A data driven method for prioritizing invasive species to aid policy and management

Natural resource managers overseeing large regions are often challenged by an overwhelmingly long list of invasive species to prioritize for management and surveys. Often, managers determine priorities through subjective experience and not regional data, contributing to a lack of objectivity, consistency, and transparency. Using the invasion curve as a guiding principle, we developed a data-driven process to guide expert input in creating regionally specific invasive species lists based on management priorities. The invasive species tiers framework uses a standardized set of definitions, data from locational databases and invasiveness assessments, and expert review to categorize highly invasive species present in and surrounding the target regions. The analysis process was evaluated and improved by feedback from the structured network of invasive species managers in New York State. Results of the invasive species tiers process for eight management regions and at the state-scale were made publicly available, and demonstrated variation in invasive species diversity across the management landscape. The approach developed here can be replicated in and scaled to other regions of the U.S. or other countries with comparable data, and it can provide a common management language to better coordinate invasive species management efforts.


Introduction
Non-native invasive species were estimated to cost US$47 billion to US$163 billion in 2017 worldwide, with mean annual cost tripling every 10 years as they outcompete, predate, or parasitize native species, agricultural crops and livestock, clog pipes, damage houses, decrease water quality, and produce a host of other harmful impacts (Diagne et al. 2021). In the coming decades, the economic losses associated with these species will increase in regions such as the northeastern United States, where the climate will be suitable to hundreds of new species by 2050 (Allen and Bradley 2016). There are approximately 11,400 non-native species already established in the United States (Simpson et al. 2018), although, only a fraction of these established non-native species exhibit invasive behaviors that may cause negative ecological impacts and economic costs (Williamson and Fitter 1996). Even so, the number of invasive species can be regionally very large and it can easily outstrip financial, technical, and personnel resources to manage them. Accordingly, invasive species managers need tools to help them decide which species most deserve the limited management resources.
For decades, a guiding framework for invasive species managers has been the invasion curve, which suggests that preventing invasive species from establishing is the most cost-effective management strategy (if feasible), followed by eradication, while the containment and long-term management of successful invaders are more difficult and costly over time (Hobbs and Humphries 1995). However, the invasion curve provides only a very general management guideline, rather than specific management metrics that would help prioritize which invasive species out of many should be allocated the limited resources. For example, since there is no specific population threshold at which a species shifts from an eradication to a more costly containment phase (Hobbs and Humphries 1995), managers have to interpret where a species may be along the invasion curve, leaving the door open to subjective bias and an overreliance on expert opinion. Decision making processes that rely on expert opinions alone risk neglecting relevant criteria and are rarely documented sufficiently (Hiebert and Stubbendieck 1993;Fox and Gordon 2009).
Seeking a more objective and quantitative framework for invasive species management, natural resource agencies are increasingly turning to prioritization schemes to make resource allocation decisions on invasive species and/or invaded sites (Forsyth et al. 2012;Gallardo and Aldridge 2013;McGeoch et al. 2016). Prioritization schemes focused on species are typically structured around one of three main variables: the species' demonstrated impact, its potential risk, and the feasibility of controlling it (Booy et al. 2017;Forner et al. 2022). Within these broad categories, schemes may comprise dozens of different criteria, including ecosystem-wide impacts like altered fire regimes, threats to specific native species or habitats, threats to agriculture and industry, human health impacts, distribution, cost of control, and/or availability of biological control agents, and so on (Fox and Gordon 2009;Epanchin-Niell and Hastings 2010;Forner et al. 2022).
In these prioritization schemes, assessors work through a series of quantitative and qualitative questions based on the selected criteria and provide answers that correspond to scores, or pathways on a decision tree then used to produce a ranking of the species most worthy of control efforts (Branquart et al. 2016;McGeoch et al. 2016;Ziller et al. 2020). Documenting sources of information and rating information uncertainty are key components in this process (Branquart et al. 2016). Ideally, species with the highest rankings are ones that pose the highest risk and require the fewest resources to manage (Dawson et al. 2009;Booy et al. 2017). In practice, however, implementation of these prioritization rankings may fall into the trappings of less structured approaches when data on abundance, distribution, impacts, and management feasibility are not readily analyzed, as relying on various assumptions may allow bias to enter prioritization. However, combining existing data with expert knowledge can make prioritization schemes more objective and consistent than an expert-only alternative (Hiebert and Stubbendieck 1993;Cipollini et al. 2005).
