Abstract
Protected areas (PAs) are crucial conservation tools implemented worldwide to conserve biodiversity. Although PAs can positively impact wildlife populations, their ecological outcomes vary substantially depending on PA management and governance. Recent calls have highlighted the need to better assess the role of area-based conservation in preventing biodiversity loss. This is crucial to improve PA effectiveness in order to meet global biodiversity goals. Here we take advantage of a unique dataset composed of 2230 surveys conducted with koala detection dogs across Eastern Australia, to assess how protection status affected the occurrence of a threatened specialist folivore. We assessed if coverage of protected forest influenced koala presence or absence at two spatial scales (1 and 3 km), for (i) strictly and (ii) all protected areas. We also investigated if PA effects were explained by differences in habitat composition (percentage of secondary forest) between protected and unprotected areas. Taking confounding factors into account, we showed that forest protection (all IUCN categories) had a significant positive effect on koala occurrence, which increased by ~ 10% along the forest protection gradient. Contrarily, koala occurrence was not affected by strictly protected areas. In addition, adding the percentage of secondary forests in our models did not modify the statistical effect of PAs on koala occurrence, suggesting that forest composition is not the driver of the observed difference along the protection gradient. Our results contribute to a broader understanding of the effects of PAs on a threatened marsupial and call for further attention to assessments of PA effectiveness in Eastern Australia, a global biodiversity hotspot.
Similar content being viewed by others
Avoid common mistakes on your manuscript.
Introduction
Protected areas (PAs) are crucial policy instruments in halting ongoing biodiversity loss resulting from the global anthropogenic alteration of ecosystems. The importance of PAs for conservation has been demonstrated in both marine and terrestrial ecosystems across biological systems (Andam et al. 2008; Duckworth and Altwegg 2018; Edgar et al. 2014; Zupan et al. 2018). For example, Gray et al. (2016) showed that PAs harbor higher species richness and abundance than unprotected areas. Threatened species are often the main target of conservation actions and are expected to benefit from land protection and management actions associated to protected area status. For instance, Cazalis et al. (2020) showed that PAs located in tropical forests are effective at retaining bird species at greater risk of extinction (Threatened or Near Threatened categories). Protected areas can positively influence wildlife distribution and abundance through different pathways, by preventing habitat loss and fragmentation (Jones et al. 2018), preserving or enhancing habitat quality inside their boundaries or through additional effects of management actions, e.g. limiting poaching, human disturbance or predation by commensal predators (Kearney et al. 2018).
Six different categories of PAs have been defined by the International Union for Conservation of Nature (IUCN) with different management objectives and protection levels, from access restricted areas (category I) to areas where sustainable extractive activities are allowed (category VI) (IUCN & VCMC, 1994). Differences in conservation outcomes between IUCN categories have been shown, with low protection categories being less efficient at avoiding forest loss (Leberger et al. 2020) or at conserving larger and more threatened wildlife species (Ferreira et al. 2020). Similarly, Jones et al. (2018) found a strong effect of protection status (IUCN PA category) on mammal species richness and encounter rates of the most common species in forests of Tanzania.
However, available evidence indicates that despite an increase in the total area covered by PAs worldwide and the reported positive effects of land protection (Gray et al. 2016), biodiversity continues to decline globally, highlighting a paradox (Maxwell et al. 2020). Recent research has shown that ecological effectiveness of PAs can vary substantially depending on a variety of socio-economic factors. Local governance, funding, national development indices or external pressures (e.g. human density in the periphery of PAs) can have adverse effects on biodiversity inside PAs (Barnes et al. 2016; Eklund & Cabeza-Jaimejuan 2017; Amano et al. 2018; Geldmann et al. 2019; Veldhuis et al. 2019). This is related to addressing key threats to species of concern, which are mostly limited to PAs with sufficient resources (Coad et al. 2019). Ultimately, for optimal ecological outcomes, PAs need to be strategically located in order to protect high-quality habitat, which is not the norm in most parts of the world (Venter et al. 2018). As a result, the effectiveness of PAs in terms of wildlife conservation is sometimes hard to predict, and recent studies have recorded counterintuitive effects of PAs in several taxa: similar densities inside and outside PAs (no PA effect) or even negative population trends inside PAs (Kiffner et al. 2020; Terraube et al. 2020; Bayraktarov et al. 2021).
Yet, studies assessing the outcomes of PAs for wildlife conservation and identifying the drivers of PA effectiveness (IUCN categories, external factors or poor local management) remain too rare. This is despite it having been recently identified as an urgent priority for the long-term success of area-based conservation (Maxwell et al. 2020). Under the post-2020 global biodiversity framework, attention is increasingly focusing beyond quantity (total PA coverage) and including the quality of PAs (Visconti et al. 2019), providing targets that are often more important for species occurrence and persistence.
The koala (Phascolarctos cinereus) is a specialist arboreal marsupial distributed across eastern and southern Australia. Populations in Queensland (QLD) and New South Wales (NSW) have been declining steeply over the last decades (Adams-Hosking et al. 2016). Consequently, the species status has been up-listed to Vulnerable by the IUCN in 2012 and to Endangered in three Australian states in 2022. Pressure is rising to further up-list the species to globally Endangered (Lam et al. 2020). Habitat loss and fragmentation due to deforestation is the main threat to this species (McAlpine et al. 2015). Diseases, dog predation and vehicle collisions are other additive mortality factors threatening koala populations (Beyer et al. 2018). In addition, the impacts of climate change are multiple and likely increasing, a recent example are the mega-fires that have impacted Eastern Australia during the summer of 2019–2020 (Phillips et al. 2021; Ward et al. 2020a) estimated that the species has lost 11% of its total habitat area following these unprecedented fires.
