of thermal controls, analyses were both differentiated according to meteorological season and integrated across the entire year. The pronounced interspecies and temporal variability of thermal constraints revealed the thermal niches were much more nuanced and variable than they have commonly been represented. This finding challenges us to present, interrogate, and interpret data representing these thermal niches, which seems to be required in order to move beyond purely probabilistic and correlative descriptions of species' range limits. Thus, this information will help improve predictions of species distributions in complex arctic-alpine landscapes.

Predicting species distribution from fishers’ local ecological knowledge: a new alternative for data-poor management

Many developing countries lack information to manage their endangered species, urging the need for affordable and reliable information. We used Bayesian hierarchical spatial models, with oceanographic variables, to predict the distribution range of Epinephelus marginatus, the dusky grouper, for the entire Southwest Atlantic. We ran a model using scientific information gathered from the literature and another using information gathered from fishers on species presence or absence. In both models, temperature was an important determinant of species occurrence. The predicted occurrence of the dusky grouper overlapped widely (Schoener’s D = 0.71; Warren’s I = 0.91) between the models, despite small differences on the southern and northern extremes of the distribution. These results suggest that basic information provided by fishers on species occurrence in their area can be reliable enough to predict species occurrence over large scales and can be potentially useful for marine spatial planning. Fishers’ knowledge may be an even more viable alternative to data collection than what was previously thought, for countries that both struggle with financial limitations and have urgent conservation needs.

Integrating remote sensing and diver observations to predict the distribution of invasive lionfish on Bahamian coral reefs

MEPS Marine Ecology Progress Series Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsTheme Sections MEPS 623:1-11 (2019) - DOI: https://doi.org/10.3354/meps13067 FEATURE ARTICLE Integrating remote sensing and diver observations to predict the distribution of invasive lionfish on Bahamian coral reefs Alexandra C. D. Davis* Oregon State University, Department of Integrative Biology, 3029 Cordley Hall, 2701 SW Campus Way, Corvallis, OR 97331, USA Present address: Department of Biological Sciences, University of Alberta, CW 405, Biological Sciences Building, Edmonton, AB T6E 4R4, Canada *Corresponding author: acdavis@ualberta.ca ABSTRACT: The ongoing invasion of coral reefs in the greater Caribbean region by Indo-Pacific lionfish (Pterois spp.) poses challenges for managers. Although the dynamics and ecological effects of lionfish invasions have been examined in detail at the scale of individual reefs, relatively little information is available on the patterns of the invasion over larger areas. This study combined species counts and microhabitat observations from SCUBA surveys with satellite-derived bathymetry and habitat data to create predictive species distribution maps of lionfish in a 58 km2 region along the southern edge of the island of Eleuthera in the Bahamas. Models predicted lionfish presence, absence, and density, and were created iteratively using various resolutions and types of data to mimic the datasets that may be available to management in the region. The best-fit model for presence and absence, which combined physical variables derived from remotely sensed satellite data and diver-collected microhabitat data, predicted 89% of lionfish presence and absence. The best-fit model for lionfish density used 2 resolutions of physical habitat data as well as biological data on densities of large native groupers and accurately predicted 67% lionfish density. These results suggest that physical habitat may be more important for initial lionfish presence and that biotic interactions influence lionfish density on the reefs. Understanding the limitations and value of each type of data when creating models may be advantageous for planning targeted lionfish removals and prioritizing sites for conservation efforts. KEY WORDS: Lionfish · Pterois · Species distribution models · Remote sensing · Invasive species · Habitat mapping Full text in pdf format Information about this Feature Article NextCite this article as: Davis ACD (2019) Integrating remote sensing and diver observations to predict the distribution of invasive lionfish on Bahamian coral reefs. Mar Ecol Prog Ser 623:1-11. https://doi.org/10.3354/meps13067 Export citation RSS - Facebook - Tweet - linkedIn Cited by Published in MEPS Vol. 623. Online publication date: July 30, 2019 Print ISSN: 0171-8630; Online ISSN: 1616-1599 Copyright © 2019 Inter-Research.