To help resource managers prioritize invasive species management across different regions, we had two objectives. The first objective was to develop a general process for combining publicly available data on species locations and impacts with expert knowledge to classify invasive species into categories (tiers) that would allow regionally variable management priorities. These tiers were based off two main data ingredients: species' abundance and invasiveness. We derived species' abundance from observation records contributed by community scientists and natural resource professionals contained in online species observation databases, a largely underutilized resource in invasive species prioritization ). For 'invasiveness,' a comprehensive term in this case which included ecological and socioeconomic impacts as well as difficulty of control and biological characteristics indicating invasive potential, we obtained scores from 600 species assessments completed by the New York State Department of Environmental Conservation (NYS DEC). For each region, after we assigned tiers to all non-native species using this available data, we submitted these 'data tiers' to a group of regional experts for their assessment. These experts finalized these species' tier placements through a structured and transparent review process enabling them to provide details on other considerations such as the regionally specific impacts of each species and the estimated costs and feasibility of managing them. As control costs and organizational budgets can vary widely and are difficult to estimate, even for management of the same species at different sites (Epanchin-Niell and Hastings 2010), we did not attempt to integrate financial considerations into the data process developed here.
The second objective was to apply this approach to different regions of New York State in the U.S. to both demonstrate the applicability of the approach and to provide data-supported recommendations to regional invasive species managers making prioritization decisions. Ultimately, our goal was to provide a standardized set of definitions and initial data-based species lists for natural resource professionals dealing with different combinations of invasive species and at different phases of invasion. In addition to categorizing species based on possible management actions (such as conducting early detection surveys versus managing for localized suppression), this would allow regional managers to learn from neighboring regions, and give statewide managers a better sense of which invasive species were of higher concern and where. The resulting general framework and approach can be replicated in other regions of the U.S. or other countries with comparable data, and it can drive the development of necessary data sources and expert networks elsewhere.

Study area
The study area encompasses the entire New York State (NYS) in northeastern U.S., ranging from New York Harbor in the south to the St. Lawrence Seaway in the north. NYS is a hub of global commerce and one of the most heavily trafficked states in the country, offering multiple paths of entry for new species (Taylor 2013). Of nearly 1,500 non-native plants documented in NYS, 726 are naturalized, and 249 have an unknown status (Werier 2017). With regards to invasive forest pests, the Northeast U.S. has the highest number of damaging non-native insects and pathogens, with NYS leading with over 40 species established (Liebhold et al. 2013). Currently, 155 species are listed as invasive species under the NYS Prohibited and Regulated Invasive Species regulations (NYS Department of Environmental Conservation 2015).
In response to the economic and ecological threats of invasive species, NYS has created robust invasive species management infrastructure. Guided by the NYS Invasive Species Council and funded primarily by the NYS Environmental Protection Fund, a suite of government agencies and other organizations work in tandem to combat invasives (NYS Department of Environmental Conservation 2018). An essential element of the state framework is the network of Partnerships for Regional Invasive Species Management (PRISM) (Fig. 1). Each of the eight PRISMs covering NYS coordinate regionally-specific invasive species issues while maintaining elements of consistency through oversight by the state's Department of Environmental Conservation. This infrastructure, along with a centralized locational database for the state, was particularly well-suited for the developing, testing, revising, and evaluating of a data-driven approach to invasive species prioritization.

Species tiers framework
The invasive species tiers framework offers a standardized set of definitions to categorize species into regionally specific lists based on invasiveness and feasibility of control. The general framework follows the concepts of the 'invasion curve' (Hobbs and Humphries 1995), which describes the phases of invasion over time and priorities for action at each phase. Tier 1 species are not yet reported in the region of interest but have reasonable potential for introduction and are highly invasive elsewhere. Tier 1 species can further be divided into Tier 1a (present within a 160 km (100 mile) buffer), Tier 1b (not within the buffer, but present in Eastern North America), and Tier 1c (not present in Eastern North America, but with a viable pathway of invasion). Tiers 2 through 4 represent highly invasive species currently present, but at differing abundances, which broadly correlates with difficulty of eradication and control costs, within the area for which the list is created. Tier 2 species have a low enough abundance that their eradication may be feasible, Tier 3 species may be contained from spreading further, and Tier 4 species are widespread across the region, making local management efforts only reasonable when protecting valued resources. Tier 5 species are present in the area of concern, but their invasiveness remains unknown, and they require further monitoring and research. These working definitions were developed from the general concepts of Hobbs & Humphries (1995) by the New York Natural Heritage Program, revised with input from the PRISM coordinators and NYS DEC agency staff, and accepted by these stakeholders in 2017 (Fig. 2).