Despite the precarious status of this species, the effect of PAs on koala populations, e.g. occurrence and abundance, is poorly understood across its distribution (Tisdell et al. 2017). Here we take advantage of a substantial dataset of > 2000 surveys conducted across QLD and NSW to provide the first assessment of how PAs influence koala occurrence. We assessed whether forest protection (i.e. the percentage of forest cover under protection) had an effect on koala occurrence at several spatial scales, while considering potential confounding factors that are likely to cause biases in PA effectiveness studies (ecological and anthropogenic variables potentially influencing koala occurrence). We also aimed to identify (i) the effects of IUCN categories on the relationship between forest protection and koala occurrence and (ii) if this relationship was driven by variations in forest composition between protected and unprotected areas.
Materials and methods
Study area
Koala surveys were carried out in eight local government areas (hereafter councils) distributed across South-East Queensland (SEQ): Sunshine Coast, Noosa shire, Gympie, Toowoomba, Moreton Bay, Redlands City, Fraser Coast and Stradbroke Island (Fig. 1). Surveys were also carried out in the Northern Tablelands in New South Wales (NSW). The climate in Queensland is temperate to subtropical, with habitats ranging from open dry sclerophyll forests, acacia and callitris dominated forests, and patchy rainforests. The Northern Tablelands are temperate characterized by higher altitude, and cooler temperatures. The region consists of a mixture of lowland plains, and tablelands dominated by a mixture of dry eucalyptus forests and grasslands. Native vegetation across both regions is fragmented by the agricultural expansion for cattle and crop, and urban settlements. Koalas within our study area inhabited a variety of habitats, including remnant and non-remnant forest patches surrounded by an agricultural matrix or even suburban settlements with low tree cover that could be generally characterized as low-quality habitat for koalas.
Protected areas in Eastern Australia
Each protected area was categorized using IUCN classification (Dudley and Phillips 2006) based on the protected area’s management strategies. Protected areas in IUCN management categories I through to IV are strict PAs primarily managed for biodiversity conservation, while other IUCN management categories allow sustainable use of natural resources and light extractive activities (UNEP-WCMC and IUCN, 2018).
Currently, according to the World Database on Protected Areas, nationally designated PAs located in SEQ, cover around 10.2% of its terrestrial area, while designated PAs located in New-South Wales (Northern Tablelands), cover around 19.4% of its terrestrial area. Of this total protected area, strict PAs in IUCN management categories I-IV cover about 7.8% of SEQ terrestrial area and 15.3% of the Northern Tableland total area (Fig. 1; Table 1 in Supplementary Material).
Koala occurrence
Given koalas are cryptic and often difficult to find, we used detection dogs to find evidence of koala presence (i.e., scat; Cristescu et al. 2020). A team consisting of a handler and detection dog specifically trained to detect koala scats were deployed across the study area between 2015 and 2021 for different purposes (e.g. koala habitat mapping, genetic sampling).
All surveys were conducted in the same manner with no prerequisite in terms of habitat type. Each survey began once the detection dogs were motivated to find the target scent and ended when the handler found the target scent or when the area was thoroughly searched. The dogs were only prompted by the handler to ensure the target area was covered or moved down-wind to benefit from wind direction. Based on previous surveys, in koala-positive sites, this survey method is highly efficient in confirming koala presence (Cristescu et al. 2020). If a detection dog indicated on a target scent, the handler identified the scat to species level (Triggs 2004) and recorded koala scat location using a handheld Garmin GPS (Alpha ® 100, Garmin Ltd., Olathe). We classified a site as used by koalas if koala scats were found, and not used when scats were not found during the survey. The teams also recorded the total search time to account for survey effort.
We conducted a total of 2230 surveys. We filtered our data to remove potential biases as follows. Regarding surveys conducted with detection dogs specially trained to find specific scents (i.e., very fresh scats or individual koalas) we only included surveys that were positive and removed negative surveys. Indeed, negative surveys with fresh scat or koala detection dogs cannot be interpreted as evidence of koalas not using the area, only as koalas not being there on that day (‘koala’ dog) or the few days prior (‘fresh scat’ dog). As a result, a total of 1463 surveys were used for subsequent analyses: koalas were detected in 861 of them and were absent from the remaining 602 surveys.
Environmental variables
Variables were extracted at two different buffer scales: (i) 1 km and (ii) 3 km. If a site was positive, variables were extracted for every first scat found across the survey. If the site was negative, variables were extracted from the survey’s starting point as the area covered by detection dogs during casual surveys is not linear but rather has a circular shape from the starting point. Koala home ranges and dispersal distances can vary significantly depending on region and individuals (Dique et al. 2003). Therefore, size of the different buffers was chosen according to previous research on koala ecology to reflect home range size and dispersal of koalas across the landscape (McAlpine et al. 2006, 2008; Rhodes et al. 2008).
We extracted both potential confounding factors (forest extent, distance to road, total water area, phosphorous levels in soil, average rainfall and amount of non-remnant Eucalyptus forest) and the variable of interest (percentage of protected forest) at these two spatial scales in ArcGIS (10.8) as follows:
Data sources
-
i
Forest extent: We used the Forests of Australia 2018 dataset created by the Australian Bureau of Agricultural and Resource Economics and Sciences (data.gov.au), with a 1-hectare resolution. Given koalas are considered specialist folivores of the Eucalyptus genus, we aggregated and clipped forest type layers and calculated the extent of Eucalyptus forest in each buffer size.
-
ii
Protection status: We assessed the amount of Eucalyptus forest that was classified under protection within each buffer. For this, we calculated the percentage of intersection between each buffer area with a Protected Area from the World Database of Protected Areas (WPDA, 2020). For subsequent analyses, we calculated this percentage both for (i) PAs under strict protection (IUCN categories I-IV) and (ii) PAs from all IUCN categories (IUCN categories I-IV and IUCN category VI). Of note, areas protected at the Council level (such as reserves) are not classified as protected under IUCN categories.
