Integrating Species Distribution Research Articles

Climate suitability for the moisture-sensitive conifer species may not be universally declining in a warming world

Amid ongoing and escalating climate change, understanding changes in habitat suitability for forest tree species is crucial for anticipating risks to their growth and ecosystem services. Despite advancements in this area, challenges remain, particularly in addressing spatial and temporal discrepancies in suitability change. This study evaluated habitat suitability changes for Qinghai spruce, a moisture-sensitive conifer species, during the warming period from 1960 to 2020, by integrating species distribution modeling with dendrochronological methods. Maxent model outputs revealed a distinct dipole pattern in habitat suitability changes: an increase in the west (56.2%) and a decrease in the east (43.8%). This dipole pattern was corroborated by the spatial pattern of forest population dynamics, highlighting much higher adult-tree mortality frequency, rate, and density decline in regions with decreased suitability, while showing minimal values in most (3 out of 4) regions with increased suitability. Climate factors and their changes explained 80.9% of the variance in suitability changes. Precipitation change, with a significant positive relationship to suitability changes, contributed the most (54.5%) to this explained variance, followed by post-warming temperature, which contributed 34.3% with a significant negative relationship. Thresholds of +20mm for precipitation change and 0°C for post-warming temperature distinguished regions with a high probability of increased suitability from those with a high probability of decreased suitability. This study provides nuanced insights into the responses of moisture-sensitive conifer species to climate change, helping inform and enhance conservation strategies for Qinghai spruce.

Integrating Systematic Surveys With Historical Data to Model the Distribution of Ornithodoros turicata americanus, a Vector of Epidemiological Concern in North America.

Globally, vector-borne diseases are increasing in distribution and frequency, affecting humans, domestic animals, and wildlife. Science-based management and prevention of these diseases requires a sound understanding of the distribution and environmental requirements of the vectors and hosts involved in disease transmission. Integrated Species Distribution Models (ISDM) account for diverse data types through hierarchical modeling and represent a significant advancement in species distribution modeling. We assessed the distribution of the soft tick subspecies Ornithodoros turicata americanus. This tick species is a potential vector of African swine fever virus (ASFV), a pathogen responsible for an ongoing global epizootic that threatens agroindustry worldwide. Given the novelty of this method, we compared the results to a conventional Maxent SDM and validated the results through data partitioning. Our input for the model consisted of systematically collected detection data from 591 sampled field sites and 12 historical species records, as well as four variables describing climatic and soil characteristics. We found that a combination of climatic variables describing seasonality and temperature extremes, along with the amount of sand in the soil, determined the predicted intensity of occurrence of this tick species. When projected in geographic space, this distribution model predicted 62% of Florida as suitable habitat for this tick species. The ISDM presented a higher TSS and AUC than the Maxent conventional model, while sensitivity was similar between both models. Our case example shows the utility of ISDMs in disease ecology studies and highlights the broad range of geographic suitability for this important disease vector. These results provide important foundational information to inform future risk assessment work for tick-borne relapsing fever surveillance and potential ASF introduction and maintenance in the United States.

PPDC: An online platform for the prediction of plant distributions in China

Abstract The survival and reproduction of plants in a particular region are closely related to the local ecological niche. The use of species distribution models based on the ecological niche concept to predict potential distributions can effectively guide the protection of endangered plants, prevention and control of invasive plants, and plant introduction and ex-situ conservation. However, traditional methods and processes for predicting potential distributions of plants are tedious and complex, requiring the collection and processing of large amounts of data and the manual operation of multiple tools. Therefore, it is difficult to achieve large-scale prediction of the potential distributions of plants. To address these limitations, by collecting and organizing a large amount of basic data, occurrence records, and environmental data and integrating species distribution models and mapping techniques, a workflow to automatically predict the potential distributions of Chinese plants was established, thus the innovative work of predicting the potential distributions of 32,000 species of plants in China was completed. Furthermore, an online platform for predicting plant distributions in China (PPDC) based on visualization technology was developed, providing a basis for sharing the prediction results across a wide range of scientists and technologists. Users can quickly access information about the potential distributions of plants in China, providing a reference for the collection, preservation, and protection of plant resources. In addition, users can quickly predict the potential distribution of a certain plant in a certain region in China according to specific needs, thus providing technical support for research and a basis for biodiversity conservation.

