Information was obtained from the ANARE Health Register. See Metadata record entitled ANARE Health Register.

INDICATOR DEFINITION Human population in stations and ships expressed in person-days.

TYPE OF INDICATOR There are three types of indicators used in this report: 1.Describes the CONDITION of important elements of a system; 2.Show the extent of the major PRESSURES exerted on a system; 3.Determine RESPONSES to either condition or changes in the condition of a system.

This indicator is one of: PRESSURE

RATIONALE FOR INDICATOR SELECTION It is generally accepted that the potential impact on the natural environment is proportional to the human population. This is the 'human footprint'. Human activities can cause disruption in physical, chemical and biological systems. As stated by the Australian Bureau of Statistics (1996): 'To understand the human impact on the Australian environment, it is necessary to know how many people live here, and how they are distributed across the continent.'

This indicator reveals where the greatest direct pressures related to size of the human population (e.g. fuel usage, sewerage and other waste generation etc) occur.

DESIGN AND STRATEGY FOR INDICATOR MONITORING PROGRAM Spatial scale: Antarctic and sub-Antarctic stations and ANARE ships travelling to and from these stations.

Frequency: Monthly figures reported annually.

Measurement technique: The Polar Medicine Branch collects data on all expeditioner movements. These data are entered into the Health Register and updated as personnel arrive on or leave a station.

RESEARCH ISSUES Now that this figure is available, research is required to ascertain the quantitive relationships of station and ship population to other indicators such as fuel usage and waste generation. This measure may be able to deliver a quantitative estimate of human pressure on the Antarctic environment.

LINKS TO OTHER INDICATORS SOE Indicator 47 - Number and nature of incidents resulting in environmental impact SOE Indicator 49 - Medical consultations per 1000 person years SOE Indicator 50 - Effluent monitoring - Volume of coastal discharge from Australian stations SOE Indicator 51 - Effluent monitoring - Biological oxygen demand SOE Indicator 52 - Effluent monitoring - Suspended solids content SOE Indicator 53 - Recycled and quarantine waste returned to Australia SOE Indicator 54 - Amount of waste incinerated at Australian Stations SOE Indicator 56 - Monthly fuel usage of the generator sets and boilers SOE Indicator 57 - Monthly total of fuel used by station incinerators SOE Indicator 58 - Monthly total of fuel used by station vehicles SOE Indicator 59 - Monthly electricity usage SOE Indicator 60 - Total helicopter hours SOE Indicator 61 - Total potable water consumption

The fields in this dataset are: Location Date Population (person-days) Illness Rate (per 1000 person years) Injury Rate (per 1000 person years)

Evaluating the status of threatened and endangered salmonid populations requires information on the current status of the threats (e.g., habitat, hatcheries, hydropower, and invasives) and the risk of extinction (e.g., status and trend in the Viable Salmonid Population criteria). For salmonids in the Pacific Northwest, threats generally result in changes to physical and biological characteristi...

Significance of selected factors to the distribution of S. depressiusculum individuals.

The "Forest Proximate People" (FPP) dataset is one of the data layers contributing to the development of indicator #13, “number of forest-dependent people in extreme poverty,” of the Collaborative Partnership on Forests (CPF) Global Core Set of forest-related indicators (GCS). The FPP dataset provides an estimate of the number of people living in or within 5 kilometers of forests (forest-proximate people) for the year 2019 with a spatial resolution of 100 meters at a global level.

For more detail, such as the theory behind this indicator and the definition of parameters, and to cite this data, see: Newton, P., Castle, S.E., Kinzer, A.T., Miller, D.C., Oldekop, J.A., Linhares-Juvenal, T., Pina, L. Madrid, M., & de Lamo, J. 2022. The number of forest- and tree-proximate people: A new methodology and global estimates. Background Paper to The State of the World’s Forests 2022 report. Rome, FAO.