-
iii
Distance to the closest road: to account for anthropogenic disturbance, we calculated the distance to the closest major primary road using the Near tool. Data was extracted from spatial layer Global Roads Inventory Project dataset (globio.org).
-
iv
Total water area: the total water area in each buffer was used as a proxy for leaf water content and / or forest resilience to drought, as the presence of water was shown to be an important driver of occurrence for populations at the edge of their range (Gordon, 1988, Davies et al. 2013). We extracted the area of water from the National Surface Hydrology spatial layer (ga.gov.au). We excluded water bodies classified as ‘foreshore flats’ as these are associated to beaches and likely to include saltwater bodies which have no positive influences on koala habitat.
-
v
Soil phosphorous: Phosphorous is considered important for the health of Eucalyptus forests and previously highlighted as a contributor to koala presence (Ullrey et al. 1981; Thomas et al. 2006; DES, 2021). We extracted mean mass fraction of total phosphorous in soil by weight (%) within topsoil layers of 0–5 cm from the Soil and Landscape Grid National Soil Attribute (data.csiro.au) at the survey start (for negative surveys) or evidence of presence (for positive surveys).
-
vi
Average rainfall levels: We collected rainfall statistics from weather stations in the closest towns to our survey points, calculating average rainfall recorded from the year prior to conducting surveys. This was obtained from the Bureau of Meteorology (bom.gov.au).
-
vii
Habitat composition (% of secondary forest): We assessed the amount of Eucalyptus forest that was classified as secondary habitat (original vegetation has been removed and has less than 50% cover remaining) within each buffer. Note that primary (i.e. old growth) vegetation is classified as woody vegetation that is undisturbed with more than 50% forest cover. This variable was available only for Queensland surveys. Eucalyptus forest within buffers was intersected with primary versus secondary classification variables from the regional ecosystem mapping 2019 (Queensland Spatial https://qldspatial.information.qld.gov.au/).
Statistical analyses
We assessed whether the percentage of protected Eucalyptus forest affected koala occurrence considering other potential confounding factors at two different spatial scales (1 and 3 km buffer scales) and for two levels of protection (strict PAs/all PAs). In the end, we removed all surveys from NSW as habitat composition variables were not available for this state and we therefore performed the following analyses on 1164 surveys. The analyses followed a two-step approach at both spatial scales. First, we used Generalized Linear Mixed Models (GLMMs) with a binomial distribution (logit link function) to evaluate the effect of forest protection status on the occurrence of koalas, then we aimed at understanding if the protected area effect could be explained by differences in habitat composition associated to protection status.
-
i)
Effect of forest protection status on koala occurrence.
Models contained eleven fixed effects: (i) the variable of interest: percentage of protected forest out of total forest area available per buffer (only Eucalyptus species); (ii) potential confounding factors influencing koala occurrence in the wild: percentage of forest out of total buffer area; average rainfall one year prior to survey; distance to closest major road; proportion of water area out of total buffer area; elevation; phosphorous mass; and (iii) total search time (seconds) for each survey. The latter aimed at controlling for the potential effect of difference in survey length on the probability of detection for koalas. We used council as a random effect and year as a random effect nested within council to account for repeated observations in each council over the years (as surveys were never conducted twice at the exact same location). We also added dog ID as a random factor to account for individual differences in detection ability. However, in this case the model residuals were non-linear and heteroscedastic. Considering that the results of the model were not affected by the inclusion or not of dog ID as a random effect (see Table 11 in the Supplementary Material) and that the model with a simpler structure had the best goodness of fit, we decided to include only year nested within council as a random effect. Longitude and latitude of surveys were also added as covariates in the models to describe latitudinal/longitudinal gradients across the study area.
-
ii)
Role of habitat composition in explaining PA effect on koala occurrence.
Second, we wanted to understand if the effect of protected areas was caused by differences in habitat composition along the gradient of forest protection. To achieve this we added a covariate to the set of four models described previously: the ‘percentage of secondary forest out of total forest area available per buffer’ (Eucalyptus species only). We hypothesized that differences in the percentage of primary and secondary habitat between protected and unprotected areas could explain the potential effect of PAs on koala occurrence. Therefore, by incorporating the amount of secondary forest in our models, we tested whether the statistical effect of protected areas on koala occurrence would change.
As a result, we built a total of eight different models including two estimates of percentage of protected forests, (i) for all protected area categories (IUCN categories I-VI) and (ii) only for strictly protected areas (IUCN categories I-IV), for each buffer size (1.5 and 3 km), with and without the additional ‘habitat composition’ covariate (see Table 1 for the structure of the eight models).
We also repeated the analyses on a dataset where presence-only data were removed (surveys conducted with ‘koala dogs’ and ‘fresh scat dogs’) in order to check that these data did not introduce a bias in our dataset in terms of habitat selection.
All covariates were standardized. Multicollinearity among explanatory variables was evaluated using the variance inflation factor (VIF), where variables with VIF values > 5 were removed. All covariates had VIF < 3.5. All statistical analyses were performed using the software R 4.0.0. We used package lme4 for all GLMMs and the package MUMIn to calculate the proportion of variance in koala occurrence explained by the fixed effects and both fixed and random effects in the eight different GLMMs. Goodness-of-fit of the models were assessed using residual diagnostics following the procedures described in the DHARMa package (Hartig 2020). All the results of the different GLMMs (parameter estimates, confidence intervals, z and p values, conditional and marginal coefficients of determination) are presented in the Results section or in the Supplementary Material.