Applying portfolio theory to benefit endangered amphibians in coastal wetlands threatened by climate change, high uncertainty, and significant investment risk

The challenge of selecting strategies to adapt to climate change is complicated by the presence of irreducible uncertainties regarding future conditions. Decisions regarding long-term investments in conservation actions contain significant risk of failure due to these inherent uncertainties. To address this challenge, decision makers need an arsenal of sophisticated but practical tools to help guide spatial conservation strategies. Theory asserts that managing risks can be achieved by diversifying an investment portfolio to include assets – such as stocks and bonds – that respond inversely to one another under a given set of conditions. We demonstrate an approach for formalizing the diversification of conservation assets (land parcels) and actions (restoration, species reintroductions) by using correlation structure to quantify the degree of risk for any proposed management investment. We illustrate a framework for identifying future habitat refugia by integrating species distribution modeling, scenarios of climate change and sea level rise, and impacts to critical habitat. Using the plains coqui (Eleutherodactylus juanariveroi), an endangered amphibian known from only three small wetland populations on Puerto Rico’s coastal plains, we evaluate the distribution of potential refugia under two model parameterizations and four future sea-level rise scenarios. We then apply portfolio theory using two distinct objective functions and eight budget levels to inform investment strategies for mitigating risk and increasing species persistence probability. Models project scenario-specific declines in coastal freshwater wetlands from 2% to nearly 30% and concurrent expansions of transitional marsh and estuarine open water. Conditional on the scenario, island-wide species distribution is predicted to contract by 25% to 90%. Optimal portfolios under the first objective function – benefit maximization – emphasizes translocating frogs to existing protected areas rather than investing in the protection of new habitat. Alternatively, optimal strategies using the second objective function – a risk-benefit tradeoff framework – include significant investment to protect parcels for the purpose of reintroduction or establishing new populations. These findings suggest that leveraging existing protected areas for species persistence, while less costly, may contain excessive risk and could result in diminished conservation benefits. Although our modeling includes numerous assumptions and simplifications, we believe this framework provides useful inference for exploring resource dynamics and developing robust adaptation strategies using an approach that is generalizable to other conservation problems which are spatial or portfolio in nature and subject to unresolvable uncertainty.

A continental-wide decline of occupancy and diversity in five Neotropical carnivores

The Neotropics are a global biodiversity hotspot that has undergone dramatic land use changes over the last decades. However, a temporal perspective on the continental-wide distributions of species in this region is still missing. To unveil it, we model the entire area of occupancy of five Neotropical carnivore species at two time periods (2000–2013 and 2014–2021) using integrated species distribution models (ISDMs) in a Bayesian framework. The carnivores are the jaguarundi (Herpailurus yagouaroundi), margay (Leopardus wiedii), maned wolf (Chrysocyon brachyurus), tayra (Eira barbara), and giant otter (Pteronura brasiliensis). We mapped the temporal change, the areas where gains and losses accumulated for all species (hotspots of change) and calculated the temporal species turnover and change in spatial turnover. We show that (1) most carnivore species have declined their area of occupancy (i.e., range size) in the last two decades, (2) their diversity has decreased over time, mostly in the Chaco region, and (3) that hotspots of fast species composition turnover are in Chaco, the Caatinga region, and northwest of Mexico. We discuss how these newly identified hotspots of change overlap with regions of well-known and pronounced land use transformation. These estimated patterns of overall decline are alarming, more so given that four out of the five species had been classified as not threatened by IUCN. The official global threat status of these species may need to be re-evaluated. All this would be invisible if standard forecasts, local expert knowledge, or static threat criteria, such as range size, were used. We thus provide a new approach to evaluate past species range dynamics based on multiple lines of evidence, which can be employed over more species in the future, particularly in under-sampled regions.