Contact points:

Maintainer: Leticia Pina

Maintainer: Sarah E., Castle

Data lineage:

The FPP data are generated using Google Earth Engine. Forests are defined by the Copernicus Global Land Cover (CGLC) (Buchhorn et al. 2020) classification system’s definition of forests: tree cover ranging from 15-100%, with or without understory of shrubs and grassland, and including both open and closed forests. Any area classified as forest sized ≥ 1 ha in 2019 was included in this definition. Population density was defined by the WorldPop global population data for 2019 (WorldPop 2018). High density urban populations were excluded from the analysis. High density urban areas were defined as any contiguous area with a total population (using 2019 WorldPop data for population) of at least 50,000 people and comprised of pixels all of which met at least one of two criteria: either the pixel a) had at least 1,500 people per square km, or b) was classified as “built-up” land use by the CGLC dataset (where “built-up” was defined as land covered by buildings and other manmade structures) (Dijkstra et al. 2020). Using these datasets, any rural people living in or within 5 kilometers of forests in 2019 were classified as forest proximate people. Euclidean distance was used as the measure to create a 5-kilometer buffer zone around each forest cover pixel. The scripts for generating the forest-proximate people and the rural-urban datasets using different parameters or for different years are published and available to users. For more detail, such as the theory behind this indicator and the definition of parameters, and to cite this data, see: Newton, P., Castle, S.E., Kinzer, A.T., Miller, D.C., Oldekop, J.A., Linhares-Juvenal, T., Pina, L., Madrid, M., & de Lamo, J. 2022. The number of forest- and tree-proximate people: a new methodology and global estimates. Background Paper to The State of the World’s Forests 2022. Rome, FAO.

References:

Buchhorn, M., Smets, B., Bertels, L., De Roo, B., Lesiv, M., Tsendbazar, N.E., Herold, M., Fritz, S., 2020. Copernicus Global Land Service: Land Cover 100m: collection 3 epoch 2019. Globe.

Dijkstra, L., Florczyk, A.J., Freire, S., Kemper, T., Melchiorri, M., Pesaresi, M. and Schiavina, M., 2020. Applying the degree of urbanisation to the globe: A new harmonised definition reveals a different picture of global urbanisation. Journal of Urban Economics, p.103312.

WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University, 2018. Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00645

Online resources:

GEE asset for "Forest proximate people - 5km cutoff distance"

Metadata record for data from ASAC Project 102 See the link below for public details on this project.

From the abstracts of some of the referenced papers:

Six species of marine microalgae, namely Phaeodactylum tricornutum Bohlin, Dunaliella tertiolecta Butcher, Isochrysis galbana Parke, Porphyridium purpureum (Bory) Ross, Chroomonas sp., and Oscillatoria woronichinii Anis., have been examined with respect to their gas exchange characteristics and the inorganic carbon species taken up by the cells from the bulk medium. All species showed a high affinity, in photosynthesis, for inorganic carbon and low CO2 compensation concentrations. Such data are suggestive of operation of a 'CO2-concentrating mechanism' in these microalgae. Direct measurements of internal organic carbon pools in four of the species studied confirm this (O. woronichinii and Chroomonas were not tested). By comparison of achieved photosynthetic rates with calculated rates of CO2 supply from the dehydration of bicarbonate, it was shown that Phaeodactylum, Porphyridium and Dunaliella could utilise the bicarbonate present in the medium. Data for the other species were inconclusive although the pH dependence of K 1/2CO2 for photosynthesis by Oscillatoria indicated that this species too could utilise bicarbonate. Such observations could, however, not be used as evidence that, at least in the eucaryotic algae examined, bicarbonate was the inorganic carbon species crossing the plasmalemma as Phaeodactylum, Porphyridium and Dunaliella, and Isochrysis all showed the presence of carbonic anhydrase activity in intact cells as well as in crude extracts. 'External' carbonic anhydrase activity represented from 1/4 to 1/2 of the total activity in the cells of these algae. It is concluded that, as a consequence of a CO2-concentrating mechanism, photorespiration was suppressed in the marine microalgae examined although the data obtained did not allow any firm conclusions to be drawn regarding the species of inorganic carbon transported into the cell.

Analysis of the age composition of a given species within a community is fundamental to any study of population dynamics and to the subsequent analyses of community interactions such as competition, succession and productivity. A problem exists in that calendar age often provides little information on the role played by any given individual plant within a population. For many populations the most useful definition of population structure is obtained from an analysis of both the functional age and the vitality of the component plants. Data from such studies on populations of marine macroalgae are lacking mainly because of the lack of suitable methods. This paper provides a review of the methods which have ben applied to such analyses in both terrestrial and marine communities, discusses these methods in the context of marine algae and presents the results of a case study on the analysis of population structure in the large brown alga Durvillaea potatorum.

