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Southwell D, Skroblin A, Moseby K, Southgate R, Indigo N, Backhouse B, Bellchambers K, Brandle R, Brenton P, Copley P, Dziminski MA, Galindez-Silva C, Lynch C, Newman P, Pedler R, Rogers D, Roshier DA, Ryan-Colton E, Tuft K, Ward M, Zurell D, Legge S. Designing a large-scale track-based monitoring program to detect changes in species distributions in arid Australia. Ecol Appl 2023; 33:e2762. [PMID: 36218186 DOI: 10.1002/eap.2762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 04/27/2022] [Accepted: 07/06/2022] [Indexed: 06/16/2023]
Abstract
Monitoring trends in animal populations in arid regions is challenging due to remoteness and low population densities. However, detecting species' tracks or signs is an effective survey technique for monitoring population trends across large spatial and temporal scales. In this study, we developed a simulation framework to evaluate the performance of alternative track-based monitoring designs at detecting change in species distributions in arid Australia. We collated presence-absence records from 550 2-ha track-based plots for 11 vertebrates over 13 years and fitted ensemble species distribution models to predict occupancy in 2018. We simulated plausible changes in species' distributions over the next 15 years and, with estimates of detectability, simulated monitoring to evaluate the statistical power of three alternative monitoring scenarios: (1) where surveys were restricted to existing 2-ha plots, (2) where surveys were optimized to target all species equally, and (3) where surveys were optimized to target two species of conservation concern. Across all monitoring designs and scenarios, we found that power was higher when detecting increasing occupancy trends compared to decreasing trends owing to the relatively low levels of initial occupancy. Our results suggest that surveying 200 of the existing plots annually (with a small subset resurveyed twice within a year) will have at least an 80% chance of detecting 30% declines in occupancy for four of the five invasive species modeled and one of the six native species. This increased to 10 of the 11 species assuming larger (50%) declines. When plots were positioned to target all species equally, power improved slightly for most compared to the existing survey network. When plots were positioned to target two species of conservation concern (crest-tailed mulgara and dusky hopping mouse), power to detect 30% declines increased by 29% and 31% for these species, respectively, at the cost of reduced power for the remaining species. The effect of varying survey frequency depended on its trade-off with the number of sites sampled and requires further consideration. Nonetheless, our research suggests that track-based surveying is an effective and logistically feasible approach to monitoring broad-scale occupancy trends in desert species with both widespread and restricted distributions.
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Affiliation(s)
- Darren Southwell
- School of Ecosystem and Forest Sciences, University of Melbourne, Parkville, Victoria, Australia
| | - Anja Skroblin
- School of Ecosystem and Forest Sciences, University of Melbourne, Parkville, Victoria, Australia
| | - Katherine Moseby
- University of NSW School of Biological, Earth and Environmental Science, Sydney, New South Wales, Australia
| | - Richard Southgate
- Envisage Environmental Services, Kingscote, South Australia, Australia
| | - Naomi Indigo
- Centre for Biodiversity and Conservation Research, University of Queensland, St Lucia, Queensland, Australia
| | - Brett Backhouse
- Alinytjara Wilurara Landscape Board, Adelaide, South Australia, Australia
| | | | - Robert Brandle
- Department for Environment and Water, South Australian Government, Adelaide, South Australia, Australia
- South Australian Arid Lands Landscape Board, Port Augusta, South Australia, Australia
| | - Peter Brenton
- Atlas of Living Australia, CSIRO National Collections and Marine Infrastructure, Docklands, Victoria, Australia
| | - Peter Copley
- Department for Environment and Water, South Australian Government, Adelaide, South Australia, Australia
| | - Martin A Dziminski
- Department of Biodiversity, Conservation and Attractions, Biodiversity and Conservation Science, Kensington, Western Australia, Australia
| | - Carolina Galindez-Silva