Effects of altitude, land use and microsites on early life performance of a high mountain tree: Insights from an in situ sowing experiment

AbstractAimUnderstanding the forces that drive range shifts in forest landscapes is imperative for predicting species distributions under anthropogenic climate and land use change. However, empirical studies exploring how these components jointly influence critical early life stages of mountain tree species across environmental gradients are scarce. We used the high mountain tree Polylepis australis as model species to investigate the relative importance of altitude and associated climatic conditions, land use for livestock and microsite characteristics on early life performance.LocationCórdoba Sierras, central Argentina.MethodsWe set up an extensive in situ sowing experiment with a robust split‐plot design that integrated spatial scales ranging from 0.4 m2 subplots at the microsite level (associated with vegetative and microtopographic structures), to livestock exclosure and enclosure plots of several hectares, to an altitudinal gradient of 1,000 m. Components of early life performance were monitored across two subsequent growing seasons.ResultsMicrosite characteristics played a fundamental role in P. australis establishment, whereby interactions with altitude and/or land use suggested alternate mechanisms: facilitation (likely reduced desiccation) dominated at low altitude while at high altitude, abiotic stress (likely intensive frost and radiation) overruled any microsite effects. At mid‐altitude, benefits of competition release prevailed over facilitation and microsite effects gained importance under livestock presence. Inconsistencies between pre‐ and post‐emergence responses illustrated potential trade‐offs between beneficial and detrimental effects of microsite conditions upon performance throughout early life: a favourable location for seeds may abruptly turn adverse for seedlings.Main conclusionsWe unravel how changes in altitude, anthropogenic disturbances and microsite characteristics jointly modulate P. australis performance across stages of early establishment. Such information is fundamental when categorizing specific microhabitats as “safe sites” for tree regeneration especially in mountain environments with high spatio‐temporal heterogeneity.

Tropical bird species richness is strongly associated with patterns of primary productivity captured by the Dynamic Habitat Indices

Biodiversity science and conservation alike require environmental indicators to understand species richness and predict species distribution patterns. The Dynamic Habitat Indices (DHIs) are a set of three indices that summarize annual productivity measures from satellite data for biodiversity applications, and include: a) cumulative annual productivity; b) minimum annual productivity; and c) variation in annual productivity. At global scales and in temperate regions the DHIs predict species diversity patterns well, but the DHIs have not been tested in the tropics, where higher levels of productivity lead to the saturation of many remotely sensed vegetation indices. Our goal was to explain bird species richness patterns based on the DHIs in tropical areas. We related the DHIs to species richness of resident landbirds for five guilds (forest, scrub, grassland, generalist, and all resident birds) based on a) species distribution model (SDM) maps for 217 species, and b) range map for 564 species across Thailand. We also quantified the relative importance of the DHIs in multiple regression models that included two measures of topography, and two climate metrics using multiple regression, best-subsets, and hierarchical partitioning analyses. We found that the three DHIs alone explained forest bird richness best (R2adj 0.61 for both SDM- and rangemap based richness; 0.15–0.54 for the other guilds). When combining the DHIs with topography and climate, the richness of both forest birds and all resident bird species was equally well explained (R2adj 0.85 and 0.67 versus 0.81 and 0.68). Among the three DHIs, cumulative annual productivity had the greatest explanatory power for all guilds based on SDM richness maps (R2adj 0.54–0.61). The strong relationship between the DHIs and bird species richness in Thailand suggests that the DHIs capture energy availability well and are useful in biodiversity assessments and potentially bird conservation in tropical areas.

Integrating anthropogenic factors into regional‐scale species distribution models—A novel application in the imperiled sagebrush biome

Species distribution models (SDMs) that rely on regional-scale environmental variables will play a key role in forecasting species occurrence in the face of climate change. However, in the Anthropocene, a number of local-scale anthropogenic variables, including wildfire history, land-use change, invasive species, and ecological restoration practices can override regional-scale variables to drive patterns of species distribution. Incorporating these human-induced factors into SDMs remains a major research challenge, in part because spatial variability in these factors occurs at fine scales, rendering prediction over regional extents problematic. Here, we used big sagebrush (Artemisia tridentata Nutt.) as a model species to explore whether including human-induced factors improves the fit of the SDM. We applied a Bayesian hurdle spatial approach using 21,753 data points of field-sampled vegetation obtained from the LANDFIRE program to model sagebrush occurrence and cover by incorporating fire history metrics and restoration treatments from 1980 to 2015 throughout the Great Basin of North America. Models including fire attributes and restoration treatments performed better than those including only climate and topographic variables. Number of fires and fire occurrence had the strongest relative effects on big sagebrush occurrence and cover, respectively. The models predicted that the probability of big sagebrush occurrence decreases by 1.2% (95% CI: -6.9%, 0.6%) when one fire occurs and cover decreases by 44.7% (95% CI: -47.9%, -41.3%) if at least one fire occurred over the 36 year period of record. Restoration practices increased the probability of big sagebrush occurrence but had minimal effect on cover. Our results demonstrate the potential value of including disturbance and land management along with climate in models to predict species distributions. As an increasing number of datasets representing land-use history become available, we anticipate that our modeling framework will have broad relevance across a range of biomes and species.