Evidence is presented for the occurrence of sexual reproduction including plasmogamy and meiosis, events previously undescribed in the life history of Ascoseira mirabilis. Ascoseira is monoecious. Gametangia are formed in chains within conceptacles. Synaptonemal complexes, structures concerned with chromosome pairing in meiosis, have been observed in the nucleus of gametangial initials. Mature male and female gametes have the same size and appearance, and resemble typical brown algal zoids. Sexual interaction begins after the female gamete settles down, and both zygotes and unfused gametes develop into sporophytes. It is concluded that Ascoseira has the same basic pattern of life history that characterises the order Fucales, and it is argued that this is probably the result of convergent evolution rather than being indicative of close phylogenetic relationship.

Life histories are of central importance in understanding evolution and phylogeny of brown algae. Like other hereditary traits, life history characteristics evolve by processes of natural selection, but because they are important determinants of biological fitness they have special evolutionary significance. Concepts of life history, as traditionally applied to brown algae, do not adequately reflect this, and they need to be broadened to include consideration of additional characteristics such as longevity and reproductive span. Life histories can be interpreted as adaptive strategies. Experimental evidence indicates that heteromorphic life histories probably evolved in response to seasonal change. Isomorphic life histories are possible adapted to stale environments, although some may also possess certain features which are adaptations to seasonal change. Life histories that lack an independent gametophyte generation may have evolved through reduction of heteromorphic life histories. It is argued that a significant increase in the longevity of sporophytes may have ben critical...

Abstract

1. Boreal forests provide numerous ecological services, including the ability to store large amounts of carbon, and are of significance to global biodiversity. Increases in industrial activities in boreal landscapes since the mid-20th century have added to concerns over biodiversity loss and climate change. Boreal forests are home to dwindling populations of boreal caribou (Rangifer tarandus caribou) in Canada, a species at risk that requires large, undisturbed landscapes for persistence. In 2012, the Canadian government defined critical habitat for boreal caribou by relating calf recruitment to disturbances. Some have questioned whether the recruitment-relationship can be extrapolated beyond the environmental conditions represented in the analysis.

2. We examined the effects of human disturbances and fire (alone and in combination) on variation in recruitment and adult female survival using data from 58 study areas in Canada. Top models were used in aspatial scenarios of landscape change to evaluate the efficacy of the critical habitat definition in achieving the recovery objectives for boreal caribou in two contrasting landscapes: Little Smoky, dominated by high levels of human disturbances, and SK1, dominated by fire.

3. The top recruitment model suggested the negative effect of fire was 3-4 times smaller than human disturbances. The top adult female survival model included human disturbances only. These results re-affirm that human disturbances are the primary factor contributing to boreal caribou declines.

4. Our aspatial scenarios suggested that undisturbed habitat would have to increase to ≥68% for Little Smoky to maintain a self-sustaining population of boreal caribou with some degree of certainty. In contrast, the SK1 population was self-sustaining with 40% undisturbed habitat when fire disturbance predominates, but could become vulnerable with increases in human disturbances (8–9%). 5. Policy implications: Our results suggest that the 65% undisturbed critical habitat designation may serve as a reasonable proxy for achieving boreal caribou recovery in landscapes dominated by human disturbances. However, some populations may be less or more vulnerable, as illustrated by the SK1 scenarios. Continued population monitoring will be essential to assessing the effectiveness of land management strategies developed for boreal caribou recovery, especially with climate change.

Methods Estimates of disturbance were calculated for 58 study areas with demographic data on boreal caribou. We derived estimates of anthropogenic disturbances, buffered by 500 m, from maps prepared by Environment and Climate Change Canada (Environment Canada, 2011; Pasher et al., 2013; Environment and Climate Change Canada, 2018). Provincial and territorial jurisdictions provided the fire data. Fire disturbance was characterized as unbuffered areas burnt 40 years prior to the first year of available demographic data used in the analyses.

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.

Pocillopora damicornis was sampled from 28 sites in the northern, central and southern regions of the Great Barrier Reef. In the northern region samples were collected from Martin Reef (1 site), Eyrie Reef (1 site), Lizard Island (9 sites), MacGillivray Reef (1 site) and Yonge Reef (1 site). In the central region samples were collected from Fantome Island (1 site), Orpheus Island (4 sites), Pelorus Island (3 sites), Trunk Reef (2 sites), Dip Reef (1 site) and Myrmidon Reef (1 site). Samples were collected in the southern region from Chinaman Reef (1 site), East Cay Reef (1 site) and Frigate Reef (1 site).

Fragments of Pocillopora damicornis, approximately two cm long, were collected from each of 1040 colonies. Each colony was photographed and the location recorded by towing a handheld Garmin Etrex GPS unit in a waterproof container on the surface. Between 6 and 99 individuals were sampled per site. Collections were made haphazardly on the upper reef slope along a zigzag transect approximately 200 m long, between 2 m and 10 m of depth. Unattached or asymmetrical colonies within one metre of a colony already sampled were not sampled due to the possibility that these were clones by fragmentation. Coral branches were fixed in absolute ethanol.