- Anangu Pitjantjatjara Yankunytjatjara Land Management, Alice Springs, Northwest Territories, Australia
| | - Catherine Lynch
- South Australian Arid Lands Landscape Board, Port Augusta, South Australia, Australia
| | - Peggy Newman
- Atlas of Living Australia, CSIRO National Collections and Marine Infrastructure, Docklands, Victoria, Australia
| | - Reece Pedler
- University of NSW School of Biological, Earth and Environmental Science, Sydney, New South Wales, Australia
| | - Daniel Rogers
- Department for Environment and Water, South Australian Government, Adelaide, South Australia, Australia
| | - David A Roshier
- Australian Wildlife Conservancy, Subiaco, Western Australia, Australia
| | - Ellen Ryan-Colton
- Research Institute for the Environment and Livelihoods, Charles Darwin University, Alice Springs, Northwest Territories, Australia
| | | | - Matt Ward
- Department for Environment and Water, South Australian Government, Adelaide, South Australia, Australia
| | - Damaris Zurell
- Geography Department, Humboldt-University Berlin, Berlin, Germany
- Institute for Biochemistry and Biology, University of Potsdam, Potsdam, Germany
| | - Sarah Legge
- Centre for Biodiversity and Conservation Research, University of Queensland, St Lucia, Queensland, Australia
- Research Institute for the Environment and Livelihoods, Charles Darwin University, Alice Springs, Northwest Territories, Australia
- Fenner School of Environment and Society, The Australian National University, Canberra, Australian Capital Territory, Australia
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Martín‐Forés I, Guerin GR, Munroe SEM, Sparrow B. Applying conservation reserve design strategies to define ecosystem monitoring priorities. Ecol Evol 2021; 11:17060-17070. [PMID: 34938492 PMCID: PMC8668797 DOI: 10.1002/ece3.8344] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2021] [Accepted: 10/28/2021] [Indexed: 11/16/2022] Open
Abstract
In an era of unprecedented ecological upheaval, monitoring ecosystem change at large spatial scales and over long-time frames is an essential endeavor of effective environmental management and conservation. However, economic limitations often preclude revisiting entire monitoring networks at high frequency. We aimed here to develop a prioritization strategy for monitoring networks to select a subset of existing sites that meets the principles of complementarity and representativeness of the whole ecological reality, and maximizes ecological complementarity (species accumulation) and the spatial and environmental representativeness. We applied two well-known approaches for conservation design, the "minimum set" and the "maximal coverage" problems, using a suite of alpha and beta biodiversity metrics. We created a novel function for the R environment that performs biodiversity metric comparisons and site prioritization on a plot-by-plot basis. We tested our procedures using plot data provided by the Terrestrial Ecosystem Research Network (TERN) AusPlots, an Australian long-term monitoring network of 774 vegetation and soil monitoring plots. We selected 250 plots and 80% of the total species recorded as targets for the maximal coverage and minimum set problems, respectively. We compared the subsets selected by the different biodiversity metrics in terms of complementarity and spatial and environmental representativeness. We found that prioritization based on species turnover (i.e., iterative selection of the most dissimilar plot to a cumulative sample in terms of species replacement) maximized ecological complementarity and spatial representativeness, while also providing high environmental coverage. Species richness was an unreliable metric for spatial representation. Selection based on range-rarity-richness was balanced in terms of complementarity and representativeness, whereas its richness-corrected implementation failed to capture ecological and environmental variation. Prioritization based on species turnover is desirable to cover the maximum variability of the whole network. Synthesis and applications: Our results inform monitoring design and conservation priorities, which can benefit by considering the turnover component of beta diversity in addition to univariate metrics. Our tool is computationally efficient, free, and can be readily applied to any species versus sites dataset, facilitating rapid decision-making.