Bayesian integrated species distribution models for hierarchical resource selection by a soaring bird

Migratory birds exhibit seasonal geographic range (hereafter, range) dynamics during the annual cycle. Few studies have examined how migratory birds select their habitats for range occupancy at the species level and space use at the individual level simultaneously. We hypothesized that environmental variables directly related to fitness components would affect the range occupancy probabilities of migrants, whereas environment variables related to movements and flights would affect the space use intensities of migrants. We built Bayesian integrated species distribution models (ISDMs) to evaluate the effects of climate conditions, wind conditions, and landcover compositions on the seasonal range dynamics of American white pelicans Pelecanus erythrorhynchos (hereafter, pelican) during summer and winter. The ISDMs estimated the summer range occupancy probabilities of pelicans with Breeding Bird Survey data, winter range occupancy probabilities with Christmas Bird Count data, and summer and winter space-use intensity rates with eBird data jointly. We evaluated the predictive performance of ISDMs using independent datasets of pelican GPS locations. Integrated species distribution models outperformed the occupancy-only models in the predictive performance of occupancy probabilities. Climate conditions had opposite effects on the range occupancy probabilities between the breeding and non-breeding grounds, whereas landcovers had relatively consistent effects on range occupancy probabilities between the seasons.

Integrating species distribution and piecewise linear regression model to identify functional connectivity thresholds to delimit urban ecological corridors

Urban ecological corridors are essential for sustainable urban development, but determining their width remains challenging. This paper addresses this issue by focusing on the unique habitat requirements of urban undercanopy bird species. We employ Species Distribution Model to simulate their potential living spaces in Shanghai and quantify their functional connectivity in urban mobility. We then use segmented linear regression models to identify turning points in functional connectivity within different buffer zones, which represent the physical width of the corridor. Our findings show that urban undercanopy birds are less sensitive to human activity and building distribution compared to surface temperature, land cover types, and vegetation canopy height. We also find that conventional linear weighting methods tend to overestimate the impact of environmental factors on undercanopy birds, leading to subtle deviations in corridor path recognition. Finally, we demonstrate that employing segmented linear regression helps to quantify the turning points of functional connectivity for each urban ecological corridor, allowing us to determine their physical width range. This study is the first attempt to quantitatively assess the functional connectivity of urban ecological corridors from the perspective of undercanopy birds and demarcate their extent.

Predicting undetected native vascular plant diversity at a global scale

Vascular plants are diverse and a major component of terrestrial ecosystems, yet their geographic distributions remain incomplete. Here, I present a global database of vascular plant distributions by integrating species distribution models calibrated to species' dispersal ability and natural habitats to predict native range maps for 201,681 vascular plant species into unsurveyed areas. Using these maps, I uncover unique patterns of native vascular plant diversity, endemism, and phylogenetic diversity revealing hotspots in underdocumented biodiversity-rich regions. These hotspots, based on detailed species-level maps, show a pronounced latitudinal gradient, strongly supporting the theory of increasing diversity toward the equator. I trained random forest models to extrapolate diversity patterns under unbiased global sampling and identify overlaps with modeled estimations but unveiled cryptic hotspots that were not captured by modeled estimations. Only 29% to 36% of extrapolated plant hotspots are inside protected areas, leaving more than 60% outside and vulnerable. However, the unprotected hotspots harbor species with unique attributes that make them good candidates for conservation prioritization.

Investigating fish reproduction phenology and essential habitats by identifying the main spatio-temporal patterns of fish distribution

Abstract Fish spawning phenology is a major concern for conservation and fisheries management. New intensive data sources, such as GPS-based tracking data and high-resolution catch declaration data, are becoming increasingly available in the field of marine ecology. These data benefit from high spatiotemporal resolution and open new research avenues for investigating the interannual variability in fish phenology. In this paper, we demonstrate how an integrated species distribution model informed by commercial catch data combined with spatiotemporal dimension reduction methods known as empirical orthogonal functions (EOFs) can be used to synthesize spatiotemporal signals in fish reproduction phenology. Specifically, we address the following questions: (1) Can we identify seasonal spatial patterns that can be interpreted in terms of reproductive phenology and essential habitats? (2) Can we identify changes in reproductive phenology over time? (3) Are these changes related to environmental drivers? The analysis illustrates the reproductive phenology of three key commercial species in the Bay of Biscay (sole, hake, and sea bass). The EOF analysis emphasized strong seasonal spatiotemporal patterns that correspond to reproduction patterns and feeding patterns. Based on this methodology, we identified seasonal variations in the timing of reproduction, and we related these variations to sea surface temperature, a key driver of fish reproduction.