Results
All IUCN categories
Our analysis revealed a significant effect of PAs on koala occurrence across our study at 1 km (estimate = 0.292; CI = 0.085;0.499; z = 2.772; p = 0.005) and 3 km (estimate = 0.274; CI = 0.039; 0.508; z = 2.292; p = 0.021), when considering forests protected by all IUCN management categories (see Fig. 2; Tables 2 and 6 in Supplementary material). The positive effect of forest protection was consistent across spatial scales and robust to the inclusion of other environmental confounding factors (see below). Our results showed that koala occurrence increases by ~ 10% along the forest protection gradient (see Fig. 3). We also calculated the marginal (R2m) and conditional (R2c) coefficients of determination for the two Generalized Mixed Models mentioned above: (i) GLMM_AllIUCNcat_1km: R2m = 0.169; R2c = 0.544; (ii) GLMM_AllIUCNcat_3km: R2m = 0.177; R2c = 0.544.
Strictly protected areas
The percentage of strictly protected forest had no significant effect on koala occurrence at 1 km (estimate=-0.054; CI=-0.217; 0.108; z=-0.657; p = 0.511) and 3 km (estimate=-0.077; CI=-0.011; 0.007; z=-0.854; p = 0.393) (see Fig. 2; Tables 4 and 8 in the Supplementary Material). We calculated the marginal (R2m) and conditional (R2c) coefficients of determination for the two Generalized Mixed Models mentioned above: (i) GLMM_StrictIUCNcat_1km: R2m = 0.169; R2c = 0.565; (ii) GLMM_StrictIUCNcat_3km: R2m = 0.181; R2c = 0.566.
Therefore, for models including all PAs and only strictly protected areas, our results show that > 35% of the variance in koala occurrence across our study area was explained by the nested random effect (‘Year’ nested within ‘Council’).
Confounding factors
Search time had a strong effect on the probability of koala occurrence (see Fig. 2; Tables 3a and 3c) in all models at both 1 and 3 km buffer scale. The amount of rainfall one year prior to scat collection and elevation also had a consistent positive effect on koala occurrence in all models at 1 and 3 km buffers, although the effect of rainfall was weaker at 1 km buffer scale. In other words, koalas were more likely to be detected during surveys conducted at higher elevation and in areas with higher rainfall. A trend for a negative latitudinal effect was detected in models including all PAs at 3 km buffer scale, meaning that southernmost sites had higher koala occurrence across our study area. We also detected a trend for a positive effect of the percentage of Eucalyptus forest cover in models including strictly PAs at 3 km scale (see Tables 3e and 3 g in Supplementary material).
Effect of habitat composition on the relationship between forest protection and koala occurrence
Adding the ‘percentage of secondary forest’ covariate did not significantly modify the effect size or the statistical significance of the ‘% of protected forest’ covariate in any model (for both scales and types of PAs considered). The addition of this covariate did not impact either the marginal coefficient of determination of any model presented in this study. This result suggests that the effect of forest protection on koala occurrence highlighted earlier is not driven by differences in the percentage of secondary forest along the protection gradient (see Tables 3, 5, 7 and 9 in Supplementary material).
Analyses performed on the datasets excluding specifically trained dogs (in which presence-only data had been removed) showed similar results than the analyses performed on the complete dataset (see Table 10 in Supplementary Material).
Discussion
Effects of forest protection on koala occurrence
We identified a positive impact of PAs on koala occurrence (~ 10% increase in occurrence along the protection gradient). Both habitat quality and quantity (total forest extent) have been shown to play an important role in determining koala abundance and distribution (McAlpine et al. 2006; Callaghan et al. 2011; Phillips and Callaghan 2011). Therefore, one could expect the effect of PAs on koala occurrence to be mediated by the conservation of patches of high-quality Eucalyptus forests. However, our results suggest that the effect of protection on koala occurrence is independent of habitat amount or composition, although the landscape categories used here might not allow us to capture small-scale variations in habitat quality between protected and unprotected areas, e.g. foliar palatability (Moore et al. 2010) and soil fertility (Dargan et al. 2019).
The positive effect of forest protection on koala occurrence could be linked to specific management actions that have been shown to positively impact koala populations (e.g. disease management, predator control or active restoration of main food tree species, e.g. Beyer et al. 2018). Yet, to the best of our knowledge these conservation actions are rather limited inside PAs in Eastern Australia. Low anthropogenic disturbance inside PAs could still explain this positive effect of forest protection on koala occurrence.
The overall limited effect of strict PAs is surprising but could be linked to the spatial structure of our dataset (limited number of surveys inside strictly protected areas, see limitations section below). Overall Australian PAs are thought to conserve poorly threatened forest specialists such as koalas. Historically, Australian PAs had been created on unproductive lands at higher elevation and with low human population density (Watson et al. 2009), far from the high-quality forests in lowlands that are optimal habitat for koalas (Smith 2004) but also preferred land for agricultural development (Bradshaw et al. 2012). These areas on private land generally have more fertile soils, which is hypothesized to favour trees with higher nitrogen and lower formylated phloroglucinol compounds (FPCs) that are linked to foliage palatability (Moore et al. 2004). Therefore, previous research has underlined that most National Parks and State Forests occurring on low fertility soils at high elevation do not provide optimal habitat for koalas, arguing that koalas are mainly dependent on the optimal management of private lands (Crowther et al. 2009). Yet, more recent research has also shown high habitat suitability for koalas in large reserves located on public land and at mid-elevation (Law et al. 2017; Whisson et al. 2023). Our results build on this evidence and highlight a consistent positive effect of elevation and forest protection on koala occurrence suggesting that nowadays this species might be confined to sub-optimal forests at higher elevation in certain areas of Eastern Australia, see the ‘refugee species concept’, (Kerley et al. 2012). This indicates that, although koala conservation in anthropized lowlands is crucial, protected sites located at mid-or high elevation may play a role in koala conservation (e.g. as climate refugia).