While the tier ranking definitions provided a useful language for categorizing and communicating invasive species priorities, the initial lists were subjective and based solely on expert opinion. To be more objective, consistent, and transparent in these rankings, a data-driven process that blended expert opinion with data from multiple sources was needed.

Data sources
The data used in this study can be divided into two categories: (1) data on the occurrence of invasive species and (2) data on the invasiveness of species.

Occurrence data
Occurrence data came from four species observation databases, spanning professionally collected data to reports from the general public. Species locations from iMapInvasives (NatureServe 2021) were used to assemble baseline species lists. iMapInvasives is an online, collaborative, GIS-based database and mapping tool which has served as the official invasive species database for NYS since 2010, with data for the state managed by the New York Natural Heritage Program (NYS Department of Environmental Conservation 2018). Throughout NYS, trained volunteers Only species considered to have high negative impacts were considered for inclusion in Tiers 1 through 4. Impacts were determined by New York State invasiveness and socio-economic ranks. For species that are not ranked yet, or region-specific adjustments of state ranks are deemed necessary, expert opinion was used and documented.
Low-impact species were not included since it cannot be justified to spend resources to control these. The buffer applied in the data tiers analyses for Tier 1 is 160 km surrounding the region of interest. This ranking system takes into account populations that have escaped into natural areas, but not intentionally (and legally) distributed individuals. For example, a landscape planting would not be counted and natural resource professionals use iMapInvasives to report invasive species, record treatment effectiveness, and document non-detection results (Jewitt et al. 2021). New reports through the mobile applications or the online interface are marked as unconfirmed until the submitted photo of the species is reviewed by a designated taxonomic expert or verified by project leaders based on professional expertise. Reports of exact species locations are typically mapped as points, lines, or polygons. In some cases, records have locations approximated (e.g., centroids of a county or waterbody) when sourced from herbarium or museum records, or other historical archives.
Three additional data sources complemented species occurrence data outside of NYS (within a 160 km buffer surrounding the state). The Early Detection and Distribution Mapping System (EDDMapS) is a national, web-based mapping system that documents invasive species and pest distributions aggregated from multiple databases and public reports (EDD-MapS 2021). We also included observations from Nonindigenous Aquatic Species (NAS), a database from the United States Geological Survey that monitors, analyzes, and records sightings of introduced aquatic species throughout the U.S. (U.S. Geological Survey 2021). A third dataset, iNaturalist, supplied data for both the 160 km buffer area around the state as well as for the areas within NYS. iNaturalist is a global online social network, composed of naturalists, professionals, and the public, who map observations of all types of species, including non-native ones (iNaturalist 2021). We included all observations in iNaturalist that were classified as introduced, not captive/cultivated, and research grade (includes reported location, available photographs, and identification verified by at least two additional users). Using these multiple data sources meant the data included multiple scientific names for the same species. We used a power query in Excel to match species with synonymic scientific names between systems, then manually matched species synonyms to align with the name used in iMapInvasives, which follows the taxonomic standards of NatureServe. Only observations recorded during or after the date 1 January 2000, were included in the downloads.