DNA was extracted using a modified protocol of the salt precipitation method. Samples were genotyped using nine microsatellite markers ( xxxxxx) according to the multiplex groups, primers and protocols described in Torda et al. (2013). Samples from four sites, Dip Reef, Chinaman Reef, East Cay Reef and Frigate Reef were not genotyped for marker Pd4.

To determine the lineage identity of each sample, a rapid genetic assay was used. The vast majority of samples (72%; i.e.745 samples) were 118 identified as Type alpha. The remaining samples were Type beta (22%; i.e. 228 samples), 'other Pocillopora' (5%; i.e. 56 samples) or did not give reliable results (1%; 11 samples) (Table 1). All subsequent analyses were carried out on Type alpha and Type beta samples separately, omitting 'other Pocillopora' and unidentified samples, which potentially include the poorly resolved genetic lineage Type gamma and P. verrucosa (Schmidt-Roach et al. 2013).

Data analyses To assess the discriminative power of sets of loci, Genotype Probability (GP) was calculated for each sample and each locus in GENALEX 6.4 (Peakall & Smouse 2006). Repeated multilocus genotypes (MLG) were considered to be clone mates if the product of GP for all 128 loci was < 0.001. Allopatric clone mates (for this study defined as originating from different 129 sampling sites) were kept, while all but one copy of sympatric clone mates were removed 130 before subsequent analyses. Deviations of populations from Hardy¿Weinberg Equilibrium (HWE) and genotypic linkage disequilibrium (LD) were tested in Genepop web version 4.0.10 (Raymond & Rousset 1995; Rousset 2008) using the log likelihood ratio statistic (G-test). Descriptive statistics were obtained in GENALEX. The Fis analogue Gis was calculated in GenoDive (Meirmans & Van 136 Tienderen 2004). Allelic richness was calculated in FSTAT v2.9.3.2 (Goudet 1995) for each locus and population. Populations that lacked data for a locus (DIP, CH, EC and FR for locus Pd4) were excluded from analyses based on all nine microsatellite loci, and instead analysed separately for the eight loci for which data were available. Allelic richness was compared between populations with Kruskal-Wallis tests.

To estimate genetic differentiation among populations, we used Dest (Jost 2008), calculated in SMOGD 1.2.5 (Crawford 2010), because this statistic is not sensitive to genetic diversity and because it accounts for both migration and mutation rates, being based on the finite island model. Significance levels of Dest values were determined by a permutation test, randomizing alleles over all compared populations, using R code from Alberto et al. (2011). For easier comparison with results of other studies, we explored other statistics as well, including (i) uncorrected pairwise Fst values by the ¿weighted" analysis of variance method (Weir & Cockerham 1984), as implemented in Genepop; (ii) the standardised pairwise F¿st, estimated using an AMOVA (Meirmans 2006) in GenoDive; (iii) pairwise Fst values corrected for null alleles (ENA correction), computed in FreeNA (Chapuis & Estoup 2007). To account for unbalanced sample sizes, the significance of uncorrected Fst values was assessed by a Fisher exact test (Goudet 1995) in Genepop with the default Markov chain parameters. To facilitate direct comparison of our results with those of previous allozyme studies, we also carried out a hierarchical analysis of standardised genetic variance as Weir & Cockerham¿s (1984) ¿ using the program TFPGA, following Ayre & Hughes (2000). To approximate the sampling design of Ayre & Hughes (2000), only samples collected from around Lizard Island and the Palm Islands were used for this analysis and samples from Type ¿ and s were pooled, as information on these distinct genetic lineages was not available in the earlier studies.

To detect putative first generation migrants, the probability that each individual belongs to each reference population was computed in GeneClass2 (Piry et al. 2004) using the criteria and probability computation algorithm of Rannala and Mountain (1997), with 10,000 simulated genotypes. Individuals were excluded from a reference population if the probability of exclusion was greater than 0.99 (¿<=0.01), and assigned to another reference population as a potential source if assignment probabilities were greater than 0.1. Genetic structuring of samples without prior definition of populations was analysed using the Bayesian clustering method implemented in InStruct (Gao et al. 2007). As opposed to the more commonly used method implemented in Structure (Pritchard et al. 2000), InStruct accounts for potential selfing. Five independent chains were run for each K from K = 1 to K = 20, with a burn-in of 100,000 and another 100,000 MCMC replications after the burn-in, using the ¿infer population structure and population selfing rates¿ function with the default samplers.