Further research is needed to decipher the linkages between protection status, management actions and koala occurrence.
Interestingly, a large part of the variance in koala occurrence was explained by the random effect used in our models (year nested within council), suggesting that large-scale climate variability might have a strong effect on koala population dynamics, particularly in areas located further from the coast (Seabrook et al. 2011).
Limitations of the methodological approach
We identified several limitations in this study. First, the percentage of Eucalyptus forests and the percentage of secondary habitat included in our analyses may not capture small-scale variations in forests quality (e.g. abundance of main food trees or nutritional value of trees, Moore and Foley 2005; Au et al. 2019), that are important drivers of koala occurrence at the local scale. The available landscape categories could thus be too simplistic to assess accurately drivers of koala occurrence in modern ecosystems (Cristescu et al. 2019; Mitchell et al. 2021). Second, strictly protected areas (categories I-IV) primarily aimed at protecting biodiversity cover in average only 8.63% of the 1 km buffers analyzed in this study (versus 38.01% for all protected areas categories, n = 1164 surveys). This means that a higher proportion of koala surveys were conducted in protected areas where extractive activities (categories V-VI) are allowed than in strictly protected areas despite strict PAs covering a higher proportion of terrestrial areas in the different councils considered here (Table 1, Supplementary Material). Lack of representativeness of surveys conducted inside strictly protected areas in our dataset may explain the low effect size of PAs on koala occurrence found in this study. Third, Australia’s protected area coverage could be inclusive of a wide range of levels of designation, management and condition as reported recently in the UK (Starnes et al. 2021). As a result, even PAs of the highest IUCN management categories may not be effectively protected for nature in Eastern Australia (Craigie and Pressey 2022), blurring effectiveness assessments of PAs. Fourth, dog ID would account for a large proportion of the variance in our models given that the surveys used in this study were pooled from dogs trained on different target scent (dogs trained to search for koala scats of any age, dogs trained to search for fresh scats, and dogs trained to search for the koala itself). In terms of survey design, dogs trained on fresh scats are deployed where koalas are known to occur, whereas dogs trained to search for koala scats of any age are deployed in areas with no prior knowledge (Cristescu et al. 2020). However, this survey design does not affect the robustness of our results as shown previously.
Implications for PA management and conservation planning
Further monitoring of koala occupancy using a structured sampling design (similar habitats inside and outside PAs) and cost-efficient survey methods such as detection dogs and passive acoustic monitoring (Hagens et al. 2018) would help estimating fine-scale effects of PAs on koala occurrence and would guide PA management plans and prescribed actions at local scale. Utilizing fresh scats to obtain koala abundance and genetic diversity estimates was limited by sample size in this study but would provide an important assessment of the effects of PAs on these parameters, which is crucial for holistic conservation management. Finally, we highlighted the low coverage of strictly protected areas across our study area (average cover = 15.72%, n = 9 councils), which could have serious consequences in terms of wildlife occupancy and abundance across the PA network, although our results suggest that multiple-use PAs can have a positive impact on koala occupancy which deserves further attention. The establishment of strictly protected areas focused on conserving high-quality forests for koalas should continue to be a priority. Yet, establishing new PAs in high-priority lowland areas is particularly challenging throughout Eastern Australia as remnants of high-quality forests overlap with private lands where agriculture expansion and development projects are the priority. Engaging private land holders and First Nations communities into regional conservation efforts and increasing conservation status around PAs could partially solve this challenge (Ivanova and Cooke 2020; Belote and Wilson 2020). Finally, koalas have been recently highlighted as an efficient umbrella species and conservation actions targeting main threats that this species faces will likely benefit many Australian threatened species (Ward et al. 2020b).
Data Availability
The datasets will be available from the corresponding author on request.
References
Adams-Hosking C, McBride MF, Baxter G, Burgman M, de Villiers D, Kavanagh R et al (2016) Use of expert knowledge to elicit population trends for the koala (Phascolarctos cinereus). Divers Distrib 22:249–262. https://doi.org/10.1111/ddi.12400
Amano T, Székely T, Sandel B, Nagy S, Mundkur T, Langendoen T, Blanco D, Soykan CU, Sutherland WJ (2018) Successful conservation of global waterbird populations depends on effective governance. Nature 553:199. https://doi.org/10.1038/nature25139
Andam K, Ferraro PJ, Pfaff A, Sanchez-Azofeifa A, Robalino J (2008) Measuring the effectiveness of protected area networks in reducing deforestation. Proc Natl Acad Sci USA 105:16089–16094. https://doi.org/10.1073/pnas.0800437105