Species invasiveness ranks
We used the invasiveness ranks for non-native species that were generated under the direction of NYS DEC with the standardized assessment forms developed for the state's invasive species regulatory system (NYS Department of Environmental Conservation 2015). Over 600 non-native plant and animal species have been assessed since 2010 by invasive species biologists and other experts for ecological invasiveness and impacts on social and economic values. Ecological invasiveness scores were based off ecological impacts including changes to ecosystem structure like reduced light availability or altered fire regimes and species-specific impacts like hybridization (0-40 points); biological characteristics and dispersal ability such as mode and rates of reproduction and traits that confer competitive advantages like allelopathy (0-25 points); distribution within both its native landscape and other places it has been introduced, including density of populations (0-20 points); and difficulty of detection and control, including seed bank longevity and the level of effort required to remove an individual (0-10 points) (Jordan et al. 2012). Assessors compiled individual scores from each of these categories for a relative maximum score on a scale of 0-100 points. Species with a score 80 or above were given a 'very high' invasiveness rank, > 70 and < 80 a 'high' rank, > 50 and < 70 a 'moderate' rank, > 40 and < 50 a 'low' rank, and 40 or below an 'insignificant' rank (Jordan et al. 2012). The ecological invasiveness assessment protocol and plant and animal assessment forms for many of the species included in this project can be accessed from the New York Invasive Species Information Clearinghouse website (nyis.info/invasiveness-rankings).
A second assessment quantified each species' socio-economic impact based on its human health, economic, and cultural benefit (positive score) or detriment (negative score). Negative human health impacts included a source of poison or allergens, economic impacts included damaging industry and real estate, and cultural impacts included disrupting aesthetic or recreational experiences. Scores from these three socio-economic components were aggregated for a net score on a scale of − 100 to + 100, with negative scores meaning the species had an overall negative socio-economic impact and positive scores meaning the species had an overall positive socio-economic impact. Species with scores lower than a − 80 were considered to have a very high negative impact and between − 70 and − 79.99, a high negative impact (NYS Invasive Species Council 2010).
Both ecological and socio-economic assessments required the assessor to provide a numeric score for each component question and document the reasoning and sources of information. For component questions in which the answer is unknown, the point value for that question was subtracted from the 'Total Answered Points Possible', and species with less than 70 out of 100 possible points were given an 'Unknown' rank. Compiled ecological invasiveness and socio-economic impact scores were provided by the NYS DEC Invasive Species Coordination Section. The full protocols for both assessments can be found in the final report on the state regulatory system for non-native species (NYS Invasive Species Council 2010).

Data tiers
To provide a provisional baseline set of regional species tier lists for expert review, we generated standardized tier rankings based solely on the occurrence data and invasiveness assessment ranks (or status if unknown or not assessed), henceforth 'data tiers,' for all nonnative species (Fig. 3). We produced lists of these data tiers for each of the eight PRISMs and for the entirety of NYS, with the region of interest henceforth referred to as the 'target region.'

Within target regions (tiers 2 through 4)
We began by counting the number of documented populations for all non-native species located within the target regions (to approximate their abundance in each region), using observation records in iMapInvasives and iNaturalist. This generated population estimates of each species that were often inflated due to overreporting of the same species in the same locations. To account for this, we employed a separation distance of 100 m in ArcMap that consolidated individual records of the same species within 100 m of each other into a single population. We initially followed a 50 m separation distance suggested by Rew and Pokorny (2006). However, after receiving input from initial expert reviewers who felt that certain species with known population numbers had been overreported in iMapInvasives, we worked with these experts to select a different separation distance to generate populations for the final version. We did this Step 3: Experts review Fig. 3 Process for prioritizing invasive species to aid in management and policy making. See Methods for full explanation by running a sensitivity analysis, with separation distance values of 50, 100, 200, and 500 m. We generated populations at each of these separation distances and compared them to Tier 2 species in the Lower Hudson PRISM for which experts knew exact population numbers. A 100 m separation distance most closely aligned with the known population numbers for these species. We only used species observations with known coordinates when applying this 100 m separation distance. Observations with 'approximate' locations in iMapInvasives were counted separately for each species and added together with the populations generated through the separation distance to get final estimate of the number of populations for each species in each target region.
Any non-native species that had either a 'high' or 'very high' ecological invasiveness score, or a 'high negative' or 'very high negative' socio-economic impact score according to the assessments (referred to collectively as 'highly invasive') were placed in the data tiers 2, 3, or 4 lists. These species were then sorted by population count, with the list divided evenly into tiers 2, 3, and 4 based on its population count, with the lowest 33% of populations landing a species in Tier 2, the middle 33% of populations in Tier 3, and the upper 33% of populations in Tier 4. Within each of these tier categories, there was no further ranking. Other criteria for binning species into data tiers were tested, but this simple 'thirds' method aligned most closely to the expert tiers (Table S2, Supplementary Information). We placed non-native species that were not ranked as highly invasive (either unassessed or scoring insignificant, low, or moderate) in an 'untiered' list. We did not generate data tier 5 species (those with unknown invasiveness), as this category was to be developed solely from expert input (given the lack of data).