To assist in the interpretation of the results of the genetic analyses, the potential dispersal capacity of brooded larvae was simulated using virtual Lagrangian particle transport modelling in the 0.025° x 0.025°-cell circulation model of the GBR in Connie 2.0 (CSIRO Connectivity Interface, http://www.csiro.au/connie2/). Collection sites were selected as both sources and sinks for dispersal of passive particles at a depth of 5 m over a dispersal period of 1, 15, 50 and 100 days.

The movement of organisms between habitats is a fundamental process that occurs within all ecological disciplines. Once the organism has entered a recipient habitat, its behavior generates a movement pattern as it may remain, move on, return, or constantly move between, producing different population dynamics and environmental changes. Originally, “spillover” was one of these distinct patterns - the uni-directional movement from a donor habitat to a different, adjacent recipient habitat. However, ecological definitions of spillover have become overly generalized to include any cross-habitat movement. As spillover research has nearly doubled since 2018, it is imperative to have universal nomenclature and methods that can quantify the term. This will allow us to advance our understanding of organism behavior in dynamic landscapes. To assess how spillover has been addressed within empirical literature, we reviewed 337 studies and organized them into the ecological disciplines of agriculture, disease, fisheries, and habitat fragmentation. Each study’s definition of spillover and the methods used to quantify the term was analyzed using four definition criteria that differentiates different cross-habitat movement patterns. We then determined the specificity of the definition and if the methods used matched the definition provided. Authors often define spillover as a movement (45%) but assess the location of organisms instead (96%). Additionally, 98% of studies assume direct movement out of the donor habitat, which can lead to an over-estimation of movement distance and spillover effect within the recipient habit. Overall, few studies (12%) included methods that matched their own definition, revealing a distinct mismatch. This was particularly noticeable within the fisheries discipline, likely the result of studies incorporating 1.5-fold more criteria within their definitions. There is much theory of the potential impact of organism movement, yet different movement patterns are often not differentiated empirically. Thus, the actual impact within natural systems is unclear. This ambiguity additionally limits communication and collaboration that need universal definitions and methodology. Techniques that quantify movement directly, such as tracking and capture-mark-recapture, are more suited to understanding effective long-term management implications and the impacts of disturbance on populations and communities. Methods We compiled a database of spillover studies that were found in the Web of Science (http:www.webofknowledge.com) records up to March 31, 2021. We used the search terms “spillover” and “spill over”. We then excluded categories outside of the biology discipline (e.g., economic or chemical spillover) as well as human epidemiology literature, which uses language very distinct from ecological work. As the remaining publications included several hundred papers disparate from organismal spillover, we further refined our search using the terms “organism”, “biodiversity”, or “population”. Articles included in our database 1) specifically mentioned “spillover” or “spill over” within the text, 2) used living organisms as the propagule of movement, and 3) empirically assessed spillover or the consequences of spillover. In this way, we collected a broad spectrum of papers that included ecological, behavioral, evolutionary, applied, and basic science articles.

Details on the theoretical comparison of the WN and OUSS null hypotheses.

Details on periodogram analysis (OUSS estimation, calculation of P values).

Details on the simulation of the LGSS models.

The right most column shows the result of Welsh’s t-test between the populations of Staats Island and each its three potential continental source populations. n: number of haplotypes; np: number of private haplotypes; h: haplotype diversity; π: nucleotide diversity; A: mean number of alleles per locus; Ap: privates alleles; He: mean expected heterozygosity; Ho: mean observed heterozygosity; Welch p-value: significance value of Welch's t-test between the SI population and each of the other populations (e.g. Welch t-test between SI and ML has a p-value of 0.2948), n.p.: test not performed. Deviations from zero *p

Details of the statistical analysis of the GPDD.

Derivation of the expected periodogram of the OUSS process.

For each age (i.e. 2, 4, 9 or 13years from planting), the results are based on an across-site analysis that was conducted by combining breast-height diameter (DBH) data measured at the Geeveston and Weilangta trial sites.DD =  net difference between the additive effects of the genes in the intra-regional ♀N♂N and ♀S♂S crosses.REC =  overall reciprocal effect (confounding maternal and non-maternal reciprocal effects at theregional level).HET =  total mid-parent heterosis attributed to both inter-regional ♀N♂S and ♀S♂N hybrids.