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Affiliation(s)
- Irene Martín‐Forés
- School of Biological SciencesThe University of AdelaideAdelaideSAAustralia
- Terrestrial Ecosystem Research Network (TERN)University of AdelaideAdelaideSAAustralia
| | - Greg R. Guerin
- School of Biological SciencesThe University of AdelaideAdelaideSAAustralia
- Terrestrial Ecosystem Research Network (TERN)University of AdelaideAdelaideSAAustralia
| | - Samantha E. M. Munroe
- School of Biological SciencesThe University of AdelaideAdelaideSAAustralia
- Terrestrial Ecosystem Research Network (TERN)University of AdelaideAdelaideSAAustralia
| | - Ben Sparrow
- School of Biological SciencesThe University of AdelaideAdelaideSAAustralia
- Terrestrial Ecosystem Research Network (TERN)University of AdelaideAdelaideSAAustralia
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Nunes LA, Ribic CA, Zuckerberg B. Identifying mismatches between conservation area networks and vulnerable populations using spatial randomization. Ecol Evol 2021; 11:16006-16020. [PMID: 34824807 PMCID: PMC8601911 DOI: 10.1002/ece3.8270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2021] [Revised: 09/27/2021] [Accepted: 10/12/2021] [Indexed: 11/12/2022] Open
Abstract
Grassland birds are among the most globally threatened bird groups due to substantial degradation of native grassland habitats. However, the current network of grassland conservation areas may not be adequate for halting population declines and biodiversity loss. Here, we evaluate a network of grassland conservation areas within Wisconsin, U.S.A., that includes both large Focal Landscapes and smaller targeted conservation areas (e.g., Grassland Bird Conservation Areas, GBCAs) established within them. To date, this conservation network has lacked baseline information to assess whether the current placement of these conservation areas aligns with population hot spots of grassland-dependent taxa. To do so, we fitted data from thousands of avian point-count surveys collected by citizen scientists as part of Wisconsin's Breeding Bird Atlas II with multinomial N-mixture models to estimate habitat-abundance relationships, develop spatially explicit predictions of abundance, and establish ecological baselines within priority conservation areas for a suite of obligate grassland songbirds. Next, we developed spatial randomization tests to evaluate the placement of this conservation network relative to randomly placed conservation networks. Overall, less than 20% of species statewide populations were found within the current grassland conservation network. Spatial tests demonstrated a high representation of this bird assemblage within the entire conservation network, but with a bias toward birds associated with moderately tallgrasses relative to those associated with shortgrasses or tallgrasses. We also found that GBCAs had higher representation at Focal Landscape rather than statewide scales. Here, we demonstrated how combining citizen science data with hierarchical modeling is a powerful tool for estimating ecological baselines and conducting large-scale evaluations of an existing conservation network for multiple grassland birds. Our flexible spatial randomization approach offers the potential to be applied to other protected area networks and serves as a complementary tool for conservation planning efforts globally.
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Affiliation(s)
- Laura A. Nunes
- Department of Forest and Wildlife EcologyUniversity of Wisconsin ‐ MadisonMadisonWisconsinUSA
| | - Christine A. Ribic
- U.S. Geological Survey, Wisconsin Cooperative Wildlife Research UnitUniversity of WisconsinMadisonWisconsinUSA
| | - Benjamin Zuckerberg
- Department of Forest and Wildlife EcologyUniversity of Wisconsin ‐ MadisonMadisonWisconsinUSA
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Southwell D, Legge S, Woinarski J, Lindenmayer D, Lavery T, Wintle B. Design considerations for rapid biodiversity reconnaissance surveys and long‐term monitoring to assess the impact of wildfire. DIVERS DISTRIB 2021. [DOI: 10.1111/ddi.13427] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Affiliation(s)
- Darren Southwell
- National Environmental Science Program Threatened Species Recovery Hub School of Ecosystem and Forest Sciences, University of Melbourne Parkville VIC Australia
| | - Sarah Legge
- National Environmental Science Program Threatened Species Recovery Hub Fenner School of Environment and Society The Australian National University Canberra ACT Australia
- National Environmental Science Program Threatened Species Recovery Hub Centre for Biodiversity and Conservation Science University of Queensland St Lucia QLD Australia
| | - John Woinarski
- National Environmental Science Program Threatened Species Recovery Hub School of Ecosystem and Forest Sciences, University of Melbourne Parkville VIC Australia
- National Environmental Science Program Threatened Species Recovery Hub Charles Darwin University Darwin NT Australia
| | - David Lindenmayer
- National Environmental Science Program Threatened Species Recovery Hub Fenner School of Environment and Society The Australian National University Canberra ACT Australia
| | - Tyrone Lavery
- National Environmental Science Program Threatened Species Recovery Hub Fenner School of Environment and Society The Australian National University Canberra ACT Australia
| | - Brendan Wintle
- National Environmental Science Program Threatened Species Recovery Hub School of Ecosystem and Forest Sciences, University of Melbourne Parkville VIC Australia
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