Outside of target region-160 km buffer (tier 1a)
Next, we created initial Tier 1a lists by analyzing the 160 km buffer around each target region and selecting species ranked as highly invasive. For the buffer around NYS, data came from iMapInvasives, iNaturalist, EDDMapS, and USGS NAS. Any highly invasive species present in a buffer surrounding the target region, but not in the region itself was placed in Tier 1a. Any unassessed species or ones with moderate or low invasiveness ranks present in this buffer received an 'untiered in buffer' rank. We did not include any species that exclusively fell outside of this 160 km buffer; these species were considered for Tiers 1b and 1c in the expert review process.

Expert review of data tiers
Invasive species and taxonomic experts were recruited to review the data tiers and provide feedback on the analysis process and create finalized Table 1 The number of species and populations (based on a separation distance of 100 m), and population density for all target regions Tiers 2 through 4 refer to high impact species present in each target region. Sq km is the area of the target region, and pops/sq km is the population density. Tier 1a refers to high-impact species present in a 160-km buffer around the target region but not present in the target region. See Fig. 1  'expert tiers' (Fig. 3). For the eight PRISMs, the expert groups consisted of PRISM coordinators and staff for that target region. PRISM coordinators, who work full-time on invasive species issues, hailed from diverse backgrounds, including conservation biology, ecology, science education, and aquatic biology. Almost all obtained graduate degrees related to their field of study, and many held decades of experience researching and working in natural resource management. Many had specific areas of invasive species expertise, such as forest pests. When presented with species outside of their comfort zone during the tier review process, coordinators generally deferred to staff members who specialized in dealing with those species. Staff member positions included terrestrial and aquatic invasive species managers, environmental educators, and project coordinators. The teams of experts reviewing the PRISM data tiers ranged significantly in size; at minimum there were 3 reviewers. However, some PRISMs created their own partner workgroups to review species, involving up to a dozen additional reviewers. For the statewide tiers we assembled a committee of invasive species professionals from across NYS to provide feedback. This committee consisted of a similarly wide range of backgrounds: invasive species professionals from NY state agencies, botanists, invasive species biologists, research scientists, foresters and forest health diagnosticians. We divided the members into three groups based on expertise: terrestrial plants, aquatic organisms, and plant pests/terrestrial animals. Each group discussed its species tier placements until consensus was reached. For all target regions we convened webinar meetings with the experts to establish agreement on the definitions and management implications of each tier. Then we explained the standardized process for providing feedback in their tier review spreadsheets, which listed all species given a data tier, and included a place for the expert to override the data tier. Experts assessed each species and determined an expert tier, either agreeing or disagreeing with the data tier. If the experts disagreed with the data tier (referred to as tier mismatches), they chose one of the options from a specified list of potential reasons for change (Table S1, Supplementary Information). For example, experts might suggest that a species with a data tier of 2 was more abundant than reported in the online data, and may in turn give it an expert tier of 3. Or they might disagree with the state invasiveness ranks and consider a species not as invasive in their region and reclassify it with an expert tier of 5 (i.e., species requires more monitoring or research to understand its invasiveness). Experts could also add species to Tiers 1b and 1c based on their knowledge of species to watch for beyond the state boundaries. We included an 'Other Reason' column to allow for alternative reasons for change, and a 'Notes' column to justify any of these changes in greater detail. We designed this feedback process to encourage experts to think critically about each species tier placement, to incorporate the data into their decision making, and to provide documentation on their reasoning.
Once all target regions provided expert tiers in May of 2021, we made the tiers publicly viewable in an online table (https:// www. nynhp. org/ invas ives/ speci es-tiers-table), allowing anyone to see how invasive species are being prioritized across the state. We also created an Esri StoryMap explaining in detail the tiering process to accompany the table (https:// www. arcgis. com/ apps/ MapJo urnal/ index. html? appid= c7af9 3ee62 314f7 89b2b fc180 2a5cc 4a).