Au J, Clark RG, Allen C, Marsh KJ, Foley WJ, Youngentob KN (2019) A nutritional mechanism underpinning folivore occurrence in disturbed forests. For Ecol Manag 453:117585. https://doi.org/10.1016/j.foreco.2019.117585
Barnes MD, Craigie ID, Harrison LB, Geldmann J, Collen B, Whitmee S et al (2016) Wildlife population trends in protected areas predicted by national socio-economic metrics and body size. Nat Commun 7:12747. https://doi.org/10.1038/ncomms12747
Bayraktarov E, Correa DF, Suarez-Castro AF et al (2021) Variable effects of protected areas on long-term multispecies trends for Australia’s imperiled birds. Conserv Sci Pract 3:e443. https://doi.org/10.1111/csp2.443
Belote RT, Wilson MB (2020) Delineating greater ecosystems around protected areas to guide conservation. Conserv Sci Pract 196:1–10. https://doi.org/10.1111/csp2.196
Beyer HL, de Villiers D, Loader J, Robbins A, Stigner M, Forbes N et al (2018) Management of multiple threats achieves meaningful koala conservation outcomes. J Appl Ecol 55:1966. https://doi.org/10.1111/1365-2664.13127
Bradshaw CJA (2012) Little left to lose: Deforestation and forest degradation in Australia since european colonization. J Plant Ecol 5:109–120. https://doi.org/10.1093/jpe/rtr038
Callaghan J, McAlpine C, Thompson J, Mitchell D, Bowen M, Rhodes J et al (2011) Ranking and mapping koala habitat quality for conservation planning on the basis of indirect evidence of tree-species use: a case study of Noosa Shire, south-eastern Queensland. Wildl Res 38:89–102. https://doi.org/10.1071/WR07177
Cazalis V, Princé, Mihoub J, Kelly J, Butchart SHM, Rodrigues ASL (2020) Effectiveness of protected areas in conserving tropical forest birds. Nat Commun 11:4461. https://doi.org/10.1038/s41467-020-18230-0
Coad L, Watson JEM, Geldmann J, Burgess ND, Leverington F, Hockings M, Knights K, Di Marco M (2019) Widespread shortfalls in protected area resourcing undermine efforts to conserve biodiversity. Front Ecol Environ 17:259–264. https://doi.org/10.1002/fee.2042
Craigie ID, Pressey RL (2022) Fine-grained data and models of protected-area management costs reveal cryptic effects of budget shortfalls. Biol Conserv 272:109589. https://doi.org/10.1016/j.biocon.2022.109589
Cristescu R, Scales K, Schultz A, Miller R, Schoeman D, Dique D, Frere C (2019) Environmental impact assessments can misrepresent species distributions: a case study of koalas in Queensland, Australia. Anim Conserv 22:314–323. https://doi.org/10.1111/acv.12455
Cristescu RH, Miller RL, Frère CH (2020) Sniffing out solutions to enhance conservation: how detection dogs can maximise research and management outcomes, through the example of koalas. Aust Zool 40:416–432. https://doi.org/10.7882/az.2019.030
Crowther MS, McAlpine CA, Lunney D, Shannon I, Bryant JV (2009) Using broad-scale, community survey data to compare species conservation strategies across regions: a case study of the Koala in a set of adjacent “catchments. Ecol Manage Restor 10:S88–S96. https://doi.org/10.1111/j.1442-8903.2009.00465.x
Dargan JR, Moriyama M, Mella VSA, Lunney D, Crowther MS (2019) The challenge for koala conservation on private land: koala habitat use varies with season on a fragmented rural landscape. Anim Conserv 22:543–555. https://doi.org/10.1111/acv.12487
Davies NA, Gramotnev G, McAlpine C, Seabrook L, Baxter G, Lunney D, Rhodes JR, Bradley A (2013) Physiological stress in Koala populations near the arid edge of their distribution. PLoS ONE 8:e79136
Department of Environment and Science (DES). 8 (2021) Spatial modelling for koalas in South East Queensland: Report version 2.0. Koala Habitat Areas (KHA) v2.0, Locally Refined Koala Habitat Areas (LRKHA) v2.0, Koala Priority Areas (KPA) v1.0, Koala Habitat Restoration Areas (KHRA) v1.0. Brisbane: Department of Environment and Science, Queensland Government
Dique DS, Thompson J, Preece HJ, De Villiers DL, Carrick FN (2003) Dispersal patterns in a regional koala population in south-east Queensland. Wildl Res 30:281–290. https://doi.org/10.1071/WR02043
Duckworth G, Altwegg R (2018) Effectiveness of protected areas for bird conservation depends on guild. Divers Distrib 24:1083–1091. https://doi.org/10.1111/ddi.12756
Dudley N, Phillips A (2006) Forests and protected areas: Guidance on the use of the IUCN protected area management categories. IUCN, Gland, Switzerland
Edgar GJ et al (2014) Global conservation outcomes depend on marine protected areas with five key features. Nature 506:216–220. https://doi.org/10.1038/nature13022
Eklund J, Cabeza M (2017) Quality of governance and effectiveness of protected areas: crucial concepts for conservation planning. Ann N Y Acad Sci 1399:27–41. https://doi.org/10.1111/nyas.13284
Ferreira GB, Collen B, Newbold T, Oliveira MJR, Pinheiro MS, de Pinho FF, Rowcliffe M, Carbone C (2020) Strict protected areas are essential for the conservation of larger and threatened mammals in a priority region of the brazilian Cerrado. Biol Conserv 251:108762. https://doi.org/10.1016/j.biocon.2020.108762
Geldmann J, Manica A, Burgess ND, Coad L, Balmford A (2019) A global-level assessment of the effectiveness of protected areas at resisting anthropogenic pressures. Proc Natl Acad Sci USA 116:23209–23215. https://doi.org/10.1073/pnas.1908221116
Gordon G, Brown A, Pulsford TJAJE (1988) A koala (Phascolarctos cinereus Goldfuss) population crash during drought and heatwave conditions in south-western Queensland. Austral Ecol 13:451–461. https://doi.org/10.1111/j.1442-9993.1988.tb00993.x