Using mobile acoustic monitoring and false‐positive N‐mixture models to estimate bat abundance and population trends

AbstractEstimating the abundance of unmarked animal populations from acoustic data is challenging due to the inability to identify individuals and the need to adjust for observation biases including detectability (false negatives), species misclassification (false positives), and sampling exposure. Acoustic surveys conducted along mobile transects were designed to avoid counting individuals more than once, where raw counts are commonly treated as an index of abundance. More recently, false‐positive abundance models have been developed to estimate abundance while accounting for imperfect detection and misclassification. We adapted these methods to model summertime abundance and trends of three species of bats at multiple spatial scales using acoustic recordings collected along mobile transects by partners of the North American Bat Monitoring Program (NABat) from 2012 to 2020. This multiscale modeling spanned individual transect routes, larger NABat grid cells (10 km × 10 km), and across the entire extent of modeled species ranges. We estimated relationships between species abundances and a suite of abiotic and biotic predictors (landcover types, climatological variables, physiographic diversity, building density, and the impacts of white‐nose syndrome [WNS]) and found varying levels of support between species. We present clear evidence of substantial declines in populations of tricolored bats (Perimyotis subflavus) and little brown bats (Myotis lucifugus), declines that corresponded in space and time with the progression of WNS, a devastating disease of hibernating bats. In contrast, our analysis revealed that similar population‐wide declines probably have not occurred in big brown bats (Eptesicus fuscus), a species known to be less affected by WNS. This study provides the first abundance‐based species distribution predictions and population trends for bats in their summer ranges in North America. These models will probably be applicable to assessing wildlife populations in other monitoring programs where acoustic data are used or where false‐negative and false‐positive detections are present. Finally, our abundance framework (as a spatial point pattern process) can serve as a foundation from which more sophisticated integrated species distribution models that incorporate additional streams of monitoring data (e.g., stationary acoustics, captures) can be developed for North American bats.

Estimating the spatial distribution of the white shark in the Mediterranean Sea via an integrated species distribution model accounting for physical barriers

AbstractConserving oceanic apex predators, such as sharks, is of utmost importance. However, scant abundance and distribution data often challenge understanding the population status of many threatened species. Occurrence records are often scarce and opportunistic, and fieldwork aimed to retrieve additional data is expensive and prone to failure. Integrating various data sources becomes crucial to developing species distribution models for informed sampling and conservation purposes. The white shark, for example, is a rare but persistent inhabitant of the Mediterranean Sea. Here, it is considered Critically Endangered by the IUCN, while population abundance, distribution patterns, and habitat use are still poorly known. This study uses available occurrence records from 1985 to 2021 from diverse sources to construct a spatial log‐Gaussian Cox process, with data‐source specific detection functions and thinning, and accounting for physical barriers. This model estimates white shark presence intensity alongside uncertainty through a Bayesian approach with Integrated Nested Laplace Approximation (INLA) and the inlabru R package. For the first time, we projected species occurrence hot spots and landscapes of relative abundance (continuous measure of animal density in space) throughout the Mediterranean Sea. This approach can be used with other rare species for which presence‐only data from different sources are available.

Integrating presence‐only and detection/non‐detection data to estimate distributions and expected abundance of difficult‐to‐monitor species on a landscape‐scale