Results
Within NYS, we assembled population numbers on 2078 non-native species for possible inclusion in the tiers. Of these, 11.6% had invasiveness assessment scores of high or very high impact and thus included in the data tiers, with the remainder going to the long untiered list. Through the expert tiering process, many species that had not received assessment scores or received scores less than high or very high were pulled into the tier categories, resulting in 399 species placed by experts in either Tier 1a, 2, 3, or 4 for at least one region (Table 1). Terrestrial plant species represented 66.2% of these species, with the remainder comprised of aquatic animals (14.5%), aquatic plants (6.5%), terrestrial animals (10.8%), and pathogens/fungi (2%). An additional 438 species were assigned to Tier 5 by experts, as these species were considered needing more research and monitoring to determine invasiveness.
The data-driven tiering method significantly increased the number of species under management consideration. Before the introduction of this method in 2020, when PRISMs were assembling prioritization lists using less structured and differing approaches, 379 unique species were mentioned on PRISM prioritization lists across the state, including Tier 5 species that required more research. With the inclusion of the new data-driven method, across all PRISMs and the statewide list, there are 837 species under management consideration or monitoring.
Importantly, the regional tiers also showed broad differences between the regions (PRISMs) in the number of populations and diversity of non-native species (Table 1). While some regions (e.g., APIPP, SLELO, and CRISP) were relatively less invaded (with less than 5,000 mapped populations of tier 2 through 4 species), others (e.g., LH) were heavily invaded (with over 20,000 mapped populations of tier 2 through 4 species). While one of the least invaded regions (CRISP) had the lowest counts of non-native tier 2 through 4 species populations, its proximity to the most heavily invaded region (LH) contributed to its very large number of Tier 1 species population (over 15,000). The PRISMs with the highest populations of non-native species were located in the southern part of NYS, close to New York City. While the regions (PRISMs) are far from equal in size, their non-native species population density bears out a similar result, with the two regions with the highest density of invasive species in the southern portion of the state (LH and LIISMA).

Mismatches between data tier and expert tier
Across all regions evaluated, experts tiered an aggregated total of 770 region-by-species instances, with much overlap in the species lists for each region. Of these region-by-species reviews, experts disagreed with the data tiers 269 times, referred to as mismatches, and provided reasons for their disagreement. The most commonly cited reason was that a species was 'under-reported in the data' (45.7% of mismatch reasons given). Less commonly, a species was changed due to the population being over-reported in the data (11.5% of mismatch reasons), indicating that the separation distance for that species was too low. Sometimes, reviewers moved species that were in the data tiers 2 through 4 list into the untiered list. This typically occurred when they considered the species to have a lower ecological invasiveness or socioeconomic impact than assessed (8.9% of mismatches) or there was disagreement about the species' nativity. If the difficulty of controlling a particular species was higher than expected given its abundance, the experts could move it to a higher data tier (5.2% of mismatches). Conversely, species with easy control methods, and thus more likely to be contained or eradicated than the data tier assumed by abundance, were sometimes moved to lower tiers by the experts (3.0% of mismatches). 'Other reasons' were listed for 25.7% of the mismatches by experts, and included reasons such as the species was common in neighboring regions, biocontrol options were in development, or management programs for it had already begun.
Though there were similar numbers of species in each of the data tiers 2 through 4 (approximately 33.3% each), mismatches between expert and data tiers were not proportional. Species placed in Data Tier 3 were most likely to be moved by an expert reviewer, representing 49.2% of the mismatches. Species given a Data Tier 3 were much more likely to be given an Expert Tier 4 (32.7% of mismatches; indicating a higher abundance than revealed by the data), than an Expert Tier 2 (13.0%). Data Tier 2 accounted for 42.1% of the mismatches, with over half of these moved off the Tiers 2-4 list by the experts, with the species either being moved to the tier 5 category (unknown invasiveness) or deemed low or insignificantly invasive and thus should not be prioritized for management. Data Tier 4 had the smallest portion of the total mismatches (8.7%).

