Connectivity is a central concept in ecology, wildlife management and conservation science. Understanding the role of connectivity in determining species persistence is increasingly important in the face of escalating anthropogenic impacts on climate and habitat. These connectivity augmenting processes can severely impact species distributions and community and ecosystem functioning. One general definition of connectivity is an emergent process arising from a set of spatial interdependencies between individuals or populations, and increasingly realistic representations of connectivity are being sought. Generally, connectivity consists of a structural component, relating to the distribution of suitable and unsuitable habitat, and a functional component, relating to movement behavior, yet the interaction of both components often better describes ecological processes. Additionally, although implied by ‘movement’, demographic measures such as the occurrence or abundance of organisms are regularly overlooked when quantifying connectivity. Integrating demographic contributions based on the knowledge of species distribution patterns is critical to understanding the dynamics of spatially structured populations. Demographically-informed connectivity draws from fundamental concepts in metapopulation ecology while maintaining important conceptual developments from landscape ecology, and the methodological development of spatially-explicit hierarchical statistical models that have the potential to overcome modeling and data challenges. Together, this offers a promising framework for developing ecologically realistic connectivity metrics. This review synthesizes existing approaches for quantifying connectivity and advocates for demographically-informed connectivity as a general framework for addressing current problems across ecological fields reliant on connectivity-driven processes such as population ecology, conservation biology, and landscape ecology. Using supporting simulations to highlight the consequences of commonly made assumptions that overlook important demographic contributions, we show that even small amounts of demographic information can greatly improve model performance. Ultimately, we argue demographic measures are central to extending the concept of connectivity and resolves long-standing challenges associated with accurately quantifying the influence of connectivity on fundamental ecological processes.

Methods This file contains simulation code implemented in R that created data used in the manuscript DOI:10.1111/ecog.05552

As well, The data the simulation code creates is provided as the simulation does take some time to run (up to several weeks depending on the parameter combinations).

These will be found in 4 zips, each reflecting a different scenario found in the text. Combinations of the patch area to abundance relationship or it being disrupted; this intersects with whether those abundance are high or low within simulated patches. Within each of these will be found the model runs that correspond to combinations of 5 and 10 years and 30, 50, and 100 patches.

Rapid global climate change is resulting in novel abiotic and biotic conditions and interactions. Identifying management strategies that maximize probability of long-term persistence requires an understanding of the vulnerability of species to environmental changes. We sought to quantify the vulnerability of Kirtland’s Warbler (Setophaga kirtlandii), a rare Neotropical migratory songbird that breeds almost exclusively in the Lower Peninsula of Michigan and winters in the Bahamian Archipelago, to projected environmental changes on the breeding and wintering grounds. We developed a population-level simulation model that incorporates the influence of annual environmental conditions on the breeding and wintering grounds, and parameterized the model using empirical relationships. We simulated independent and additive effects of reduced breeding grounds habitat quantity and quality, and wintering grounds habitat quality, on population viability. Our results indicated the Kirtland’s Warbler population is stable under current environmental and management conditions. Reduced breeding grounds habitat quantity resulted in reductions of the stable population size, but did not cause extinction under the scenarios we examined. In contrast, projected large reductions in wintering grounds precipitation caused the population to decline, with risk of extinction magnified when breeding habitat quantity or quality also decreased. Our study indicates that probability of long-term persistence for Kirtland’s Warbler will depend on climate change impacts to wintering grounds habitat quality, and contributes to the growing literature documenting the importance of considering the full annual cycle for understanding population dynamics of migratory species. KIWA STELLA ModelText file containing the code needed to create the KIWA simulation model in STELLA, organized by model sector. The habitat module code shows input values for contemporary conditions and each environmental change scenario.

1. In species providing extended parental care, one or both parents care for altricial young over a period including more than one breeding season. We expect large parental investment and long-term dependency within family units to cause high variability in life trajectories among individuals with complex consequences at the population level. So far, models for estimating demographic parameters in free-ranging animal populations mostly ignore extended parental care, thereby limiting our understanding of its consequences on parents and offspring life histories.

2. We designed a capture-recapture multi-event model for studying the demography of species providing extended parental care. It handles statistical multiple-year dependency among individual demographic parameters grouped within family units, variable litter size, and uncertainty on the timing at offspring independence. It allows for the evaluation of trade-offs among demographic parameters, the influence of past reproductive history on the caring parent’s survival status, breeding probability and litter size probability, while accounting for imperfect detection of family units. We assess the model performance using simulated data, and illustrate its use with a long-term dataset collected on the Svalbard polar bears (Ursus maritimus).

3. Our model performed well in terms of bias and mean square error and in estimating demographic parameters in all simulated scenarios, both when offspring departure probability from the family unit occurred at a constant rate or varied during the field season depending on the date of capture. For the polar bear case study, we provide estimates of adult and dependent offspring survival rates, breeding probability and litter size probability. Results showed that the outcome of the previous reproduction influenced breeding probability.

4. Overall, our results show the importance of accounting for i) the multiple-year statistical dependency within family units, ii) uncertainty on the timing at offspring independence, and iii) past reproductive history of the caring parent. If ignored, estimates obtained for breeding probability, litter size, and survival can be biased. This is of interest in terms of conservation because species providing extended parental care are often long-living mammals vulnerable or threatened with extinction.