Abstract Estimating species distribution and abundance is foundational to effective management and conservation. Using an integrated species distribution model that combines presence‐only data from various sources with detection/non‐detection data from structured surveys, we estimated the distribution and expected abundance of three difficult‐to‐monitor mammals of management concern across New York State, namely, coyotes (Canis latrans), bobcats (Lynx rufus) and black bears (Ursus americanus). Three distinct landscape‐scale camera trap surveys provided detection/non‐detection data over 9 years between 2013 and 2021, and we augmented those data with incidental records of our focal species from public repositories. We used an inhomogeneous Poisson point process to construct an integrated model that fit both data types simultaneously. We demonstrate a simple application of spatial point density of all species records in the accessed public databases to inform the thinning process to account for unknown spatial sampling in the presence‐only data, often referred to as the ‘magic covariate’. Using this approach, we examine habitat associations and provide spatially explicit estimates in expected abundance across the entirety of New York State for all three focal species. As expected, coyotes were the most widely distributed and abundant species, with a strong positive association with agricultural land uses. Bobcats exhibited low expected abundance throughout the state and showed positive associations with deciduous forest and forest edge, and a negative association with road density. Finally, we observed considerable spatial variation in abundance of black bears with expected abundance increasing in association with various forest cover and composition covariates and decreasing with crop cover. We present insights into habitat associations and spatial variation in abundance, and provide management implications for each of the species of interest. Synthesis and applications. Our integrated modelling method allows for managers to use citizen sightings combined with detection/non‐detection surveys to estimate robust indices of abundance for both high‐ and low‐density, and wide‐spread versus patchily distributed species. Through comparison with previous studies, we highlight how broad‐scale programmes, such as the statewide efforts to estimate species distributions undertaken here, can benefit substantively from integrated models that leverage additional data (here, incidental records) from a larger region of space, and thus capture more landscape heterogeneity than is plausible within formalized surveys alone.

Exploring the Climatic Niche Evolution of the Genus Falco (Aves: Falconidae) in Europe.

By integrating species distribution modeling techniques, phylogenetic comparative methods, and climatic data, we analyzed how European falcon climatic niches have changed over evolutionary time in order to understand their tempo and mode of evolution and gain phylogenetic insights related to the ecological context of falcon evolution. For this purpose, we tested the relative contributions of niche conservatism, convergent evolution, and divergent evolution in the evolutionary history of this group of species in Europe. The occupation of climatic niche spaces by falcon species in Europe was not similar, considering that their climatic niche evolution was characterized by heterotachy, especially after ca. 4 Mya. Our results indicate that convergent evolution and niche divergence played an important role in the evolutionary history of these species, with no significant evidence of closely related species retaining their fundamental niche over time (phylogenetic niche conservatism). In most analyses, less closely related falcon species occupied similar climatic environments. We found that speciation in the European genus Falco was influenced by climatic niche differentiation, more prevalent in the last 4 million years, with the main climatic niche shifts occurring between closely related falcon species.

Integrated species distribution models to account for sampling biases and improve range‐wide occurrence predictions

AbstractAimSpecies distribution models (SDMs) that integrate presence‐only and presence–absence data offer a promising avenue to improve information on species' geographic distributions. The use of such ‘integrated SDMs’ on a species range‐wide extent has been constrained by the often limited presence–absence data and by the heterogeneous sampling of the presence‐only data. Here, we evaluate integrated SDMs for studying species ranges with a novel expert range map‐based evaluation. We build new understanding about how integrated SDMs address issues of estimation accuracy and data deficiency and thereby offer advantages over traditional SDMs.LocationSouth and Central America.Time Period1979–2017.Major Taxa StudiedHummingbirds.MethodsWe build integrated SDMs by linking two observation models – one for each data type – to the same underlying spatial process. We validate SDMs with two schemes: (i) cross‐validation with presence–absence data and (ii) comparison with respect to the species' whole range as defined with IUCN range maps. We also compare models relative to the estimated response curves and compute the association between the benefit of the data integration and the number of presence records in each data set.ResultsThe integrated SDM accounting for the spatially varying sampling intensity of the presence‐only data was one of the top performing models in both model validation schemes. Presence‐only data alleviated overly large niche estimates, and data integration was beneficial compared to modelling solely presence‐only data for species which had few presence points when predicting the species' whole range. On the community level, integrated models improved the species richness prediction.Main ConclusionsIntegrated SDMs combining presence‐only and presence–absence data are successfully able to borrow strengths from both data types and offer improved predictions of species' ranges. Integrated SDMs can potentially alleviate the impacts of taxonomically and geographically uneven sampling and to leverage the detailed sampling information in presence–absence data.