Discussion
The invasive species tiers framework was designed to help regional and state managers think strategically about how to assign species to management priorities from overwhelmingly long lists of non-native species. The data driven process developed here brings efficiency and standardization to categorizing invasive species into rankings for a target region, which is often done with varying degrees of subjectivity by resource managers. However, since data gaps over large regions and lack of published information on invasiveness are common, the expertise of professionals dealing with invasive species was invaluable to informing the finalized tiers. While many data tier placements were revised with expert input, the process of starting with a data-driven baseline and requiring the experts to provide standardized reasons for mismatches allowed tier lists to be transparent and comparable from region to region.
Though the tier lists generated are particular to the invasive species of NYS, this process can be replicated with other similar sources of locational data and invasiveness information. Federal and non-governmental databases in the United States are available for focused invasive species reporting, such as USGS NAS, EDDMapS, and iMapInvasives . However, in the absence of resources and political will for invasive species mapping programs, a region or state might have limited data entered into these systems.
With the rise of simple and mobile mapping interfaces, species location data from the public has become more abundant (Rapacciuolo et al. 2021). Globally, there are at least 26 community sciencefocused databases containing nonnative species observations (Johnson et al. 2020). In our method, incorporating several of these databases not only provided rough population estimates of species that had previously only been mentioned anecdotally, it also increased the total number of species under management consideration by almost five hundred. Combining multiple databases, an approach unique to this methodology, allowed us to ensure that we had taken all possible invasive species records into account. Any nonnative species reported on any available database has the potential to be easily reviewed by managers, and can quickly be flagged if the species has been assessed for invasiveness.
In the development stages of the tiers process, iNaturalist data was not included, since the initial plan was to only use data from systems focused on invasive species reporting. However, after a preliminary round of expert review, it became apparent that many species, particularly ones in low abundance, were underreported in the state invasive species database, yet captured by public reports in iNaturalist. Bringing iNaturalist data into the tier analysis increased the number and populations of invasive species. There were, however, some limitations important to note. Since many of the reports in iNaturalist are crowdsourced, information such as species identification and species' nonnative status may be incorrect. Also, the comprehensive list of all non-native species in iNaturalist greatly increased the number of species in consideration making the review process longer for experts. And while the iNaturalist data was useful for new presumed invasive species, such as ornamental species that had been noted by the expert reviewers as growing aggressively outside of cultivation, many reports were obviously intentional plantings based on the photos submitted. The field in iNaturalist for 'captive/cultivated' species is underutilized; none of the approximately 200,000 introduced species records in New York downloaded for the analysis were marked as captive. Finally, not all species are reported evenly in unstructured community science databases (Ward 2014; Rapacciuolo et al. 2021;Callaghan et al. 2021). Expert reviewers noted improved alignment of data and expert tiers with iNaturalist data for showy species, such as Japanese primrose (Primula japonica), but not for less detectable species, such as grasses. Awareness of these and other caveats are important, yet in the absence of other data sources, iNaturalist data can offer means to generating lists of invasive species observed in a region and watchlists of species in a buffer surrounding the region of interest .
In addition to spatial information, the data tier analysis relied on ecological invasiveness and socio-economic assessments for each species. It can take months, if not years of work to produce comprehensive assessments for regions with many species (Heikkilä 2011). We had the luxury of using invasiveness assessments for over 600 species that were previously completed by the NYS DEC over the course of a decade using a standardized protocol (Jordan et al. 2012). However, realizing that producing detailed assessments for hundreds of candidate invasive species is not feasible for many jurisdictions, Bradley et al. (2022) proposes looking to neighboring states and the federal government for shared assessments and lists of regulated invasive species. Tapping into existing resources would be made easier if states made their assessments publicly available, and if there was a federal clearinghouse of protocols and completed assessments (Marshall Meyers et al. 2020;Bradley et al. 2022). In the absence of existing assessments, an expertdriven consensus approach could also be used to produce the initial list of highly invasive species for data-tiering. This technique was used to produce a list of highly invasive species not yet present in, but likely to invade, the European Union (Roy et al. 2019). For states or regions that can apply resources towards assessing many species to determine the highly invasive ones for inclusion in data tiers, there are many existing protocols from which to choose (examples reviewed in Heikkilä 2011;Buerger et al. 2016;Bradley et al. 2022). As the tiers framework does not define invasiveness, new groups adopting the process have leeway in choosing which priorities (e.g., ecological, economic, or control feasibility criteria) are emphasized in this assessment step.