Gray CL, Hill SLL, Newbold T, Hudson LN, Börger L, Contu S et al (2016) Local biodiversity is higher inside than outside terrestrial protected areas worldwide. Nat Commun 7:12306. https://doi.org/10.1038/ncomms12306
Hagens SV, Rendall AR, Whisson DA (2018) Passive acoustic surveys for predicting species’ distributions: optimising detection probability. PLoS ONE 13:e0199396. https://doi.org/10.1371/journal.pone.0199396
Hartig F (2020) DHARMa: Residual Diagnostics for Hierarchical (Multi-Level / Mixed) Regression Models. R Package Version 0.2.7. https://CRAN.R-project.org/package=DHARMa
Ivanova IM, Cook CN (2020) The role of privately protected areas in achieving biodiversity representation within a national protected area network. Conserv Sci Pract 2:e307. https://doi.org/10.1111/csp2.307
IUCN and WCMC (1994) Guidelines for Protected Area Management Categories, Gland, Switzerland
Jones KR, Venter O, Fuller RA, Allan JR, Maxwell SL, Negret PJ, Watson JEM (2018) One-third of global protected land is under intense human pressure. Science 360:788–791. https://doi.org/10.1126/science.aap9565
Kearney SG, Adams VM, Fuller RA, Possingham HP, Watson JEM (2018) Estimating the benefit of well-managed protected areas for threatened species conservation. Oryx 54:276–284. https://doi.org/10.1017/S0030605317001739
Kerley GIH, Kowalczyk R, Cromsigt JPGM (2012) Conservation implications of the refugee species concept and the european bison: King of the forest or refugee in a marginal habitat? Ecography 35:519–529. https://doi.org/10.1111/j.1600-0587.2011.07146.x
Kiffner C, Binzen G, Cunningham L, Jones M, Spruiell F, Kioko J (2020) Wildlife population trends as indicators of protected area effectiveness in northern Tanzania. Ecol Indic 110:105903. https://doi.org/10.1016/j.ecolind.2019.105903
Lam SS, Waugh C, Peng WX, Sonne C (2020) Wildfire puts koalas at risk of extinction. Science 367:750. https://doi.org/10.1126/science.aba8372
Law B, Caccamo G, Roe P, Truskinger A, Brassil T, Gonsalves L, McConville A, Stanton M (2017) Development and Field Validation of a Regional, Management-Scale Habitat Model: a Koala Phascolarctos Cinereus Case Study. Ecol Evol 7:7475–7489. https://doi.org/10.1002/ece3.3300
Leberger R, Rosa IMD, Guerra CA et al (2020) Global patterns of forest loss across IUCN categories of protected areas. Biol Conserv 241:108299. https://doi.org/10.1016/j.biocon.2019.108299
Maxwell SL, Cazalis V, Dudley N et al (2020) Area-based conservation in the 21st century. Nature 586:217–227. https://doi.org/10.1038/s41586-020-2773-z
McAlpine CA, Rhodes JR, Callaghan J et al (2006) The importance of forest area and configuration relative to local habitat factors for conserving forest mammals: a case study of koalas in Queensland, Australia. Biol Conserv 132:153–165. https://doi.org/10.1016/j.biocon.2006.03.021
McAlpine CA, Rhodes JR, Bowen ME, Lunney D, Callaghan JG, Mitchell DL, Possingham HP (2008) Can multi-scale models of a species’ distribution be generalised from region to region? A case study of the koala. J Appl Ecol 45:558–567. https://doi.org/10.1111/j.1365-2664.2007.01431.x
McAlpine CA, Lunney D, Melzer A, Menkhorst P, Stephen Phillips S, Phalen D et al (2015) Conserving koalas: a review of the contrasting regional trends, outlooks and policy challenges. Biol Conserv 192:226–236. https://doi.org/10.1016/j.biocon.2015.09.020
Mitchell DL, Soto-Berelov M, Langford WT, Jones SD (2021) Factors confounding koala habitat mapping at multiple decision-making scales. Ecol Manag Restor 22:171–182. https://doi.org/10.1111/emr.12468
Moore BD, Foley WJ (2005) Tree use by koalas in a chemically complex landscape. Nature 435:488–490. https://doi.org/10.1038/nature03551
Moore BD, Wallis IR, Marsh KJ, Foley WJ (2004) The role of nutrition in the conservation of the marsupial folivores of eucalypt forests. In: Lunney D (ed) Conservation of Australia’s Forest Fauna. Second Edition. Royal Zoological Society of New South Wales, Mosman, NSW, Australia, 549–575
Moore BD, Lawler IR, Wallis IR, Beale CM, Foley WJ (2010) Palatability mapping: a koala’s eye view of spatial variation in habitat quality. Ecology 91:3165–3176. https://doi.org/10.1890/09-1714.1
Phillips S, Callaghan J (2011) The spot assessment technique: a tool for determining localized levels of habitat use by koalas Phascolarctos cinereus. Aust Zool 35:774–780. https://doi.org/10.7882/AZ.2011.029
Phillips S, Wallis K, Lane A (2021) Quantifying the impacts of bushfire on populations of wild koalas (Phascolarctos cinereus): insights from the 2019/20 fire season. Ecol Manag Restor 22:80–88. https://doi.org/10.1111/emr.12458
Rhodes JR, Callaghan JG, McAlpine CA, De Jong C, Bowen ME, Mitchell DL, Lunney D, Possingham HP (2008) Regional variation in habitat-occupancy thresholds: a warning for conservation planning. J Appl Ecol 45:549–557. https://doi.org/10.1111/j.1365-2664.2007.01407.x
Seabrook L, McAlpine C, Baxter G, Rhodes J, Bradley A, Lunney D (2011) Drought-driven change in wildlife distribution and numbers: A case study of koalas in south west Queensland. Wildl Res 38:509–524. https://doi.org/10.1071/wr11064
Smith AP (2004) Koala conservation and habitat requirements in a timber production forest in north-east New South Wales. In: Lunney D (ed) Conservation of Australia’s Forest Fauna, vol 611. Royal Zoological Society of New South Wales, Sydney, NSW, p 591
Starnes T, Beresford AE, Buchanan GM, Lewis M, Hughes A, Gregory RD (2021) The extent and effectiveness of protected areas in the UK. Glob Ecol Conserv 30:e01745. https://doi.org/10.1016/j.gecco.2021.e01745