A fast method for fitting integrated species distribution models

Abstract Integrated distribution models (IDMs) predict where species might occur using data from multiple sources, a technique thought to be especially useful when data from any individual source are scarce. Recent advances allow us to fit such models with latent terms to account for dependence within and between data sources, but they are computationally challenging to fit. We propose a fast new methodology for fitting integrated distribution models using presence/absence and presence‐only data, via a spatial random effects approach combined with automatic differentiation. We have written an R package (called scampr) for straightforward implementation of our approach. We use simulation to demonstrate that our approach has comparable performance to INLA—a common framework for fitting IDMs—but with computation times up to an order of magnitude faster. We also use simulation to look at when IDMs can be expected to outperform models fitted to a single data source, and find that the amount of benefit gained from using an IDM is a function of the relative amount of additional information available from incorporating a second data source into the model. We apply our method to predict 29 plant species in NSW, Australia, and find particular benefit in predictive performance when data from a single source are scarce and when compared to models for presence‐only data. Our faster methods of fitting IDMs make it feasible to more deeply explore the model space (e.g. comparing different ways to model latent terms), and in future work, to consider extensions to more complex models, for example the multi‐species setting.

Long-distance dispersal drives the genetic variation and historical demography of Quercus magnoliifolia and Quercus resinosa (Fagaceae) in the Mexican highlands

While hybridization and introgression can have a strong adaptive importance, it can impede divergence of species. Quercus magnoliifolia and Q. resinosa are two endemic oak species distributed across the Mexican highlands. These species diverged ecological and morphologically; however, no nuclear genetic differentiation is evident. In this study, we determined the mechanisms that shape patterns of genetic variation and establish the role of migration and hybridization in the evolutionary history of these two oak species. To do this, Bayesian approaches were used for inference on migration rates and directionality and timing of divergence between species using chloroplast microsatellites. We then integrated species distribution models to infer the geographic distribution of Q. magnoliifolia and Q. resinosa during Last Interglacial, Last Glacial Maximum, and Mid-Holocene time frames. We failed in distinguishing a unique genetic composition for each species. Chloroplast differentiation was more congruent with geography than the taxonomic status of each species. Our study revealed that after the divergence (c. 10 Mya) of these two oak species, high rates of introgression took place at the end of the Pleistocene. Furthermore, past distribution models predicted that Q. magnoliifolia and Q. resinosa have likely been in sympatry presumable since Last Glacial Maximum and Q. resinosa probably expanded geographically towards its current distribution around Mid-Holocene. This expansion was supported by testing migration models, suggesting recent establishment of Q. resinosa to the north of Trans-Mexican Volcanic Belt. We hypothesized that after Q. magnoliifolia and Q. resinosa diverged, colonization events followed by hybridization between oaks and long-distance seed dispersal occurred, explaining the present-day patterns of distribution of chloroplast diversity. We propose that divergence of species remains mainly on loci under natural selection, providing evidence on the “porous” nature of species boundaries among oaks.

Identifying conservation and restoration priorities for degraded coastal wetland vegetations: Integrating species distribution model and GeoDetector

The ongoing degradation of seagrass and seaweed is of global concern. Comprehending the spatial distribution of these wetland vegetation types and the threats they face becomes critical for effective conservation and restoration efforts. In this study, we combined a species distribution model and geographical detector to propose a novel framework for mapping the distribution and disturbance of degraded coastal wetland vegetation in sparsely recorded areas and identifying conservation and restoration priorities. Guangxi is a province in China known for its extensive coastal wetland vegetation. In our study of Guangxi, habitats suitable for two degraded vegetation types, i.e., seagrass and seaweed, were mapped using the maximum entropy model; 669.44 km2 of seagrass habitat and 929.69 km2 of seaweed habitat were identified. The geographical detector model was used to analyze anthropogenic disturbance caused by four local disturbance factors: shoreline development, fisheries, waterways, and ports and anchorages. Shoreline development was identified as the disturbance factor with the strongest impact on potential habitats of both vegetation types. According to these findings, 48.40 %–64.23 % of the vegetation habitats suffered from high anthropogenic disturbance. Preexisting nature reserves had not effectively protected wetland vegetation from human disturbance. Based on the spatial pattern of vegetation habitat and comprehensive anthropogenic disturbance, conservation and restoration priorities for seagrasses and seaweeds covering an area of 302.26 km2 were further mapped. Our results thus help improve wetland vegetation conservation by providing basic information, and they provide a tool to support site planning for seagrass and seaweed conservation and restoration.