Incorporating management costs, which is often left out of invasive species prioritization strategies, would increase the usefulness of this species-focused process (Courtois et al. 2018). Ideally, managers want to focus control projects on species that maximize reduction of negative impacts while minimizing costs, which typically occurs in the early phases of the invasion curve for a particular species (Hobbs and Humphries 1995). Costs vary widely by species and site-specific factors, but estimates can be taken into account through modeling of management costs over invasion dynamics (Epanchin-Niell and Hastings 2010), surveys of managers (Oreska and Aldridge 2011), and detailed record keeping of control projects over many years (Lowry et al. 2022). The development of standardized protocols for collecting and estimating management costs across species and stages of invasion, in addition to a centralized platform to share this data, are needed to further include management costs into prioritization systems.
The final critical element in this prioritization scheme is expert feedback. Expert reviewers filled in gaps in the data and provided a counterbalance to our automated approach. Additionally, expert feedback was responsible for several improvements notably: increasing the separation distance to 100 m, using socio-economic impacts as a criterion in addition to ecological impacts, and including observation records from iNaturalist. Also, comparing data tiers to expert tiers allowed us to evaluate the effectiveness of different variables to bin the highly invasive species into appropriate tiers; we found the simplistic method of binning the species into thirds by number of populations outperformed more complex approaches. Also, the expert feedback on species that were 'underreported in the data' prompted a push for new observations to be reported to iMapInvasives so future tier analyses will be more accurate. And while we did not have the cost of management of each species to incorporate into the data tiering process, experts did have the leeway to push a species to a higher or lower tier if its management difficulty was different than expected. If a group did have data on management cost, they could include that in the initial data tiering process to better align with priorities of the resource managers.
These new tier lists are currently being used in New York by state and regional invasive species managers in their prioritization decisions. Generally, Tier 1 and 2 species are the focus of early detection surveys and rapid response measures, if detected. Spread prevention methods are often employed for Tier 3 species, such as boat washing stations upon exit from waterbodies containing these species. And Tier 4 species are only managed with careful consideration of the assets being protected at a particular site, as these species are impractical to manage at a large scale. All eight NY PRISMs incorporate the tier framework into their priority species webpages targeting regional partner organizations (Table S3, Supplementary Information), and PRISM and statewide tier rankings are informing which species projects receive management contracts and where managers direct their resources. At the state level, the tier lists are being used within environmental permitting scenarios, such as the review of large-scale energy development proposals, to identify the regional significance of invasive species distribution which may be subject to control and management (J. Thiel, personal communication, 6 July 2022). The Tier 1 lists are also being used for a 'horizon scanning' efforts that will assess risks posed by new incoming invasive species, which is a recommended action set forth in the NYS Invasive Species Comprehensive Management Plan (NYS Department of Environmental Conservation 2018).
The online searchable tiers table and StoryMap developed to explain the process helps NYS managers communicate and coordinate invasive species efforts with national invasive species organizations and neighboring states, as well as between NYS agencies and regions. These resources also educate the public about how invasive species management decisions are made across the state.
The tiers process will be repeated each year by NYNHP with updated data from the previous field season. Scripts are being developed to automate the processing steps, such as taxonomy matching and separation distance analyses. The expert review spreadsheets will have the previous year's data and expert tiers, with changes in the new data tiers highlighted to facilitate expert review. Any changes to the expert tiers will be reflected on the publicly available online tiers table.
This process developed and tested for NYS and the NY PRISMs is replicable and scalable to other regions of interest. Jurisdictions or organizations can start by creating a list of high-ranking invasive species within and approaching the target region through standardized risk assessments, shared assessment information, or expert consensus. Available spatial data on these species' locations must also be gathered, whether from professional resource mapping programs, opportunistic publiclycontributed data, or both. Mapping software is then used to develop the data tiers lists for within the region (Tiers 2-4) by clustering observations with separation distances and splitting the final population numbers into thirds. Mapping software can also be used to develop data tier 1 species that are approaching the region within a designated buffer. Experts knowledgeable on invasive species and familiar with the target region are then convened to review the baseline tier lists and adjust as needed. Finally, the tier lists and methods used should be transparent and accessible to the public. Widespread adoption of similar data-driven tiers would allow neighboring states and provinces to share a common management language and better coordinate invasive species management efforts.