Terraube J, Van Doninck J, Helle P, Cabeza M (2020) Assessing the effectiveness of a national protected area network for carnivore conservation. Nat Commun 11:2957. https://doi.org/10.1038/s41467-020-16792-7
Thomas DS, Montagu KD, Conroy JP (2006) Why does phosphorus limitation increase wood density in Eucalyptus grandis seedlings? Tree Physiol 26:35–42. https://doi.org/10.1093/treephys/26.1.35
Tisdell CA, Preece HJ, Abdullah S, Beyer HL (2017) Strategies to conserve the koala: cost-effectiveness considerations. Australas J Environ Manag 24:302–318. https://doi.org/10.1080/14486563.2017.1349693
Triggs B (2004) Tracks, scats, and other traces: a field guide to Australian mammals., 2nd revised ed (Oxford University Press: Melbourne)
Ullrey D, Robinson P, Whetter P (1981) Composition of preferred and rejected eucalyptus browse offered to captive koalas, Phascolarctos cinereus (Marsupialia). Aust J Zool 29:839–846
UNEP-WCMC IUCN (2018) Protected Planet: The World Database on Protected Areas (WDPA), On-line [November 2018]. UNEP-WCMC and IUCN. Retrieved from www.protectedplanet.net
Veldhuis MP, Ritchie ME, Ogutu JO, Morrison TA, Beale CM, Estes AB et al (2019) Cross boundary human impacts compromise the serengeti-Mara ecosystem. Science 363:1424–1428. https://doi.org/10.1126/science.aav0564
Venter O, Magrach A, Outram N, Klein CJ, Possingham HP, Di Marco M, Watson JEM (2018) Bias in protected area location and its effects on long-term aspirations of biodiversity conventions. Conserv Biol 32:127–134. https://doi.org/10.1111/cobi.12970
Visconti P, Butchart SH, Brooks TM, Langhammer PF, Marnewick D, Vergara S, Yanosky A, Watson JE (2019) Protected area targets post-2020. Science 364:239–241. https://doi.org/10.1126/science.aav6886
Ward M, Tulloch AIT, Radford JQ, Williams BA, Reside AE, Macdonald SL, Mayfield HJ, Maron M et al (2020a) Impact of 2019–2020 mega-fires on australian fauna habitat. Nat Ecol Evol 4:1321–1326. https://doi.org/10.1038/s41559-020-1251-1
Ward M, Rhodes JR, Watson JE, Lefevre J, Atkinson S, Possingham HP (2020b) Use of surrogate species to cost-effectively prioritize conservation actions. Conserv Biol 34:600–610. https://doi.org/10.1111/cobi.13430
Watson JEM, Fuller RA, Watson AWT et al (2009) Wilderness and future conservation priorities in Australia. Divers Distrib 15:1028–1036. https://doi.org/10.1111/j.1472-4642.2009.00601.x
Whisson DA, Rivera P, Rendall AR (2023) Systematic acoustic surveys inform priority conservation areas for koalas in a modified landscape. Landsc Ecol 38:1279–1290. https://doi.org/10.1007/s10980-023-01620-2
The World Database on Protected Areas (2021) Available from http://www.protectedplanet.net/ Accessed 23.2.2021
Zupan ME, Fragkopoulou E, Claudet J, Erzini K, Horta e Costa B, Gonçalves EJ (2018) Marine partially protected areas: drivers of ecological effectiveness. Front Ecol Environ 16:381–387. https://doi.org/10.1002/fee.1934
Acknowledgements
In the spirit of reconciliation we acknowledge the Traditional Custodians of country where data was collected throughout these surveys, the Kabi Kabi First Nation, the Quandamooka People, the Danggan Balun (Five Rivers) People, the Gomeroi People, the Western Bundjalung People and the Githabul People, we acknowledge their connections to land, sea and community. We pay our respect to their Elders past, present and emerging. We would like to thank all our partners who supported the survey work of the Detection Dogs for Conservation team: Future-Plus Environmental Pty Ltd (Glenview Koala Offset Project), Gympie Regional Council, Koala Action Inc., O2 Ecology, Queensland Koala Crusaders, Future Plus Noosa Biosphere Reserve Foundation, Noosa Council, Northern Tablelands Local Land Services, Sunshine Coast Council, WWF-Australia. We are grateful to our long-term partner the International Fund for Animal Welfare (IFAW) for their continuous support. We would like to thank the Redland City Council and its Koala Conservation Officer Cathryn Dexter for their support to publish data from koala surveys undertaken across the City. For collaboration with this study, we thank Queensland Department of Transport and Main Roads (TMR) for use of unpublished data, including collected during the 2018 Oakey survey and are grateful to Rick Haywood (TMR) for his assistance. Finally, we would like to thank two anonymous reviewers for their thoughtful comments and Alexandre Villers for suggestions on the methodological approach.
Funding
Open Access funding enabled and organized by CAUL and its Member Institutions
Author information
Authors and Affiliations
Contributions
JT and CF conceived the ideas; RG, KH and RC collected the koala occurrence data; RG and KH extracted the spatial data; JT led the statistical analyses; JT led the writing of the manuscript. All authors contributed critically to the drafts and gave final approval for publication.
Corresponding author
Ethics declarations
Competing interests
The authors have no relevant financial or non-financial interests to disclose.
Additional information
Communicated by Alison Nazareno.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Terraube, J., Gardiner, R., Hohwieler, K. et al. Protected area coverage has a positive effect on koala occurrence in Eastern Australia. Biodivers Conserv 32, 2495–2511 (2023). https://doi.org/10.1007/s10531-023-02615-w
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10531-023-02615-w