An integrated species distribution modelling framework for heterogeneous biodiversity data

Most knowledge about species and habitats is in-homogeneously distributed, with biases existing in space, time and taxonomic and functional knowledge. Yet, controversially the total amount of biodiversity data has never been greater. A key challenge is thus how to make effective use of the various sources of biodiversity data in an integrated manner. Particularly for widely used modelling approaches, such as species distribution models (SDMs), the need for integration is urgent, if spatial and temporal predictions are to be accurate enough in addressing global challenges.Here, I present a modelling framework that brings together several ideas and methodological advances for creating integrated species distribution models (iSDM). The ibis.iSDM R-package is a set of modular convenience functions that allows the integration of different data sources, such as presence-only, community survey, expert ranges or species habitat preferences, in a single model or ensemble of models. Further it supports convenient parameter transformations and tuning, data preparation helpers and allows the creation of spatial-temporal projections and scenarios. Ecological constraints such as projection limits, dispersal, connectivity or adaptability can be added in a modular fashion thus helping to prevent unrealistic estimates of species distribution changes.The ibis.iSDM R-package makes use of a series of methodological advances and is aimed to be a vehicle for creating more realistic and constrained spatial predictions. Besides providing convenience functions for a range of different statistical models as well as an increasing number of wrappers for mechanistic modules, ibis.iSDM also introduces several innovative concepts such as sequential or weighted integration, or thresholding by prediction uncertainty. The overall framework will be continued to be improved and further functionalities be added.

A hierarchical modeling approach to predict the distribution and density of Sierra Nevada Red Fox (Vulpes vulpes necator).

Carnivores play critical roles in ecosystems, yet many species are declining worldwide. The Sierra Nevada Red Fox (Vulpes vulpes necator; SNRF) is a rare and endangered subspecies of red fox limited to upper montane forests, subalpine, and alpine environments of California and Oregon, United States. Having experienced significant distribution contractions and population declines in the last century, the subspecies is listed as at-risk by relevant federal and state agencies. Updated information on its contemporary distribution and density is needed to guide and evaluate conservation and management actions. We combined 12 years (2009-2020) of detection and nondetection data collected throughout California and Oregon to model the potential distribution and density of SNRFs throughout their historical and contemporary ranges. We used an integrated species distribution and density modeling approach, which predicted SNRF density in sampled locations based on observed relationships between environmental covariates and detection frequencies, and then projected those predictions to unsampled locations based on the estimated correlations with environmental covariates. This approach provided predictions that serve as density estimates in sampled regions and projections in unsampled areas. Our model predicted a density of 1.06 (95% credible interval = 0.8-1.36) foxes per 100 km2 distributed throughout 22,926 km2 in three distinct regions of California and Oregon-Sierra Nevada, Lassen Peak, and Oregon Cascades. SNRFs were most likely to be found in areas with low minimum temperatures and high snow water equivalent. Our results provide a contemporary baseline to inform the development and evaluation of conservation and management actions, and guide future survey efforts.

Combining fishery data through integrated species distribution models

Abstract Species Distribution Models are pivotal for fisheries management. There has been an increasing number of fishery data sources available, making data integration an attractive way to improve model predictions. A wide range of methods have been applied to integrate different datasets in different disciplines. We focus on the use of Integrated Species Distribution Models (ISDMs) due to their capacity to formally accommodate different types of data and scale proportional gear efficiencies. ISDMs use joint modelling to integrate information from different data sources to improve parameter estimation by fitting shared environmental, temporal and spatial effects. We illustrate this method first using a simulated example, and then apply it to a case study that combines data coming from a fishery-independent trawl survey and a fishery-dependent trammel net observations on Solea solea. We explore the sensitivity of model outputs to several weightings for the commercial data and also compare integrated model results with ensemble modelling to combine population trends in the case study. We obtain similar results but discuss that ensemble modelling requires both response variables and link functions to be the same across models. We conclude by discussing the flexibility and requirements of ISDMs to formally combine different fishery datasets.