Methods Polar bears were caught and individually marked as part of a long-term monitoring program on the ecology of polar bears in the Barents Sea region (Derocher 2005). All bears one year or older were immobilized by remote injection of a dart (Palmer Cap-Chur Equipment, Douglasville, GA, USA) with the drug Zoletil® (Virbac, Carros, France) (Stirling et al. 1989). The dart was fired from a small helicopter (Eurocopter 350 B2 or B3), usually from a distance of about 4 to 10 meters. Cubs of the year were immobilized by injection with a syringe. Cubs and yearlings were highly dependent on their mother; therefore, they remained in her vicinity and were captured together with their mother.

The file "CR.txt" contains the capture-histories of n= 158 family units captured between 1992 to 2019 arranged in a matrix with the status of each family unit (in rows) provided each year (in columns). Code 1 is for two-year-old independent juvenile female, code 2 is three-year-old independent juvenile female, code 3 is four-year-old subadult female, code 4 is four-year-old subadult female, code 5 is adult female with one cub, code 6 is adult female with two cubs, code 7 is adult female with one yearling, code 8 is adult female with two yearlings, code 9 is adult female with one depdendent two-year-old bear, code 10 is adult female with two dependent two-year-old bears, code 11 is adult female without dependent offspring, code 0 means a no-capture event.

The file "captureday.txt" contains the date of capture, in day of the year, for each family unit (in row) each year (in column). "NA" means a no-capture event.

The file "weaning.txt" contains the status (code 1 is independent from its mother, code 0 means still dependent from its mother), identification number and date of capture of each two-year-old bear (n=120 males and females) captured on the field.

[THIS DATASET HAS BEEN WITHDRAWN]. Site indices, as a relative measure of the actual population size, for UK butterfly species calculated from data from the UK Butterfly Monitoring Scheme (UKBMS). Site indices are a relative rather than an absolute measure of the size of a population, and have been shown to relate closely to other, more intensive, measures of population size such as mark, release, recapture (MRR) methods. The site index can be thought of as a relative measure of the actual population size, being a more or less constant proportion of the number of butterflies present. The proportion seen is likely to vary according to species; some butterfly species are more conspicuous and thus more easily detected, whereas others are much less easy to see. Site indices are only calculated at sites with sufficient monitoring visits throughout the season, or for targeted reduced effort surveys (timed observations, larval web counts and egg counts) where counts are generally obtained as close to the peak of the flight period as possible and are subsequently adjusted for the time of year and size of the site (area of suitable habitat type for a given species). Wider Countryside Butterfly Survey (WCBS) sites are thus excluded because they are based on very few visits from which accurate indices of abundance cannot currently be calculated. For transect sites a statistical model (a General Additive Model, 'GAM') is used to impute missing values and to calculate a site index. Each year most transect sites (over 90%) produce an index for at least one species and in recent years site indices are calculated for almost 1,500 sites across the UK. Site indices are subsequently collated to contribute to the overall 'Collated Index' for each species, which are relative measures of the abundance of each species across a geographical area, for example, across the whole UK or at country level in England, Scotland, Wales or Northern Ireland. Individual site indices are important in informing conservation management as not all sites show the same patterns for each species and likely reflect a combination of local climate and habitat management at the site. Although the Centre for Ecology & Hydrology (CEH) and Butterfly Conservation (BC) are responsible for the calculation and interpretation of site indices, the collection of the data used in its creation is ultimately reliant on a large volunteer community. The UKBMS is run by BC, the CEH and the British Trust for Ornithology (BTO), in partnership with the Joint Nature Conservation Committee (JNCC), and supported and steered by Forestry Commission (FC), Natural England (NE), Natural Resources Wales (NRW), Northern Ireland Environment Agency (NIEA), and Scottish Natural Heritage (SNH). The UKBMS is indebted to all volunteers who contribute data to the scheme. Full details about this dataset can be found at https://doi.org/10.5285/cec0fd19-688b-4e3e-8332-cf6506bb4612

Aim: A common pattern in biogeography is the scale-dependent effect of environmental variables on the spatial distribution of species. We tested the role of climatic and land cover variables in structuring the distribution of genetic variation in the grey long-eared bat, Plecotus austriacus, across spatial scales. Although landscape genetics has been widely used to describe spatial patterns of gene flow in a variety of taxa, volant animals have generally been neglected because of their perceived high dispersal potential.Location: England and Europe. Methods: We used a multiscale integrated approach, combining population genetics with species distribution modelling and geographical information under a causal modelling framework, to identify landscape barriers to gene flow and their effect on population structure and conservation status. Genotyping involved 23 polymorphic microsatellites and 259 samples from across the species' range. Results: We identified distinct population structure shaped by geographical barriers and evidence of population fragmentation at the northern edge of the range. Habitat suitability (as captured by species distribution models, SDMs) was the most important landscape variable affecting genetic connectivity at the broad spatial scale, while at the fine scale, lowland unimproved grasslands, the main foraging habitat of P. austriacus, played a pivotal role in promoting genetic connectivity. Main conclusions: The importance of lowland unimproved grasslands in determining the biogeography and genetic connectivity in P. austriacus highlights the importance of their conservation as part of a wider landscape management for fragmented edge populations. This study illustrates the value of using SDMs in landscape genetics and highlights the need for multiscale approaches when studying genetic connectivity in volant animals or taxa with similar dispersal abilities.